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+ Check where you are using command
+Listing the contents using command
+Change directory using command
+
+
+
+Command line
+Command line
+Command
+Command
+Commands
+Command
+Command
+Text data counts
+Pipe
+Basic
+Create new text file with
+
+ Linux for Bioinformatics
+Navigating in Linux file system
+You are in your home directory after you log into the system and are directed to the shell command prompt. This section will show you hot to explore Linux file system using shell commands.
+Path
+To understand Linux file system, you can image it as a tree structure.
+
In Linux, a path is a unique location of a file or a directory in the file system.
+For convenience, Linux file system is usually thought of in a tree structure. On a standard Linux system you will find the layout generally follows the scheme presented below.
+The tree of the file system starts at the trunk or slash, indicated by a forward slash (/). This directory, containing all underlying directories and files, is also called the root directory or “the root” of the file system.
%%bash
+## In your account, you will see a folder
+## with you account ID as the name
+cd ~
+echo $HOME
+/home/xie186
+Relative and absolute path
+-
+
- Absolute path +
An absolute path is defined as the location of a file or directory from the root directory(/). An absolute path starts from the root of the tree (/).
Here are some examples:
+/home/xie186
+/home/xie186/.bashrc
+-
+
- Relative path +
Relative path is a path related to the present working directory:
+data/sample1/ and ../doc/.
If you want to get the absolute path based on relative path, you can use readlink with parameter -f:
pwd
+readlink -f ../
+Once we enter into a Linux file system, we need to 1) know where we are; 2) how to get where we want; 3) how to know what files or directories we have in a particular path.
+Check where you are using command pwd
+In order to know where we are, we need to use pwd command. The command pwd is short for “print name of current/working directory”. It will return the full path of current directory.
Command pwd is almost always used by itself. This means you only need to type pwd and press ENTER
%%bash
+pwd
+Listing the contents using command ls
+After you know where you are, then you want to know what you have in that
+directory, we can use command ls to list directory contents
Its syntax is:
+ls [option]... [file]...
+
+ls with no option will list files and directories in bare format. Bare format means the detailed information (type, size, modified date and time, permissions and links etc) won’t be viewed. When you use ls by itself, it will list files and directories in the current directory.
ls ~/
+ls -a
+ls -ld
+Linux command options can be combined without a space between them and with a single - (dash).
+The following command is a faster way to use the l and a options and gives the same output as the Linux command shown above.
+ls -lt ~/.bashrc
+-rw-r--r--. 1 xie186 zt-bioi611 1067 Aug 22 22:27 /home/xie186/.bashrc
+
+
+Change directory using command cd
+Unlike pwd, when you use cd you usually need to provide the path (either absolute or relative path) which we want to enter.
If you didn’t provide any path information, you will change to home directory by default.
+| Path | +Shortcuts | +Description | +
|---|---|---|
| Single dot | +. | +The current folder | +
| Double dots | +.. | +The folder above the current folder | +
| Tilde character | +~ | +Home directory (normally the directory:/home/my_login_name) | +
| Dash | +- | +Your last working directory | +
Here are some examples:
+cd ~
+pwd
+ls
+ls ../
+##
+pwd
+cd ../
+pwd
+cd ./
+pwd
+Each directory has two entries in it at the start, with names . (a link to itself) and .. (a link to its parent directory). The exception, of course, is the root directory, where the .. directory also refers to the root directory.
Sometimes you go to a new directory and do something, then you remember that you need to go to the previous working direcotry. To get back instantly, use a dash.
+%%bash
+
+# This is our current directory
+pwd
+
+# Let us go our home diretory
+cd ~
+
+# Check where we are
+pwd
+
+# Let us go to your previous working directory
+cd -
+# Check where we are now
+pwd
+/home/xie186/BIOI611_lab/docs
+/home/xie186
+/home/xie186/BIOI611_lab/docs
+/home/xie186/BIOI611_lab/docs
+Manipulations of files and directories
+In Linux, manipulations of files and directories are the most frequent work. In this section, you will learn how to copy, rename, remove, and create files and directories.
+Command line cp
+In Linux, command cp can help you copy files and directories into a target directory.
Command line mv
+Move files/folders and rename file/folders using mv:
# move file from one location to another
+mv file1 target_direcotry/
+# rename
+mv file1 file2
+mv file1 file2 file3 target_direcotry/
+
+Command mkdir
+The syntax is shown as below:
+mkdir [OPTION ...] DIRECTORY ...
+Multiple directories can be specified when calling mkdir
mkdir directory1 directory2
+mkdir -p foo/bar/baz
+How to defining complex directory trees with one command:
+mkdir -p project/{software,results,doc/{html,info,pdf},scripts}
+
+Then you can view the directory using tree.
Command rm
+You can use rm to remove both files and directories.
+## You can remove one file.
+rm file1
+## `rm` can remove multiple files simutaneously
+rm file2 file3
+You can also use 'rm' to remove a folder. If a folder is empty, you can remove it using rm with -r.
rm -r FOLDER
+If a folder is not empty, you can remove it using rm with -r and -f.
mkdir test_folder
+rm -r test_folder
+View text files in Linux
+Commands cat, more and less
+The command cat is short for concatenate files and print on the standard output.
+The syntax is shown as below:
+cat [OPTION]... [FILE]...
+For small text file, cat can be used to view the files on the standard output.
+The command more is old utility. When the text passed to it is too large to fit on one screen, it pages it. You can scroll down but not up.
+The syntaxt of more is shown below:
more [options] file [...]
+The command less was written by a man who was fed up with more’s inability to scroll backwards through a file. He turned less into an open source project and over time, various individuals added new features to it. less is massive now. That’s why some small embedded systems have more but not less. For comparison, less’s source is over 27000 lines long. more implementations are generally only a little over 2000 lines long.
+The syntaxt of less is shown below:
+less [options] file [...]
+Command head and tail
+The command head is used to output the first part of files. By default, it outputs the first 10 lines of the file.
head [OPTION]... [FILE]...
+Here is an exmaple of printing the first 5 files of the file:
+head -n 5 code_perl/variable_assign.pl
+In fact, the letter n does not even need to be used at all. Just the hyphen and the integer (with no intervening space) are sufficient to tell head how many lines to return. Thus, the following would produce the same result as the above commands:
+head -5 target_file.txt
+The command tail is used to output the last part of files. By default, it prints the last 10 lines of the file to standard output.
The syntax is shown below:
+tail [OPTION]... [FILE]...
+Here is an exmaple of printing the last 5 files of the file:
+tail -5 target_file.txt
+To view lines from a specific point in a file, you can use -n +NUMBER with the tail command. For example, here is an example of viewing the file from the 2nd line of the line.
tail -n +2 target_file.txt
+
+Auto-completion
+In most Shell environment, programmable completion feature will also improve your speed of typing. It permits typing a partial name of command or a partial file (or directory), then pressing TAB key to auto-complete the command. If there are more than one possible completions, then TAB will list all of them.
A handy autocomplete feature also exists. Type one or more letters, press the Tab key twice, and then a list of functions starting with these letters appears. For example: type so, press the Tab key twice, and then you get the list as:
soelim sort sotruss soundstretch source
+Demonstration of programmable completion feature.
+File permissions
+In Linux, file permissions are a vital aspect of system security and resource management. This is particularly important in bioinformatics, where large datasets and scripts are often shared across teams. Permissions determine who can read, write, or execute a file, ensuring that critical data is not accidentally modified or deleted.
+Three Permission Categories:
+-
+
- User (u): The owner of the file. +
- Group (g): A group of users who share access to the file. +
- Other (o): All other users on the system. +
Permission Types :
+-
+
- Read (r): Ability to view the contents of a file. +
- Write (w): Ability to modify or delete the file. +
- Execute (x): Ability to run the file as a program (for scripts or executables). +
%%bash
+groups $USER animako eunal gstewar1 mjames17 mjeakle nmilza rahooper
+xie186 : zt-bioi611 zt-bioi611_mgr
+animako : zt-bioi611
+eunal : zt-bioi611
+gstewar1 : zt-bioi611
+mjames17 : zt-bioi611
+mjeakle : zt-bioi611
+nmilza : zt-bioi611
+rahooper : zt-bioi611
+%%bash
+mkdir -p ~/test_permission/
+touch ~/test_permission/test.txt
+ls -l ~/test_permission/
+rm -rf ~/test_permission/
+total 0
+-rw-r--r--. 1 xie186 zt-bioi611 0 Sep 8 22:52 test.txt
+Here, the first character represents the type of file (e.g., - for a regular file or d for a directory), followed by three groups of three characters, each representing the permissions for the user, group, and others, respectively.
Examples:
+-rwxr-xr--: The owner has read, write, and execute permissions. The group has read and execute permissions, while others can only read the file.
+drwxr-x---: A directory where the owner can read, write, and access (execute). The group can only read and access, while others have no permissions.
Modify file permissions using the chmod command. Permissions can be set in two ways:
Symbolic Mode:
+In symbolic mode, you modify permissions by referencing the categories (user, group, other) and specifying whether you're adding (+), removing (-), or setting (=) permissions.
+# Add execute permission for the user:
+chmod u+x filename
+# Remove write permission for the group:
+chmod g-w filename
+# Set read-only permission for others:
+chmod o=r filename
+Symbolic mode is intuitive and flexible, especially when you want to make precise adjustments to permissions without affecting other categories. This is useful for common file-sharing tasks in bioinformatics where you need to tweak access for specific collaborators.
+Numeric Mode (Octal representation):
+In numeric mode, file permissions are set using a three-digit number. Each digit represents the permissions foruser,group, andother, respectively. The digits are calculated by adding the values of theread,write, andexecute` permissions:
-
+
- Read (r) = 4 +
- Write (w) = 2 +
- Execute (x) = 1 +
Example Permission Breakdown:
+Read (r), Write (w), and Execute (x) for user = 7
+Read (r) and Execute (x) for group = 5
+Read (r) only for others = 4
+chmod 754 filename
+An example to help you understand executable:
%%bash
+printf '#!/user/bin/python\nprint("Hello, Welcome to Course BIOI611!")' > ~/test.py
+%%bash
+ls -l ~/test.py
+python ~/test.py
+-rw-r--r--. 1 xie186 zt-bioi611 61 Sep 8 23:06 /home/xie186/test.py
+Hello, Welcome to Course BIOI611!
+Error message below will be thrown out if you consider ~/test.py as a program:
bash: line 1: /home/xie186/test.py: No such file or directory
+%%bash
+chmod u+x ~/test.py
+ls -l ~/test.py
+python ~/test.py
+rm ~/test.py
+-rwxr--r--. 1 xie186 zt-bioi611 61 Sep 8 23:06 /home/xie186/test.py
+Hello, Welcome to Course BIOI611!
+Disk Usage of Files and Directories
+The Linux du (short for Disk Usage) is a standard Unix/Linux command, used to check the information of disk usage of files and directories on a machine. The du command has many parameter options that can be used to get the results in many formats. The du command also displays the files and directory sizes in a recursively manner.
%%bash
+du -h ~/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref
+2.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref
+%%bash
+du -ah ~/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref
+2.9M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/sjdbList.fromGTF.out.tab
+7.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/Log.out
+936M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/SA
+1.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/SAindex
+3.0M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/transcriptInfo.tab
+2.3M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/sjdbList.out.tab
+1.5M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/geneInfo.tab
+1.0K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/genomeParameters.txt
+512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrLength.txt
+512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrNameLength.txt
+512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrStart.txt
+7.6M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/exonGeTrInfo.tab
+3.1M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/exonInfo.tab
+2.8M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/sjdbInfo.txt
+512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrName.txt
+119M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/Genome
+2.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref
+%%bash
+du -csh /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/*
+19G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/raw_data
+0 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/raw_data_smart_seq
+1.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_data.sub
+575K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_smart_seq-7478223-xie186.err
+0 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_smart_seq-7478223-xie186.out
+8.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_smart_seq.sub
+2.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s2_star.sub
+34G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_align
+2.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref
+512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/test.sub
+512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/test.txt
+55G total
+Symbolic link
+Symbolic link, similar to shortcuts, can point to another file/folder.
+ln -s <path_to_files/folder_to_be_linked> <symlink_to_be_created>
+ls -l <symlink>
+unlink <symlink>
+
+File Management and Data Handling
+Compressing and decompressing files (gzip, gunzip, tar).
+Compress one file:
+%%bash
+perl -e 'for($i=0; $i<10000; ++$i){ print "test\n";}' > test.txt
+du -h test.txt
+gzip test.txt
+du -h test.txt.gz
+gunzip test.txt
+ls test.txt
+rm test.txt
+52K test.txt
+4.0K test.txt.gz
+test.txt
+Compress multiple files:
+%%bash
+perl -e 'for($i=0; $i<10000; ++$i){ print "test\n";}' > test1.txt
+perl -e 'for($i=0; $i<10000; ++$i){ print "test\n";}' > test2.txt
+du -h test1.txt test2.txt
+tar zcvf test.tar.gz test1.txt test2.txt
+du -sh test.tar.gz
+ls test1.txt test2.txt
+52K test1.txt
+52K test2.txt
+test1.txt
+test2.txt
+4.0K test.tar.gz
+test1.txt
+test2.txt
+z: This option tells tar to compress the archive using gzip. The resulting archive will have a .gz extension to indicate that it has been compressed with the gzip utility.
c: This option stands for create. It instructs tar to create a new archive.
v: This stands for verbose. When used, tar will display detailed information about the files being added to the archive, such as their names.
f: This stands for file. It tells tar that the next argument (test.tar.gz) is the name of the archive file to create.
%%bash
+tar tvf test.tar.gz
+rm test.tar.gz test1.txt test2.txt
+-rw-r--r-- xie186/zt-bioi611 50000 2024-08-25 21:52 test1.txt
+-rw-r--r-- xie186/zt-bioi611 50000 2024-08-25 21:52 test2.txt
+t: List the contents of archive.tar.
v: Display additional details about each file (like file permissions, size, and modification date).
f: Specifies that archive.tar is the archive file to operate on.
To uncompress a tar.gz file, use tar zxvf:
tar zxvf test.tar.gz
+Transferring files within the network
+Basic Syntax of scp:
scp [options] source destination
+
+Copy a Local File to a Remote Server
+scp file.txt username@remote_host:/path/to/destination/
+Alternative command is rsync.
File searching, filtering, and text processing
+Command find
+The find command is designed for comprehensive file and directory sesarches.
find [path] [options] [expression]
+%%bash
+find /home/xie186/scratch/bioi611/bulk_RNAseq -name "*.fastq.gz"
+/home/xie186/scratch/bioi611/bulk_RNAseq/raw_data/N2_day7_rep3.fastq.gz
+/home/xie186/scratch/bioi611/bulk_RNAseq/raw_data/N2_day1_rep3.fastq.gz
+/home/xie186/scratch/bioi611/bulk_RNAseq/raw_data/N2_day1_rep1.fastq.gz
+/home/xie186/scratch/bioi611/bulk_RNAseq/raw_data/N2_day7_rep1.fastq.gz
+/home/xie186/scratch/bioi611/bulk_RNAseq/raw_data/N2_day1_rep2.fastq.gz
+/home/xie186/scratch/bioi611/bulk_RNAseq/raw_data/N2_day7_rep2.fastq.gz
+Text data counts wc
+%%bash
+find /home/xie186/scratch/bioi611/bulk_RNAseq -name "*.fastq.gz" |wc -l
+6
+Pipe |
+In Linux and Unix-based systems, the pipe (|) is used in the command line to redirect the output of one command as the input to another command. This allows you to chain commands together and perform more complex tasks in a single line.
%%bash
+grep '>' ~/scratch/bioi611/reference/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa |wc -l
+7
+Column filering
+Command cut can be used to print selected parts of lines from each FILE to standard output.
