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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: Apr 3, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

htsint: a Python library for sequencing pipelines that combines data through gene set generation.

Adam J Richards¹, Anthony Herrel^2,3, Camille Bonneaud^4,5

¹Station d'Ecologie Expérimentale du CNRS, USR 2936, Route du CNRS, Moulis, 09200, France. adamricha@gmail.com.

BMC Bioinformatics

|September 25, 2015

Summary

This summary is machine-generated.

This study introduces htsint, a tool that groups genes into functional modules by analyzing annotation data. This approach enhances RNA-Seq analysis by integrating genomic information and detecting subtle expression changes.

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Last Updated: Apr 3, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Genomics and Bioinformatics
Computational Biology
Systems Biology

Background:

Sequencing technologies generate vast genomic and transcriptomic data, including gene expression and polymorphisms.
Standard analysis pipelines often overlook relationships between ontology terms, limiting functional context.
Gene-level analyses in RNA-Seq can miss coordinated changes, necessitating group-level approaches.

Purpose of the Study:

To develop a high-throughput data integration tool, htsint, for compiling and analyzing functional annotation information.
To create a method for calculating functional distances between genes and partitioning them into functional modules.
To extend gene set enrichment frameworks for RNA-Seq data analysis.

Main Methods:

The htsint tool compiles annotation data from specified taxa to compute functional distances among genes.
Spectral clustering is employed to partition genes into distinct functional modules.
The generated gene sets can be tested for enrichment or differential expression using existing packages.

Main Results:

htsint generates functional modules by integrating diverse annotation data and applying spectral clustering.
The tool can analyze gene spaces ranging from specific pathways to entire genomes.
Functional modules derived from htsint facilitate the detection of coordinated changes in transcriptomic features.

Conclusions:

htsint provides a toolkit for generating functional modules, designed for sequencing pipelines but applicable to broader genomics research.
The software integrates annotation data and aids in analyzing RNA-Seq experiments by considering genes in functional groups.
htsint is available as a free Python library on GitHub, promoting accessibility and further development.