genotypes
This program exports a Stacks data set either as a set of observed haplotypes at each locus in the population, or with
the haplotypes encoded into genotypes. The -r option allows only loci that exist in a certain
number of population individuals to be exported. In a mapping context, raising or lowering this limit is an effective way
to control the quality level of markers exported as genuine markers will be found in a large number of progeny. If
exporting a set of observed haplotypes in a population, the -m option can be used to restict
exported loci to those that have a minimum depth of reads.
By default, when executing the pipeline (either denovo_map.pl or ref_map.pl)
the genotypes program will be executed last and will identify mappable markers in the population and
export both a set of observed haplotypes and a set of generic genotypes with -r 1. If SQL interaction
is enabled, these files will be uploaded to the database where Stacks will store the genotyping information in a neutral way.
From the web interface, additional, manual corrections can be made, as well as marker annotations and all of this data can
be exported directly from the web, after specifying a particular map type (if exporting data from a genetic cross).
Making Corrections
If enabled with the -c option, the genotypes program
will make automated corrections to the data. Since loci are matched up in the population, the script
can correct false-negative heterozygote alleles since it knows the existence of alleles at a particular
locus in the other individuals. For example, the program will identify loci with SNPs that didn’t have high
enough coverage to be identified by the SNP caller. It will also check that homozygous tags have a minimum
depth of coverage, since a low-coverage polymorphic locus may appear homozygous simply because the other allele
wasn’t sequenced.
Correction Thresholds
The thresholds for automatic corrections can be modified by changing the default values
for the min_hom_seqs, min_het_seqs, and
max_het_seqs parameters to genotypes. min_hom_seqs
is the minimum number of reads required to consider a stack homozygous (default of 5). The
min_het_seqs and max_het_seqs variables represent fractions. If
the ratio of the depth of the the smaller allele to the bigger allele is greater than
max_het_seqs (default of 1/10) a stack is called a het. If the ratio is less than
min_het_seqs (default of 1/20) a stack is called homozygous. If the ratio is in between
the two values it is is unknown and a genotype will not be assigned.
Automated corrections made by the program are shown in the output file in capital letters.
Making genotypes appear in the web interface
If the -s option is specified, a second file will be output containing the
genotypes in SQL format — which can be imported back in to the database (into the
catalog_genotypes table). These genotypes can then be seen in the web interface
and additional, manual corrections can be made through the web. The manual corrections can then be included
in the output by exporting the results directly from the web interface.
Example Usage
Exporting a set of observed haplotypes, with a minimum stack depth of 5 reads:
~/% genotypes -P ./stacks/ -b 1 -m 5 -r 3
Exporting a set of generic, map-agnostic genotypes, requiring a marker to be present in at least three progeny:
~/% genotypes -P ./stacks/ -b 1 -t gen -c -r 3 -s
Exporting a set of map-specific genotypes:
~/% genotypes -P ./stacks/ -b 1 -t BC1 -c -o joinmap -r 3 -s
Other Pipeline Programs
Raw reads
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Core
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Execution control
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Utility programs
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The process_radtags program examines raw reads from an Illumina sequencing run and
first, checks that the barcode and the restriction enzyme cutsite are intact (correcting minor errors).
Second, it slides a window down the length of the read and checks the average quality score within the window.
If the score drops below 90% probability of being correct, the read is discarded. Reads that pass quality
thresholds are demultiplexed if barcodes are supplied.
The process_shortreads program performs the same task as process_radtags
for fast cleaning of randomly sheared genomic or transcriptomic data. This program will trim reads that are below the
quality threshold instead of discarding them, making it useful for genomic assembly or other analyses.
The clone_filter program will take a set of reads and reduce them according to PCR
clones. This is done by matching raw sequence or by referencing a set of random oligos that have been included in the sequence.
The kmer_filter program allows paired or single-end reads to be filtered according to the
number or rare or abundant kmers they contain. Useful for both RAD datasets as well as randomly sheared genomic or
transcriptomic data.
The ustacks program will take as input a set of short-read sequences and align them into
exactly-matching stacks. Comparing the stacks it will form a set of loci and detect SNPs at each locus using a
maximum likelihood framework.
A catalog can be built from any set of samples processed
by the ustacks program. It will create a set of consensus loci, merging alleles together. In the case
of a genetic cross, a catalog would be constructed from the parents of the cross to create a set of
all possible alleles expected in the progeny of the cross.
Sets of stacks constructed by the ustacks
program can be searched against a catalog produced by the cstacks program. In the case of a
genetic map, stacks from the progeny would be matched against the catalog to determine which progeny
contain which parental alleles.
The tsv2bam program will transpose data so that it is oriented by locus, instead of by sample.
In additon, if paired-ends are available, the program will pull in the set of paired reads that are associate with each
single-end locus that was assembled de novo.
The gstacks - For de novo analyses, this program will pull in paired-end
reads, if available, assemble the paired-end contig and merge it with the single-end locus, align reads
to the locus, and call SNPs. For reference-aligned analyses, this program will build loci from the
single and paired-end reads that have been aligned and sorted.
This populations program will compute population-level summary statistics such
as π, FIS, and FST. It can output site level SNP calls in VCF format and
can also output SNPs for analysis in STRUCTURE or in Phylip format for phylogenetics analysis.
The denovo_map.pl program executes each of the Stacks components to create a genetic
linkage map, or to identify the alleles in a set of populations.
The ref_map.pl program takes reference-aligned input data and executes each of the Stacks
components, using the reference alignment to form stacks, and identifies alleles. It can be used in a genetic map
of a set of populations.
The load_radtags.pl program takes a set of data produced by either the denovo_map.pl or
ref_map.pl progams (or produced by hand) and loads it into the database. This allows the data to be generated on
one computer, but loaded from another. Or, for a database to be regenerated without re-executing the pipeline.
The stacks-dist-extract script will pull data distributions from the log and distribs
files produced by the Stacks component programs.
The stacks-integrate-alignments script will take loci produced by the de novo pipeline,
align them against a reference genome, and inject the alignment coordinates back into the de novo-produced data.
The stacks-private-alleles script will extract private allele data from the populations program
outputs and output useful summaries and prepare it for plotting.
