Search Results for "hmm"
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Showing 69 open source projects for "hmm"
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gse
Go efficient multilingual NLP and text segmentation
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text use Viterbi algorithm. Support NLP by TensorFlow (in work). Named Entity Recognition (in work). ... -
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Prokka
Rapid prokaryotic genome annotation
...Given a FASTA file of contigs, it predicts genes, rRNAs, tRNAs, and other functional elements, then assigns functions by comparing to reference protein databases and HMM profiles. It outputs GenBank, GFF, and other formats compatible with downstream tools and genome browsers. Prokka handles common complications—overlapping ORFs, frameshifts, alternate start codons—while providing customizable databases so researchers can bias domain or strain-specific annotations. The pipeline is optimized for speed, using multithreading and caching to annotate large microbial genomes in minutes. ... -
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
...Generalized linear models with support for all of the one-parameter exponential family distributions. Markov switching models (MSAR), also known as Hidden Markov Models (HMM). Vector autoregressive models, VAR and structural VAR. Vector error correction model, VECM. Robust linear models with support for several M-estimators. statsmodels supports specifying models using R-style formulas and pandas DataFrames. -
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ASRT Speech Recognition
A Deep-Learning-Based Chinese Speech Recognition System
ASRT is an end-to-end deep-learning Chinese ASR system built with TensorFlow/Keras, using convolution + CTC and a Max-Entropy HMM language model. It provides a REST/gRPC server backend and client SDKs in multiple languages (Python, Java, Go, Windows). Notably lightweight, it performs well without needing GPU acceleration and runs across platforms, targeting developers and researchers building Chinese voice interfaces. -
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MaryTTS
An open-source, multilingual text-to-speech synthesis system
...As of version 5.2, MaryTTS supports German, British and American English, French, Italian, Luxembourgish, Russian, Swedish, Telugu, and Turkish; more languages are in preparation. MaryTTS comes with toolkits for quickly adding support for new languages and for building unit selection and HMM-based synthesis voices. -
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GenSeed-HMM
Progressive assembly tool using DNA, protein or profile HMMs as seeds.
GenSeed-HMM is a program for seed-driven progressive DNA assembly. It differs from the previously published GenSeed in many aspects, but more pointedly by being able to use an HMM profile as seed. Basically, GenSeed-HMM performs three operations, repeatedly: - recruit reads by matching them to a seed - assemble the recruited reads (including previously assembled contigs) - generate new seeds (for the next round of progressive assembly) from the extremities of all generated contigs The first seed is provided by the user, and can be a piece of DNA or protein sequence, or an HMM profile, for increased sensitivity. ... -
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MetaErg
Metagenome Annotation Pipeline
MetaErg is a stand-alone and fully automated metagenome and metaproteome annotation pipeline published at: https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full. If you are using this pipeline for your work, please cite: Dong X and Strous M (2019) An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs. Front. Genet. 10:999. doi: 10.3389/fgene.2019.00999 The instructions on configuring and running the MetaErg pipeline is available at GitHub... -
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hiddenDomains
hiddenDomains: a modern HMM to identify ChIP-seq enrichment
hiddenDomains uses a Hidden Markov Model to identify enriched domains in ChIP-seq data. It accepts BAM files for input and can perform an analysis with or without control data. The output is a BED file, ready for the UCSC genome browser, that contains the domains and is color coded according to their posterior probabilities. -
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Extract 16S rRNA from genomes
This script extracts 16S rRNA sequences from multifasta files
...It works only in unix system and requires nhmmer and samtools software installed in the system path (if not in system path modify the path at lines 21,72 (for nhmmer),51,100 (for samtools) of the script by adding the path to your nhmmer executable). The HMM files arc.ssu.rnammer.hmm and bac.ssu.rnammer.hmm have to be in the same software were executable file is saved. Inside the folder creates two sub-folders named "bacteria" and "archaea" where you will save your multifasta files (multifasta of bacteria in one and of archaea in the other one). MORE DETAILS IN THE FILE INCLUDED... -
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GPRED-GC
a Gene PREDiction model accounting for 5'-3' GC gradient
A new hidden Markov model (HMM)-based ab initio gene prediction tool for finding genes with highly variable GC contents such as the genes with negative GC gradients in grass genomes. -
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GENSENG
GENSENG is a software detecting CNVs from NGS data
