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Related Concept Videos

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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...

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...

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

Pre-mRNA Processing: Modification of pre-mRNA Ends

Pre-mRNA Processing: Modification of pre-mRNA Ends

In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a cap to the 5' end of the growing transcript. In this process, a 5' phosphate is replaced by modified guanosine that has a methyl group attached (7-methyl guanosine). This 5' cap helps...

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Related Experiment Video

Updated: Feb 23, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Integrative deep models for alternative splicing.

Anupama Jha¹, Matthew R Gazzara^1,2,3, Yoseph Barash^1,2

¹Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, PA, USA.

Bioinformatics (Oxford, England)

|September 9, 2017

Summary

This summary is machine-generated.

This study enhances computational models for alternative splicing (AS) prediction by integrating diverse experimental data. The new framework improves accuracy in predicting splicing regulatory factors and offers biological insights.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Alternative splicing (AS) increases transcriptome complexity and aberrant splicing is linked to diseases.
Computational models (splicing codes) predict splicing outcomes from genomic sequence, but previously only used expression data.
Integrating diverse data sources can improve AS regulatory factor prediction.

Purpose of the Study:

To improve computational models for alternative splicing (AS) outcome prediction.
To integrate additional data sources for better AS regulatory factor predictions.
To develop a scalable framework for splicing code modeling.

Main Methods:

Compared Bayesian and Deep Neural Network models for AS prediction.
Developed a novel target function for exon skipping events to enhance model accuracy.
Utilized transfer learning to integrate noisy and incomplete data from CLIP-Seq, knockdown, and overexpression experiments.

Main Results:

The new target function significantly improved AS prediction accuracy.
The developed framework successfully integrated diverse experimental data, including CLIP-Seq and gene perturbation experiments.
Demonstrated prediction improvements and biological insights using mouse brain, muscle, and heart datasets.

Conclusions:

The proposed framework offers a scalable and integrative solution for improving splicing code modeling.
The study highlights the potential of integrating multiple data types for a deeper understanding of AS regulation.
The findings pave the way for more accurate predictions of AS regulatory factors.