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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Sep 23, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Analysing high-throughput sequencing data in Python with HTSeq 2.0.

Givanna H Putri^1,2, Simon Anders³, Paul Theodor Pyl⁴

¹School of Clinical Medicine, University of New South Wales, Sydney, NSW 2033, Australia.

Bioinformatics (Oxford, England)

|May 13, 2022

Summary

This summary is machine-generated.

HTSeq 2.0 enhances genomic data analysis with a new sparse data representation and improved htseq-count for single-cell omics. This open-source software update offers better documentation and Python 3 support for researchers.

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Last Updated: Sep 23, 2025

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Area of Science:

Bioinformatics
Computational Biology
Genomic Data Analysis

Background:

HTSeq is a widely used Python toolkit for processing high-throughput sequencing data.
Previous versions of HTSeq provided essential functionalities for genomic data analysis.

Purpose of the Study:

To introduce HTSeq 2.0, detailing its new features and improvements.
To enhance the capabilities of HTSeq for modern omics data, including single-cell applications.

Main Methods:

Development of a new sparse genomic data representation.
Enhancements to the htseq-count script for single-cell omics data.
Implementation of a new script for analyzing data with cell and molecular barcodes.
Inclusion of improved documentation, testing, and deployment procedures.
Transition to Python 3 support.

Main Results:

HTSeq 2.0 offers an extended application programming interface (API).
The update includes specific optimizations for single-cell omics data analysis.
New functionalities facilitate the analysis of complex genomic datasets using barcodes.
Bug fixes and improved stability are incorporated.

Conclusions:

HTSeq 2.0 represents a significant upgrade for genomic data processing.
The new version is better equipped to handle the demands of single-cell and barcode-based omics studies.
Enhanced usability and broader compatibility are key benefits for the research community.