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Navigating the unexplored seascape of pre-miRNA candidates in single-genome approaches
Author(s) -
Nuno D. Mendes,
Steffen Heyne,
Ana T. Freitas,
Marie-France Sagot,
Rolf Backofen
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts574
Subject(s) - computer science , cluster analysis , perl , robustness (evolution) , computational biology , data mining , scripting language , identification (biology) , feature vector , artificial intelligence , biology , genetics , gene , world wide web , operating system , botany
The computational search for novel microRNA (miRNA) precursors often involves some sort of structural analysis with the aim of identifying which type of structures are prone to being recognized and processed by the cellular miRNA-maturation machinery. A natural way to tackle this problem is to perform clustering over the candidate structures along with known miRNA precursor structures. Mixed clusters allow then the identification of candidates that are similar to known precursors. Given the large number of pre-miRNA candidates that can be identified in single-genome approaches, even after applying several filters for precursor robustness and stability, a conventional structural clustering approach is unfeasible.

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