Ubuntu Manpage: variantCaller - variant-calling algorithms for PacBio sequencing data

Provided by: pbgenomicconsensus_2.0.0+20151210-1_all

NAME

       variantCaller - variant-calling algorithms for PacBio sequencing data

SYNOPSIS

       variantCaller.py is invoked from the command line.  For example, a simple invocation is:

          variantCaller.py -j8 --algorithm=quiver \
                           -r lambdaNEB.fa        \
                           -o variants.gff        \
                           aligned_reads.cmp.h5

       which  requests  that  variant  calling  proceed,  -  using  8  worker  processes, - employing the quiver
       algorithm, - taking input from the file aligned_reads.cmp.h5, - using the FASTA file lambdaNEB.fa as  the
       reference, - and writing output to variants.gff (see pbgff(5)).

       A  particularly  useful option is --referenceWindow/-w: this option allows the user to direct the tool to
       perform variant calling exclusively on a window of the reference genome, where the

OPTIONS

          variantCaller.py --help

       will provide a help message explaining all available options.

NOTES

Input and output
variantCaller.py requires two input files:

• A file of reference-aligned reads in PacBio's standard cmp.h5 format;

• A FASTA file that has been processed by ReferenceUploader.

The tool's output is formatted in the GFF format, as described in (how to link to other file?). External
tools can be used to convert the GFF file to a VCF or BED file---two other standard interchange formats
for variant calling.

NOTE:
Input cmp.h5 file requirements

variantCaller.py requires its input cmp.h5 file to be be sorted. An unsorted file can be sorting
using the tool cmpH5Sort.py.

The quiver(1) algorithm in variantCaller requires its input cmp.h5 file to have the following pulse
features:

System Message: ERROR/3 (doc/VariantCallerFunctionalSpecification.rst:, line 69)
Unexpected indentation.

• InsQV,

• SubsQV,

• DelQV,

• DelTag,

• MergeQV.

The plurality(1) algorithm can be run on cmp.h5 files that lack these features.

The input file is the main argument to variantCaller.py, while the output file is provided as an argument
to the -o flag. For example,

variantCaller.py aligned_reads.cmp.h5 -r lambda.fa -o variants.gff

will read input from aligned_reads.cmp.h5, using the reference lambda.fa, and send output to the file
variants.gff. The extension of the filename provided to the -o flag is meaningful, as it determines the
output file format. The file formats presently supported, by extension, are

.gff GFFv3 format

.txt a simplified human readable format used primarily by the developers

If the -o flag is not provided, the default behavior is to output to a variants.gff in the current
directory.

NOTE:
variantCaller.py does not modify its input cmp.h5 file in any way. This is in contrast to previous
variant callers in use at PacBio, which would write a consensus dataset to the input cmp.h5 file.

Available algorithms
At this time there are two algorithms available for variant calling: plurality and quiver.

Plurality is a simple and very fast procedure that merely tallies the most frequent read base or bases
found in alignment with each reference base, and reports deviations from the reference as potential
variants.

Quiver is a more complex procedure based on algorithms originally developed for CCS. Quiver leverages
the quality values (QVs) provided by upstream processing tools, which provide insight into whether
insertions/deletions/substitutions were deemed likely at a given read position. Use of quiver requires
the ConsensusCore library as well as trained parameter set, which will be loaded from a standard location
(TBD). Quiver can be thought of as a QV-aware local-realignment procedure.

Both algorithms are expected to converge to zero errors (miscalled variants) as coverage increases;
however quiver should converge much faster (i.e., fewer errors at low coverage), and should provide
greater variant detection power at a given error level.

Confidence values
Both quiver and plurality make a confidence metric available for every position of the consensus
sequence. The confidence should be interpreted as a phred-transformed posterior probability that the
consensus call is incorrect; i.e.

QV = -10 \log_{10}(p_{err})

variantCaller.py clips reported QV values at 93---larger values cannot be encoded in a standard FASTQ
file.

Chemistry specificity
The Quiver algorithm parameters are trained per-chemistry. SMRTanalysis software loads metadata into the
cmp.h5 to indicate the chemistry used per movie. Quiver sees this table and automatically chooses the
appropriate parameter set to use. This selection can be overridden by a command line flag.

When multiple chemistries are represented in the reads in a cmp.h5, Quiver will model each read
appropriately using the parameter set for its chemistry, thus yielding optimal results.

Performance Requirements
variantCaller.py performs variant calling in parallel using multiple processes. Work splitting and
inter-process communication are handled using the Python multiprocessing module. Work can be split among
an arbitrary number of processes (using the -j command-line flag), but for best performance one should
use no more worker processes than there are CPUs in the host computer.

The running time of the plurality algorithm should not exceed the runtime of the BLASR process that
produced the cmp.h5. The running time of the quiver algorithm should not exceed 4x the runtime of BLASR.

The amount of core memory (RAM) used among all the python processes launched by a variantCaller.py run
should not exceed the size of the uncompressed input .cmp.h5 file.

NAME

SYNOPSIS

OPTIONS

NOTES

SEE ALSO