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Isolation of High-density Lipoproteins for Non-coding Small RNA Quantification

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Computational Methods for Mapping, Assembly and Quantification for Coding and Non-coding Transcripts.

Isaac A Babarinde¹, Yuhao Li¹, Andrew P Hutchins¹

¹Department of Biology, Southern University of Science and Technology, 1088 Xueyuan Lu, Shenzhen, China.

Computational and Structural Biotechnology Journal

|June 14, 2019

Summary

This summary is machine-generated.

High-throughput RNA sequencing (RNA-Seq) offers deep biological insights but increases computational demands. This review details RNA-Seq analysis pipelines, their challenges, and their utility for studying coding and non-coding RNAs.

Keywords:

Genome Long non-coding RNA RNA-Seq Transcript Transposable element

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Gene expression measurement is crucial for understanding biological functions.
High-throughput sequencing technologies like RNA-Seq have revolutionized the scale of transcriptional analysis.
Increased data complexity necessitates advanced computational approaches.

Purpose of the Study:

To review common computational pipelines for RNA-Seq data analysis.
To discuss the strengths and weaknesses of these pipelines for RNA analysis.
To address challenges in analyzing repetitive elements like transposable elements.

Main Methods:

Review of existing literature on RNA-Seq computational pipelines.
Analysis of strengths and weaknesses of assembly, quantification, and analysis methods.
Discussion of strategies for handling transposable elements in transcript assembly.

Main Results:

RNA-Seq analysis pipelines vary in their suitability for different biological questions.
Assembly and quantification of coding and non-coding RNAs present distinct challenges.
Repetitive elements, such as transposable elements, complicate transcript assembly.

Conclusions:

RNA-Seq is a powerful and versatile technology for biological research.
Understanding computational pipeline limitations is essential for accurate RNA-Seq data interpretation.
Continued development in bioinformatics is needed to fully leverage RNA-Seq data.