Roman E. Reggiardo, Vikas Peddu
23 July, 2023
The lab needs you to process patient sequencing data to try and identify mutations in our final gene panel
“If you’ve thought of it, someone has probably done it, and if not, you should do it right now” – My brain
Complex but repeatable and general tasks often will attract developers who can and do program tools to do whatever it is
In this case, we need a Variant Caller
Check out freeBayes here
freebayes is a Bayesian genetic variant detector designed to find small polymorphisms, specifically SNPs (single-nucleotide polymorphisms), indels (insertions and deletions), MNPs (multi-nucleotide polymorphisms), and complex events (composite insertion and substitution events) smaller than the length of a short-read sequencing alignment.
Their GitHub also has binaries available that we can execute in releases
We need to download the following .gz file
We’ve worked with .gz before, what do we do next?
With an unzipped, executable freeBayes binary
usage: /usr/local/bin/freebayes -f [REFERENCE] [OPTIONS] [BAM FILES] >[OUTPUT]
Bayesian haplotype-based polymorphism discovery.
parameters:
-h --help For a complete description of options.
freebayes is maintained by Erik Garrison and Pjotr Prins.
citation: Erik Garrison, Gabor Marth
"Haplotype-based variant detection from short-read sequencing"
arXiv:1207.3907 (http://arxiv.org/abs/1207.3907)
author: Erik Garrison <erik.garrison@gmail.com>
version: v1.3.6complex software like this has A LOT of instructions:
usage: /usr/local/bin/freebayes -f [REFERENCE] [OPTIONS] [BAM FILES] >[OUTPUT]
Bayesian haplotype-based polymorphism discovery.
parameters:
-h --help For a complete description of options.
freebayes is maintained by Erik Garrison and Pjotr Prins.
citation: Erik Garrison, Gabor Marth
"Haplotype-based variant detection from short-read sequencing"
arXiv:1207.3907 (http://arxiv.org/abs/1207.3907)
author: Erik Garrison <erik.garrison@gmail.com>
version: v1.3.6
overview:
To call variants from aligned short-read sequencing data, supply BAM files and
a reference. FreeBayes will provide VCF output on standard out describing SNPs,
indels, and complex variants in samples in the input alignments.
By default, FreeBayes will consider variants supported by at least 2
observations in a single sample (-C) and also by at least 20% of the reads from
a single sample (-F). These settings are suitable to low to high depth
sequencing in haploid and diploid samples, but users working with polyploid or
pooled samples may wish to adjust them depending on the characteristics of
their sequencing data.
FreeBayes is capable of calling variant haplotypes shorter than a read length
where multiple polymorphisms segregate on the same read. The maximum distance
between polymorphisms phased in this way is determined by the
--max-complex-gap, which defaults to 3bp. In practice, this can comfortably be
set to half the read length.
Ploidy may be set to any level (-p), but by default all samples are assumed to
be diploid. FreeBayes can model per-sample and per-region variation in
copy-number (-A) using a copy-number variation map.
FreeBayes can act as a frequency-based pooled caller and describe variants
and haplotypes in terms of observation frequency rather than called genotypes.
To do so, use --pooled-continuous and set input filters to a suitable level.
Allele observation counts will be described by AO and RO fields in the VCF output.
examples:
# call variants assuming a diploid sample
freebayes -f ref.fa aln.bam >var.vcf
# call variants assuming a diploid sample, providing gVCF output
freebayes -f ref.fa --gvcf aln.bam >var.gvcf
# require at least 5 supporting observations to consider a variant
freebayes -f ref.fa -C 5 aln.bam >var.vcf
# discard alignments overlapping positions where total read depth is greater than 200
freebayes -f ref.fa -g 200 aln.bam >var.vcf
# use a different ploidy
freebayes -f ref.fa -p 4 aln.bam >var.vcf
# assume a pooled sample with a known number of genome copies
freebayes -f ref.fa -p 20 --pooled-discrete aln.bam >var.vcf
# generate frequency-based calls for all variants passing input thresholds
freebayes -f ref.fa -F 0.01 -C 1 --pooled-continuous aln.bam >var.vcf
# use an input VCF (bgzipped + tabix indexed) to force calls at particular alleles
freebayes -f ref.fa -@ in.vcf.gz aln.bam >var.vcf
# generate long haplotype calls over known variants
freebayes -f ref.fa --haplotype-basis-alleles in.vcf.gz \
--haplotype-length 50 aln.bam
# naive variant calling: simply annotate observation counts of SNPs and indels
freebayes -f ref.fa --haplotype-length 0 --min-alternate-count 1 \
--min-alternate-fraction 0 --pooled-continuous --report-monomorphic aln.bam >var.vcf
parameters:
-h --help Prints this help dialog.
