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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...

RNA Splicing

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

RNA Splicing

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Alternative RNA Splicing

Alternative RNA Splicing

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There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Identification Exon Skipping Events From High-Throughput RNA Sequencing Data.

Yang Bai, Shufan Ji, Qinghua Jiang

IEEE Transactions on Nanobioscience

|May 3, 2015

Summary

ESFinder accurately identifies exon skipping events from RNA sequencing data. This novel random forest classifier improves genome-wide alternative splicing analysis in NGS studies.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Next-generation high-throughput RNA sequencing (RNA-Seq) enables genome-wide alternative splicing analysis.
Accurate identification of alternative splicing events, particularly exon skipping (ES), from RNA-Seq data remains a challenge in next-generation sequencing (NGS) studies.

Purpose of the Study:

To propose and evaluate ESFinder, a novel random forest classifier for identifying exon skipping events from RNA-Seq data.
To identify and utilize relevant features for accurate ES event prediction.

Main Methods:

Development of ESFinder, a random forest classification method.
Feature selection and relevance analysis for predicting exon skipping events.
Application of ESFinder to real human skeletal muscle and brain RNA-Seq datasets.

Main Results:

ESFinder effectively predicts exon skipping events.
High predictive accuracy was achieved in experimental results.
The method demonstrates robust performance on diverse human tissue data.

Conclusions:

ESFinder offers an effective solution for identifying exon skipping events in RNA-Seq data.
The tool enhances the accuracy of genome-wide alternative splicing analysis.
ESFinder contributes to advancing NGS-based transcriptomic research.