The SSQC pipeline and all its dependencies are Linux based, typically running under Linux operating system. You can install the SSQC pipeline and all its dependencies manually using the following steps.
git clone https://github.com/tbmorrison/ssqc.git
or
curl -sSL -o ssqc.zip https://github.com/tbmorrison/ssqc/archive/master.zip
mkdir -p ssqc
unzip ssqc.zip -d ssqc'
ssqc [PARAM_TSV_FILE_PATH]
[PARAM_TSV_FILE_PATH] Parameter file indicating which sequences to process
The paramater file is a tab seperated
*paired-end true or false if sequence is paired end fastq-files *contains a list of fastq.gz files, paired ends alternate with naming convension name-R1.fastq.gz and name-R2.fastq.gz is_input copies of internal standard added to sample. cc_expected determined after several runs..expected count of unique CC sequences when no significant NT competition. nt2is </path/to/file> contains the mapping of NT to IS, base changes in lowercase ref </path/to/files> path to the reference genome seqSplit <true/false> determine if the NT and IS reads are to divided into separate bam and fastq multiple-lanes true or false Not implemented. Script does not handle illumina 4 lane fastq. Use catMultLane to concatenate lanes qScore Sorting reads into NT, IS, REC, CC, & ukn bins uses base change position, to use position for splitting qScore must be at or above (zero ignores) goodBaseChange Number of base change positions required for sorting, otherwise ends up in ukn bin calcCov true or false generate coverage table to be used for viral load, otherwise use NT and IS read counts covBed table used by samtools bedcov for coverage analysis
Program flow: read parameters; if needed--align reads; call remRecombinants to count CC and remove recombinants; calculate NT and IS reads; create runQC table; calculate viral load viral load calculated from total amplicon reads NT / IS * IS_INPUT runQC uses the NT and IS CC-reads to adjust expected complexity capture (cc_expected) for competition. Unique CC sequences calculated from -CC-counts.txt Coverage calculated using bamstats amplicon counting method
viralLoad.txt contains the estimated viral genomes present when IS was mixed with sample runQC.txt indicates how far the complexity capture deviated from expected.
-NT-R1.fastq or -NT-R2.fastq are the viral reads, and should be compatible with your viral assembly pipeline
-IS-R1.fastq or -IS-R2.fastq are the IS reads
-bad-R1.fastq or -bad-R2.fastq are the recombinant reads
-IS.coverage, -cc.coverage, -NT.coverage, or -bad.coverage a table listing the reads for each amplicon, used for viral load.
-tallies.txt a table indicating occurrences and position of recombinants
-CC.txt table the source/quality of a samples CC sequences.
-CC-counts.txt a summary table created from the CC.txt file.
Bam and sam intermediate files used to create fastq and coverage.
We welcome contributions! The code has room for optimization but we should settle on the final deliverable before moving to optimization. Also, I’m a hacker, so I’m happy to hear of any suggestions for code improvements. Please have a look here on how you can help.