This repository holds scripts I have written for working with next-generation sequencing (NGS) data. Manuals for each script are described below.
axtToSyn.py
This script elongates synteny blocks from a pairwise whole genome alignment, chained and netted and in the .axt file format.
./axtToSyn.py -h
usage: axtToSyn [-h] file outfile [s] [l]
Generates synteny blocks from pairwise genome alignment .axt file by block
elongation.
positional arguments:
file Relative path to net.axt alignment file.
outfile Path to output synteny blocks file.
s Min alignment score to be considered for elongation (defalt:
1e6)
l Min block len to be considered for elongation (defalt: 2e5)
optional arguments:
-h, --help show this help message and exit
length_dist.py
length_dist.py generates a read length frequency distribution from an input fasta file.
fasta2line
The fasta2line.py script converts a fasta file with multiple sequence lines per entry to a fasta file with one sequence line per entry.
common_seqs.py -h usage: common_seqs [-h] fasta [k]
Determins k most common sequences with length greater than 30nts from fasta file, returns seq and counts
positional arguments: fasta input fasta file (path) k Specify the k most common seqs. Default: 10
optional arguments: -h, --help show this help message and exit
knorm
The knorm.py program normalizes reads based on kmer coverage, throwing out reads whose median kmer coverage is above the coverage limit. This program is meant to be run before kspec.py.
kspec
The kspec.py script generates a kmer distribution from a fastq file, sending kmer frequency-distribution data to an output file.
count_stats.py
This script generates two summary files after the alignment of RNA-seq reads to a reference genome using STAR.
The first file that is generated counts_per_gene.tsv is a summary of counts/gene for each file of RNA-seq reads, these are meant to be used downstream for differential expression analysis.
The second file that is generated count_stats.tsv, contains four columns with the following contents:
| column | content |
|---|---|
| column 1 | File name |
| column 2 | Total reads per file |
| column 3 | Number uniquely mapped reads |
| column 4 | Number of reads mapped to gene models |
annotation_lookup.py
usage: annotation lookup [-h] chrom_file ann_file
Looks up the annotation gene name from a file containing chromosome and position outputs a file with the gene name added
positional arguments: chrom_file chromosome file (path) ann_file annotation file (path)
optional arguments: -h, --help show this help message and exit
report_fastq.py -h
usage: report_fastq.py [-h] [--help] crawl_dir
This script recursively searches through a directory to look for fastq files and prints out the file name with the percent of sequences in that file that are greater than 30 nucleotides long.
positional argument: crawl_dir relative or abs path to directory
optional arguments: -h, --help print this help message and exit