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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 microarray-based...

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Updated: May 17, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Tools for mapping high-throughput sequencing data.

Nuno A Fonseca¹, Johan Rung, Alvis Brazma

¹EMBL Outstation, European Bioinformatics Institute (EBI), Hinxton, Cambridge CB10 ISD, UK. nf@ebi.ac.uk

Bioinformatics (Oxford, England)

|October 13, 2012

Summary

This summary is machine-generated.

Choosing the right read alignment software is crucial for high-throughput sequencing analysis. This survey classifies sequence mappers by various characteristics to aid researchers in selecting the most suitable tools for their specific applications.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Read alignment is a critical step in high-throughput sequencing data analysis.
Numerous software tools (mappers) exist for sequence alignment.
Selecting the optimal mapper for a specific application is challenging.

Purpose of the Study:

To classify sequence mappers based on a comprehensive set of characteristics.
To facilitate easier comparison of different alignment tools.
To assist practitioners in identifying mappers best suited for their unique research problems.

Main Methods:

Survey and classification of existing sequence mappers.
Analysis of mapper characteristics relevant to alignment performance and application suitability.
Comparative evaluation framework for sequence alignment tools.

Main Results:

A structured classification of sequence mappers is presented.
Key characteristics differentiating mappers are identified.
Guidance is provided for selecting appropriate mappers based on application needs.

Conclusions:

Understanding mapper characteristics is essential for effective high-throughput sequencing analysis.
This classification aids researchers in making informed decisions about alignment tools.
Improved mapper selection can enhance the accuracy and efficiency of genomic data analysis.