%%bash
+wget -O GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz "https://ncbi.nlm.nih.gov/geo/download/?type=rnaseq_counts&acc=GSE102537&format=file&file=GSE102537_raw_counts_GRCh38.p13_NCBI.tsv.gz"
+--2024-08-25 21:08:03-- https://ncbi.nlm.nih.gov/geo/download/?type=rnaseq_counts&acc=GSE102537&format=file&file=GSE102537_raw_counts_GRCh38.p13_NCBI.tsv.gz
+Resolving ncbi.nlm.nih.gov (ncbi.nlm.nih.gov)... 2607:f220:41e:4290::110, 130.14.29.110
+Connecting to ncbi.nlm.nih.gov (ncbi.nlm.nih.gov)|2607:f220:41e:4290::110|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 349584 (341K) [application/octet-stream]
+Saving to: ‘GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz’
+
+ 0K .......... .......... .......... .......... .......... 14% 6.66M 0s
+ 50K .......... .......... .......... .......... .......... 29% 16.9M 0s
+ 100K .......... .......... .......... .......... .......... 43% 27.5M 0s
+ 150K .......... .......... .......... .......... .......... 58% 10.1M 0s
+ 200K .......... .......... .......... .......... .......... 73% 17.2M 0s
+ 250K .......... .......... .......... .......... .......... 87% 37.6M 0s
+ 300K .......... .......... .......... .......... . 100% 10.5M=0.02s
+
+2024-08-25 21:08:04 (13.4 MB/s) - ‘GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz’ saved [349584/349584]
+%%bash
+zcat GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz |head
+GeneID GSM2740270 GSM2740272 GSM2740273 GSM2740274 GSM2740275
+100287102 9 17 14 14 19
+653635 336 470 467 310 370
+102466751 8 56 46 31 31
+107985730 0 2 2 3 3
+100302278 0 1 0 0 2
+645520 0 3 8 4 7
+79501 0 2 2 1 4
+100996442 16 25 34 20 28
+729737 19 39 33 22 26
+%%bash
+zcat GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz |cut -f1,2,3 |head
+GeneID GSM2740270 GSM2740272
+100287102 9 17
+653635 336 470
+102466751 8 56
+107985730 0 2
+100302278 0 1
+645520 0 3
+79501 0 2
+100996442 16 25
+729737 19 39
+Row filtering
+%%bash
+grep '>' ~/scratch/bioi611/reference/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa
+>I dna:chromosome chromosome:WBcel235:I:1:15072434:1 REF
+>II dna:chromosome chromosome:WBcel235:II:1:15279421:1 REF
+>III dna:chromosome chromosome:WBcel235:III:1:13783801:1 REF
+>IV dna:chromosome chromosome:WBcel235:IV:1:17493829:1 REF
+>V dna:chromosome chromosome:WBcel235:V:1:20924180:1 REF
+>X dna:chromosome chromosome:WBcel235:X:1:17718942:1 REF
+>MtDNA dna:chromosome chromosome:WBcel235:MtDNA:1:13794:1 REF
+%%bash
+zcat GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz |wc -l
+zcat GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz |awk '$2>500' |wc -l
+zcat GSE164073_raw_counts_GRCh38.p13_NCBI.tsv.gz |awk '$2>500 && $3>500' |wc -l
+39377
+8773
+3820
+Text processing
+%%bash
+grep '>' ~/scratch/bioi611/reference/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa |sed 's/>//' |sed 's/ .*//'
+I
+II
+III
+IV
+V
+X
+MtDNA
+Regular Expressions
+Regular expressions are sequences of characters that define search patterns. They are commonly used for string matching, searching, and text processing.
+Regex is used in text editors, programming languages, command-line tools (likegrep and sed), and many bioinformatics tools to search, replace, or extract data from text.
-
+
- Metacharacters: Special characters that have specific meanings in regex syntax.
+
.(dot): Matches any single character except a newline. +Example:A.Gmatches "AAG", "ATG", "ACG", etc.
+
^: Matches the start of a line.
+Example: ^A matches any line starting with "A".
$: Matches the end of a line.
+Example: end$ matches any line ending with "end".
*: Matches 0 or more occurrences of the preceding character.
+Example: ca*t matches "ct", "cat", "caat", "caaat", etc.
+: Matches 1 or more occurrences of the preceding character.
+Example: ca+t matches "cat", "caat", "caaat", etc.
?: Matches 0 or 1 occurrence of the preceding character.
+Example: colou?r matches both "color" and "colour".
[]: Matches any one of the characters inside the brackets.
+Example: [aeiou] matches any vowel.
|: Alternation (OR) operator.
+Example: cat|dog matches either "cat" or "dog".
-
+
- Character Classes: Represents a set of characters. +
\d: Matches any digit (equivalent to [0-9]).
\w: Matches any word character (alphanumeric or underscore).
\s: Matches any whitespace character (spaces, tabs, etc.).
\D: Matches any non-digit character.
\W: Matches any non-word character.
\S: Matches any non-whitespace character.
-
+
- Quantifiers: Specify the number of occurrences to match +
{n}: Matches exactly n occurrences.
+Example: A{3} matches "AAA".
{n,}: Matches n or more occurrences.
+Example: T{2,} matches "TT", "TTT", "TTTT", etc.
{n,m}: Matches between n and m occurrences.
+Example: G{1,3} matches "G", "GG", or "GGG".
An example of the command line used
+%%bash
+grep -v '#' ~/scratch/bioi611/reference/Caenorhabditis_elegans.WBcel235.111.gtf \
+ |awk '$3=="gene"' \
+ |sed 's/.*gene_biotype "//' \
+ |sed 's/";//'|sort |uniq -c \
+ | sort -k1,1n
+ 22 rRNA
+ 100 antisense_RNA
+ 129 snRNA
+ 194 lincRNA
+ 261 miRNA
+ 346 snoRNA
+ 634 tRNA
+ 2128 pseudogene
+ 7764 ncRNA
+ 15363 piRNA
+ 19985 protein_coding
+Environment variables
+Environment variables are dynamic values that affect the behavior of processes and programs in Linux. They are commonly used to store configuration data and are essential in bioinformatics workflows for defining paths to software, libraries, and datasets.
+Commonly Used Environment Variables:
+-
+
PATH:
+
The PATH variable specifies directories where the system looks for executable files when a command is run.
%%bash
+echo $PATH
+/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/compiler/linux-rhel8-zen2/gcc/11.3.0/texlive/bin/x86_64-linux:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/compiler/linux-rhel8-zen2/gcc/11.3.0/imagemagick/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/compiler/linux-rhel8-zen2/gcc/11.3.0/graphviz/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/compiler/linux-rhel8-zen2/gcc/11.3.0/ghostscript/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/compiler/linux-rhel8-zen2/gcc/11.3.0/ffmpeg/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/mpi-nocuda/linux-rhel8-zen2/gcc/11.3.0/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/nompi-nocuda/linux-rhel8-zen2/gcc/11.3.0/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/views/2023/linux-rhel8-zen2/gcc@11.3.0/python-3.10.10/compiler/linux-rhel8-zen2/gcc/11.3.0/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/linux-rhel8-x86_64/gcc-rh8-8.5.0/gcc-11.3.0-oedkmii7vhd6rbnqm6xufmg7d3jx4w6l/bin:/cvmfs/hpcsw.umd.edu/spack-software/2023.11.20/linux-rhel8-zen2/gcc-11.3.0/py-jupyter-1.0.0-trwwgzwljql55mhmaygcuxb3nvaevjsu/bin:/software/acigs-utilities/bin:/home/xie186/miniforge3/bin:/home/xie186/miniforge3/condabin:/home/xie186/SHELL.bioi611/software/STAR_2.7.11b/Linux_x86_64_static:/home/xie186/.local/bin:/home/xie186/bin:/software/acigs-utilities/bin:/usr/share/Modules/bin:/usr/lib/heimdal/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/symas/bin:/opt/dell/srvadmin/bin
+-
+
HOME:
+
The HOME variable stores the path to the user’s home directory.
%%bash
+echo $HOME
+/home/xie186
+%%bash
+echo $SHELL
+/bin/bash
+Setting Environment Variables:
+Temporarily setting a variable (valid only for the current shell session):
+export PATH=value:PATH
+Permanently setting a variable:
+To make the environment variable persistent across sessions,
+it needs to be added to configuration files like .bashrc or .bash_profile.
+Example: Add the following line to .bashrc:
Software installation
+Installation via Conda
+Conda is a popular package management system, especially in bioinformatics, +due to its ability to create isolated environments. This is crucial when working with tools that have conflicting dependencies.
+-
+
- Install
conda/miniforge
+
Go to: https://github.com/conda-forge/miniforge/releases +Download the corresponding installtion file
+%%bash
+uname -m
+x86_64
+wget https://github.com/conda-forge/miniforge/releases/download/24.7.1-0/Mambaforge-24.7.1-0-Linux-x86_64.sh
+-
+
- Create conda environment and install software +
conda create -n bioi611
+conda activate bioi611
+conda install bioconda::fastqc==0.11.8
+Installation via Source Code (Manual Compilation)
+git clone https://github.com/lh3/bwa.git
+cd bwa; make
+./bwa index ref.fa
+Using Container for Bioinformatics Tools
+https://hub.docker.com/r/biocontainers/bwa/
+module load singularity
+singularity build bwa_v0.7.17_cv1.sif docker://biocontainers/bwa:v0.7.17_cv1
+Text editor in Linux
+In Linux, we sometimes need to create or edit a text file like writing a new perl script. So we need to use text editor.
+As a newbie, someone would prefer a basic, GUI-based text editor with menus and traditional CUA key bindings. Here we recommend Sublime, ATOM and Notepad++.
+But GUI-based text editor is not always available in Linux.
+A powerful screen text editor vi (pronounced “vee-eye”) is available on nearly all Linux system. We highly recommend vi as a text editor, because something we’ll have to edit a text file on a system without a friendlier text editor. Once we get familiar with vi, we’ll find that it’s very fast and powerful.
But remember, it’s OK if you think this part is too difficult at the beginning. You can use either Sublime, ATOM or Notepad++. If you are connecting to a Linux system without Sublime, ATOM and Notepad++, you can write the file in a local computer and then upload the file onto Linux system.
Basic vi skills
+As vi uses a lot of combination of keystrokes, it may be not easy for newbies to remember all the combinations in one fell swoop. Considering this, we’ll first introduce the basic skills someone needs to know to use vi. We need to first understand how three modes of vi work and then try to remember a few basic vi commonds. Then we can use these skills to write Perl or R scripts in the following chaptors for Perl and R (Figure \@ref(fig:workingModeVi)).
Three modes of vi:
Create new text file with vi
+mkdir test_vi ## generate a new folder
+cd test_vi ## go into the new folder
+echo "Using \`ls\` we don't expect files in this folder."
+ls
+echo "No file displayed!"
+Using the code above, we made a new directory named test_vi. We didn't see any file.
If we type vi test.py, an empty file and screen are created into which you may enter text because the file does not exist((Figure \@ref(fig:ViNewFile))).
vi test.py
+A screentshot of the vi test.py.
Now if you are in vi mode. To go to Input mode, you can type i, 'a' or 'o' (Figure \@ref(fig:ViInpuMode)).
A screentshot of the vi test.py.
Now you can type the content (codes or other information) (\@ref(fig:ViInpuType)).
+Once you are done typing. You need to go to Command mode(Figure \@ref(fig:workingModeVi)) if you want to save and exit the file. To do this, you need to press ESC button on the keyboard.
Now we just wrote a Perl script. We can run this script.
+
+python test.py
+High-Performance Computing (HPC) for Bioinformatics
+HPC resources enable bioinformatics analyses that require significant computational power and memory.
+Basics of HPC clusters and job schedulers (SLURM).
+An example of an job file (s1_star.sh):
#!/bin/bash
+#SBATCH --partition=standard
+#SBATCH -t 40:00:00
+#SBATCH -n 1
+#SBATCH -c 20
+#SBATCH --job-name=s1_star_aln
+#SBATCH --mail-type=FAIL,BEGIN,END
+#SBATCH --error=%x-%J-%u.err
+#SBATCH --output=%x-%J-%u.out
+conda activate bioi611
+mkdir -p STAR_align/
+STAR --genomeDir STAR_ref \
+ --outSAMtype BAM SortedByCoordinate \
+ --twopassMode Basic \
+ --quantMode TranscriptomeSAM GeneCounts \
+ --readFilesCommand zcat \
+ --outFileNamePrefix STAR_align/N2_day1_rep1. \
+ --runThreadN 20 \
+ --readFilesIn raw_data/N2_day1_rep1.fastq.gz
+To submit this job, run:
+sbatch s1_star.sh
+Check quota infomation
+%%bash
+scratch_quota
+# shell_quota
+# Group quotas
+ Group name Space used Space quota % quota used
+ zt-bioi611 285.811 MB 4.000 TB 0.01%
+ zt-bioi611_mgr 98.163 GB unlimited 0
+ total 98.449 GB unlimited 0
+# User quotas
+ User name Space used Space quota % quota used % of GrpTotal
+ xie186 98.449 GB unlimited 0 100.00%
+View information about Slurm nodes and partitions.
+%%bash
+sinfo
+PARTITION AVAIL TIMELIMIT NODES STATE NODELIST
+debug up 15:00 1 maint compute-b8-60
+debug up 15:00 1 drng compute-b8-57
+debug up 15:00 1 mix compute-b8-59
+debug up 15:00 1 alloc compute-b8-58
+scavenger up 14-00:00:0 1 inval compute-b8-48
+scavenger up 14-00:00:0 4 drain$ compute-b8-[53-56]
+scavenger up 14-00:00:0 84 maint compute-a7-[5,9,14-16,28,49],compute-a8-[2-4,8-9,15,18,22,24,29,37,44,51],compute-b5-[4,16,26,29-30,33,44,51-52],compute-b6-[7,12,21,28-29,32,34,43-46,50-51,59],compute-b7-[12-13,19-22,25,27,29,31,35,37,39,42,45-46,49-50,54,56-59],compute-b8-[16,19,21,23-24,29,32,35-37,39-45,60]
+scavenger up 14-00:00:0 2 drain* compute-a7-[13,43]
+scavenger up 14-00:00:0 13 drng compute-a8-[7,14],compute-b7-[14-15,18,38,43-44],compute-b8-[2,20,51,57],gpu-b9-5
+scavenger up 14-00:00:0 2 drain compute-a7-8,gpu-b10-5
+scavenger up 14-00:00:0 182 mix bigmem-a9-[1-2,4-5],compute-a5-[3-11],compute-a7-[2-3,6-7,10,12,17-19,21-22,30,38-40,45-46,48,54-56,60],compute-a8-[5-6,10-12,16-17,19-21,25,28,31-35,39,41,45,47,50,52,54,57-59],compute-b5-[1-3,5-8,11,13-15,17-25,27-28,31-32,34-43,45-50,53-55,57-58],compute-b6-[1-5,14-15,17-20,22-24,35-36,48-49,52,54],compute-b7-[1,7-8,16-17,23-24,26,28,30,32-34,36,40-41,47-48,51-52,55,60],compute-b8-[1,15,17-18,22,25-27,30-31,33,46-47,49-50,59],gpu-b9-[1-4,6-7],gpu-b10-[1-3,6-7],gpu-b11-[1-6]
+scavenger up 14-00:00:0 93 alloc bigmem-a9-[3,6],compute-a7-[1,4,11,20,23-27,29,31-37,41-42,44,47,50-53,57-59],compute-a8-[1,13,23,26-27,30,36,38,40,42-43,46,48-49,53,55-56,60],compute-b5-[9-10,12,56,59-60],compute-b6-[6,8-11,13,16,27,30-31,58,60],compute-b7-[2-6,9-11,53],compute-b8-[3-14,28,34,38,52,58],gpu-b10-4
+scavenger up 14-00:00:0 14 idle compute-b6-[25-26,33,37-42,47,53,55-57]
+standard* up 7-00:00:00 1 inval compute-b8-48
+standard* up 7-00:00:00 4 drain$ compute-b8-[53-56]
+standard* up 7-00:00:00 82 maint compute-a7-[5,9,14-16,28,49],compute-a8-[2-4,8-9,15,18,22,24,29,37,44,51],compute-b5-[4,16,26,29-30,33,44,51-52],compute-b6-[7,12,21,28-29,32,34,43-46,50-51],compute-b7-[12-13,19-22,25,27,29,31,35,37,39,42,45-46,49-50,54,56-59],compute-b8-[16,19,21,23-24,29,32,35-37,39-45]
+standard* up 7-00:00:00 2 drain* compute-a7-[13,43]
+standard* up 7-00:00:00 11 drng compute-a8-[7,14],compute-b7-[14-15,18,38,43-44],compute-b8-[2,20,51]
+standard* up 7-00:00:00 1 drain compute-a7-8
+standard* up 7-00:00:00 159 mix compute-a5-[3-11],compute-a7-[2-3,6-7,10,12,17-19,21-22,30,38-40,45-46,48,54-56,60],compute-a8-[5-6,10-12,16-17,19-21,25,28,31-35,39,41,45,47,50,52,54,57-59],compute-b5-[1-3,5-8,11,13-15,17-25,27-28,31-32,34-43,45-50,53-55,57-58],compute-b6-[1-5,14-15,17-20,22-24,35-36,48-49,52],compute-b7-[1,7-8,16-17,23-24,26,28,30,32-34,36,40-41,47-48,51-52,55,60],compute-b8-[1,15,17-18,22,25-27,30-31,33,46-47,49-50]
+standard* up 7-00:00:00 87 alloc compute-a7-[1,4,11,20,23-27,29,31-37,41-42,44,47,50-53,57-59],compute-a8-[1,13,23,26-27,30,36,38,40,42-43,46,48-49,53,55-56,60],compute-b5-[9-10,12,56,59-60],compute-b6-[6,8-11,13,16,27,30-31],compute-b7-[2-6,9-11,53],compute-b8-[3-14,28,34,38,52]
+standard* up 7-00:00:00 10 idle compute-b6-[25-26,33,37-42,47]
+serial up 14-00:00:0 1 maint compute-b6-59
+serial up 14-00:00:0 1 mix compute-b6-54
+serial up 14-00:00:0 2 alloc compute-b6-[58,60]
+serial up 14-00:00:0 4 idle compute-b6-[53,55-57]
+gpu up 7-00:00:00 1 down$ gpu-a6-3
+gpu up 7-00:00:00 1 drng gpu-b9-5
+gpu up 7-00:00:00 1 drain gpu-b10-5
+gpu up 7-00:00:00 19 mix gpu-a6-[6,8],gpu-b9-[1-4,6-7],gpu-b10-[1-3,6-7],gpu-b11-[1-6]
+gpu up 7-00:00:00 1 alloc gpu-b10-4
+gpu up 7-00:00:00 6 idle gpu-a5-1,gpu-a6-[2,4-5,7,9]
+bigmem up 7-00:00:00 4 mix bigmem-a9-[1-2,4-5]
+bigmem up 7-00:00:00 2 alloc bigmem-a9-[3,6]
+Check partitial information
+%%bash
+scontrol show partition standard
+PartitionName=standard
+ AllowGroups=ALL AllowAccounts=ALL AllowQos=ALL
+ AllocNodes=ALL Default=YES QoS=N/A
+ DefaultTime=00:15:00 DisableRootJobs=NO ExclusiveUser=NO GraceTime=0 Hidden=NO
+ MaxNodes=UNLIMITED MaxTime=7-00:00:00 MinNodes=0 LLN=NO MaxCPUsPerNode=UNLIMITED MaxCPUsPerSocket=UNLIMITED
+ Nodes=compute-a5-[3-11],compute-a7-[1-60],compute-a8-[1-60],compute-b5-[1-60],compute-b6-[1-52],compute-b7-[1-60],compute-b8-[1-56]
+ PriorityJobFactor=1 PriorityTier=1 RootOnly=NO ReqResv=NO OverSubscribe=YES:4
+ OverTimeLimit=NONE PreemptMode=REQUEUE
+ State=UP TotalCPUs=45696 TotalNodes=357 SelectTypeParameters=NONE
+ JobDefaults=(null)
+ DefMemPerNode=UNLIMITED MaxMemPerNode=UNLIMITED
+ TRES=cpu=45696,mem=178500G,node=357,billing=45696
+ TRESBillingWeights=CPU=1.0,Mem=0.25G
+Display node config information
+%%bash
+scontrol show node compute-a5-3
+NodeName=compute-a5-3 Arch=x86_64 CoresPerSocket=64
+ CPUAlloc=71 CPUEfctv=128 CPUTot=128 CPULoad=68.89
+ AvailableFeatures=rhel8,amd,epyc_7702,ib
+ ActiveFeatures=rhel8,amd,epyc_7702,ib
+ Gres=(null)
+ NodeAddr=compute-a5-3 NodeHostName=compute-a5-3 Version=23.11.9
+ OS=Linux 4.18.0-553.5.1.el8_10.x86_64 #1 SMP Tue May 21 03:13:04 EDT 2024
+ RealMemory=512000 AllocMem=296960 FreeMem=326630 Sockets=2 Boards=1
+ State=MIXED ThreadsPerCore=1 TmpDisk=300000 Weight=1 Owner=N/A MCS_label=N/A
+ Partitions=scavenger,standard
+ BootTime=2024-08-08T18:32:48 SlurmdStartTime=2024-08-12T17:43:23
+ LastBusyTime=2024-08-12T17:43:19 ResumeAfterTime=None
+ CfgTRES=cpu=128,mem=500G,billing=128
+ AllocTRES=cpu=71,mem=290G
+ CapWatts=n/a
+ CurrentWatts=630 AveWatts=294
+ ExtSensorsJoules=n/a ExtSensorsWatts=0 ExtSensorsTemp=n/a
+CPU Details: +* Total CPUs: 128 +* Allocated CPUs: 71
+Memory: +* Total Memory: 500 GB +* Allocated Memory: 290 GB +* Free Memory: ~319 GB
+View information about jobs located in the Slurm scheduling queue.
