Abstract
Given the current cost-effectiveness of next-generation sequencing, the amount of DNA-seq and RNA-seq data generated is ever increasing. One of the primary objectives of NGS experiments is calling genetic variants. While highly accurate, most variant calling pipelines are not optimized to run efficiently on large data sets. However, as variant calling in genomic data has become common practice, several methods have been proposed to reduce runtime for DNA-seq analysis through the use of parallel computing. Determining the effectively expressed variants from transcriptomics (RNA-seq) data has only recently become possible, and as such does not yet benefit from efficiently parallelized workflows. We introduce Halvade-RNA, a parallel, multi-node RNA-seq variant calling pipeline based on the GATK Best Practices recommendations. Halvade-RNA makes use of the MapReduce programming model to create and manage parallel data streams on which multiple instances of existing tools such as STAR and GATK operate concurrently. Whereas the single-threaded processing of a typical RNA-seq sample requires ∼28h, Halvade-RNA reduces this runtime to ∼2h using a small cluster with two 20-core machines. Even on a single, multi-core workstation, Halvade-RNA can significantly reduce runtime compared to using multi-threading, thus providing for a more cost-effective processing of RNA-seq data. Halvade-RNA is written in Java and uses the Hadoop MapReduce 2.0 API. It supports a wide range of distributions of Hadoop, including Cloudera and Amazon EMR.
Highlights
All authors were affiliated with ExaScience Life Lab which is a consortium of Recently, a number of methods have been introduced to accelerate read mapping and variant calling through the use of parallel and distributed computing techniques: HugeSeq [1], MegaSeq [2], Churchill [3] and Halvade [4] implement a DNA-seq variant calling pipeline according to the Best Practices recommendations [5] for use with the GATK [6, 7] variant caller
These tools exploit the fact that read mapping is parallel by read, i.e., aligning one read is independent of the alignment of other reads, while variant calling is parallel by genomic region, i.e., variant calling in a certain genomic region is independent of variant calling in other regions
The GATK Split‘N’Trim module is used to split reads into different exon segments and trim reads that overlap with intronic regions
Summary
A number of methods have been introduced to accelerate read mapping and variant calling through the use of parallel and distributed computing techniques: HugeSeq [1], MegaSeq [2], Churchill [3] and Halvade [4] implement a DNA-seq variant calling pipeline according to the Best Practices recommendations [5] for use with the GATK [6, 7] variant caller. These companies provided support in the form of salaries for these authors but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of each author is articulated in the “author contributions” section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
