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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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Related Experiment Video

Updated: May 27, 2025

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

HTSinfer: inferring metadata from bulk Illumina RNA-Seq libraries.

Máté Balajti^1,2, Rohan Kandhari¹, Boris Jurič³

¹Biozentrum, University of Basel, Basel 4056, Switzerland.

Bioinformatics (Oxford, England)

|February 19, 2025

Summary

This summary is machine-generated.

HTSinfer is a new tool that automatically infers sequencing metadata from bulk RNA-sequencing data. This tool improves data accuracy and usability for the scientific community.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

The Sequencing Read Archive is a major repository for sequencing data, crucial for scientific research.
Accurate metadata is essential for reusing sequencing data, but manual entry is error-prone and often incomplete.
Existing tools for verifying metadata completeness and consistency are limited.

Purpose of the Study:

To introduce HTSinfer, a Python-based tool for inferring metadata from bulk RNA-sequencing data.
To address the limitations of manual metadata entry and improve data discoverability and reuse.

Main Methods:

HTSinfer analyzes bulk RNA-sequencing data from Illumina platforms.
It uses genome sequence information and diagnostic genes to infer library source, type, read orientation, adapter sequences, and read length statistics.
The tool is modular and open-source to encourage community contributions.

Main Results:

HTSinfer accurately infers critical metadata directly from sequencing data.
It automates a previously manual and error-prone process, enhancing data reliability.
The tool provides insights into library source, type, and technical parameters.

Conclusions:

HTSinfer offers a robust solution for automated metadata inference in RNA-sequencing.
By improving metadata quality, HTSinfer facilitates broader and more accurate data reuse.
The open-source nature of HTSinfer promotes its adoption and further development within the scientific community.