+%%bash
+squeue -u $USER
+ JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
+ 7563417 standard sys/dash xie186 R 48:15 1 compute-a5-5
+Cancel a job
+%%bash
+scancel <JOBID>
+
+
+
+
+ Bix4UMD/BIOI611_lab
+
+
+
+
+ Next »
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/basic_linux_files/9aa63990-e065-4ee7-aee6-c0e56c67cc38.png b/basic_linux_files/9aa63990-e065-4ee7-aee6-c0e56c67cc38.png
new file mode 100644
index 0000000..a0052c9
Binary files /dev/null and b/basic_linux_files/9aa63990-e065-4ee7-aee6-c0e56c67cc38.png differ
diff --git a/bulkRNAseq_lab.ipynb b/bulkRNAseq_lab.ipynb
new file mode 100644
index 0000000..994c98f
--- /dev/null
+++ b/bulkRNAseq_lab.ipynb
@@ -0,0 +1,293 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c5f7823",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @hidden_cell\n",
+ "import os\n",
+ "os.chdir('/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fae78bdc-2102-4b84-92b6-86966c1cc232",
+ "metadata": {},
+ "source": [
+ "## Download reference genome "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13fff280-eecc-4ddc-93a5-d4bf7d7baf46",
+ "metadata": {},
+ "source": [
+ "\n",
+ "To download the reference for this lab, we use [ENSEMBL database](https://useast.ensembl.org/Caenorhabditis_elegans/Info/Index). \n",
+ "In ENSEMBL database, each species may have different releases of genome build. We use `release-111` in this project. \n",
+ "\n",
+ "The genome sequences can be obtained from the link below:\n",
+ "https://ftp.ensembl.org/pub/release-111/fasta/caenorhabditis_elegans/dna/\n",
+ "\n",
+ "The genoe anntation file in gtf format can be obtained here: \n",
+ "https://ftp.ensembl.org/pub/release-111/gtf/caenorhabditis_elegans/\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "177be9c6",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "%%bash\n",
+ "wget -O Caenorhabditis_elegans.WBcel235.dna.toplevel.fa.gz https://ftp.ensembl.org/pub/release-111/fasta/caenorhabditis_elegans/dna/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa.gz\n",
+ "gunzip Caenorhabditis_elegans.WBcel235.dna.toplevel.fa.gz\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6a910ba",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "%%bash\n",
+ "## A *fai file will be generated\n",
+ "samtools faidx ref/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa \n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a636c9e5",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "%%bash\n",
+ "wget -O Caenorhabditis_elegans.WBcel235.111.gtf.gz -nv https://ftp.ensembl.org/pub/release-111/gtf/caenorhabditis_elegans/Caenorhabditis_elegans.WBcel235.111.gtf.gz\n",
+ "gunzip Caenorhabditis_elegans.WBcel235.111.gtf.gz\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d71d18a",
+ "metadata": {},
+ "source": [
+ "In this course, the reference files have been downloaded and stored in shared folder for BIOI611:\n",
+ "/scratch/zt1/project/bioi611/shared/reference/\n",
+ "\n",
+ "As you already leart, you can create a symbolic link for you to use in your scratch folder: \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "59184e9e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "cd /scratch/zt1/project/bioi611/user/$USER\n",
+ "ln -s /scratch/zt1/project/bioi611/shared/reference/ ."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13b0bad9-a31a-4d0b-9b0c-445a4752f0d1",
+ "metadata": {},
+ "source": [
+ "### How many chromsomes there are "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4123f560-aceb-4e54-a0ea-b52ce813a83a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">I dna:chromosome chromosome:WBcel235:I:1:15072434:1 REF\n",
+ ">II dna:chromosome chromosome:WBcel235:II:1:15279421:1 REF\n",
+ ">III dna:chromosome chromosome:WBcel235:III:1:13783801:1 REF\n",
+ ">IV dna:chromosome chromosome:WBcel235:IV:1:17493829:1 REF\n",
+ ">V dna:chromosome chromosome:WBcel235:V:1:20924180:1 REF\n",
+ ">X dna:chromosome chromosome:WBcel235:X:1:17718942:1 REF\n",
+ ">MtDNA dna:chromosome chromosome:WBcel235:MtDNA:1:13794:1 REF\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%bash\n",
+ "cd /scratch/zt1/project/bioi611/user/$USER\n",
+ "grep '>' reference/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "888e6856-762d-419c-b2a8-5e65c057dc54",
+ "metadata": {},
+ "source": [
+ "### How many genes there are "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "32313747-7131-4cfa-9b4f-1eb1728aa81d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 22 rRNA\n",
+ " 100 antisense_RNA\n",
+ " 129 snRNA\n",
+ " 194 lincRNA\n",
+ " 261 miRNA\n",
+ " 346 snoRNA\n",
+ " 634 tRNA\n",
+ " 2128 pseudogene\n",
+ " 7764 ncRNA\n",
+ " 15363 piRNA\n",
+ " 19985 protein_coding\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%bash\n",
+ "cd /scratch/zt1/project/bioi611/user/$USER\n",
+ "\n",
+ "grep -v '#' reference/Caenorhabditis_elegans.WBcel235.111.gtf \\\n",
+ " |awk '$3==\"gene\"' \\\n",
+ " |sed 's/.*gene_biotype \"//' \\\n",
+ " |sed 's/\";//'|sort |uniq -c \\\n",
+ " | sort -k1,1n"
+ ]
+ },
+ {
+ "attachments": {
+ "ffadf6f7-cfd1-4cdd-907e-7d4ac12c2a76.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "7a7bde26-6670-4df7-a5f8-6b7f3d2100f7",
+ "metadata": {},
+ "source": [
+ "\n",
+ "Source: https://useast.ensembl.org/Help/Faq?id=468eudogene\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f1c8a42",
+ "metadata": {},
+ "source": [
+ "## Download raw fastq files"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d85fdda7",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "%%bash\n",
+ "mkdir -p raw_data/ \n",
+ "curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/002/SRR15694102/SRR15694102.fastq.gz -o raw_data/N2_day7_rep1.fastq.gz\n",
+ "curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/001/SRR15694101/SRR15694101.fastq.gz -o raw_data/N2_day7_rep2.fastq.gz\n",
+ "curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/000/SRR15694100/SRR15694100.fastq.gz -o raw_data/N2_day7_rep3.fastq.gz\n",
+ "curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/099/SRR15694099/SRR15694099.fastq.gz -o raw_data/N2_day1_rep1.fastq.gz\n",
+ "curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/098/SRR15694098/SRR15694098.fastq.gz -o raw_data/N2_day1_rep2.fastq.gz\n",
+ "curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/097/SRR15694097/SRR15694097.fastq.gz -o raw_data/N2_day1_rep3.fastq.gz\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e574da47",
+ "metadata": {},
+ "source": [
+ "## Quality control "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9298b90",
+ "metadata": {},
+ "source": [
+ "1. Use FastQC to check the quality of fastq files:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6951d0e5",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "%%bash\n",
+ "cd /scratch/zt1/project/bioi611/user/$USER\n",
+ "sbatch ../../shared/scripts/bulkRNA_s1_fastqc.sub\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a223f5a",
+ "metadata": {},
+ "source": [
+ "2. Use `trim galore` to remove adaptors, low quality bases and low quality reads. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f673a41",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "%%bash\n",
+ "cd /scratch/zt1/project/bioi611/user/$USER\n",
+ "sbatch ../../shared/scripts/bulkRNA_s2_trim_galore.sub\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84fb878c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bulkRNAseq_lab/index.html b/bulkRNAseq_lab/index.html
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@@ -0,0 +1,233 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ # @hidden_cell
+import os
+os.chdir('/')
+Download reference genome
+To download the reference for this lab, we use ENSEMBL database.
+In ENSEMBL database, each species may have different releases of genome build. We use release-111 in this project.
The genome sequences can be obtained from the link below: +https://ftp.ensembl.org/pub/release-111/fasta/caenorhabditis_elegans/dna/
+The genoe anntation file in gtf format can be obtained here: +https://ftp.ensembl.org/pub/release-111/gtf/caenorhabditis_elegans/
+%%bash
+wget -O Caenorhabditis_elegans.WBcel235.dna.toplevel.fa.gz https://ftp.ensembl.org/pub/release-111/fasta/caenorhabditis_elegans/dna/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa.gz
+gunzip Caenorhabditis_elegans.WBcel235.dna.toplevel.fa.gz
+%%bash
+## A *fai file will be generated
+samtools faidx ref/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa
+%%bash
+wget -O Caenorhabditis_elegans.WBcel235.111.gtf.gz -nv https://ftp.ensembl.org/pub/release-111/gtf/caenorhabditis_elegans/Caenorhabditis_elegans.WBcel235.111.gtf.gz
+gunzip Caenorhabditis_elegans.WBcel235.111.gtf.gz
+In this course, the reference files have been downloaded and stored in shared folder for BIOI611: +/scratch/zt1/project/bioi611/shared/reference/
+As you already leart, you can create a symbolic link for you to use in your scratch folder:
+%%bash
+cd /scratch/zt1/project/bioi611/user/$USER
+ln -s /scratch/zt1/project/bioi611/shared/reference/ .
+How many chromsomes there are
+%%bash
+cd /scratch/zt1/project/bioi611/user/$USER
+grep '>' reference/Caenorhabditis_elegans.WBcel235.dna.toplevel.fa
+>I dna:chromosome chromosome:WBcel235:I:1:15072434:1 REF
+>II dna:chromosome chromosome:WBcel235:II:1:15279421:1 REF
+>III dna:chromosome chromosome:WBcel235:III:1:13783801:1 REF
+>IV dna:chromosome chromosome:WBcel235:IV:1:17493829:1 REF
+>V dna:chromosome chromosome:WBcel235:V:1:20924180:1 REF
+>X dna:chromosome chromosome:WBcel235:X:1:17718942:1 REF
+>MtDNA dna:chromosome chromosome:WBcel235:MtDNA:1:13794:1 REF
+How many genes there are
+%%bash
+cd /scratch/zt1/project/bioi611/user/$USER
+
+grep -v '#' reference/Caenorhabditis_elegans.WBcel235.111.gtf \
+ |awk '$3=="gene"' \
+ |sed 's/.*gene_biotype "//' \
+ |sed 's/";//'|sort |uniq -c \
+ | sort -k1,1n
+ 22 rRNA
+ 100 antisense_RNA
+ 129 snRNA
+ 194 lincRNA
+ 261 miRNA
+ 346 snoRNA
+ 634 tRNA
+ 2128 pseudogene
+ 7764 ncRNA
+ 15363 piRNA
+ 19985 protein_coding
+
+Source: https://useast.ensembl.org/Help/Faq?id=468eudogene
Download raw fastq files
+%%bash
+mkdir -p raw_data/
+curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/002/SRR15694102/SRR15694102.fastq.gz -o raw_data/N2_day7_rep1.fastq.gz
+curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/001/SRR15694101/SRR15694101.fastq.gz -o raw_data/N2_day7_rep2.fastq.gz
+curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/000/SRR15694100/SRR15694100.fastq.gz -o raw_data/N2_day7_rep3.fastq.gz
+curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/099/SRR15694099/SRR15694099.fastq.gz -o raw_data/N2_day1_rep1.fastq.gz
+curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/098/SRR15694098/SRR15694098.fastq.gz -o raw_data/N2_day1_rep2.fastq.gz
+curl -L ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR156/097/SRR15694097/SRR15694097.fastq.gz -o raw_data/N2_day1_rep3.fastq.gz
+Quality control
+-
+
- Use FastQC to check the quality of fastq files: +
%%bash
+cd /scratch/zt1/project/bioi611/user/$USER
+sbatch ../../shared/scripts/bulkRNA_s1_fastqc.sub
+-
+
- Use
trim galoreto remove adaptors, low quality bases and low quality reads.
