HTSeq – A Python framework to work with high-throughput sequencing data

Abstract

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 work flows, 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, 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.

Integrated analysis of mRNA and miRNA expression in response to interleukin-6 in hepatocytes

“…[2]. In this data, we identify and validate IL-6-induced changes in gene expression [3][4][5][6] and their functional relationships over time and between cell types by gene ontology [7,8]. We also provide data showing the enrichment of miRNA binding motifs in the 3'UTRs of differentially expressed genes [9], and their predicted gene targets derived from our RNA-seq data [10] …”

The draft genome of tropical fruit durian (Durio zibethinus)

“…Previously aligned RNA-seq data were counted against the predicted gene models using HTSeq-count 69 . Raw counts were fitted on a negative binomial distribution, and differential expression was tested for using DESeq2.…”

Digital image analysis outperforms manual biomarker assessment in breast cancer

“…Stranded RNAseq libraries were then constructed using TruSeq Stranded Total RNA Library Prep Kit (Illumina) at the Science for Life Laboratory (Stockholm, Sweden). Gene-level expression estimates were calculated using HTSeq count version 0.6.1, 31 and data were normalized using the TMM method 32 in the edgeR package. 33 Unaligned RNAseq data from the 'Cancer Genome Atlas' breast cancer data set 34 were downloaded (n = 1073) and processed through an identical bioinformatics pipeline as the primary data set.…”