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RNA Editing

RNA Editing

RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...

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.
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Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

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Ribosome Profiling

Ribosome Profiling

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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: Oct 29, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

Published on: December 2, 2009

WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction.

Lian Liu¹, Bowen Song², Kunqi Chen³

¹School of Computer Sciences, Shannxi Normal University, Xi'an, Shaanxi 710119, China.

Methods (San Diego, Calif.)

|July 10, 2021

Summary

Introducing the WHISTLE server, a novel machine learning platform utilizing genomic coordinates. This tool enhances biological predictions by integrating genomic features alongside sequence data, outperforming traditional methods.

Keywords:

Epitranscriptome Genomic coordinate Web server

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Last Updated: Oct 29, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

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Published on: December 2, 2009

A Nonsequencing Approach for the Rapid Detection of RNA Editing

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Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

Published on: May 14, 2020

Area of Science:

Bioinformatics
Computational Biology
Machine Learning in Genomics

Background:

Machine learning models often rely solely on primary sequences (DNA, RNA, protein) for biological predictions.
Genomic context and non-sequence features are crucial for understanding biological events but are underutilized in current tools.

Purpose of the Study:

To develop the first machine learning platform, WHISTLE, that integrates genomic coordinates and features.
To provide a tool for building and deploying machine learning models incorporating genomic evidence.

Main Methods:

Developed the WHISTLE server, a platform for machine learning based on genomic coordinates.
Integrated 46 distinct genomic features with conventional sequence features for covariate extraction.
Enabled convenient model web deployment.

Main Results:

Models incorporating genomic features significantly outperformed sequence-only models in predicting m⁶A sites.
Demonstrated the utility of genomic evidence in machine learning for biological predictions.

Conclusions:

The WHISTLE server is a valuable tool for studying biological attributes linked to genomic coordinates.
This platform advances machine learning applications in genomics by leveraging genomic evidence.