+
%%bash
+cd /scratch/zt1/project/bioi611/user/$USER
+sbatch ../../shared/scripts/bulkRNA_s2_trim_galore.sub
+
+
+
+
+
+ Bix4UMD/BIOI611_lab
+
+
+
+ « Previous
+
+
+ Next »
+
+
+
+
+
+
+
+
+
+
+
+
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dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt{display:table;margin:6px 0;font-size:90%;line-height:normal;background:#e7f2fa;color:#2980b9;border-top:3px solid #6ab0de;padding:6px;position:relative}html.writer-html4 .rst-content dl:not(.docutils)>dt:before,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:before{color:#6ab0de}html.writer-html4 .rst-content dl:not(.docutils)>dt .headerlink,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink{color:#404040;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt{margin-bottom:6px;border:none;border-left:3px solid #ccc;background:#f0f0f0;color:#555}html.writer-html4 .rst-content dl:not(.docutils) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink{color:#404040;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils)>dt:first-child,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:first-child{margin-top:0}html.writer-html4 .rst-content dl:not(.docutils) code.descclassname,html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descclassname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{background-color:transparent;border:none;padding:0;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .optional,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .optional{display:inline-block;padding:0 4px;color:#000;font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .property,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .property{display:inline-block;padding-right:8px;max-width:100%}html.writer-html4 .rst-content dl:not(.docutils) .k,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .k{font-style:italic}html.writer-html4 .rst-content dl:not(.docutils) .descclassname,html.writer-html4 .rst-content dl:not(.docutils) .descname,html.writer-html4 .rst-content dl:not(.docutils) .sig-name,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .sig-name{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#000}.rst-content .viewcode-back,.rst-content .viewcode-link{display:inline-block;color:#27ae60;font-size:80%;padding-left:24px}.rst-content .viewcode-back{display:block;float:right}.rst-content p.rubric{margin-bottom:12px;font-weight:700}.rst-content code.download,.rst-content tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>.kbd,.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>kbd{color:inherit;font-size:80%;background-color:#fff;border:1px solid #a6a6a6;border-radius:4px;box-shadow:0 2px grey;padding:2.4px 6px;margin:auto 0}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) 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diff --git a/css/theme_extra.css b/css/theme_extra.css
new file mode 100644
index 0000000..ab0631a
--- /dev/null
+++ b/css/theme_extra.css
@@ -0,0 +1,197 @@
+/*
+ * Wrap inline code samples otherwise they shoot of the side and
+ * can't be read at all.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/313
+ * https://github.com/mkdocs/mkdocs/issues/233
+ * https://github.com/mkdocs/mkdocs/issues/834
+ */
+.rst-content code {
+ white-space: pre-wrap;
+ word-wrap: break-word;
+ padding: 2px 5px;
+}
+
+/**
+ * Make code blocks display as blocks and give them the appropriate
+ * font size and padding.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/855
+ * https://github.com/mkdocs/mkdocs/issues/834
+ * https://github.com/mkdocs/mkdocs/issues/233
+ */
+.rst-content pre code {
+ white-space: pre;
+ word-wrap: normal;
+ display: block;
+ padding: 12px;
+ font-size: 12px;
+}
+
+/**
+ * Fix code colors
+ *
+ * https://github.com/mkdocs/mkdocs/issues/2027
+ */
+.rst-content code {
+ color: #E74C3C;
+}
+
+.rst-content pre code {
+ color: #000;
+ background: #f8f8f8;
+}
+
+/*
+ * Fix link colors when the link text is inline code.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/718
+ */
+a code {
+ color: #2980B9;
+}
+a:hover code {
+ color: #3091d1;
+}
+a:visited code {
+ color: #9B59B6;
+}
+
+/*
+ * The CSS classes from highlight.js seem to clash with the
+ * ReadTheDocs theme causing some code to be incorrectly made
+ * bold and italic.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/411
+ */
+pre .cs, pre .c {
+ font-weight: inherit;
+ font-style: inherit;
+}
+
+/*
+ * Fix some issues with the theme and non-highlighted code
+ * samples. Without and highlighting styles attached the
+ * formatting is broken.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/319
+ */
+.rst-content .no-highlight {
+ display: block;
+ padding: 0.5em;
+ color: #333;
+}
+
+
+/*
+ * Additions specific to the search functionality provided by MkDocs
+ */
+
+.search-results {
+ margin-top: 23px;
+}
+
+.search-results article {
+ border-top: 1px solid #E1E4E5;
+ padding-top: 24px;
+}
+
+.search-results article:first-child {
+ border-top: none;
+}
+
+form .search-query {
+ width: 100%;
+ border-radius: 50px;
+ padding: 6px 12px;
+ border-color: #D1D4D5;
+}
+
+/*
+ * Improve inline code blocks within admonitions.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/656
+ */
+ .rst-content .admonition code {
+ color: #404040;
+ border: 1px solid #c7c9cb;
+ border: 1px solid rgba(0, 0, 0, 0.2);
+ background: #f8fbfd;
+ background: rgba(255, 255, 255, 0.7);
+}
+
+/*
+ * Account for wide tables which go off the side.
+ * Override borders to avoid weirdness on narrow tables.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/834
+ * https://github.com/mkdocs/mkdocs/pull/1034
+ */
+.rst-content .section .docutils {
+ width: 100%;
+ overflow: auto;
+ display: block;
+ border: none;
+}
+
+td, th {
+ border: 1px solid #e1e4e5 !important;
+ border-collapse: collapse;
+}
+
+/*
+ * Without the following amendments, the navigation in the theme will be
+ * slightly cut off. This is due to the fact that the .wy-nav-side has a
+ * padding-bottom of 2em, which must not necessarily align with the font-size of
+ * 90 % on the .rst-current-version container, combined with the padding of 12px
+ * above and below. These amendments fix this in two steps: First, make sure the
+ * .rst-current-version container has a fixed height of 40px, achieved using
+ * line-height, and then applying a padding-bottom of 40px to this container. In
+ * a second step, the items within that container are re-aligned using flexbox.
+ *
+ * https://github.com/mkdocs/mkdocs/issues/2012
+ */
+ .wy-nav-side {
+ padding-bottom: 40px;
+}
+
+/* For section-index only */
+.wy-menu-vertical .current-section p {
+ background-color: #e3e3e3;
+ color: #404040;
+}
+
+/*
+ * The second step of above amendment: Here we make sure the items are aligned
+ * correctly within the .rst-current-version container. Using flexbox, we
+ * achieve it in such a way that it will look like the following:
+ *
+ * [No repo_name]
+ * Next >> // On the first page
+ * << Previous Next >> // On all subsequent pages
+ *
+ * [With repo_name]
+ *
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ To be added
+
+
+
+
+
+ Bix4UMD/BIOI611_lab
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/img/favicon.ico b/img/favicon.ico
new file mode 100644
index 0000000..e85006a
Binary files /dev/null and b/img/favicon.ico differ
diff --git a/index.html b/index.html
new file mode 100644
index 0000000..ee20d52
--- /dev/null
+++ b/index.html
@@ -0,0 +1,132 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ BIOI611 lab
+Welcome to BIOI 611! I’m excited to have you in this course, where we will delve into the fascinating world of transcriptomics and explore the intricacies of gene and transcript-level expression analysis.
+This course focuses on the analysis of transcriptomics data, and specifically on the analysis of gene and transcript-level expression. Material covered includes transcript and gene expression estimation from RNA-seq data (short and long-read), basic experimental design and statistical methods for differential expression analysis, discovery of novel transcripts via reference-guided and de novo assembly, and the analysis of single-cell gene expression data (e.g., single-cell expression quantification, dimensionality reduction, clustering, pseudotime analysis). Prerequisite: BIOI 604. Core.
+ +
+
+
+
+ Bix4UMD/BIOI611_lab
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/js/html5shiv.min.js b/js/html5shiv.min.js
new file mode 100644
index 0000000..1a01c94
--- /dev/null
+++ b/js/html5shiv.min.js
@@ -0,0 +1,4 @@
+/**
+* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ To be added
+
+
+
+
+
+ Bix4UMD/BIOI611_lab
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/ref.ipynb b/ref.ipynb
new file mode 100644
index 0000000..a610add
--- /dev/null
+++ b/ref.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cddc4857-4def-4e55-988f-43ff2d033833",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ref/index.html b/ref/index.html
new file mode 100644
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--- /dev/null
+++ b/ref/index.html
@@ -0,0 +1,126 @@
+
+
+
+
+
+
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+
+
+
+
+
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+
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+
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+
+
+
+ Bix4UMD/BIOI611_lab
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/search.html b/search.html
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--- /dev/null
+++ b/search.html
@@ -0,0 +1,127 @@
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Search Results
+ + + +
+ Searching...
+
+
+
+
+
+
+
+ Bix4UMD/BIOI611_lab
+
+
+
+
+
+
+
+
+
+
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+
+
diff --git a/search/lunr.js b/search/lunr.js
new file mode 100644
index 0000000..aca0a16
--- /dev/null
+++ b/search/lunr.js
@@ -0,0 +1,3475 @@
+/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
+ * Copyright (C) 2020 Oliver Nightingale
+ * @license MIT
+ */
+
+;(function(){
+
+/**
+ * A convenience function for configuring and constructing
+ * a new lunr Index.
+ *
+ * A lunr.Builder instance is created and the pipeline setup
+ * with a trimmer, stop word filter and stemmer.
+ *
+ * This builder object is yielded to the configuration function
+ * that is passed as a parameter, allowing the list of fields
+ * and other builder parameters to be customised.
+ *
+ * All documents _must_ be added within the passed config function.
+ *
+ * @example
+ * var idx = lunr(function () {
+ * this.field('title')
+ * this.field('body')
+ * this.ref('id')
+ *
+ * documents.forEach(function (doc) {
+ * this.add(doc)
+ * }, this)
+ * })
+ *
+ * @see {@link lunr.Builder}
+ * @see {@link lunr.Pipeline}
+ * @see {@link lunr.trimmer}
+ * @see {@link lunr.stopWordFilter}
+ * @see {@link lunr.stemmer}
+ * @namespace {function} lunr
+ */
+var lunr = function (config) {
+ var builder = new lunr.Builder
+
+ builder.pipeline.add(
+ lunr.trimmer,
+ lunr.stopWordFilter,
+ lunr.stemmer
+ )
+
+ builder.searchPipeline.add(
+ lunr.stemmer
+ )
+
+ config.call(builder, builder)
+ return builder.build()
+}
+
+lunr.version = "2.3.9"
+/*!
+ * lunr.utils
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A namespace containing utils for the rest of the lunr library
+ * @namespace lunr.utils
+ */
+lunr.utils = {}
+
+/**
+ * Print a warning message to the console.
+ *
+ * @param {String} message The message to be printed.
+ * @memberOf lunr.utils
+ * @function
+ */
+lunr.utils.warn = (function (global) {
+ /* eslint-disable no-console */
+ return function (message) {
+ if (global.console && console.warn) {
+ console.warn(message)
+ }
+ }
+ /* eslint-enable no-console */
+})(this)
+
+/**
+ * Convert an object to a string.
+ *
+ * In the case of `null` and `undefined` the function returns
+ * the empty string, in all other cases the result of calling
+ * `toString` on the passed object is returned.
+ *
+ * @param {Any} obj The object to convert to a string.
+ * @return {String} string representation of the passed object.
+ * @memberOf lunr.utils
+ */
+lunr.utils.asString = function (obj) {
+ if (obj === void 0 || obj === null) {
+ return ""
+ } else {
+ return obj.toString()
+ }
+}
+
+/**
+ * Clones an object.
+ *
+ * Will create a copy of an existing object such that any mutations
+ * on the copy cannot affect the original.
+ *
+ * Only shallow objects are supported, passing a nested object to this
+ * function will cause a TypeError.
+ *
+ * Objects with primitives, and arrays of primitives are supported.
+ *
+ * @param {Object} obj The object to clone.
+ * @return {Object} a clone of the passed object.
+ * @throws {TypeError} when a nested object is passed.
+ * @memberOf Utils
+ */
+lunr.utils.clone = function (obj) {
+ if (obj === null || obj === undefined) {
+ return obj
+ }
+
+ var clone = Object.create(null),
+ keys = Object.keys(obj)
+
+ for (var i = 0; i < keys.length; i++) {
+ var key = keys[i],
+ val = obj[key]
+
+ if (Array.isArray(val)) {
+ clone[key] = val.slice()
+ continue
+ }
+
+ if (typeof val === 'string' ||
+ typeof val === 'number' ||
+ typeof val === 'boolean') {
+ clone[key] = val
+ continue
+ }
+
+ throw new TypeError("clone is not deep and does not support nested objects")
+ }
+
+ return clone
+}
+lunr.FieldRef = function (docRef, fieldName, stringValue) {
+ this.docRef = docRef
+ this.fieldName = fieldName
+ this._stringValue = stringValue
+}
+
+lunr.FieldRef.joiner = "/"
+
+lunr.FieldRef.fromString = function (s) {
+ var n = s.indexOf(lunr.FieldRef.joiner)
+
+ if (n === -1) {
+ throw "malformed field ref string"
+ }
+
+ var fieldRef = s.slice(0, n),
+ docRef = s.slice(n + 1)
+
+ return new lunr.FieldRef (docRef, fieldRef, s)
+}
+
+lunr.FieldRef.prototype.toString = function () {
+ if (this._stringValue == undefined) {
+ this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef
+ }
+
+ return this._stringValue
+}
+/*!
+ * lunr.Set
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A lunr set.
+ *
+ * @constructor
+ */
+lunr.Set = function (elements) {
+ this.elements = Object.create(null)
+
+ if (elements) {
+ this.length = elements.length
+
+ for (var i = 0; i < this.length; i++) {
+ this.elements[elements[i]] = true
+ }
+ } else {
+ this.length = 0
+ }
+}
+
+/**
+ * A complete set that contains all elements.
+ *
+ * @static
+ * @readonly
+ * @type {lunr.Set}
+ */
+lunr.Set.complete = {
+ intersect: function (other) {
+ return other
+ },
+
+ union: function () {
+ return this
+ },
+
+ contains: function () {
+ return true
+ }
+}
+
+/**
+ * An empty set that contains no elements.
+ *
+ * @static
+ * @readonly
+ * @type {lunr.Set}
+ */
+lunr.Set.empty = {
+ intersect: function () {
+ return this
+ },
+
+ union: function (other) {
+ return other
+ },
+
+ contains: function () {
+ return false
+ }
+}
+
+/**
+ * Returns true if this set contains the specified object.
+ *
+ * @param {object} object - Object whose presence in this set is to be tested.
+ * @returns {boolean} - True if this set contains the specified object.
+ */
+lunr.Set.prototype.contains = function (object) {
+ return !!this.elements[object]
+}
+
+/**
+ * Returns a new set containing only the elements that are present in both
+ * this set and the specified set.
+ *
+ * @param {lunr.Set} other - set to intersect with this set.
+ * @returns {lunr.Set} a new set that is the intersection of this and the specified set.
+ */
+
+lunr.Set.prototype.intersect = function (other) {
+ var a, b, elements, intersection = []
+
+ if (other === lunr.Set.complete) {
+ return this
+ }
+
+ if (other === lunr.Set.empty) {
+ return other
+ }
+
+ if (this.length < other.length) {
+ a = this
+ b = other
+ } else {
+ a = other
+ b = this
+ }
+
+ elements = Object.keys(a.elements)
+
+ for (var i = 0; i < elements.length; i++) {
+ var element = elements[i]
+ if (element in b.elements) {
+ intersection.push(element)
+ }
+ }
+
+ return new lunr.Set (intersection)
+}
+
+/**
+ * Returns a new set combining the elements of this and the specified set.
+ *
+ * @param {lunr.Set} other - set to union with this set.
+ * @return {lunr.Set} a new set that is the union of this and the specified set.
+ */
+
+lunr.Set.prototype.union = function (other) {
+ if (other === lunr.Set.complete) {
+ return lunr.Set.complete
+ }
+
+ if (other === lunr.Set.empty) {
+ return this
+ }
+
+ return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))
+}
+/**
+ * A function to calculate the inverse document frequency for
+ * a posting. This is shared between the builder and the index
+ *
+ * @private
+ * @param {object} posting - The posting for a given term
+ * @param {number} documentCount - The total number of documents.
+ */
+lunr.idf = function (posting, documentCount) {
+ var documentsWithTerm = 0
+
+ for (var fieldName in posting) {
+ if (fieldName == '_index') continue // Ignore the term index, its not a field
+ documentsWithTerm += Object.keys(posting[fieldName]).length
+ }
+
+ var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)
+
+ return Math.log(1 + Math.abs(x))
+}
+
+/**
+ * A token wraps a string representation of a token
+ * as it is passed through the text processing pipeline.
+ *
+ * @constructor
+ * @param {string} [str=''] - The string token being wrapped.
+ * @param {object} [metadata={}] - Metadata associated with this token.
+ */
+lunr.Token = function (str, metadata) {
+ this.str = str || ""
+ this.metadata = metadata || {}
+}
+
+/**
+ * Returns the token string that is being wrapped by this object.
+ *
+ * @returns {string}
+ */
+lunr.Token.prototype.toString = function () {
+ return this.str
+}
+
+/**
+ * A token update function is used when updating or optionally
+ * when cloning a token.
+ *
+ * @callback lunr.Token~updateFunction
+ * @param {string} str - The string representation of the token.
+ * @param {Object} metadata - All metadata associated with this token.
+ */
+
+/**
+ * Applies the given function to the wrapped string token.
+ *
+ * @example
+ * token.update(function (str, metadata) {
+ * return str.toUpperCase()
+ * })
+ *
+ * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.
