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Finding Genes in DNA with a Hidden Markov Model

JOHN HENDERSON, STEVEN SALZBERG, KENNETH H. FASMAN
1997 Journal of Computational Biology  
This study describes a new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions.  ...  The models were then tied together to form a biologically feasible topology.  ...  Hidden Markov Models can be constructed in an in nite variety of shapes and sizes, and their e ectiveness varies widely depending on the design.  ... 
doi:10.1089/cmb.1997.4.127 pmid:9228612 fatcat:wsbwveivnnhuzcneluhve54dvm

Finding Genes by Hidden Markov Models with a Protein Motif Dictionary

Kiyoshi Asai, Tetsushi Yada, Katunobu Itou
1996 Genome Informatics Series  
A new method for combining protein motif dictionary to gene finding system is proposed. The system consists of Hidden Markov Models (HMMs) and a dictionary.  ...  As a result, while the system parses DNA sequences and finds the coding regions, the protein motifs are automatically annotated in the regions.  ...  In this paper, we propose a new method for combining a protein motif dictionary to gene finding system based on hidden Markov models (HMMs).  ... 
doi:10.11234/gi1990.7.88 fatcat:vdycwpuol5ebfgderwc5iofaki

Prediction of homologous genes by extracting Glycine max transcriptome using Hidden Markov Model

Rakesh Sharma, Monika ., Vandana Nunia, Shailesh Kumar, S. L. Kothari, Sumita Kachhwaha
2019 Asian Journal of Pharmacy and Pharmacology  
Objective: The objective of the work is to develop a Hidden Markov Model (HMM) based approach for finding gene family from RNAseq data in Glycine max.  ...  Conclusion: The method applied in this work is a novel method for identification of homologous gens for RNAseq datasets.  ...  In the present work we have developed a novel method for extracting of RNAseq data through hidden Markov models (HMMs) and introduce an HMM-based solution for finding gene family.  ... 
doi:10.31024/ajpp.2019.5.6.6 fatcat:v4zcth5jqjdj5muavaml2wrequ

GeneMark.hmm: new solutions for gene finding

A. Lukashin
1998 Nucleic Acids Research  
The idea was to embed the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states.  ...  Interestingly, the high gene finding accuracy was observed even in the case when Markov models of order zero, one and two were used.  ...  Anders Krogh kindly helped with using the ECOPARSE e-mail server, Andy Link provided the data on proteins with verified N-terminals, Steven Salzberg helped in the H.pylori gene prediction comparisons.  ... 
doi:10.1093/nar/26.4.1107 pmid:9461475 pmcid:PMC147337 fatcat:t6wgrephvngypkvbverxgprgea

Hidden Markov models applied to a subsequence of the Xylella fastidiosa genome

Cibele Q. da Silva
2003 Genetics and Molecular Biology  
In this study we present an application of hidden Markov models to a subsequence of the Xylella fastidiosa DNA data.  ...  Hidden Markov models are more realistic than Markov models since they allow for the identification of heterogeneous regions of a DNA sequence.  ...  A Hidden Markov Model for DNA Sequences In this section we are going to present some hidden Markov models developed by Churchill (1989) .  ... 
doi:10.1590/s1415-47572003000400018 fatcat:zw35znukxrdhbdlcb3tmpymjl4

Hidden Markov Model Approaches for Biological Studies

Xiang Yang Lou
2017 Biometrics & Biostatistics International Journal  
Hidden Markov models and more generally hidden Markov random fields can capture both random signals and inherent correlation structure typically in time and space, and have emerged as a powerful approach  ...  The hidden Markov process is a class of doubly stochastic processes, characterized by Markov property and the output independence, in which an underlying Markov process is hidden, meaning the variable  ...  This project was supported in part by NSF grant DMS1462990 and UAMS Research Scholar Pilot Grant Awards in Child Health G1-51898 to X.-Y.L. The author declares no conflict of interest on this work.  ... 
doi:10.15406/bbij.2017.05.00139 fatcat:x5mqdr44gnbcteffu5g4ocl7c4

