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, F_IS, and F_ST. 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.