Gonomics: uniting high performance and readability for genomics with Go
Eric H Au, Christiana Fauci, Yanting Luo, Riley J Mangan, Daniel A Snellings, Chelsea R Shoben, Seth Weaver, Shae K Simpson, Craig B Lowe- Computational Mathematics
- Computational Theory and Mathematics
- Computer Science Applications
- Molecular Biology
- Biochemistry
- Statistics and Probability
Abstract
Summary
Many existing software libraries for genomics require researchers to pick between competing considerations: the performance of compiled languages and the accessibility of interpreted languages. Go, a modern compiled language, provides an opportunity to address this conflict. We introduce Gonomics, an open-source collection of command line programs and bioinformatic libraries implemented in Go that unites readability and performance for genomic analyses. Gonomics contains packages to read, write, and manipulate a wide array of file formats (e.g. FASTA, FASTQ, BED, BEDPE, SAM, BAM, and VCF), and can convert and interface between these formats. Furthermore, our modular library structure provides a flexible platform for researchers developing their own software tools to address specific questions. These commands can be combined and incorporated into complex pipelines to meet the growing need for high-performance bioinformatic resources.
Availability and implementation
Gonomics is implemented in the Go programming language. Source code, installation instructions, and documentation are freely available at https://github.com/vertgenlab/gonomics.