International Journal of Bioinformatics Research and Applications
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
A metastate HMM with application to gene structure identification in eukaryotes
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
Using protein domains to improve the accuracy of Ab Initio gene finding
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
Using compositions of Markov models to determine functional gene fragments
Cybernetics and Systems Analysis
Hi-index | 3.84 |
Summary: We describe two new Generalized Hidden Markov Model implementations for ab initio eukaryotic gene prediction. The C/C++ source code for both is available as open source and is highly reusable due to their modular and extensible architectures. Unlike most of the currently available gene-finders, the programs are re-trainable by the end user. They are also re-configurable and include several types of probabilistic submodels which can be independently combined, such as Maximal Dependence Decomposition trees and interpolated Markov models. Both programs have been used at TIGR for the annotation of the Aspergillus fumigatus and Toxoplasma gondii genomes. Availability: Source code and documentation are available under the open source Artistic License from http://www.tigr.org/software/pirate.