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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 (lncRNA)...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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Related Experiment Video

Updated: Jun 2, 2026

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

Published on: August 21, 2014

Databases and resources for human small non-coding RNAs.

Eneritz Agirre¹, Eduardo Eyras

¹Department of Computational Genomics, Universitat Pompeu Fabra, Dr. Aiguader 88, E08003 Barcelona, Spain.

|April 21, 2011

Summary

This summary is machine-generated.

This review surveys human small non-coding RNA (sRNA) databases, highlighting challenges in integrating diverse sRNA data and functional annotation for gene regulation and disease research.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

High-throughput sequencing enables genome-wide studies of small non-coding RNAs (sRNAs).
sRNAs play crucial roles in gene regulation and disease.
The rapid increase in sRNA data necessitates organized annotation databases.

Purpose of the Study:

To review currently available human sRNA databases.
To discuss challenges in integrating diverse sRNA datasets.
To highlight issues in functional annotation of novel sRNA classes.

Main Methods:

Literature review of existing sRNA databases.
Analysis of publicly available high-throughput sequencing datasets for human sRNAs.
Discussion of data integration and annotation strategies.

Main Results:

Several databases for human sRNAs exist, with tools for accessing micro-RNA data.
Large sRNA datasets are often supplementary data in publications.
Heterogeneity of new sRNA classes complicates integration and annotation.

Conclusions:

Effective integration and annotation of diverse human sRNA datasets remain challenging.
Improved bioinformatics tools and standardized annotation are needed.
Further research is required to fully understand the roles of various sRNAs in biological processes and diseases.