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Related Experiment Video

Updated: Jun 21, 2026

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA

Published on: December 2, 2009

RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data.

Nicole Cloonan¹, Qinying Xu, Geoffrey J Faulkner

¹Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au

Bioinformatics (Oxford, England)

|August 4, 2009

Summary

This summary is machine-generated.

RNA sequencing (RNAseq) mapping requires specialized tools. RNA-MATE is a new computational pipeline that improves mapping of strand-specific RNAseq data, maximizing mappable tags and reducing sequencing costs.

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A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA

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Published on: December 2, 2009

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNAseq) data presents unique mapping challenges compared to DNA sequencing data.
Standard mapping tools struggle with exon-junction spanning reads and preserving strand specificity.
Efficiently mapping RNAseq data is crucial for accurate gene expression analysis.

Purpose of the Study:

To present RNA-MATE, a novel computational pipeline for mapping strand-specific RNAseq data.
To address the challenges of mapping RNAseq data, including exon-junction spanning reads.
To maximize the number of mappable tags, thereby reducing sequencing experiment costs.

Main Methods:

RNA-MATE employs a recursive mapping strategy specifically designed for RNAseq data.
The pipeline handles strand-specific data, ensuring accurate gene expression profiling.
It integrates alignment, collation, and generation of files for genomic context examination.

Main Results:

RNA-MATE effectively maps strand-specific RNAseq data to a reference genome.
The pipeline maximizes the number of mappable sequencing tags.
It provides an automated and integrated solution for RNAseq data analysis.

Conclusions:

RNA-MATE offers an efficient solution for mapping RNAseq data, overcoming common challenges.
Improved mapping accuracy and tag utilization can lead to significant cost savings in sequencing experiments.
The pipeline facilitates comprehensive gene expression analysis within a genomic context.