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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...

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...

Alternative RNA Splicing

Alternative RNA Splicing

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Related Experiment Video

Updated: Jan 25, 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

Deep learning of the back-splicing code for circular RNA formation.

Jun Wang¹, Liangjiang Wang¹

¹Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.

Bioinformatics (Oxford, England)

|May 12, 2019

Summary

This summary is machine-generated.

Researchers developed DeepCirCode, a deep learning model, to predict circular RNA (circRNA) formation. This model identifies sequence motifs crucial for back-splicing, offering new insights into circRNA biogenesis.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Circular RNAs (circRNAs) are a novel class of RNA molecules formed by back-splicing.
The precise mechanisms governing circRNA formation remain largely unknown.
circRNAs are implicated in various biological processes and human diseases.

Purpose of the Study:

To develop a predictive model for human circRNA formation.
To elucidate the sequence features and motifs involved in circRNA back-splicing.
To provide insights into the regulatory code of circRNA biogenesis.

Main Methods:

Application of state-of-the-art machine learning, specifically deep learning.
Development of a convolutional neural network (CNN) model named DeepCirCode.
Analysis of sequence motifs identified by the deep learning model.

Main Results:

DeepCirCode accurately predicts human circRNA formation.
The model outperforms traditional machine learning algorithms.
Identified sequence motifs are associated with RNA splicing, transcription, and translation, and their distribution is critical for back-splicing.
Some identified motifs show conservation across species (human, mouse, fruit fly).

Conclusions:

DeepCirCode offers a powerful tool for predicting circRNA formation.
Sequence motifs and their distribution play a significant role in circRNA biogenesis.
The findings contribute to understanding the 'back-splicing code' governing circRNA formation.