A non-biased framework for the annotation and classification of the non-mirna small RNA transcriptome

Lorena Pantano, Xavier P. Estivill, Eulalia Martí

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Motivation: Recent progress in high-throughput sequencing technologies has largely contributed to reveal a highly complex landscape of small non-coding RNAs (sRNAs), including novel non-canonical sRNAs derived from long non-coding RNA, repeated elements, transcription start sites and splicing site regions among others. The published frameworks for sRNA data analysis are focused on miRNA detection and prediction, ignoring further information in the dataset. As a consequence, tools for the identification and classification of the sRNAs not belonging to miRNA family are currently lacking. Results: Here, we present, SeqCluster, an extension of the currently available SeqBuster tool to identify and analyze at different levels the sRNAs not annotated or predicted as miRNAs. This new module deals with sequences mapping onto multiple locations and permits a highly versatile and user-friendly interaction with the data in order to easily classify sRNA sequences with a putative functional importance. We were able to detect all known classes of sRNAs described to date using SeqCluster with different sRNA datasets.

Original languageEnglish
Article numberbtr527
Pages (from-to)3202-3203
Number of pages2
JournalBioinformatics
Volume27
Issue number22
DOIs
Publication statusPublished - Nov 2011
Externally publishedYes

    Fingerprint

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

Cite this