Merge branch 'master' of github.com:transcript/samsa2

README.md

@@ -26,10 +26,11 @@ The following programs can be downloaded OR can be installed from the binaries p
 1. Download SAMSA2:   
     `git clone https://github.com/kaedenn/samsa2.git`
 2. Either install the dependencies from the links above, or use the setup_and_test/package_installation.bash script provided with SAMSA2 for installing from the included binaries.
-3. Make changes to the master_script.bash, which performs the first 3 of 4 steps in the SAMSA2 pipeline (preprocessing, annotation, aggregation)
-4. If not using master_script, use DIAMOND to annotate your reads against a database of your choosing (note that database must be local and DIAMOND-indexed).  See "example\_DIAMOND\_annotation\_script.bash" for more details.
-5. If not using master_script, use "DIAMOND\_analysis\_counter.py" to create a ranked abundance summary of the DIAMOND results from each metatransciptome file.
-6. Import these abundance summaries into R and use "run\_DESeq\_stats.R" to determine the most significantly differing features between either individual metatranscriptomes, or control vs. experimental groups.
+3. Access the full databases by downloading them using the full\_database\_download.bash script, located in the setup\_and\_test folder.  This downloads the full RefSeq bacteria database, for organism and specific functional results, and the SEED Subsystems database, which is used for hierarchical functional ontology.
+4. Make changes to the master_script.bash, which performs the first 3 of 4 steps in the SAMSA2 pipeline (preprocessing, annotation, aggregation)
+5. If not using master_script, use DIAMOND to annotate your reads against a database of your choosing (note that database must be local and DIAMOND-indexed).  See "example\_DIAMOND\_annotation\_script.bash" for more details.
+6. If not using master_script, use "DIAMOND\_analysis\_counter.py" to create a ranked abundance summary of the DIAMOND results from each metatransciptome file.
+7. Import these abundance summaries into R and use "run\_DESeq\_stats.R" to determine the most significantly differing features between either individual metatranscriptomes, or control vs. experimental groups.
 
 ## SAMSA: Simple Analysis of Metatranscriptomes by Sequence Annotation
 Metatranscriptome, RNA-seq data from multiple members of a microbial community, offers incredibly powerful insights into the workings of a complex ecosystem.  RNA sequences are able to not only identify the individual members of a community down to the strain level, but can also provide information on the activity of these microbes at the time of sample collection - something that cannot be determined through other meta- (metagenome, 16S rRNA sequencing) method.  

setup_and_test/package_installation.bash

@@ -63,6 +63,7 @@ bash ./build.sh
 echo "SortMeRNA extraction finished at: "; date
 
 # Index silva-bac-16s-id90.fasta for sortmerna use:
+echo "Indexing the SILVA rRNA database for SortMeRNA; feel free to abort at this step if this isn't needed."
 $SORTMERNA_DIR/indexdb_rna --ref $SORTMERNA_DIR/rRNA_databases/silva-bac-16s-id90.fasta,$SORTMERNA_DIR/index/silva-bac-16s-db
 
 echo "Finished extracting and installing all SAMSA2 package dependencies at: "; date