Motivation: General-purpose processors can now contain many dozens of processor cores and support hundreds of simultaneous threads of execution. To make best use of these threads, genomics software must contend with new and subtle computer architecture issues. We discuss some of these and propose methods for improving thread scaling in tools that analyze each read independently, such as read aligners. Results: We implement these methods in new versions of Bowtie, Bowtie 2 and HISAT. We greatly improve thread scaling in many scenarios, including on the recent Intel Xeon Phi architecture. We also highlight how bottlenecks are exacerbated by variable-record-length file formats like FASTQ and suggest changes that enable superior scaling. Availability and implementation: Experiments for this study: https://github.com/BenLangmead/bowtie-scaling. Bowtie: http://bowtie-bio.sourceforge.net. Bowtie 2: http://bowtie-bio.sourceforge.net/bowtie2. HISAT: http://www.ccb.jhu.edu/software/hisat. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract TGICL is a pipeline for analysis of large Expressed Sequence Tags (EST) and mRNA databases in which the sequences are first clustered based on pairwise sequence similarity, and then assembled by individual clusters (optionally with quality values) to produce longer, more complete consensus sequences. The system can run on multi-CPU architectures including SMP and PVM. Availability: http://www.tigr.org/tdb/tgi/software/ Contact: [email protected]; [email protected] * To whom correspondence should be addressed.