# 1. clone repository
>>> git clone https://github.com/DRL/gimble.git
# 2. Install miniconda from https://conda.io/miniconda.html
# ...
# 3. Create the following conda environment
>>> conda create -n gimble python=3.7.12 agemo bedtools bcftools samtools vcflib mosdepth=0.3.2 pysam numpy docopt tqdm pandas tabulate zarr scikit-allel parallel matplotlib msprime demes dask numcodecs python-newick nlopt -c conda-forge -c bioconda
# 4. Load the environment (needs to be activated when using gimble)
>>> conda activate gimble
# 5. Start gimble'ing ...
>>> (gimble) gIMble/gimble --help
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).
usage: gimble <module> [<args>...] [-V -h]
[Input]
preprocess Preprocess input files
parse Parse files into GimbleStore
blocks Generate blocks from parsed data in GimbleStore (requires 'parse')
windows Generate windows from blocks in GimbleStore (requires 'blocks')
tally Tally variation for inference (requires 'blocks' or 'windows')
[Simulation]
simulate Simulate data based on specific parameters or gridsearch results
[Inference]
optimize Perform global parameter optimisation on tally/simulation
makegrid Precalculate grid of parameters
gridsearch Evaluate tally/simulation against a precomputed grid (requires 'makegrid')
[Info]
info Print metrics about data in GimbleStore
list List information saved in GimbleStore
query Extract information from GimbleStore
delete Delete information in GimbleStore
[Experimental]
partitioncds Partition CDS sites in BED file by degeneracy in sample GTs
[Options]
-h, --help Show this screen
-V, --version Show version
The preprocess module 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.
./gimble preprocess -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
- reads input data into GimbleStore
./gimble parse -v gimble.vcf.gz -b gimble.intergenic.bed -g gimble.genomefile -s gimble.samples.csv -z analysis
- The output of this module determines which parts of the sampled sequences are available for analysis.
- It uses the "callable" regions specified in the BED file and the variants contained in the VCF file to define sequence blocks of a fixed number of callable sites.
- The blocking of genomic data is controlled by the parameters
--block_length(number of callable sites in each block) and--block_span(maximum distance between the first and last site in a block) - Blocks are constructed independently for each sample pair, which ameliorates the asymmetry in coverage profiles among the samples due to stochastic variation in sequencing depth between samples and/or reference bias.
- Optimal block length will be different for each dataset. The user is encouraged to explore parameter space.
./gimble blocks -z analysis.z -l 64
- Windows are constructed by traversing each sequence of the reference from start to end, incorporating the heterospecific pair-blocks (X) as they appear based on their start positions.
- The parameter
--blockscontrols how many blocks are incorporated into each window and the parameter--stepsby how many blocks the next window is shifted --blocksshould be chosen so that, given the number of interspecific pairs, enough blocks from each pair can be placed in a window.
./gimble windows -z analysis.z -w 500 -s 100
- Lists basic metrics about the GimbleStore
- Computes standard population genetic summary statistics (
$\pi$ ,$d_{xy}$ and$H$ mean heterozygosity) based on blocks sampled between (X) and within species/populations (A and B). For for details see gIMble_info.pdf
./gimble info -z analysis.z
- Tallies variation for blocks or for blocks in windows into bSFSs
- The bSFS is a tally of the mutation configurations of blocks which are themselves described by vectors of the form
$\underline{k}_i$ , which count the four possible mutation types found within a pair-block$i$ . - parameter k-max is the max count per mutation type beyond which counts are treated as marginals. Order of mutation types is (hetB, hetA, hetAB, fixed)
./gimble tally -z analysis.z -k 2,2,2,2 -l blocks_kmax2 -t blocks
./gimble tally -z analysis.z -k 2,2,2,2 -l windows_kmax2 -t windows
- Searches parameter space for model parameters under a given model for a given data tally (based on parsed or simulated tallies) using an optimization algorithm with bounded constraints.
- Given a set of bounds, optimization can be initiated either at the midpoint of the bounded parameter space or at a random start point.
- Optimizations finalize after user-defined stopping criteria are met
- The user can assess convergence of optimizations by consulting the log-file.
./gimble optimize -z analysis.z -l IM_BA_optimize -d tally/windows_kmax2 \
-w -m IM_BA -r A -u 2.9e-09 -A 10_000,2_500_000 -B 10_000,1_000_000 \
-C 1_000_000,5_000_000 -M 0,1e-5 -T 0,5_000_000 -g CRS2 -p 1 -e 19 -i 10_000
- Computes a grid of parameter combinations for a list of parameters under a given model
- Pre-computing the probabilities of bSFS configurations in a grid across parameter space is computationally efficient (relative to using an optimization algorithm) and therefore useful when we wish to interrogate data in replicate, i.e. across windows or simulation replicates.
- Grid searches may be used either for an initial exploration of the likelihood surface (i.e. prior to defining the parameter space across which to run
optimize), or to fit models to window-wise data.
./gimble makegrid -z analysis.z -m IM_AB \
-b 64 -r A -u 2.9e-9 -k 2,2,2,2 \
-A=200_000,3_000_000,12,lin \
-B=100_000,2_000_000,12,lin \
-C 100_000,2_000_000,12,lin \
-T 4_256_034 -M 0,2.21E-06,16,lin \
-p 48 -e 19 -l IM_BA_grid
- Searches a tally or simulation against a grid made with
makegrid
gimble gridsearch -z analysis.z \
-g makegrid/IM_BA_grid -d tally/windows_kmax2 -p 50
- Simulates tallies based on fixed parameters under a model
- Simulates tallies based on results of gridsearches
# based on fixed parameters
./gimble simulate -z analysis.z \
--seed 19 --replicates 100 --windows 11217 --blocks 500 \
--block_length 64 -a 10 -b 10 \
-k 2,2,2,2 -s IM_BA_sims_100reps -p 55 -m IM_BA \
-A 1_407_027 -B 545_041 -C 923_309 -M 7.3778010583E-07 -u 2.9e-9 -T 4_256_034
# based on gridsearch result
./gimble simulate -z analysis.z \
--seed 19 --replicates 100 --windows 11217 --blocks 500 \
--block_length 64 -a 10 -b 10 \
--gridsearch_key gridsearch/windows_kmax2/IM_BA_grid \
-k 2,2,2,2 -s IM_BA_grid -p 55 -u 2.9e-9