HTSlib: C library for reading/writing high-throughput sequencing data

By James K. Bonfield, John Marshall, Petr Danecek, Heng Li, V. Ohan, Andrew Whitwham, Thomas M. Keane, Robert M. Davies. GigaScience, 2021

332 citations recorded by the source corpus.

Abstract Background Since the original publication of the VCF and SAM formats, an explosion of software tools have been created to process these data files. To facilitate this a library was produced out of the original SAMtools implementation, with a focus on performance and robustness. The file formats themselves have become international standards under the jurisdiction of the Global Alliance for Genomics and Health. Findings We present a software library for providing programmatic access to sequencing alignment and variant formats. It was born out of the widely used SAMtools and BCFtools applications. Considerable improvements have been made to the original code plus many new features including newer access protocols, the addition of the CRAM file format, better indexing and iterators, and better use of threading. Conclusion Since the original Samtools release, performance has been considerably improved, with a BAM read-write loop running 5 times faster and BAM to SAM conversion 13 times faster (both using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen HTSlib downloaded >1 million times from GitHub and conda. The C library has been used directly by an estimated 900 GitHub projects and has been incorporated into Perl, Python, Rust, and R, significantly expanding the number of uses via other languages. HTSlib is open source and is freely available from htslib.org under MIT/BSD license.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d9046e88c749d303d553b54bb80b1acc9921ed8c

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Citations in the corpus

Analysing high-throughput sequencing data in Python with HTSeq 2.0 (2021, methodology): “added support for improved dependency infrastructure such as autodetection of SAM/BAM/CRAM file type via HTSlib (Bonfield et al. 2021) .”
CoverM: read alignment statistics for metagenomics (2025, unknown): “It makes substantial use of rust-htslib (K € oster 2016), which provides Rust bindings to HTSlib (Bonfield et al. 2021) for parsing intermediate alignment (BAM) files.”
NanoPack2: population-scale evaluation of long-read sequencing data (2022, methodology): “Cramino, using rust-htslib (Köster 2016; Bonfield et al. 2021), provides a much faster alternative for gathering metrics based on the data output, mean coverage, the number of reads, their mean and median length, and sequence identity relative to the reference genome.”
Improved transcriptome assembly using a hybrid of long and short reads with StringTie (2021, methodology): “format as it now makes use of the HTSlib C library [14] and can operate in hybrid data mode, enabled by the --mix option.”