+ * @returns {lunr.Token}
+ */
+lunr.Token.prototype.update = function (fn) {
+ this.str = fn(this.str, this.metadata)
+ return this
+}
+
+/**
+ * Creates a clone of this token. Optionally a function can be
+ * applied to the cloned token.
+ *
+ * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.
+ * @returns {lunr.Token}
+ */
+lunr.Token.prototype.clone = function (fn) {
+ fn = fn || function (s) { return s }
+ return new lunr.Token (fn(this.str, this.metadata), this.metadata)
+}
+/*!
+ * lunr.tokenizer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A function for splitting a string into tokens ready to be inserted into
+ * the search index. Uses `lunr.tokenizer.separator` to split strings, change
+ * the value of this property to change how strings are split into tokens.
+ *
+ * This tokenizer will convert its parameter to a string by calling `toString` and
+ * then will split this string on the character in `lunr.tokenizer.separator`.
+ * Arrays will have their elements converted to strings and wrapped in a lunr.Token.
+ *
+ * Optional metadata can be passed to the tokenizer, this metadata will be cloned and
+ * added as metadata to every token that is created from the object to be tokenized.
+ *
+ * @static
+ * @param {?(string|object|object[])} obj - The object to convert into tokens
+ * @param {?object} metadata - Optional metadata to associate with every token
+ * @returns {lunr.Token[]}
+ * @see {@link lunr.Pipeline}
+ */
+lunr.tokenizer = function (obj, metadata) {
+ if (obj == null || obj == undefined) {
+ return []
+ }
+
+ if (Array.isArray(obj)) {
+ return obj.map(function (t) {
+ return new lunr.Token(
+ lunr.utils.asString(t).toLowerCase(),
+ lunr.utils.clone(metadata)
+ )
+ })
+ }
+
+ var str = obj.toString().toLowerCase(),
+ len = str.length,
+ tokens = []
+
+ for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {
+ var char = str.charAt(sliceEnd),
+ sliceLength = sliceEnd - sliceStart
+
+ if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {
+
+ if (sliceLength > 0) {
+ var tokenMetadata = lunr.utils.clone(metadata) || {}
+ tokenMetadata["position"] = [sliceStart, sliceLength]
+ tokenMetadata["index"] = tokens.length
+
+ tokens.push(
+ new lunr.Token (
+ str.slice(sliceStart, sliceEnd),
+ tokenMetadata
+ )
+ )
+ }
+
+ sliceStart = sliceEnd + 1
+ }
+
+ }
+
+ return tokens
+}
+
+/**
+ * The separator used to split a string into tokens. Override this property to change the behaviour of
+ * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.
+ *
+ * @static
+ * @see lunr.tokenizer
+ */
+lunr.tokenizer.separator = /[\s\-]+/
+/*!
+ * lunr.Pipeline
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.Pipelines maintain an ordered list of functions to be applied to all
+ * tokens in documents entering the search index and queries being ran against
+ * the index.
+ *
+ * An instance of lunr.Index created with the lunr shortcut will contain a
+ * pipeline with a stop word filter and an English language stemmer. Extra
+ * functions can be added before or after either of these functions or these
+ * default functions can be removed.
+ *
+ * When run the pipeline will call each function in turn, passing a token, the
+ * index of that token in the original list of all tokens and finally a list of
+ * all the original tokens.
+ *
+ * The output of functions in the pipeline will be passed to the next function
+ * in the pipeline. To exclude a token from entering the index the function
+ * should return undefined, the rest of the pipeline will not be called with
+ * this token.
+ *
+ * For serialisation of pipelines to work, all functions used in an instance of
+ * a pipeline should be registered with lunr.Pipeline. Registered functions can
+ * then be loaded. If trying to load a serialised pipeline that uses functions
+ * that are not registered an error will be thrown.
+ *
+ * If not planning on serialising the pipeline then registering pipeline functions
+ * is not necessary.
+ *
+ * @constructor
+ */
+lunr.Pipeline = function () {
+ this._stack = []
+}
+
+lunr.Pipeline.registeredFunctions = Object.create(null)
+
+/**
+ * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token
+ * string as well as all known metadata. A pipeline function can mutate the token string
+ * or mutate (or add) metadata for a given token.
+ *
+ * A pipeline function can indicate that the passed token should be discarded by returning
+ * null, undefined or an empty string. This token will not be passed to any downstream pipeline
+ * functions and will not be added to the index.
+ *
+ * Multiple tokens can be returned by returning an array of tokens. Each token will be passed
+ * to any downstream pipeline functions and all will returned tokens will be added to the index.
+ *
+ * Any number of pipeline functions may be chained together using a lunr.Pipeline.
+ *
+ * @interface lunr.PipelineFunction
+ * @param {lunr.Token} token - A token from the document being processed.
+ * @param {number} i - The index of this token in the complete list of tokens for this document/field.
+ * @param {lunr.Token[]} tokens - All tokens for this document/field.
+ * @returns {(?lunr.Token|lunr.Token[])}
+ */
+
+/**
+ * Register a function with the pipeline.
+ *
+ * Functions that are used in the pipeline should be registered if the pipeline
+ * needs to be serialised, or a serialised pipeline needs to be loaded.
+ *
+ * Registering a function does not add it to a pipeline, functions must still be
+ * added to instances of the pipeline for them to be used when running a pipeline.
+ *
+ * @param {lunr.PipelineFunction} fn - The function to check for.
+ * @param {String} label - The label to register this function with
+ */
+lunr.Pipeline.registerFunction = function (fn, label) {
+ if (label in this.registeredFunctions) {
+ lunr.utils.warn('Overwriting existing registered function: ' + label)
+ }
+
+ fn.label = label
+ lunr.Pipeline.registeredFunctions[fn.label] = fn
+}
+
+/**
+ * Warns if the function is not registered as a Pipeline function.
+ *
+ * @param {lunr.PipelineFunction} fn - The function to check for.
+ * @private
+ */
+lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {
+ var isRegistered = fn.label && (fn.label in this.registeredFunctions)
+
+ if (!isRegistered) {
+ lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn)
+ }
+}
+
+/**
+ * Loads a previously serialised pipeline.
+ *
+ * All functions to be loaded must already be registered with lunr.Pipeline.
+ * If any function from the serialised data has not been registered then an
+ * error will be thrown.
+ *
+ * @param {Object} serialised - The serialised pipeline to load.
+ * @returns {lunr.Pipeline}
+ */
+lunr.Pipeline.load = function (serialised) {
+ var pipeline = new lunr.Pipeline
+
+ serialised.forEach(function (fnName) {
+ var fn = lunr.Pipeline.registeredFunctions[fnName]
+
+ if (fn) {
+ pipeline.add(fn)
+ } else {
+ throw new Error('Cannot load unregistered function: ' + fnName)
+ }
+ })
+
+ return pipeline
+}
+
+/**
+ * Adds new functions to the end of the pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.
+ */
+lunr.Pipeline.prototype.add = function () {
+ var fns = Array.prototype.slice.call(arguments)
+
+ fns.forEach(function (fn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(fn)
+ this._stack.push(fn)
+ }, this)
+}
+
+/**
+ * Adds a single function after a function that already exists in the
+ * pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
+ * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
+ */
+lunr.Pipeline.prototype.after = function (existingFn, newFn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
+
+ var pos = this._stack.indexOf(existingFn)
+ if (pos == -1) {
+ throw new Error('Cannot find existingFn')
+ }
+
+ pos = pos + 1
+ this._stack.splice(pos, 0, newFn)
+}
+
+/**
+ * Adds a single function before a function that already exists in the
+ * pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
+ * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
+ */
+lunr.Pipeline.prototype.before = function (existingFn, newFn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
+
+ var pos = this._stack.indexOf(existingFn)
+ if (pos == -1) {
+ throw new Error('Cannot find existingFn')
+ }
+
+ this._stack.splice(pos, 0, newFn)
+}
+
+/**
+ * Removes a function from the pipeline.
+ *
+ * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.
+ */
+lunr.Pipeline.prototype.remove = function (fn) {
+ var pos = this._stack.indexOf(fn)
+ if (pos == -1) {
+ return
+ }
+
+ this._stack.splice(pos, 1)
+}
+
+/**
+ * Runs the current list of functions that make up the pipeline against the
+ * passed tokens.
+ *
+ * @param {Array} tokens The tokens to run through the pipeline.
+ * @returns {Array}
+ */
+lunr.Pipeline.prototype.run = function (tokens) {
+ var stackLength = this._stack.length
+
+ for (var i = 0; i < stackLength; i++) {
+ var fn = this._stack[i]
+ var memo = []
+
+ for (var j = 0; j < tokens.length; j++) {
+ var result = fn(tokens[j], j, tokens)
+
+ if (result === null || result === void 0 || result === '') continue
+
+ if (Array.isArray(result)) {
+ for (var k = 0; k < result.length; k++) {
+ memo.push(result[k])
+ }
+ } else {
+ memo.push(result)
+ }
+ }
+
+ tokens = memo
+ }
+
+ return tokens
+}
+
+/**
+ * Convenience method for passing a string through a pipeline and getting
+ * strings out. This method takes care of wrapping the passed string in a
+ * token and mapping the resulting tokens back to strings.
+ *
+ * @param {string} str - The string to pass through the pipeline.
+ * @param {?object} metadata - Optional metadata to associate with the token
+ * passed to the pipeline.
+ * @returns {string[]}
+ */
+lunr.Pipeline.prototype.runString = function (str, metadata) {
+ var token = new lunr.Token (str, metadata)
+
+ return this.run([token]).map(function (t) {
+ return t.toString()
+ })
+}
+
+/**
+ * Resets the pipeline by removing any existing processors.
+ *
+ */
+lunr.Pipeline.prototype.reset = function () {
+ this._stack = []
+}
+
+/**
+ * Returns a representation of the pipeline ready for serialisation.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @returns {Array}
+ */
+lunr.Pipeline.prototype.toJSON = function () {
+ return this._stack.map(function (fn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(fn)
+
+ return fn.label
+ })
+}
+/*!
+ * lunr.Vector
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A vector is used to construct the vector space of documents and queries. These
+ * vectors support operations to determine the similarity between two documents or
+ * a document and a query.
+ *
+ * Normally no parameters are required for initializing a vector, but in the case of
+ * loading a previously dumped vector the raw elements can be provided to the constructor.
+ *
+ * For performance reasons vectors are implemented with a flat array, where an elements
+ * index is immediately followed by its value. E.g. [index, value, index, value]. This
+ * allows the underlying array to be as sparse as possible and still offer decent
+ * performance when being used for vector calculations.
+ *
+ * @constructor
+ * @param {Number[]} [elements] - The flat list of element index and element value pairs.
+ */
+lunr.Vector = function (elements) {
+ this._magnitude = 0
+ this.elements = elements || []
+}
+
+
+/**
+ * Calculates the position within the vector to insert a given index.
+ *
+ * This is used internally by insert and upsert. If there are duplicate indexes then
+ * the position is returned as if the value for that index were to be updated, but it
+ * is the callers responsibility to check whether there is a duplicate at that index
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.positionForIndex = function (index) {
+ // For an empty vector the tuple can be inserted at the beginning
+ if (this.elements.length == 0) {
+ return 0
+ }
+
+ var start = 0,
+ end = this.elements.length / 2,
+ sliceLength = end - start,
+ pivotPoint = Math.floor(sliceLength / 2),
+ pivotIndex = this.elements[pivotPoint * 2]
+
+ while (sliceLength > 1) {
+ if (pivotIndex < index) {
+ start = pivotPoint
+ }
+
+ if (pivotIndex > index) {
+ end = pivotPoint
+ }
+
+ if (pivotIndex == index) {
+ break
+ }
+
+ sliceLength = end - start
+ pivotPoint = start + Math.floor(sliceLength / 2)
+ pivotIndex = this.elements[pivotPoint * 2]
+ }
+
+ if (pivotIndex == index) {
+ return pivotPoint * 2
+ }
+
+ if (pivotIndex > index) {
+ return pivotPoint * 2
+ }
+
+ if (pivotIndex < index) {
+ return (pivotPoint + 1) * 2
+ }
+}
+
+/**
+ * Inserts an element at an index within the vector.
+ *
+ * Does not allow duplicates, will throw an error if there is already an entry
+ * for this index.
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @param {Number} val - The value to be inserted into the vector.
+ */
+lunr.Vector.prototype.insert = function (insertIdx, val) {
+ this.upsert(insertIdx, val, function () {
+ throw "duplicate index"
+ })
+}
+
+/**
+ * Inserts or updates an existing index within the vector.
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @param {Number} val - The value to be inserted into the vector.
+ * @param {function} fn - A function that is called for updates, the existing value and the
+ * requested value are passed as arguments
+ */
+lunr.Vector.prototype.upsert = function (insertIdx, val, fn) {
+ this._magnitude = 0
+ var position = this.positionForIndex(insertIdx)
+
+ if (this.elements[position] == insertIdx) {
+ this.elements[position + 1] = fn(this.elements[position + 1], val)
+ } else {
+ this.elements.splice(position, 0, insertIdx, val)
+ }
+}
+
+/**
+ * Calculates the magnitude of this vector.
+ *
+ * @returns {Number}
+ */
+lunr.Vector.prototype.magnitude = function () {
+ if (this._magnitude) return this._magnitude
+
+ var sumOfSquares = 0,
+ elementsLength = this.elements.length
+
+ for (var i = 1; i < elementsLength; i += 2) {
+ var val = this.elements[i]
+ sumOfSquares += val * val
+ }
+
+ return this._magnitude = Math.sqrt(sumOfSquares)
+}
+
+/**
+ * Calculates the dot product of this vector and another vector.
+ *
+ * @param {lunr.Vector} otherVector - The vector to compute the dot product with.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.dot = function (otherVector) {
+ var dotProduct = 0,
+ a = this.elements, b = otherVector.elements,
+ aLen = a.length, bLen = b.length,
+ aVal = 0, bVal = 0,
+ i = 0, j = 0
+
+ while (i < aLen && j < bLen) {
+ aVal = a[i], bVal = b[j]
+ if (aVal < bVal) {
+ i += 2
+ } else if (aVal > bVal) {
+ j += 2
+ } else if (aVal == bVal) {
+ dotProduct += a[i + 1] * b[j + 1]
+ i += 2
+ j += 2
+ }
+ }
+
+ return dotProduct
+}
+
+/**
+ * Calculates the similarity between this vector and another vector.
+ *
+ * @param {lunr.Vector} otherVector - The other vector to calculate the
+ * similarity with.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.similarity = function (otherVector) {
+ return this.dot(otherVector) / this.magnitude() || 0
+}
+
+/**
+ * Converts the vector to an array of the elements within the vector.
+ *
+ * @returns {Number[]}
+ */
+lunr.Vector.prototype.toArray = function () {
+ var output = new Array (this.elements.length / 2)
+
+ for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {
+ output[j] = this.elements[i]
+ }
+
+ return output
+}
+
+/**
+ * A JSON serializable representation of the vector.
+ *
+ * @returns {Number[]}
+ */
+lunr.Vector.prototype.toJSON = function () {
+ return this.elements
+}
+/* eslint-disable */
+/*!
