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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 27, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing.

Krishna R Kalari, Asha A Nair, Jaysheel D Bhavsar

¹Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. kocher.jeanpierre@mayo.edu.

BMC Bioinformatics

|June 29, 2014

Summary

This summary is machine-generated.

MAP-RSeq is a new computational workflow for comprehensive RNA sequencing analysis, simplifying genomic feature extraction from transcriptomic data for any genome. This tool aids in understanding disease transcriptomics for improved diagnosis and treatment.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Next-generation sequencing (NGS) costs have decreased, but user-friendly, comprehensive RNA sequencing (RNA-Seq) analysis tools remain scarce.
A unified solution for transcriptomic genomics analysis is needed to bridge this gap.
MAP-RSeq was developed to address the lack of integrated RNA-Seq analysis platforms.

Purpose of the Study:

To develop a comprehensive computational workflow for analyzing RNA sequencing data.
To provide a user-friendly, one-stop solution for extracting genomic features from transcriptomic data for any genome.
To facilitate a deeper understanding of the transcriptomic landscape in diseases.

Main Methods:

MAP-RSeq is a modular workflow comprising six key stages: read alignment, quality assessment, gene expression and exon read counting, single nucleotide variant (SNV) identification, fusion transcript detection, and data summarization.
The workflow was validated using simulated and real RNA-Seq datasets.
It is adaptable for human transcriptome analysis and can be readily applied to other genomes.

Main Results:

MAP-RSeq provides essential outputs including gene and exon counts, fusion candidates, expressed SNVs, mapping statistics, and visualizations.
The workflow generates a detailed research data report for RNA-Seq studies.
Validation confirmed the accuracy and utility of the MAP-RSeq tools and parameters.

Conclusions:

MAP-RSeq offers a comprehensive suite of analyses for RNA-Seq data, including gene counts, SNVs, and fusion transcripts.
The workflow is versatile, supporting standalone virtual machine execution or parallel cluster computing.
MAP-RSeq has been successfully applied in clinical and research settings to advance disease transcriptomic understanding and patient care.