--version Prints the release number and the git commit id.
input:
-b --bam FILE Add FILE to the set of BAM files to be analyzed.
-L --bam-list FILE
A file containing a list of BAM files to be analyzed.
-c --stdin Read BAM input on stdin.
-f --fasta-reference FILE
Use FILE as the reference sequence for analysis.
An index file (FILE.fai) will be created if none exists.
If neither --targets nor --region are specified, FreeBayes
will analyze every position in this reference.
-t --targets FILE
Limit analysis to targets listed in the BED-format FILE.
-r --region <chrom>:<start_position>-<end_position>
Limit analysis to the specified region, 0-base coordinates,
end_position not included (same as BED format).
Either '-' or '..' maybe used as a separator.
-s --samples FILE
Limit analysis to samples listed (one per line) in the FILE.
By default FreeBayes will analyze all samples in its input
BAM files.
--populations FILE
Each line of FILE should list a sample and a population which
it is part of. The population-based bayesian inference model
will then be partitioned on the basis of the populations.
-A --cnv-map FILE
Read a copy number map from the BED file FILE, which has
either a sample-level ploidy:
sample_name copy_number
or a region-specific format:
seq_name start end sample_name copy_number
... for each region in each sample which does not have the
default copy number as set by --ploidy. These fields can be delimited
by space or tab.
output:
-v --vcf FILE Output VCF-format results to FILE. (default: stdout)
--gvcf
Write gVCF output, which indicates coverage in uncalled regions.
--gvcf-chunk NUM
When writing gVCF output emit a record for every NUM bases.
-& --gvcf-dont-use-chunk BOOL
When writing the gVCF output emit a record for all bases if
set to "true" , will also route an int to --gvcf-chunk
similar to --output-mode EMIT_ALL_SITES from GATK
-@ --variant-input VCF
Use variants reported in VCF file as input to the algorithm.
Variants in this file will included in the output even if
there is not enough support in the data to pass input filters.
-l --only-use-input-alleles
Only provide variant calls and genotype likelihoods for sites
and alleles which are provided in the VCF input, and provide
output in the VCF for all input alleles, not just those which
have support in the data.
--haplotype-basis-alleles VCF
When specified, only variant alleles provided in this input
VCF will be used for the construction of complex or haplotype
alleles.
--report-all-haplotype-alleles
At sites where genotypes are made over haplotype alleles,
provide information about all alleles in output, not only
those which are called.
--report-monomorphic
Report even loci which appear to be monomorphic, and report all
considered alleles, even those which are not in called genotypes.
Loci which do not have any potential alternates have '.' for ALT.
-P --pvar N Report sites if the probability that there is a polymorphism
at the site is greater than N. default: 0.0. Note that post-
filtering is generally recommended over the use of this parameter.
--strict-vcf
Generate strict VCF format (FORMAT/GQ will be an int)
population model:
-T --theta N The expected mutation rate or pairwise nucleotide diversity
among the population under analysis. This serves as the
single parameter to the Ewens Sampling Formula prior model
default: 0.001
-p --ploidy N Sets the default ploidy for the analysis to N. default: 2
-J --pooled-discrete
Assume that samples result from pooled sequencing.
Model pooled samples using discrete genotypes across pools.
When using this flag, set --ploidy to the number of
alleles in each sample or use the --cnv-map to define
per-sample ploidy.
-K --pooled-continuous
Output all alleles which pass input filters, regardles of
genotyping outcome or model.
reference allele:
-Z --use-reference-allele
This flag includes the reference allele in the analysis as
if it is another sample from the same population.
