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PMID: 17123865 Published · ppublish English Journal Article Review

Computational identification of microRNAs and their targets.

Computational biology and chemistry ·Vol. 30 ·No. 6 ·2006-12-00 ·Pages 395-407

Zhang B, Pan X, Wang Q, Cobb GP, Anderson TA

Abstract

MicroRNAs (miRNAs) are one class of newly identified riboregulators of gene expression in many eukaryotic organisms. They play important roles in multiple biological and metabolic processes, including developmental timing, signal transduction, cell maintenance and differentiation, diseases and cancers. miRNAs regulate gene expression at the posttranscriptional level by directly cleaving targeted mRNAs or repressing translation. Although the founding members of miRNAs were discovered by genetic screening approaches, experimental approaches were limited by their low efficiency, time consuming, and high cost. As an alternative, computational approaches were developed. Computational approaches for identifying miRNAs are based on the following major characteristics of miRNAs: hairpin-shaped secondary structures, high conservation for some miRNAs, and high minimal folding free energy index (MFEI). Computational approaches also play an important role in identifying miRNA targets. A majority of known miRNAs and their targets were identified by computational approaches. Several web-based or non-web-based computer software programs are publicly available for predicting miRNAs and their targets.

MeSH Terms
Animals Computational Biology Databases, Factual Gene Expression Regulation Gene Targeting Humans MicroRNAs/genetics Phylogeny
Chemicals
MicroRNAs
Authors & Affiliations
5 authors, click to expand affiliations / ORCID
Zhang Baohong
The Institute of Environmental and Human Health, Department of Environmental Toxicology, Texas Tech University, Lubbock, TX 79409-1163, USA. baohong.zhang@ttu.edu
Pan Xiaoping
Wang Qinglian
Cobb George P
Anderson Todd A
Article Info
Journal
Computational biology and chemistry
Abbr.
Comput Biol Chem
ISSN
1476-9271
Published
2006-12-00
Epub
2006-00-22
Pages
395-407
Language
English
Region
England
NLM ID
101157394
Subset
IM
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