BACKGROUND:SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. FINDINGS:The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. CONCLUSION:Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.

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.

Determining the evolutionary basis of cross-species transmission and immune evasion is key to understanding the mechanisms that control the emergence of either new viruses or novel antigenic variants with pandemic potential. The hemagglutinin glycoprotein of influenza A viruses is a critical host range determinant and a major target of neutralizing antibodies. Equine influenza virus (EIV) is a significant pathogen of the horse that causes periodical outbreaks of disease even in populations with high vaccination coverage. EIV has also jumped the species barrier and emerged as a novel respiratory pathogen in dogs, canine influenza virus. We studied the dynamics of equine influenza virus evolution in horses at the intrahost level and how this evolutionary process is affected by interhost transmission in a natural setting. To this end, we performed clonal sequencing of the hemagglutinin 1 gene derived from individual animals at different times postinfection. Our results show that despite the population consensus sequence remaining invariant, genetically distinct subpopulations persist during the course of infection and are also transmitted, with some variants likely to change antigenicity. We also detected a natural case of mixed infection in an animal infected during an outbreak of equine influenza, raising the possibility of reassortment between different strains of virus. In sum, our data suggest that transmission bottlenecks may not be as narrow as originally perceived and that the genetic diversity required to adapt to new host species may be partially present in the donor host and potentially transmitted to the recipient host.

Motivation: Existing sequence assembly editors struggle with the volumes of data now readily available from the latest generation of DNA sequencing instruments.Results: We describe the Gap5 software along with the data structures and algorithms used that allow it to be scalable. We demonstrate this with an assembly of 1.1 billion sequence fragments and compare the performance with several other programs. We analyse the memory, CPU, I/O usage and file sizes used by Gap5.