HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models

doi:10.1186/1471-2105-10-S1-S35

BMC Bioinformatics

Volume 10
Suppl 1

Viewing options:

Abstract
Full text
PDF (1.9MB)
Additional files

Associated material:

PubMed record

Related literature:

Articles citing this article
on Google Scholar
on ISI Web of Science
on PubMed Central
Other articles by authors
on Google Scholar

Kadri S
Hinman V
Benos PV

on PubMed

Kadri S
Hinman V
Benos PV
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Nominate for award

Post to:

This article is part of the supplement: Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009) .

Research

HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models

Sabah Kadri¹ , Veronica Hinman² and Panayiotis V Benos³

¹Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA

²Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA

³Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA

author email corresponding author email

BMC Bioinformatics 2009, 10(Suppl 1):S35doi:10.1186/1471-2105-10-S1-S35


Published:	30 January 2009

Additional files

Additional File 1:

This file contains the results of summarization of the microRNA registry (version 10.1, December 2007) [34] hairpin characteristics for each species.

Format: XLS Size: 33KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional File 2:

This file contains a more detailed description of the algorithms used for parameter estimation and classification using HHMMs.

Format: PDF Size: 1.1MB Download file

This file can be viewed with: Adobe Acrobat Reader