GENSENG is a software detecting CNVs(Copy Number Variations) from NGS(Next Generation Sequencing) data. We have uploaded the data preparation codes. Please download it from Files folder. -
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HMM-GRASPx
HMM Profile-based targeted assembly and alignment for metagenomics
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SnowyOwl
RNA-Seq based gene prediction pipeline for fungal genomes
SnowyOwl is a gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions, and to evaluate the resulting models. The pipeline has been validated and streamlined by comparing its predictions to manually curated gene models in three fungal genomes, and its results show substantial increases in sensitivity and selectivity over previous gene predictions. Sensitivity is gained by repeatedly running the HMM gene predictor Augustus with varied input parameters, and selectivity by choosing the models with best homology to known proteins and best agreement to the RNA-Seq data. ... -
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karkinos
Tumor genotyper for Exome sequence that detects SNV,CNV, aTumor purity
karkinos is tumor genotyper which detects single nucleotide variation (SNV), integer copy number variation (CNV) and calculates tumor cellularity from tumor-normal paired sequencing data. Accurate CNV calling is achieved using continuous wavelet analysis and multi-state HMM, while SNV call is adjusted by tumor cellularity and filtered by heuristic filtering algorithm and Fisher Test. Also, Noise calls in low depth region are removed using EM algorithm. -
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hts_engine is software to synthesize speech waveform from HMMs trained by the HMM-based speech synthesis system (HTS). This software is released under the Modified BSD license.
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Kinannote
Protein Kinase Identification and Classification
Kinannote identifies and classifies protein kinases in a user-provided fasta file using an HMM derived from serine/threonine protein kinases, a position specific scoring matrix derived from the HMM, and comparison with a local version of the curated kinase database from kinase.com. If the user inputs a complete proteome, additional modules evaluate the completeness of the kinome and place it in context with reference kinomes. -
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PALM
Probabilistic natural mapping for gene based association study.
PALM is a software that conducts gene-based association analysis. PALM exploits Hidden Markov Models (HMM) to capture the inner genomic structures. Then, unique gene features (termed "palm prints") are extracted for each gene set by calculating the natural gradient. Then palm-prints are tested with chi-square significance test. -
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I think cutest is a very cool test framework for embedded C. Well, mostly because it is simple, just two files, OK, I admit I am lazy, that is why I use it. Hmm, still cutest is not perfect, so I just use it for personal purpose and I will also fix some issues which I think is valuable and put it here. Just for personal purpose.
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Frame-Pro
HMM and directed graph consensus combing error-correction tool
Frame-Pro is a tool using Hidden Markov Model and directed acyclic graph to correct the errors in DNA sequencing reads. -
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Ansj Chinese word segmentation
Ansj word segmentation
...Chinese word segmentation, name recognition, part-of-speech tagging, user-defined dictionary. This is a java implementation of Chinese word segmentation based on n-Gram+CRF+HMM. The word segmentation speed reaches about 2 million words per second (tested under mac air), and the accuracy rate can reach more than 96%. At present, it has realized the functions of Chinese word segmentation, Chinese name recognition, user-defined dictionary, keyword extraction, automatic summarization, and keyword tagging. It can be applied to natural language processing and other aspects, and is suitable for various projects that require high word segmentation effects. -
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Sinsy
HMM-based singing voice synthesis system
Sinsy is an HMM-based singing voice synthesis system. This software is released under the Modified BSD license. -
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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.
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High-order HMM in Matlab
Implementation of duration high-order hidden Markov model in Matlab.
Implementation of duration high-order hidden Markov model (DHO-HMM) in Matlab with application in speech recognition. -
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ML Toolbox
Matlab toolbox for Machine Learning
Matlab toolbox designed to simplify training, validation and testing process for multiple probabilistic models, including SVM, HMM and CRF. The toolbox is designed to work with Matlab Distributed Engine, allowing a distributed training of the probabilistic models. -
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HMM-FRAME
Frameshift detection and correction for next-generation sequences
HMM-FRAME is designed to accurately locate and correct frameshift errors in next-generation sequencing (NGS) data using an augmented Viterbi algorithm on profile hidden Markov models (profile HMMs). By correcting the frameshift errors, it can classify more NGS sequences into their native protein domain families.