+ * lunr.stemmer
+ * Copyright (C) 2020 Oliver Nightingale
+ * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
+ */
+
+/**
+ * lunr.stemmer is an english language stemmer, this is a JavaScript
+ * implementation of the PorterStemmer taken from http://tartarus.org/~martin
+ *
+ * @static
+ * @implements {lunr.PipelineFunction}
+ * @param {lunr.Token} token - The string to stem
+ * @returns {lunr.Token}
+ * @see {@link lunr.Pipeline}
+ * @function
+ */
+lunr.stemmer = (function(){
+ var step2list = {
+ "ational" : "ate",
+ "tional" : "tion",
+ "enci" : "ence",
+ "anci" : "ance",
+ "izer" : "ize",
+ "bli" : "ble",
+ "alli" : "al",
+ "entli" : "ent",
+ "eli" : "e",
+ "ousli" : "ous",
+ "ization" : "ize",
+ "ation" : "ate",
+ "ator" : "ate",
+ "alism" : "al",
+ "iveness" : "ive",
+ "fulness" : "ful",
+ "ousness" : "ous",
+ "aliti" : "al",
+ "iviti" : "ive",
+ "biliti" : "ble",
+ "logi" : "log"
+ },
+
+ step3list = {
+ "icate" : "ic",
+ "ative" : "",
+ "alize" : "al",
+ "iciti" : "ic",
+ "ical" : "ic",
+ "ful" : "",
+ "ness" : ""
+ },
+
+ c = "[^aeiou]", // consonant
+ v = "[aeiouy]", // vowel
+ C = c + "[^aeiouy]*", // consonant sequence
+ V = v + "[aeiou]*", // vowel sequence
+
+ mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0
+ meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1
+ mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1
+ s_v = "^(" + C + ")?" + v; // vowel in stem
+
+ var re_mgr0 = new RegExp(mgr0);
+ var re_mgr1 = new RegExp(mgr1);
+ var re_meq1 = new RegExp(meq1);
+ var re_s_v = new RegExp(s_v);
+
+ var re_1a = /^(.+?)(ss|i)es$/;
+ var re2_1a = /^(.+?)([^s])s$/;
+ var re_1b = /^(.+?)eed$/;
+ var re2_1b = /^(.+?)(ed|ing)$/;
+ var re_1b_2 = /.$/;
+ var re2_1b_2 = /(at|bl|iz)$/;
+ var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$");
+ var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+
+ var re_1c = /^(.+?[^aeiou])y$/;
+ var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+
+ var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+
+ var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+ var re2_4 = /^(.+?)(s|t)(ion)$/;
+
+ var re_5 = /^(.+?)e$/;
+ var re_5_1 = /ll$/;
+ var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+
+ var porterStemmer = function porterStemmer(w) {
+ var stem,
+ suffix,
+ firstch,
+ re,
+ re2,
+ re3,
+ re4;
+
+ if (w.length < 3) { return w; }
+
+ firstch = w.substr(0,1);
+ if (firstch == "y") {
+ w = firstch.toUpperCase() + w.substr(1);
+ }
+
+ // Step 1a
+ re = re_1a
+ re2 = re2_1a;
+
+ if (re.test(w)) { w = w.replace(re,"$1$2"); }
+ else if (re2.test(w)) { w = w.replace(re2,"$1$2"); }
+
+ // Step 1b
+ re = re_1b;
+ re2 = re2_1b;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ re = re_mgr0;
+ if (re.test(fp[1])) {
+ re = re_1b_2;
+ w = w.replace(re,"");
+ }
+ } else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1];
+ re2 = re_s_v;
+ if (re2.test(stem)) {
+ w = stem;
+ re2 = re2_1b_2;
+ re3 = re3_1b_2;
+ re4 = re4_1b_2;
+ if (re2.test(w)) { w = w + "e"; }
+ else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); }
+ else if (re4.test(w)) { w = w + "e"; }
+ }
+ }
+
+ // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)
+ re = re_1c;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ w = stem + "i";
+ }
+
+ // Step 2
+ re = re_2;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = re_mgr0;
+ if (re.test(stem)) {
+ w = stem + step2list[suffix];
+ }
+ }
+
+ // Step 3
+ re = re_3;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = re_mgr0;
+ if (re.test(stem)) {
+ w = stem + step3list[suffix];
+ }
+ }
+
+ // Step 4
+ re = re_4;
+ re2 = re2_4;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = re_mgr1;
+ if (re.test(stem)) {
+ w = stem;
+ }
+ } else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1] + fp[2];
+ re2 = re_mgr1;
+ if (re2.test(stem)) {
+ w = stem;
+ }
+ }
+
+ // Step 5
+ re = re_5;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = re_mgr1;
+ re2 = re_meq1;
+ re3 = re3_5;
+ if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {
+ w = stem;
+ }
+ }
+
+ re = re_5_1;
+ re2 = re_mgr1;
+ if (re.test(w) && re2.test(w)) {
+ re = re_1b_2;
+ w = w.replace(re,"");
+ }
+
+ // and turn initial Y back to y
+
+ if (firstch == "y") {
+ w = firstch.toLowerCase() + w.substr(1);
+ }
+
+ return w;
+ };
+
+ return function (token) {
+ return token.update(porterStemmer);
+ }
+})();
+
+lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')
+/*!
+ * lunr.stopWordFilter
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.generateStopWordFilter builds a stopWordFilter function from the provided
+ * list of stop words.
+ *
+ * The built in lunr.stopWordFilter is built using this generator and can be used
+ * to generate custom stopWordFilters for applications or non English languages.
+ *
+ * @function
+ * @param {Array} token The token to pass through the filter
+ * @returns {lunr.PipelineFunction}
+ * @see lunr.Pipeline
+ * @see lunr.stopWordFilter
+ */
+lunr.generateStopWordFilter = function (stopWords) {
+ var words = stopWords.reduce(function (memo, stopWord) {
+ memo[stopWord] = stopWord
+ return memo
+ }, {})
+
+ return function (token) {
+ if (token && words[token.toString()] !== token.toString()) return token
+ }
+}
+
+/**
+ * lunr.stopWordFilter is an English language stop word list filter, any words
+ * contained in the list will not be passed through the filter.
+ *
+ * This is intended to be used in the Pipeline. If the token does not pass the
+ * filter then undefined will be returned.
+ *
+ * @function
+ * @implements {lunr.PipelineFunction}
+ * @params {lunr.Token} token - A token to check for being a stop word.
+ * @returns {lunr.Token}
+ * @see {@link lunr.Pipeline}
+ */
+lunr.stopWordFilter = lunr.generateStopWordFilter([
+ 'a',
+ 'able',
+ 'about',
+ 'across',
+ 'after',
+ 'all',
+ 'almost',
+ 'also',
+ 'am',
+ 'among',
+ 'an',
+ 'and',
+ 'any',
+ 'are',
+ 'as',
+ 'at',
+ 'be',
+ 'because',
+ 'been',
+ 'but',
+ 'by',
+ 'can',
+ 'cannot',
+ 'could',
+ 'dear',
+ 'did',
+ 'do',
+ 'does',
+ 'either',
+ 'else',
+ 'ever',
+ 'every',
+ 'for',
+ 'from',
+ 'get',
+ 'got',
+ 'had',
+ 'has',
+ 'have',
+ 'he',
+ 'her',
+ 'hers',
+ 'him',
+ 'his',
+ 'how',
+ 'however',
+ 'i',
+ 'if',
+ 'in',
+ 'into',
+ 'is',
+ 'it',
+ 'its',
+ 'just',
+ 'least',
+ 'let',
+ 'like',
+ 'likely',
+ 'may',
+ 'me',
+ 'might',
+ 'most',
+ 'must',
+ 'my',
+ 'neither',
+ 'no',
+ 'nor',
+ 'not',
+ 'of',
+ 'off',
+ 'often',
+ 'on',
+ 'only',
+ 'or',
+ 'other',
+ 'our',
+ 'own',
+ 'rather',
+ 'said',
+ 'say',
+ 'says',
+ 'she',
+ 'should',
+ 'since',
+ 'so',
+ 'some',
+ 'than',
+ 'that',
+ 'the',
+ 'their',
+ 'them',
+ 'then',
+ 'there',
+ 'these',
+ 'they',
+ 'this',
+ 'tis',
+ 'to',
+ 'too',
+ 'twas',
+ 'us',
+ 'wants',
+ 'was',
+ 'we',
+ 'were',
+ 'what',
+ 'when',
+ 'where',
+ 'which',
+ 'while',
+ 'who',
+ 'whom',
+ 'why',
+ 'will',
+ 'with',
+ 'would',
+ 'yet',
+ 'you',
+ 'your'
+])
+
+lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')
+/*!
+ * lunr.trimmer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.trimmer is a pipeline function for trimming non word
+ * characters from the beginning and end of tokens before they
+ * enter the index.
+ *
+ * This implementation may not work correctly for non latin
+ * characters and should either be removed or adapted for use
+ * with languages with non-latin characters.
+ *
+ * @static
+ * @implements {lunr.PipelineFunction}
+ * @param {lunr.Token} token The token to pass through the filter
+ * @returns {lunr.Token}
+ * @see lunr.Pipeline
+ */
+lunr.trimmer = function (token) {
+ return token.update(function (s) {
+ return s.replace(/^\W+/, '').replace(/\W+$/, '')
+ })
+}
+
+lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')
+/*!
+ * lunr.TokenSet
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A token set is used to store the unique list of all tokens
+ * within an index. Token sets are also used to represent an
+ * incoming query to the index, this query token set and index
+ * token set are then intersected to find which tokens to look
+ * up in the inverted index.
+ *
+ * A token set can hold multiple tokens, as in the case of the
+ * index token set, or it can hold a single token as in the
+ * case of a simple query token set.
+ *
+ * Additionally token sets are used to perform wildcard matching.
+ * Leading, contained and trailing wildcards are supported, and
+ * from this edit distance matching can also be provided.
+ *
+ * Token sets are implemented as a minimal finite state automata,
+ * where both common prefixes and suffixes are shared between tokens.
+ * This helps to reduce the space used for storing the token set.
+ *
+ * @constructor
+ */
+lunr.TokenSet = function () {
+ this.final = false
+ this.edges = {}
+ this.id = lunr.TokenSet._nextId
+ lunr.TokenSet._nextId += 1
+}
+
+/**
+ * Keeps track of the next, auto increment, identifier to assign
+ * to a new tokenSet.
+ *
+ * TokenSets require a unique identifier to be correctly minimised.
+ *
+ * @private
+ */
+lunr.TokenSet._nextId = 1
+
+/**
+ * Creates a TokenSet instance from the given sorted array of words.
+ *
+ * @param {String[]} arr - A sorted array of strings to create the set from.
+ * @returns {lunr.TokenSet}
+ * @throws Will throw an error if the input array is not sorted.
+ */
+lunr.TokenSet.fromArray = function (arr) {
+ var builder = new lunr.TokenSet.Builder
+
+ for (var i = 0, len = arr.length; i < len; i++) {
+ builder.insert(arr[i])
+ }
+
+ builder.finish()
+ return builder.root
+}
+
+/**
+ * Creates a token set from a query clause.
+ *
+ * @private
+ * @param {Object} clause - A single clause from lunr.Query.
+ * @param {string} clause.term - The query clause term.
+ * @param {number} [clause.editDistance] - The optional edit distance for the term.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.fromClause = function (clause) {
+ if ('editDistance' in clause) {
+ return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)
+ } else {
+ return lunr.TokenSet.fromString(clause.term)
+ }
+}
+
+/**
+ * Creates a token set representing a single string with a specified
+ * edit distance.
+ *
+ * Insertions, deletions, substitutions and transpositions are each
+ * treated as an edit distance of 1.
+ *
+ * Increasing the allowed edit distance will have a dramatic impact
+ * on the performance of both creating and intersecting these TokenSets.
+ * It is advised to keep the edit distance less than 3.
+ *
+ * @param {string} str - The string to create the token set from.
+ * @param {number} editDistance - The allowed edit distance to match.
+ * @returns {lunr.Vector}
+ */
+lunr.TokenSet.fromFuzzyString = function (str, editDistance) {
+ var root = new lunr.TokenSet
+
+ var stack = [{
+ node: root,
+ editsRemaining: editDistance,
+ str: str
+ }]
+
+ while (stack.length) {
+ var frame = stack.pop()
+
+ // no edit
+ if (frame.str.length > 0) {
+ var char = frame.str.charAt(0),
+ noEditNode
+
+ if (char in frame.node.edges) {
+ noEditNode = frame.node.edges[char]
+ } else {
+ noEditNode = new lunr.TokenSet
+ frame.node.edges[char] = noEditNode
+ }
+
+ if (frame.str.length == 1) {
+ noEditNode.final = true
+ }
+
+ stack.push({
+ node: noEditNode,
+ editsRemaining: frame.editsRemaining,
+ str: frame.str.slice(1)
+ })
+ }
+
+ if (frame.editsRemaining == 0) {
+ continue
+ }
+
+ // insertion
+ if ("*" in frame.node.edges) {
+ var insertionNode = frame.node.edges["*"]
+ } else {
+ var insertionNode = new lunr.TokenSet
+ frame.node.edges["*"] = insertionNode
+ }
+
+ if (frame.str.length == 0) {
+ insertionNode.final = true
+ }
+
+ stack.push({
+ node: insertionNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str
+ })
+
+ // deletion
+ // can only do a deletion if we have enough edits remaining
+ // and if there are characters left to delete in the string
+ if (frame.str.length > 1) {
+ stack.push({
+ node: frame.node,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str.slice(1)
+ })
+ }
+
+ // deletion
+ // just removing the last character from the str
+ if (frame.str.length == 1) {
+ frame.node.final = true
+ }
+
+ // substitution
+ // can only do a substitution if we have enough edits remaining
+ // and if there are characters left to substitute
+ if (frame.str.length >= 1) {
+ if ("*" in frame.node.edges) {
+ var substitutionNode = frame.node.edges["*"]
+ } else {
+ var substitutionNode = new lunr.TokenSet
+ frame.node.edges["*"] = substitutionNode
+ }
+
+ if (frame.str.length == 1) {
+ substitutionNode.final = true
+ }
+
+ stack.push({
+ node: substitutionNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str.slice(1)
+ })
+ }
+
+ // transposition
+ // can only do a transposition if there are edits remaining
+ // and there are enough characters to transpose
+ if (frame.str.length > 1) {
+ var charA = frame.str.charAt(0),
+ charB = frame.str.charAt(1),
+ transposeNode
+
+ if (charB in frame.node.edges) {
+ transposeNode = frame.node.edges[charB]
+ } else {
+ transposeNode = new lunr.TokenSet
+ frame.node.edges[charB] = transposeNode
+ }
+
+ if (frame.str.length == 1) {
+ transposeNode.final = true
+ }
+
+ stack.push({
+ node: transposeNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: charA + frame.str.slice(2)
+ })
+ }
+ }
+
+ return root
+}
+
+/**
+ * Creates a TokenSet from a string.
+ *
+ * The string may contain one or more wildcard characters (*)
+ * that will allow wildcard matching when intersecting with
+ * another TokenSet.
+ *
+ * @param {string} str - The string to create a TokenSet from.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.fromString = function (str) {
+ var node = new lunr.TokenSet,
+ root = node
+
+ /*
+ * Iterates through all characters within the passed string
+ * appending a node for each character.
+ *
+ * When a wildcard character is found then a self
+ * referencing edge is introduced to continually match
+ * any number of any characters.
+ */
+ for (var i = 0, len = str.length; i < len; i++) {
+ var char = str[i],
+ final = (i == len - 1)
+
+ if (char == "*") {
+ node.edges[char] = node
+ node.final = final
+
+ } else {
+ var next = new lunr.TokenSet
+ next.final = final
+
+ node.edges[char] = next
+ node = next
+ }
+ }
+
+ return root
+}
+
+/**
+ * Converts this TokenSet into an array of strings
+ * contained within the TokenSet.
+ *
+ * This is not intended to be used on a TokenSet that
+ * contains wildcards, in these cases the results are
+ * undefined and are likely to cause an infinite loop.
+ *
+ * @returns {string[]}
+ */
+lunr.TokenSet.prototype.toArray = function () {
+ var words = []
+
+ var stack = [{
+ prefix: "",
+ node: this
+ }]
+
+ while (stack.length) {
+ var frame = stack.pop(),
+ edges = Object.keys(frame.node.edges),
+ len = edges.length
+
+ if (frame.node.final) {
+ /* In Safari, at this point the prefix is sometimes corrupted, see:
+ * https://github.com/olivernn/lunr.js/issues/279 Calling any
+ * String.prototype method forces Safari to "cast" this string to what
+ * it's supposed to be, fixing the bug. */
+ frame.prefix.charAt(0)
+ words.push(frame.prefix)
+ }
+
+ for (var i = 0; i < len; i++) {
+ var edge = edges[i]
+
+ stack.push({
+ prefix: frame.prefix.concat(edge),
+ node: frame.node.edges[edge]
+ })
+ }
+ }
+
+ return words
+}
+
+/**
+ * Generates a string representation of a TokenSet.
+ *
+ * This is intended to allow TokenSets to be used as keys
+ * in objects, largely to aid the construction and minimisation
+ * of a TokenSet. As such it is not designed to be a human
+ * friendly representation of the TokenSet.
+ *
+ * @returns {string}
+ */
+lunr.TokenSet.prototype.toString = function () {
+ // NOTE: Using Object.keys here as this.edges is very likely
+ // to enter 'hash-mode' with many keys being added
+ //
+ // avoiding a for-in loop here as it leads to the function
+ // being de-optimised (at least in V8). From some simple
+ // benchmarks the performance is comparable, but allowing
+ // V8 to optimize may mean easy performance wins in the future.
+
+ if (this._str) {
+ return this._str
+ }
+
+ var str = this.final ? '1' : '0',
+ labels = Object.keys(this.edges).sort(),
+ len = labels.length
+
+ for (var i = 0; i < len; i++) {
+ var label = labels[i],
+ node = this.edges[label]
+
+ str = str + label + node.id
+ }
+
+ return str
+}
+
+/**
+ * Returns a new TokenSet that is the intersection of
+ * this TokenSet and the passed TokenSet.
+ *
+ * This intersection will take into account any wildcards
+ * contained within the TokenSet.
