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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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Related Experiment Video

Updated: May 23, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Improving RNA-Seq Precision with MapAl.

Paweł P Labaj¹, Bryan E Linggi, H Steven Wiley

¹Department of Biotechnology, Boku University Vienna Vienna, Austria.

Frontiers in Genetics

|April 10, 2012

Summary

This summary is machine-generated.

MapAl enhances RNA-Seq expression profiling by integrating gene models during read alignment. This tool significantly improves the reliable measurement of known and novel transcripts, boosting analytical power for differential expression analysis.

Keywords:

RNA-Seq gene expression profiling measurement precision read mapping reliability splice-form discrimination transcriptomics

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Last Updated: May 23, 2026

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Published on: November 7, 2025

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Current RNA-Seq pipelines often yield noisy gene expression estimates.
Accurate expression profiling is crucial for identifying significant biological signals.

Purpose of the Study:

To introduce MapAl, a novel tool for RNA-Seq expression profiling.
To improve the reliability and scope of transcript quantification using RNA sequencing data.

Main Methods:

MapAl integrates gene models during the read alignment stage of RNA-Seq post-processing.
It builds upon established bioinformatics tools such as Bowtie and Cufflinks.
The pipeline incorporates de novo transcript identification.

Main Results:

MapAl consistently increases the number of reliably measured known transcripts by 50%.
It enables a reliable assessment of double the total number of transcripts compared to existing pipelines.
Improved measurement precision enhances the power of differential expression analyses.

Conclusions:

MapAl offers a substantial improvement in RNA-Seq expression profiling.
The tool enhances the reliable detection of biological signals, irrespective of experimental design complexity.
MapAl provides a more comprehensive and precise assessment of the transcriptome.