SUMMARY: HTSeq 2.0 provides a more extensive application programming interface including a new representation for sparse genomic data, enhancements for htseq-count to suit single-cell omics, a new script for data using cell and molecular barcodes, improved documentation, testing and deployment, bug fixes and Python 3 support. AVAILABILITY AND IMPLEMENTATION: HTSeq 2.0 is released as an open-source software under the GNU General Public License and is available from the Python Package Index at https://pypi.python.org/pypi/HTSeq. The source code is available on Github at https://github.com/htseq/htseq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. RESULTS: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. AVAILABILITY AND IMPLEMENTATION: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq.

SUMMARY: As applications of genome sequencing, including exomes and whole genomes, are expanding, there is a need for analysis tools that are scalable to large sets of samples and/or ultra-deep coverage. Many current tool chains are based on the widely used file formats BAM and VCF or VCF-derivatives. However, for some desirable analyses, data management with these formats creates substantial implementation overhead, and much time is spent parsing files and collating data. We observe that a tally data structure, i.e. the table of counts of nucleotides × samples × strands × genomic positions, provides a reasonable intermediate level of abstraction for many genomics analyses, including single nucleotide variant (SNV) and InDel calling, copy-number estimation and mutation spectrum analysis. Here we present h5vc, a data structure and associated software for managing tallies. The software contains functionality for creating tallies from BAM files, flexible and scalable data visualization, data quality assessment, computing statistics relevant to variant calling and other applications. Through the simplicity of its API, we envision making low-level analysis of large sets of genome sequencing data accessible to a wider range of researchers. AVAILABILITY AND IMPLEMENTATION: The package H5VC for the statistical environment R is available through the Bioconductor project. The HDF5 system is used as the core of our implementation. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.