+ *
+ * @param {lunr.TokenSet} b - An other TokenSet to intersect with.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.prototype.intersect = function (b) {
+ var output = new lunr.TokenSet,
+ frame = undefined
+
+ var stack = [{
+ qNode: b,
+ output: output,
+ node: this
+ }]
+
+ while (stack.length) {
+ frame = stack.pop()
+
+ // NOTE: As with the #toString method, we are using
+ // Object.keys and a for loop instead of a for-in loop
+ // as both of these objects enter 'hash' mode, causing
+ // the function to be de-optimised in V8
+ var qEdges = Object.keys(frame.qNode.edges),
+ qLen = qEdges.length,
+ nEdges = Object.keys(frame.node.edges),
+ nLen = nEdges.length
+
+ for (var q = 0; q < qLen; q++) {
+ var qEdge = qEdges[q]
+
+ for (var n = 0; n < nLen; n++) {
+ var nEdge = nEdges[n]
+
+ if (nEdge == qEdge || qEdge == '*') {
+ var node = frame.node.edges[nEdge],
+ qNode = frame.qNode.edges[qEdge],
+ final = node.final && qNode.final,
+ next = undefined
+
+ if (nEdge in frame.output.edges) {
+ // an edge already exists for this character
+ // no need to create a new node, just set the finality
+ // bit unless this node is already final
+ next = frame.output.edges[nEdge]
+ next.final = next.final || final
+
+ } else {
+ // no edge exists yet, must create one
+ // set the finality bit and insert it
+ // into the output
+ next = new lunr.TokenSet
+ next.final = final
+ frame.output.edges[nEdge] = next
+ }
+
+ stack.push({
+ qNode: qNode,
+ output: next,
+ node: node
+ })
+ }
+ }
+ }
+ }
+
+ return output
+}
+lunr.TokenSet.Builder = function () {
+ this.previousWord = ""
+ this.root = new lunr.TokenSet
+ this.uncheckedNodes = []
+ this.minimizedNodes = {}
+}
+
+lunr.TokenSet.Builder.prototype.insert = function (word) {
+ var node,
+ commonPrefix = 0
+
+ if (word < this.previousWord) {
+ throw new Error ("Out of order word insertion")
+ }
+
+ for (var i = 0; i < word.length && i < this.previousWord.length; i++) {
+ if (word[i] != this.previousWord[i]) break
+ commonPrefix++
+ }
+
+ this.minimize(commonPrefix)
+
+ if (this.uncheckedNodes.length == 0) {
+ node = this.root
+ } else {
+ node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child
+ }
+
+ for (var i = commonPrefix; i < word.length; i++) {
+ var nextNode = new lunr.TokenSet,
+ char = word[i]
+
+ node.edges[char] = nextNode
+
+ this.uncheckedNodes.push({
+ parent: node,
+ char: char,
+ child: nextNode
+ })
+
+ node = nextNode
+ }
+
+ node.final = true
+ this.previousWord = word
+}
+
+lunr.TokenSet.Builder.prototype.finish = function () {
+ this.minimize(0)
+}
+
+lunr.TokenSet.Builder.prototype.minimize = function (downTo) {
+ for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {
+ var node = this.uncheckedNodes[i],
+ childKey = node.child.toString()
+
+ if (childKey in this.minimizedNodes) {
+ node.parent.edges[node.char] = this.minimizedNodes[childKey]
+ } else {
+ // Cache the key for this node since
+ // we know it can't change anymore
+ node.child._str = childKey
+
+ this.minimizedNodes[childKey] = node.child
+ }
+
+ this.uncheckedNodes.pop()
+ }
+}
+/*!
+ * lunr.Index
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * An index contains the built index of all documents and provides a query interface
+ * to the index.
+ *
+ * Usually instances of lunr.Index will not be created using this constructor, instead
+ * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be
+ * used to load previously built and serialized indexes.
+ *
+ * @constructor
+ * @param {Object} attrs - The attributes of the built search index.
+ * @param {Object} attrs.invertedIndex - An index of term/field to document reference.
+ * @param {Object' + noResultsText + '
'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 0000000..f719dbc --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"BIOI611 lab Welcome to BIOI 611! I\u2019m excited to have you in this course, where we will delve into the fascinating world of transcriptomics and explore the intricacies of gene and transcript-level expression analysis. This course focuses on the analysis of transcriptomics data, and specifically on the analysis of gene and transcript-level expression. Material covered includes transcript and gene expression estimation from RNA-seq data (short and long-read), basic experimental design and statistical methods for differential expression analysis, discovery of novel transcripts via reference-guided and de novo assembly, and the analysis of single-cell gene expression data (e.g., single-cell expression quantification, dimensionality reduction, clustering, pseudotime analysis). Prerequisite: BIOI 604. Core.","title":"BIOI611 lab"},{"location":"#bioi611-lab","text":"Welcome to BIOI 611! I\u2019m excited to have you in this course, where we will delve into the fascinating world of transcriptomics and explore the intricacies of gene and transcript-level expression analysis. This course focuses on the analysis of transcriptomics data, and specifically on the analysis of gene and transcript-level expression. Material covered includes transcript and gene expression estimation from RNA-seq data (short and long-read), basic experimental design and statistical methods for differential expression analysis, discovery of novel transcripts via reference-guided and de novo assembly, and the analysis of single-cell gene expression data (e.g., single-cell expression quantification, dimensionality reduction, clustering, pseudotime analysis). Prerequisite: BIOI 604. Core.","title":"BIOI611 lab"},{"location":"FASTQ_PHRED/","text":"What is PHRED Scores A Phred score is a measure of the probability that a base call in a DNA sequencing read is incorrect. It is a logarithmic scale, meaning that a small change in the Phred score represents a large change in the probability of an error. $$Q = -10 \\cdot \\log_{10}(P)$$ Where: Q is the PHRED score. P is the probability that the base was called incorrectly. For example: Q = 20 : This corresponds to a 1 in 100 probability of an incorrect base call, or an accuracy of 99%. Q = 30 : This corresponds to a 1 in 1000 probability of an incorrect base call, or an accuracy of 99.9%. Q = 40 : This corresponds to a 1 in 10,000 probability of an incorrect base call, or an accuracy of 99.99%. # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"Phred\", \"Prob of\")) cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"score\", \"Incorrect call\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { cat(sprintf(\"%-5d\\t\\t%0.5f\\n\", phred, 10^(phred / -10))) } What is ASCII ASCII (American Standard Code for Information Interchange) is used to represent characters in computers. We can represent Phred scores using ASCII characters. The advantage is that the quality information can be esisly stored in text based FASTQ file. Not all ASCII characters are printable. The first printable ASCII character is ! and the decimal code for the character for ! is 33. # Store output in a vector to fit on a slide output <- c(sprintf(\"%-8s %-8s\", \"Character\", \"ASCII #\")) # Loop through ASCII values from 33 to 89 for (i in 33:89) { output <- c(output, sprintf(\"%-8s %-8d\", intToUtf8(i), i)) } # Print the output in a single block (e.g., to fit on a slide) cat(paste(output, collapse = \"\\n\")) Phred scores in FASTQ file In a FASTQ file, Phred scores are represented as ASCII characters. These characters are converted back to numeric values (PHRED scores) based on the encoding scheme used: PHRED+33 Encoding (Sanger/Illumina 1.8+) : The ASCII character for a quality score Q is calculated as: ASCII character=chr(Q+33) For example: A PHRED score of 30 is encoded as chr(30 + 33) = chr(63) , which corresponds to the ASCII character ? . PHRED+64 Encoding (Illumina 1.3-1.7) : The ASCII character for a quality score QQQ is calculated as: ASCII character=chr(Q+64) For example: A PHRED score of 30 is encoded as chr(30 + 64) = chr(94) , which corresponds to the ASCII character ^ . # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t\\t%-10s\\n\", \"Phred\", \"Prob. of\", \"ASCII\", \"ASCII\")) cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t%-10s\\n\", \"score\", \"Error\", \"Phred+33\", \"Phred+64\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { # Calculate the probability of error prob_error <- 10^(phred / -10) # Convert Phred scores to ASCII characters ascii_phred33 <- intToUtf8(phred + 33) ascii_phred64 <- intToUtf8(phred + 64) # Print the results in a formatted table cat(sprintf(\"%-5d\\t\\t%0.5f\\t\\t%-6s\\t\\t%-10s\\n\", phred, prob_error, ascii_phred33, ascii_phred64)) }","title":"PRED Score in Bioinformatics"},{"location":"FASTQ_PHRED/#what-is-phred-scores","text":"A Phred score is a measure of the probability that a base call in a DNA sequencing read is incorrect. It is a logarithmic scale, meaning that a small change in the Phred score represents a large change in the probability of an error. $$Q = -10 \\cdot \\log_{10}(P)$$ Where: Q is the PHRED score. P is the probability that the base was called incorrectly. For example: Q = 20 : This corresponds to a 1 in 100 probability of an incorrect base call, or an accuracy of 99%. Q = 30 : This corresponds to a 1 in 1000 probability of an incorrect base call, or an accuracy of 99.9%. Q = 40 : This corresponds to a 1 in 10,000 probability of an incorrect base call, or an accuracy of 99.99%. # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"Phred\", \"Prob of\")) cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"score\", \"Incorrect call\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { cat(sprintf(\"%-5d\\t\\t%0.5f\\n\", phred, 10^(phred / -10))) }","title":"What is PHRED Scores"},{"location":"FASTQ_PHRED/#what-is-ascii","text":"ASCII (American Standard Code for Information Interchange) is used to represent characters in computers. We can represent Phred scores using ASCII characters. The advantage is that the quality information can be esisly stored in text based FASTQ file. Not all ASCII characters are printable. The first printable ASCII character is ! and the decimal code for the character for ! is 33. # Store output in a vector to fit on a slide output <- c(sprintf(\"%-8s %-8s\", \"Character\", \"ASCII #\")) # Loop through ASCII values from 33 to 89 for (i in 33:89) { output <- c(output, sprintf(\"%-8s %-8d\", intToUtf8(i), i)) } # Print the output in a single block (e.g., to fit on a slide) cat(paste(output, collapse = \"\\n\"))","title":"What is ASCII"},{"location":"FASTQ_PHRED/#phred-scores-in-fastq-file","text":"In a FASTQ file, Phred scores are represented as ASCII characters. These characters are converted back to numeric values (PHRED scores) based on the encoding scheme used: PHRED+33 Encoding (Sanger/Illumina 1.8+) : The ASCII character for a quality score Q is calculated as: ASCII character=chr(Q+33) For example: A PHRED score of 30 is encoded as chr(30 + 33) = chr(63) , which corresponds to the ASCII character ? . PHRED+64 Encoding (Illumina 1.3-1.7) : The ASCII character for a quality score QQQ is calculated as: ASCII character=chr(Q+64) For example: A PHRED score of 30 is encoded as chr(30 + 64) = chr(94) , which corresponds to the ASCII character ^ . # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t\\t%-10s\\n\", \"Phred\", \"Prob. of\", \"ASCII\", \"ASCII\")) cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t%-10s\\n\", \"score\", \"Error\", \"Phred+33\", \"Phred+64\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { # Calculate the probability of error prob_error <- 10^(phred / -10) # Convert Phred scores to ASCII characters ascii_phred33 <- intToUtf8(phred + 33) ascii_phred64 <- intToUtf8(phred + 64) # Print the results in a formatted table cat(sprintf(\"%-5d\\t\\t%0.5f\\t\\t%-6s\\t\\t%-10s\\n\", phred, prob_error, ascii_phred33, ascii_phred64)) }","title":"Phred scores in FASTQ file"},{"location":"Phred_FQ/","text":"What is PHRED Scores A Phred score is a measure of the probability that a base call in a DNA sequencing read is incorrect. It is a logarithmic scale, meaning that a small change in the Phred score represents a large change in the probability of an error. $$Q = -10 \\cdot \\log_{10}(P)$$ Where: Q is the PHRED score. P is the probability that the base was called incorrectly. For example: Q = 20 : This corresponds to a 1 in 100 probability of an incorrect base call, or an accuracy of 99%. Q = 30 : This corresponds to a 1 in 1000 probability of an incorrect base call, or an accuracy of 99.9%. Q = 40 : This corresponds to a 1 in 10,000 probability of an incorrect base call, or an accuracy of 99.99%. # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"Phred\", \"Prob of\")) cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"score\", \"Incorrect call\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { cat(sprintf(\"%-5d\\t\\t%0.5f\\n\", phred, 10^(phred / -10))) } Phred Prob of score Incorrect call 0 1.00000 1 0.79433 2 0.63096 3 0.50119 4 0.39811 5 0.31623 6 0.25119 7 0.19953 8 0.15849 9 0.12589 10 0.10000 11 0.07943 12 0.06310 13 0.05012 14 0.03981 15 0.03162 16 0.02512 17 0.01995 18 0.01585 19 0.01259 20 0.01000 21 0.00794 22 0.00631 23 0.00501 24 0.00398 25 0.00316 26 0.00251 27 0.00200 28 0.00158 29 0.00126 30 0.00100 31 0.00079 32 0.00063 33 0.00050 34 0.00040 35 0.00032 36 0.00025 37 0.00020 38 0.00016 39 0.00013 40 0.00010 41 0.00008 What is ASCII ASCII (American Standard Code for Information Interchange) is used to represent characters in computers. We can represent Phred scores using ASCII characters. The advantage is that the quality information can be esisly stored in text based FASTQ file. Not all ASCII characters are printable. The first printable ASCII character is ! and the decimal code for the character for ! is 33. # Store output in a vector to fit on a slide output <- c(sprintf(\"%-8s %-8s\", \"Character\", \"ASCII #\")) # Loop through ASCII values from 33 to 89 for (i in 33:89) { output <- c(output, sprintf(\"%-8s %-8d\", intToUtf8(i), i)) } # Print the output in a single block (e.g., to fit on a slide) cat(paste(output, collapse = \"\\n\")) Character ASCII # ! 33 \" 34 # 35 $ 36 % 37 & 38 ' 39 ( 40 ) 41 * 42 + 43 , 44 - 45 . 46 / 47 0 48 1 49 2 50 3 51 4 52 5 53 6 54 7 55 8 56 9 57 : 58 ; 59 < 60 = 61 > 62 ? 63 @ 64 A 65 B 66 C 67 D 68 E 69 F 70 G 71 H 72 I 73 J 74 K 75 L 76 M 77 N 78 O 79 P 80 Q 81 R 82 S 83 T 84 U 85 V 86 W 87 X 88 Y 89 Phred scores in FASTQ file In a FASTQ file, Phred scores are represented as ASCII characters. These characters are converted back to numeric values (PHRED scores) based on the encoding scheme used: PHRED+33 Encoding (Sanger/Illumina 1.8+) : The ASCII character for a quality score Q is calculated as: ASCII character=chr(Q+33) For example: A PHRED score of 30 is encoded as chr(30 + 33) = chr(63) , which corresponds to the ASCII character ? . PHRED+64 Encoding (Illumina 1.3-1.7) : The ASCII character for a quality score QQQ is calculated as: ASCII character=chr(Q+64) For example: A PHRED score of 30 is encoded as chr(30 + 64) = chr(94) , which corresponds to the ASCII character ^ . # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t\\t%-10s\\n\", \"Phred\", \"Prob. of\", \"ASCII\", \"ASCII\")) cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t%-10s\\n\", \"score\", \"Error\", \"Phred+33\", \"Phred+64\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { # Calculate the probability of error prob_error <- 10^(phred / -10) # Convert Phred scores to ASCII characters ascii_phred33 <- intToUtf8(phred + 33) ascii_phred64 <- intToUtf8(phred + 64) # Print the results in a formatted table cat(sprintf(\"%-5d\\t\\t%0.5f\\t\\t%-6s\\t\\t%-10s\\n\", phred, prob_error, ascii_phred33, ascii_phred64)) } Phred Prob. of ASCII ASCII score Error Phred+33 Phred+64 0 1.00000 ! @ 1 0.79433 \" A 2 0.63096 # B 3 0.50119 $ C 4 0.39811 % D 5 0.31623 & E 6 0.25119 ' F 7 0.19953 ( G 8 0.15849 ) H 9 0.12589 * I 10 0.10000 + J 11 0.07943 , K 12 0.06310 - L 13 0.05012 . M 14 0.03981 / N 15 0.03162 0 O 16 0.02512 1 P 17 0.01995 2 Q 18 0.01585 3 R 19 0.01259 4 S 20 0.01000 5 T 21 0.00794 6 U 22 0.00631 7 V 23 0.00501 8 W 24 0.00398 9 X 25 0.00316 : Y 26 0.00251 ; Z 27 0.00200 < [ 28 0.00158 = \\ 29 0.00126 > ] 30 0.00100 ? ^ 31 0.00079 @ _ 32 0.00063 A ` 33 0.00050 B a 34 0.00040 C b 35 0.00032 D c 36 0.00025 E d 37 0.00020 F e 38 0.00016 G f 39 0.00013 H g 40 0.00010 I h 41 0.00008 J i","title":"Phred FQ"},{"location":"Phred_FQ/#what-is-phred-scores","text":"A Phred score is a measure of the probability that a base call in a DNA sequencing read is incorrect. It is a logarithmic scale, meaning that a small change in the Phred score represents a large change in the probability of an error. $$Q = -10 \\cdot \\log_{10}(P)$$ Where: Q is the PHRED score. P is the probability that the base was called incorrectly. For example: Q = 20 : This corresponds to a 1 in 100 probability of an incorrect base call, or an accuracy of 99%. Q = 30 : This corresponds to a 1 in 1000 probability of an incorrect base call, or an accuracy of 99.9%. Q = 40 : This corresponds to a 1 in 10,000 probability of an incorrect base call, or an accuracy of 99.99%. # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"Phred\", \"Prob of\")) cat(sprintf(\"%-5s\\t\\t%-10s\\n\", \"score\", \"Incorrect call\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { cat(sprintf(\"%-5d\\t\\t%0.5f\\n\", phred, 10^(phred / -10))) } Phred Prob of score Incorrect call 0 1.00000 1 0.79433 2 0.63096 3 0.50119 4 0.39811 5 0.31623 6 0.25119 7 0.19953 8 0.15849 9 0.12589 10 0.10000 11 0.07943 12 0.06310 13 0.05012 14 0.03981 15 0.03162 16 0.02512 17 0.01995 18 0.01585 19 0.01259 20 0.01000 21 0.00794 22 0.00631 23 0.00501 24 0.00398 25 0.00316 26 0.00251 27 0.00200 28 0.00158 29 0.00126 30 0.00100 31 0.00079 32 0.00063 33 0.00050 34 0.00040 35 0.00032 36 0.00025 37 0.00020 38 0.00016 39 0.00013 40 0.00010 41 0.00008","title":"What is PHRED Scores"},{"location":"Phred_FQ/#what-is-ascii","text":"ASCII (American Standard Code for Information Interchange) is used to represent characters in computers. We can represent Phred scores using ASCII characters. The advantage is that the quality information can be esisly stored in text based FASTQ file. Not all ASCII characters are printable. The first printable ASCII character is ! and the decimal code for the character for ! is 33. # Store output in a vector to fit on a slide output <- c(sprintf(\"%-8s %-8s\", \"Character\", \"ASCII #\")) # Loop through ASCII values from 33 to 89 for (i in 33:89) { output <- c(output, sprintf(\"%-8s %-8d\", intToUtf8(i), i)) } # Print the output in a single block (e.g., to fit on a slide) cat(paste(output, collapse = \"\\n\")) Character ASCII # ! 