--reference-quality MQ,BQ
Assign mapping quality of MQ to the reference allele at each
site and base quality of BQ. default: 100,60
allele scope:
-n --use-best-n-alleles N
Evaluate only the best N SNP alleles, ranked by sum of
supporting quality scores. (Set to 0 to use all; default: all)
-E --max-complex-gap N
--haplotype-length N
Allow haplotype calls with contiguous embedded matches of up
to this length. Set N=-1 to disable clumping. (default: 3)
--min-repeat-size N
When assembling observations across repeats, require the total repeat
length at least this many bp. (default: 5)
--min-repeat-entropy N
To detect interrupted repeats, build across sequence until it has
entropy > N bits per bp. Set to 0 to turn off. (default: 1)
--no-partial-observations
Exclude observations which do not fully span the dynamically-determined
detection window. (default, use all observations, dividing partial
support across matching haplotypes when generating haplotypes.)
These flags are meant for testing.
They are not meant for filtering the output.
They actually filter the input to the algorithm by throwing away alignments.
This hurts performance by hiding information from the Bayesian model.
Do not use them unless you can validate that they improve results!
-I --throw-away-snp-obs Remove SNP observations from input.
-i --throw-away-indels-obs Remove indel observations from input.
-X --throw-away-mnp-obs Remove MNP observations from input.
-u --throw-away-complex-obs Remove complex allele observations from input.
If you need to break apart haplotype calls to obtain one class of alleles,
run the call with default parameters, then normalize and subset the VCF:
freebayes ... | vcfallelicprimitives -kg >calls.vcf
For example, this would retain only biallelic SNPs.
<calls.vcf vcfsnps | vcfbiallelic >biallelic_snp_calls.vcf
indel realignment:
-O --dont-left-align-indels
Turn off left-alignment of indels, which is enabled by default.
input filters:
-4 --use-duplicate-reads
Include duplicate-marked alignments in the analysis.
default: exclude duplicates marked as such in alignments
-m --min-mapping-quality Q
Exclude alignments from analysis if they have a mapping
quality less than Q. default: 1
-q --min-base-quality Q
Exclude alleles from analysis if their supporting base
quality is less than Q. default: 0
-R --min-supporting-allele-qsum Q
Consider any allele in which the sum of qualities of supporting
observations is at least Q. default: 0
-Y --min-supporting-mapping-qsum Q
Consider any allele in which and the sum of mapping qualities of
supporting reads is at least Q. default: 0
-Q --mismatch-base-quality-threshold Q
Count mismatches toward --read-mismatch-limit if the base
quality of the mismatch is >= Q. default: 10
-U --read-mismatch-limit N
Exclude reads with more than N mismatches where each mismatch
has base quality >= mismatch-base-quality-threshold.
default: ~unbounded
-z --read-max-mismatch-fraction N
Exclude reads with more than N [0,1] fraction of mismatches where
each mismatch has base quality >= mismatch-base-quality-threshold
default: 1.0
-$ --read-snp-limit N
Exclude reads with more than N base mismatches, ignoring gaps
with quality >= mismatch-base-quality-threshold.
default: ~unbounded
-e --read-indel-limit N
Exclude reads with more than N separate gaps.
default: ~unbounded
-0 --standard-filters Use stringent input base and mapping quality filters
Equivalent to -m 30 -q 20 -R 0 -S 0
-F --min-alternate-fraction N
Require at least this fraction of observations supporting
an alternate allele within a single individual in the
in order to evaluate the position. default: 0.05
-C --min-alternate-count N
Require at least this count of observations supporting
an alternate allele within a single individual in order
to evaluate the position. default: 2
-3 --min-alternate-qsum N
Require at least this sum of quality of observations supporting
an alternate allele within a single individual in order
to evaluate the position. default: 0
-G --min-alternate-total N
Require at least this count of observations supporting
an alternate allele within the total population in order
to use the allele in analysis. default: 1
--min-coverage N
Require at least this coverage to process a site. default: 0
--limit-coverage N
Downsample per-sample coverage to this level if greater than this coverage.
default: no limit
-g --skip-coverage N
Skip processing of alignments overlapping positions with coverage >N.
