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RNA-seq03:21

RNA-seq

11.6K
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...
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RNA Editing02:23

RNA Editing

9.6K
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...
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RNA Interference01:23

RNA Interference

27.6K
RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
27.6K
siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
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Related Experiment Video

Updated: Dec 20, 2025

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

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mmannot: How to improve small-RNA annotation?

Matthias Zytnicki1, Christine Gaspin1

  • 1Unité de Mathématiques et Informatique Appliquées, Toulouse INRA, Castanet Tolosan, France.

Plos One
|May 29, 2020
PubMed
Summary

This study introduces mmannot, a new bioinformatics strategy to accurately quantify small non-coding RNAs (ncRNAs) by addressing multi-mapping reads. It improves the analysis of ncRNA expression profiles from high-throughput sequencing data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-throughput sequencing enables genome-wide analysis of small non-coding RNAs (ncRNAs).
  • ncRNAs encompass diverse classes like microRNAs, tRNA fragments, snoRNAs, and snRNAs.
  • Standard RNA-seq quantification faces challenges with reads mapping to multiple genomic locations, leading to biased results.

Purpose of the Study:

  • To develop a novel strategy for accurate quantification of ncRNAs, particularly addressing the issue of multi-mapping reads.
  • To improve the identification and understanding of small non-coding RNA expression profiles.

Main Methods:

  • A new strategy was developed to compare multi-mapping reads and their associated genomic annotations.
  • When reads map to multiple positions with different annotations, existing features are merged.

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A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
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  • The strategy was implemented in a freely available bioinformatics tool called mmannot.

    • Main Results:

    • The mmannot tool provides a more accurate quantification of ncRNAs by handling multi-mapping reads effectively.
    • The strategy successfully co-localizes reads with single feature annotations, such as duplicated miRNAs or genes.
    • Merging features for reads with diverse annotations provides comprehensive expression counts.

    Conclusions:

    • The mmannot strategy offers a robust solution to the challenge of multi-mapping reads in ncRNA analysis.
    • This approach enhances the reliability of expression quantification for various small non-coding RNA classes.
    • Accurate ncRNA quantification is crucial for advancing our understanding of their biological roles.