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Updated: Jun 4, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Bioinformatics for RNomics.

Kristin Reiche¹, Katharina Schutt, Kerstin Boll

¹Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|March 4, 2011

Summary

This summary is machine-generated.

This study details computational methods for identifying and classifying RNA transcripts. It addresses the challenge of understanding novel non-coding RNA functions using advanced bioinformatics approaches.

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Last Updated: Jun 4, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

High-throughput technologies enable detailed transcriptome-wide expression analysis.
Functional information for protein-coding genes is abundant, but scarce for non-coding RNAs.

Purpose of the Study:

To describe current experimental and computational methods for identifying genome-wide functional RNA transcripts.
To focus on computational approaches for elucidating the function of novel non-coding RNA genes.
To provide detailed computational strategies for RNA transcript annotation and classification.

Main Methods:

Review of current experimental techniques for RNA transcript identification.
Detailed description of computational methods for RNA transcript functional analysis.
Exploration of bioinformatics tools for non-coding RNA gene function and evolutionary origin discovery.

Main Results:

Current methods for identifying RNA transcripts are improving rapidly.
Computational tools are crucial for uncovering the function of novel non-coding RNAs.
Established computational approaches can be used to annotate and classify RNA transcripts.

Conclusions:

Computational methods are essential for advancing the functional annotation of RNA transcripts.
Addressing the lack of functional information for non-coding RNAs is a key challenge in genomics.
This work provides a framework for the computational analysis of RNA transcripts.