`conda install -c bioconda gimbleprep`
gIMble workflow. preprocess (0) assures input data conforms to requirements; parse (1) reads data into a gIMble
store, the central data structure that holds all subsequent analysis. The modules blocks (2) and
windows (3) partition the data which is summarised as a tally (4) of blockwise mutation
configurations (bSFSs) either across all pair-blocks (blocks tally) or for pair-blocks in windows
(windows tally). Tallies may be used either in a bounded search of parameter space via the
module optimize (5) or to evaluate likelihoods over a parameter grid (which is precomputed using
makegrid (6)) via the gridsearch (7) module. The simulate (8) module allows coalescent
simulation of tallies (simulate tally) based on inferred parameter estimates (either global
estimates or gridsearch results of window-wise data). Simulated data can be analysed to quantify
the uncertainty (and potential bias) of parameter estimates. The results held within a gIMble store
can be described, written to column-based output files or removed using the modules info (9),
query (10), and delete (11).
gimbleprep assures that input files are adequately filtered and processed so that the gimble workflow can be completed successfully.
While this processing of input files could be done more efficiently with other means, it has the advantage of generating a VCF file complies with gimble data requirements but which can also be used in alternative downstream analyses.
gimbleprep -f FASTA -b BAM_DIR/ -v RAW.vcf.gz -k
Based on the supplied input files:
-f: FASTA of reference-b: directory of BAM files, composed of readgroup-labelled reads mapped to reference-v: compressed+indexed Freebayes VCF file
the module produces the following output files:
- genome file (sequence_id, length) based on FASTA file
- sample file (sample_id) based on ReadGroupIDs in BAM files
- coverage threshold report for each BAM file
- gimble VCF file (see VCF processing details)
- gimble BED file (see BAM processing details)
- log of executed commands
After running, output files require manual user input (see Manually modify files)
- MNPs are decomposed into SNPs
- variant sets are defined as
- {RAW_VARIANTS} := all variants in VCF - {NONSNP} := non-SNP variants - {SNPGAP} := all variants within +/- X b of {NONSNP} variants - {QUAL} := all variants with QUAL below --min_qual - {BALANCE} := all variants with any un-balanced allele observation (-e 'RPL<1 | RPR<1 | SAF<1 | SAR<1') - {FAIL} := {{NONSNP} U {SNPGAP} U {QUAL} U {BALANCE}} - {VARIANTS} := {RAW_VARIANTS} - {FAIL}```
The processed VCF file
- only contains variants from set
{VARIANTS} - only contains sample genotypes with sample read depths within coverage thresholds (others are set to missing, i.e.
./.)
- definition of BED region sets
- {CALLABLE_SITES} := for each BAM, regions along which sample read depths lie within coverage thresholds. - {CALLABLES} := bedtools multiintersect of all {CALLABLE_SITES} regions across all BAMs/samples - {FAIL_SITES} := sites excluded due to {FAIL} variant set (during VCF processing) - {SITES} := {CALLABLES} - {FAIL_SITES}
Resulting BED file
- only contains BED regions from set
{SITES} - lists which samples are adequately covered along each region
gimble.genomefile:- [Optional] remove sequence IDs to ignore them in the analyses
gimble.samples.csv- [Required] add population IDs the second column. Must be exactly 2 populations
- [Optional] remove sample IDs to ignore them in the analyses
gimble.bed- [Recommended] intersect with BED regions of interest to analyse particular genomic regions, e.g:
bedtools intersect -a gimble.bed -b my_intergenic_regions.bed > gimble.intergenic.bed
Building docker image
docker build -t gimbleprep --rm .
Runs command in new container. Note the list of command line arguments trailing the end of this command. Other command line options can be added as well.
docker run --name gimbleprep --rm gimbleprep \
--mount type=bind,source=”$(pwd)”,target=/home/newuser \
-f fasta-file -v vcf-file -b bam-dir