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GATK-Queue / Genome Analysis Toolkit-Queue

A command-line scripting framework for defining multi-stage genomic analysis pipelines combined with an execution manager that runs those pipelines from end-to-end. Often processing genome data includes several steps to produces outputs, for example our BAM to VCF calling pipeline include among other things: local realignment around indels; emitting raw SNP calls; emitting indels, masking the SNPs at indels; annotating SNPs using chip data; labeling suspicious calls based on filters; creating a summary report with statistics. Running these tools one by one in series may often take weeks for processing, or would require custom scripting to try and optimize using parallel resources. With a Queue script users can semantically define the multiple steps of the pipeline and then hand off the logistics of running the pipeline to completion. Queue runs independent jobs in parallel, handles transient errors, and uses various techniques such as running multiple copies of the same program on different portions of the genome to produce outputs faster.


A platform-independent mutation caller for targeted, exome, and whole-genome resequencing data generated on Illumina, SOLiD, Life/PGM, Roche/454, and similar instruments. The newest version, VarScan 2, is written in Java, so it runs on most operating systems. It can be used to detect different types of variation: 1) germline variants (SNPs and indels) in individual samples or pools of samples, 2) multi-sample variants (shared or private) in multi-sample datasets (with mpileup), 3) somatic mutations, LOH events, and germline variants in tumor-normal pairs and 4) somatic copy number alterations (CNAs) in tumor-normal exome data.


A Bayesian method to call indels from short-read sequence data in individuals and populations by realigning reads to candidate haplotypes that represent alternative sequence to the reference. The candidate haplotypes are formed by combining candidate indels and SNVs identified by the read mapper, while allowing for known sequence variants or candidates from other methods to be included. In our probabilistic realignment model we account for base-calling errors, mapping errors, and also, importantly, for increased sequencing error indel rates in long homopolymer runs.

SPLINTER / Short indel Prediction by Large deviation Inference and Nonlinear True frequency Estimation by Recursion

Detects and quantifies short indels and substitutions in large pools. SPLINTER allows accurate detection and quantification of short insertions, deletions, and substitutions by integrating information from the synthetic DNA library to tune SPLINTER and quantify specificity and sensitivity for every experiment in order to accurately detect and quantify indels and substitutions.