This filters sites above this coverage, but will also reduce data nearby.
default: no limit
--trim-complex-tail
Trim complex tails.
population priors:
-k --no-population-priors
Equivalent to --pooled-discrete --hwe-priors-off and removal of
Ewens Sampling Formula component of priors.
mappability priors:
-w --hwe-priors-off
Disable estimation of the probability of the combination
arising under HWE given the allele frequency as estimated
by observation frequency.
-V --binomial-obs-priors-off
Disable incorporation of prior expectations about observations.
Uses read placement probability, strand balance probability,
and read position (5'-3') probability.
-a --allele-balance-priors-off
Disable use of aggregate probability of observation balance between alleles
as a component of the priors.
genotype likelihoods:
--observation-bias FILE
Read length-dependent allele observation biases from FILE.
The format is [length] [alignment efficiency relative to reference]
where the efficiency is 1 if there is no relative observation bias.
--base-quality-cap Q
Limit estimated observation quality by capping base quality at Q.
--prob-contamination F
An estimate of contamination to use for all samples. default: 10e-9
--legacy-gls Use legacy (polybayes equivalent) genotype likelihood calculations
--contamination-estimates FILE
A file containing per-sample estimates of contamination, such as
those generated by VerifyBamID. The format should be:
sample p(read=R|genotype=AR) p(read=A|genotype=AA)
Sample '*' can be used to set default contamination estimates.
algorithmic features:
--report-genotype-likelihood-max
Report genotypes using the maximum-likelihood estimate provided
from genotype likelihoods.
-B --genotyping-max-iterations N
Iterate no more than N times during genotyping step. default: 1000.
--genotyping-max-banddepth N
Integrate no deeper than the Nth best genotype by likelihood when
genotyping. default: 6.
-W --posterior-integration-limits N,M
Integrate all genotype combinations in our posterior space
which include no more than N samples with their Mth best
data likelihood. default: 1,3.
-N --exclude-unobserved-genotypes
Skip sample genotypings for which the sample has no supporting reads.
-S --genotype-variant-threshold N
Limit posterior integration to samples where the second-best
genotype likelihood is no more than log(N) from the highest
genotype likelihood for the sample. default: ~unbounded
-j --use-mapping-quality
Use mapping quality of alleles when calculating data likelihoods.
-H --harmonic-indel-quality
Use a weighted sum of base qualities around an indel, scaled by the
distance from the indel. By default use a minimum BQ in flanking sequence.
-D --read-dependence-factor N
Incorporate non-independence of reads by scaling successive
observations by this factor during data likelihood
calculations. default: 0.9
-= --genotype-qualities
Calculate the marginal probability of genotypes and report as GQ in
each sample field in the VCF output.
debugging:
-d --debug Print debugging output.
-dd Print more verbose debugging output (requires "make DEBUG")
author: Erik Garrison <erik.garrison@gmail.com>
version: v1.3.6-r --region <chrom>:<start_position>-<end_position>
-b --bam FILE Add FILE to the set of BAM files to be analyzed.
-f --fasta-reference FILE
Use FILE as the reference sequence for analysis.
An index file (FILE.fai) will be created if none exists.
If neither --targets nor --region are specified, FreeBayes
will analyze every position in this reference.
-b BAM files The lab (me) has provided you with bam files are in /media/fileshare/samples*.tar.gz
-f FASTA The human genome reference is available at /media/fileshare/hg38.fa.gz
-r regions will be provided by you (we’ve already extracted them….)
copy the human genome to a directory named reference and the extract the .bam file archive, deleting the original .tar.gz
We need to proceed carefully:
ls your directory
gunzip the reference fasta file:
gunzip <fasta> -what did this do? (ls the directory again)Write a bash script call_variant.sh that:
final_gene_panel.fa to extract the relevant chr, start, and stopfreebayes,using the reference fasta, on the sample .bam files using the range extracted from final_gene_panel.fafreebayes output to an informatively named output file