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
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* Corresponding authors: Sabah Kadri sskadri@andrew.cmu.edu - Panayiotis V Benos benos@pitt.edu
1 Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2 Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3 Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
BMC Bioinformatics 2009, 10(Suppl 1):S35 doi:10.1186/1471-2105-10-S1-S35
Published: 30 January 2009Additional 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