The ISOSDB V2 is available in this repo.
To use the pseudoR pipeline, please read the following:
Clone directory using git: git clone https://github.com/joshuakirsch/pseudoR.git
Install dependencies with mamba [might work with conda but not sure]:
mamba create -n pseudoR bedtools=2.30.0 blast=2.12.0 bowtie2=2.4.5 mosdepth=0.3.3 prodigal=2.6.3 samtools=1.6 seqkit=2.2.0 r r-essentials r-readr r-tidyr r-dplyr
BBTools must be installed using the instructions from this website: [BBTools] (https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/installation-guide/)
Add the BBTools folder to your $PATH variable before running the pseudoR pipeline
pseudoR pipeline can be run with a single command:
bash pseudoR.sh
Running bash pseudoR.sh -h shows the following information:
This program begins the process of finding IS insertions in metagenomes by first finding insertions in contigs.
-s List of samples
-d Location of the repo
-c Folder containing reads
-m m for one reference per sample and s for a single reference per sample
-r Folder of assemblies if m is selected or location of single reference contigs if s is selected
-t Number of threads
-1 ending of read1 files. The sample name MUST proceed this delimiter. For instance, for sample1 the read1 file is sample1.read1.fq.gz. The read1 ending should be -1 '.read1.fq.gz'
-2 ending of read2 files. The sample name MUST proceed this delimiter. For instance, for sample1 the read2 file is sample1.read2.fq.gz. The read1 ending should be -2 '.read2.fq.gz'
-3 ending of single read files. The sample name MUST proceed this delimiter and you MUST provide a single read file. If none exists, make a blank file as placeholder
-x ending of contig files. The sample name MUST proceed this delimiter. For instance, for sample1 the assembly file is sample1.contigs.fa. The contig ending should be -x '.contigs.fa' Only used for multi-reference mode.
-i OPTIONAL: Folder location of IS element blast database (default is the same location as '-d'. However, a custom data base can be used and will replace the provided IS element database from the ISOSDB. Please make this database using the IR-Database-Generation_V2.sh script)
I recommend using over 10 cores. There is a lot of mapping and BAM sorting in this program which takes a good bit of time. Usually, I budget 1 hour per sample. I also highly recommend that reads are deduplicated before using in this program.
This program does not create a log file automatically. To obtain a record of the program's readout, run bash pseudoR.sh 1>>log.file 2>>log.file
Files in the results/ref/ folder can be used for downstream analyses if need be:
- orfs.nucl.fa – All predicted ORFs found in contigs greater than 1 kB
- orfs.nucl.dedupe.fa – Deduplicated ORFs from orfs.nucl.fa
There are many output files from this program and they can take up a lot of space. For each sample (in this case sample1), the following files in results/mapping_files/ are produced:
sample1.contig.reads_mapped.bam– sorted BAM file of reads mapped to whole contigssample1.unmapped.fq– FASTQ file of reads that did not map to the contigssample1.unmapped.numbered.fq- FASTQ file with the same reads as in sample1.unmapped.fastq but with the names changed to seq_#, where # is a numbersample1_blast_results.txt– Output of BLASTN from comparing sample1.unmapped.numbered.fq to the IS termini databasesample1_trimmed_reads.tsv– Unmapped reads trimmed of the IS termini [is found in results/]sample1.unmapped.contig_mapping.bam– Unsorted BAM file of unmapped and trimmed reads mapped to contigssample1.unmapped.contig_mapping.filtered.sam– SAM version of sample1.unmapped.contig_mapping.bam without reads that did not mapsample1.orf.reads_mapped.bam– Sorted BAM file of reads that mapped to the contigs and also mapped to the ORF databasesample1.unmapped.orf_mapping.bam– Unsorted BAM file of unmapped and trimmed reads mapped to contigssample1.unmapped.orf_mapping.filtered.sam– SAM version of sample1.unmapped.orf_mapping.bam without reads that did not mapsample1.contig.reads_mapped.depth– Read depth of every position where an IS insertion was predicted in contigssample1.orf.reads_mapped.depth– Read depth of every position where an IS insertion was predicted in the ORF database
Useful Output files are
final_results/pseudoR_output.contig.tsv- This the contig mapping output of the pipeline. Here are what the columns in the tsv are:
sample= Sample where the insertion was foundcontig= Contig where the insertion was foundInsertion_Position= Site of highest IS read depth from insertionIS_element= IS element forming insertion5prime_IS_depth= Depth of reads from 5' terminus of IS element3prime_IS_depth= Depth of read from 3' terminus of IS elementtotal_IS_depth= Sum of 5prime_IS_depth and 3prime_IS_depthnon_IS_depth= Non-IS read depth (ie reads that originally mapped to this loci) at the insertion positiondepth_percentage= Percentage of total_IS_depth/non_IS_depth (ie 100* (total_IS_depth/non_IS_depth))ORF= ORF where insertion is found. NA if insertion is intergenic
- This the contig mapping output of the pipeline. Here are what the columns in the tsv are:
final_results/pseudoR_output.ORF.tsv- This the contig mapping output of the pipeline. Here are what the columns in the tsv are:
sample= Sample where the insertion was foundORF= ORF where the insertion was foundInsertion_Position= Site of highest IS read depth from insertionIS_element= IS element forming insertion5prime_IS_depth= Depth of reads from 5' terminus of IS element3prime_IS_depth= Depth of read from 3' terminus of IS elementtotal_IS_depth= Sum of 5prime_IS_depth and 3prime_IS_depthnon_IS_depth= Non-IS read depth (ie reads that originally mapped to this loci) at the insertion positiondepth_percentage= Percentage of total_IS_depth/non_IS_depth (ie 100* (total_IS_depth/non_IS_depth))
- This the contig mapping output of the pipeline. Here are what the columns in the tsv are: