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The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
A HMM-based algorithm for defining enriched regions from ChIP-seq data
HPeak is a hidden Markov model-based approach that can accurately pinpoint regions to where significantly more sequence reads map. Testing on real data shows that these regions are indeed highly enriched by the right protein binding sites.
Command (single-end):
perl /compbio/software/HPeak3/HPeak.pl -sp HUMAN/MOUSE -format BED -t TREATMENT.inp -c CONTROL.inp -n OUTPUTPREFIX -fmin 100 -fmax 300 -r 36 -ann -wig -seq -interfiles
Command (pair-end):
perl /compbio/software/HPeak3/HPeak.pl -sp...
The AK toolkit is another kit for building and use Hidden Markov Models (HMMs). Originally developed for handwritten text recognition (HTR) using Bernoulli HMMs, it also implements diagonal Gaussians and can be used for any other purpose.
MeCab is a fast and customizable Japanese morphological analyzer. MeCab is designed for generic purpose and applied to variety of NLP tasks, such as Kana-Kanji conversion. MeCab provides parameter estimation functionalities based on CRFs and HMM
This is a project to create a compiler that converts grammars written in SRGS standard (http://www.w3.org/TR/speech-grammar/) to a graph understandable by HMM based ASR engines. Check srgs-parser.sf.net