33 \" 34 # 35 $ 36 % 37 & 38 ' 39 ( 40 ) 41 * 42 + 43 , 44 - 45 . 46 / 47 0 48 1 49 2 50 3 51 4 52 5 53 6 54 7 55 8 56 9 57 : 58 ; 59 < 60 = 61 > 62 ? 63 @ 64 A 65 B 66 C 67 D 68 E 69 F 70 G 71 H 72 I 73 J 74 K 75 L 76 M 77 N 78 O 79 P 80 Q 81 R 82 S 83 T 84 U 85 V 86 W 87 X 88 Y 89","title":"What is ASCII"},{"location":"Phred_FQ/#phred-scores-in-fastq-file","text":"In a FASTQ file, Phred scores are represented as ASCII characters. These characters are converted back to numeric values (PHRED scores) based on the encoding scheme used: PHRED+33 Encoding (Sanger/Illumina 1.8+) : The ASCII character for a quality score Q is calculated as: ASCII character=chr(Q+33) For example: A PHRED score of 30 is encoded as chr(30 + 33) = chr(63) , which corresponds to the ASCII character ? . PHRED+64 Encoding (Illumina 1.3-1.7) : The ASCII character for a quality score QQQ is calculated as: ASCII character=chr(Q+64) For example: A PHRED score of 30 is encoded as chr(30 + 64) = chr(94) , which corresponds to the ASCII character ^ . # Print the header cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t\\t%-10s\\n\", \"Phred\", \"Prob. of\", \"ASCII\", \"ASCII\")) cat(sprintf(\"%-5s\\t\\t%-10s\\t%-6s\\t%-10s\\n\", \"score\", \"Error\", \"Phred+33\", \"Phred+64\")) # Loop through Phred scores from 0 to 41 for (phred in 0:41) { # Calculate the probability of error prob_error <- 10^(phred / -10) # Convert Phred scores to ASCII characters ascii_phred33 <- intToUtf8(phred + 33) ascii_phred64 <- intToUtf8(phred + 64) # Print the results in a formatted table cat(sprintf(\"%-5d\\t\\t%0.5f\\t\\t%-6s\\t\\t%-10s\\n\", phred, prob_error, ascii_phred33, ascii_phred64)) } Phred Prob. of ASCII ASCII score Error Phred+33 Phred+64 0 1.00000 ! @ 1 0.79433 \" A 2 0.63096 # B 3 0.50119 $ C 4 0.39811 % D 5 0.31623 & E 6 0.25119 ' F 7 0.19953 ( G 8 0.15849 ) H 9 0.12589 * I 10 0.10000 + J 11 0.07943 , K 12 0.06310 - L 13 0.05012 . M 14 0.03981 / N 15 0.03162 0 O 16 0.02512 1 P 17 0.01995 2 Q 18 0.01585 3 R 19 0.01259 4 S 20 0.01000 5 T 21 0.00794 6 U 22 0.00631 7 V 23 0.00501 8 W 24 0.00398 9 X 25 0.00316 : Y 26 0.00251 ; Z 27 0.00200 < [ 28 0.00158 = \\ 29 0.00126 > ] 30 0.00100 ? ^ 31 0.00079 @ _ 32 0.00063 A ` 33 0.00050 B a 34 0.00040 C b 35 0.00032 D c 36 0.00025 E d 37 0.00020 F e 38 0.00016 G f 39 0.00013 H g 40 0.00010 I h 41 0.00008 J i","title":"Phred scores in FASTQ file"},{"location":"basic_linux/","text":"Linux for Bioinformatics Navigating in Linux file system You are in your home directory after you log into the system and are directed to the shell command prompt. This section will show you hot to explore Linux file system using shell commands. Path To understand Linux file system, you can image it as a tree structure. In Linux, a path is a unique location of a file or a directory in the file system. For convenience, Linux file system is usually thought of in a tree structure. On a standard Linux system you will find the layout generally follows the scheme presented below. The tree of the file system starts at the trunk or slash, indicated by a forward slash ( / ). This directory, containing all underlying directories and files, is also called the root directory or \u201cthe root\u201d of the file system. %%bash ## In your account, you will see a folder ## with you account ID as the name cd ~ echo $HOME /home/xie186 Relative and absolute path Absolute path An absolute path is defined as the location of a file or directory from the root directory(/). An absolute path starts from the root of the tree ( / ). Here are some examples: /home/xie186 /home/xie186/.bashrc Relative path Relative path is a path related to the present working directory: data/sample1/ and ../doc/ . If you want to get the absolute path based on relative path , you can use readlink with parameter -f : pwd readlink -f ../ Once we enter into a Linux file system, we need to 1) know where we are; 2) how to get where we want; 3) how to know what files or directories we have in a particular path. Check where you are using command pwd In order to know where we are, we need to use pwd command. The command pwd is short for \u201cprint name of current/working directory\u201d. It will return the full path of current directory. Command pwd is almost always used by itself. This means you only need to type pwd and press ENTER %%bash pwd Listing the contents using command ls After you know where you are, then you want to know what you have in that directory, we can use command ls to list directory contents Its syntax is: ls [option]... [file]... ls with no option will list files and directories in bare format. Bare format means the detailed information (type, size, modified date and time, permissions and links etc) won\u2019t be viewed. When you use ls by itself, it will list files and directories in the current directory. ls ~/ ls -a ls -ld Linux command options can be combined without a space between them and with a single - (dash). The following command is a faster way to use the l and a options and gives the same output as the Linux command shown above. ls -lt ~/.bashrc -rw-r--r--. 1 xie186 zt-bioi611 1067 Aug 22 22:27 /home/xie186/.bashrc Change directory using command cd Unlike pwd , when you use cd you usually need to provide the path (either absolute or relative path) which we want to enter. If you didn\u2019t provide any path information, you will change to home directory by default. Path Shortcuts Description Single dot . The current folder Double dots .. The folder above the current folder Tilde character ~ Home directory (normally the directory:/home/my_login_name) Dash - Your last working directory Here are some examples: cd ~ pwd ls ls ../ ## pwd cd ../ pwd cd ./ pwd Each directory has two entries in it at the start, with names . (a link to itself) and .. (a link to its parent directory). The exception, of course, is the root directory, where the .. directory also refers to the root directory. Sometimes you go to a new directory and do something, then you remember that you need to go to the previous working direcotry. To get back instantly, use a dash. %%bash # This is our current directory pwd # Let us go our home diretory cd ~ # Check where we are pwd # Let us go to your previous working directory cd - # Check where we are now pwd /home/xie186/BIOI611_lab/docs /home/xie186 /home/xie186/BIOI611_lab/docs /home/xie186/BIOI611_lab/docs Manipulations of files and directories In Linux, manipulations of files and directories are the most frequent work. In this section, you will learn how to copy, rename, remove, and create files and directories. Command line cp In Linux, command cp can help you copy files and directories into a target directory. Command line mv Move files/folders and rename file/folders using mv : # move file from one location to another mv file1 target_direcotry/ # rename mv file1 file2 mv file1 file2 file3 target_direcotry/ Command mkdir The syntax is shown as below: mkdir [OPTION ...] DIRECTORY ... Multiple directories can be specified when calling mkdir mkdir directory1 directory2 mkdir -p foo/bar/baz How to defining complex directory trees with one command: mkdir -p project/{software,results,doc/{html,info,pdf},scripts} Then you can view the directory using tree . Command rm You can use rm to remove both files and directories. ## You can remove one file. rm file1 ## `rm` can remove multiple files simutaneously rm file2 file3 You can also use 'rm' to remove a folder. If a folder is empty, you can remove it using rm with -r . rm -r FOLDER If a folder is not empty, you can remove it using rm with -r and -f . mkdir test_folder rm -r test_folder View text files in Linux Commands cat , more and less The command cat is short for concatenate files and print on the standard output. The syntax is shown as below: cat [OPTION]... [FILE]... For small text file, cat can be used to view the files on the standard output. The command more is old utility. When the text passed to it is too large to fit on one screen, it pages it. You can scroll down but not up. The syntaxt of more is shown below: more [options] file [...] The command less was written by a man who was fed up with more\u2019s inability to scroll backwards through a file. He turned less into an open source project and over time, various individuals added new features to it. less is massive now. That\u2019s why some small embedded systems have more but not less. For comparison, less\u2019s source is over 27000 lines long. more implementations are generally only a little over 2000 lines long. The syntaxt of less is shown below: less [options] file [...] Command head and tail The command head is used to output the first part of files. By default, it outputs the first 10 lines of the file. head [OPTION]... [FILE]... Here is an exmaple of printing the first 5 files of the file: head -n 5 code_perl/variable_assign.pl In fact, the letter n does not even need to be used at all. Just the hyphen and the integer (with no intervening space) are sufficient to tell head how many lines to return. Thus, the following would produce the same result as the above commands: head -5 target_file.txt The command tail is used to output the last part of files. By default, it prints the last 10 lines of the file to standard output. The syntax is shown below: tail [OPTION]... [FILE]... Here is an exmaple of printing the last 5 files of the file: tail -5 target_file.txt To view lines from a specific point in a file, you can use -n +NUMBER with the tail command. For example, here is an example of viewing the file from the 2nd line of the line. tail -n +2 target_file.txt Auto-completion In most Shell environment, programmable completion feature will also improve your speed of typing. It permits typing a partial name of command or a partial file (or directory), then pressing TAB key to auto-complete the command. If there are more than one possible completions, then TAB will list all of them. A handy autocomplete feature also exists. Type one or more letters, press the Tab key twice, and then a list of functions starting with these letters appears. For example: type so , press the Tab key twice, and then you get the list as: soelim sort sotruss soundstretch source Demonstration of programmable completion feature. File permissions In Linux, file permissions are a vital aspect of system security and resource management. This is particularly important in bioinformatics, where large datasets and scripts are often shared across teams. Permissions determine who can read, write, or execute a file, ensuring that critical data is not accidentally modified or deleted. Three Permission Categories : User (u): The owner of the file. Group (g): A group of users who share access to the file. Other (o): All other users on the system. Permission Types : Read (r): Ability to view the contents of a file. Write (w): Ability to modify or delete the file. Execute (x): Ability to run the file as a program (for scripts or executables). %%bash groups $USER animako eunal gstewar1 mjames17 mjeakle nmilza rahooper xie186 : zt-bioi611 zt-bioi611_mgr animako : zt-bioi611 eunal : zt-bioi611 gstewar1 : zt-bioi611 mjames17 : zt-bioi611 mjeakle : zt-bioi611 nmilza : zt-bioi611 rahooper : zt-bioi611 %%bash mkdir -p ~/test_permission/ touch ~/test_permission/test.txt ls -l ~/test_permission/ rm -rf ~/test_permission/ total 0 -rw-r--r--. 1 xie186 zt-bioi611 0 Sep 8 22:52 test.txt Here, the first character represents the type of file (e.g., - for a regular file or d for a directory), followed by three groups of three characters, each representing the permissions for the user , group , and others , respectively. Examples: -rwxr-xr-- : The owner has read , write , and execute permissions. The group has read and execute permissions, while others can only read the file. drwxr-x--- : A directory where the owner can read, write, and access (execute). The group can only read and access, while others have no permissions. Modify file permissions using the chmod command. Permissions can be set in two ways: Symbolic Mode: In symbolic mode, you modify permissions by referencing the categories (user, group, other) and specifying whether you're adding (+), removing (-), or setting (=) permissions. # Add execute permission for the user: chmod u+x filename # Remove write permission for the group: chmod g-w filename # Set read-only permission for others: chmod o=r filename Symbolic mode is intuitive and flexible, especially when you want to make precise adjustments to permissions without affecting other categories. This is useful for common file-sharing tasks in bioinformatics where you need to tweak access for specific collaborators. Numeric Mode (Octal representation): In numeric mode, file permissions are set using a three-digit number. Each digit represents the permissions for user , group , and other , respectively. The digits are calculated by adding the values of the read , write , and execute` permissions: Read (r) = 4 Write (w) = 2 Execute (x) = 1 Example Permission Breakdown: Read (r), Write (w), and Execute (x) for user = 7 Read (r) and Execute (x) for group = 5 Read (r) only for others = 4 chmod 754 filename An example to help you understand executable : %%bash printf '#!/user/bin/python\\nprint(\"Hello, Welcome to Course BIOI611!\")' > ~/test.py %%bash ls -l ~/test.py python ~/test.py -rw-r--r--. 1 xie186 zt-bioi611 61 Sep 8 23:06 /home/xie186/test.py Hello, Welcome to Course BIOI611! Error message below will be thrown out if you consider ~/test.py as a program: bash: line 1: /home/xie186/test.py: No such file or directory %%bash chmod u+x ~/test.py ls -l ~/test.py python ~/test.py rm ~/test.py -rwxr--r--. 1 xie186 zt-bioi611 61 Sep 8 23:06 /home/xie186/test.py Hello, Welcome to Course BIOI611! Disk Usage of Files and Directories The Linux du (short for Disk Usage) is a standard Unix/Linux command, used to check the information of disk usage of files and directories on a machine. The du command has many parameter options that can be used to get the results in many formats. The du command also displays the files and directory sizes in a recursively manner. %%bash du -h ~/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref 2.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref %%bash du -ah ~/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref 2.9M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/sjdbList.fromGTF.out.tab 7.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/Log.out 936M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/SA 1.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/SAindex 3.0M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/transcriptInfo.tab 2.3M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/sjdbList.out.tab 1.5M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/geneInfo.tab 1.0K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/genomeParameters.txt 512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrLength.txt 512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrNameLength.txt 512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrStart.txt 7.6M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/exonGeTrInfo.tab 3.1M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/exonInfo.tab 2.8M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/sjdbInfo.txt 512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/chrName.txt 119M /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref/Genome 2.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref %%bash du -csh /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/* 19G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/raw_data 0 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/raw_data_smart_seq 1.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_data.sub 575K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_smart_seq-7478223-xie186.err 0 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_smart_seq-7478223-xie186.out 8.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s1_download_smart_seq.sub 2.5K /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/s2_star.sub 34G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_align 2.5G /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/STAR_ref 512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/test.sub 512 /home/xie186/scratch.bioi611/Analysis/bulk_RNAseq/test.txt 55G total Symbolic link Symbolic link, similar to shortcuts, can point to another file/folder. ln -s
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