Hidden Markov models in biological sequence analysis

E. Birney
2001 IBM Journal of Research and Development  
Hidden Markov models in biological sequence analysis The vast increase of data in biology has meant that many aspects of computational science have been drawn into the field.  ...  This paper reviews machine learning techniques based on the use of hidden Markov models (HMMs) for investigating biomolecular sequences.  ...  both of which utilize hidden Markov models.  ... 
doi:10.1147/rd.453.0449 fatcat:7gp4mr5ujjcl5br6pwycxblejy

Automatic Gene Recognition without Using Training Data

Kiyoshi Asai, Yutaka Ueno, Katunobu Itou, Tetsushi Yada
1997 Genome Informatics Series  
In this approach, we start from a simple model, which only uses the knowledge of start codons and the stop codons, then the recognition of the DNA sequences by the recognizer and the training of the parameters  ...  In this paper, we propose a new approach for gene recognition, which uses no training data for the recognizer.  ...  Acknowledgments This work was supported in part by a Grant-in-Aid (08283101: "Genome Science") for Scientific Research on Priority Areas from The Ministry of Education, Science, Sports and Culture of Japan  ... 
doi:10.11234/gi1990.8.15 fatcat:lcuauczyejf4jdt4uejk5olvzu

Constraint-Based System for Genomic Analysis

Nittaya Kerdprasop, Kittisak Kerdprasop
2015 International Journal of Information and Education Technology  
In this paper, we propose a different setting of sequence analysis formulation based on the nucleotide patterns using a constraint logic programming paradigm, in which the sequence alignment can be performed  ...  We propose in this paper the design of a constraint-based system for genomic sequence analysis including the algorithm for the constraint solver, a major part of the proposed system.  ...  The Glimmer gene-finding program [12] introduces a generalized hidden Markov model with variable order called the interpolated Markov model.  ... 
doi:10.7763/ijiet.2015.v5.487 fatcat:jxutnxbhvjedlfn22ksfntkz2m

A Brief Review of Computational Gene Prediction Methods

Zhuo Wang, Yazhu Chen, Yixue Li
2004 Genomics, Proteomics & Bioinformatics  
Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics.  ...  Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions.  ...  In Hidden Markov Model, transitions between sub-models corresponding to particular gene components are modeled as unobserved ("hidden") Markov processes, which determine the probability of generating particular  ... 
doi:10.1016/s1672-0229(04)02028-5 fatcat:l6lbb5wxyjhwlaqsb2j6w45jq4

Hidden Markov Model in Biological Sequence Analysis– A Systematic Review

2016 International Journal of Scientific and Innovative Mathematical Research  
This paper especially focusing on HMM and its various types like Profile Hidden Markov Models (PHMMs) and Pair Hidden Markov Models (Pair HMM).  ...  For biological sequence analysis Hidden Markov Model (HMM) have been used widely in many applications. It has provided solution for various biological sequence analysis problems.  ...  Figure 1 . 1 Example of Hidden Markov Model in gene sequence problemInFigure 1, the square boxes represent exon and intron. Inside the box indicate that emission probability of 'A' 'T' 'C' and 'D'.  ... 
doi:10.20431/2347-3142.0403001 fatcat:bauj4ftia5dzjps3bdwmrskg3e

Hidden Markov Model Reveals Biological Sequence Information
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Tetsushi YADA
2000 Seibutsu Butsuri  
biological sequence analysis / hidden Markov model / stochastic regular grammar / profile / gene finding Hidden Markov Model Reveals Biological Sequence Information Tetsushi YADA RIKEN Genomic Sciences  ...  Biological sequence analysis based on hidden Markov models HMMs is no doubt one of the most successful endeavors in the field of bioinformatics.  ... 
doi:10.2142/biophys.40.25 fatcat:uwke6hby4jhgvbxmges7gg5oxa

A generalized hidden Markov model for the recognition of human genes in DNA

D Kulp, D Haussler, M G Reese, F H Eeckman
1996 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994).  ...  For a cross-validated standard test set of 304 genes [ftp:@www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity  ...  Thanks is also extended to Gregg Helt for early work on the gene data set and Nomi HHarris for her graphical interface, which proved helpful to study individual predictions.  ... 
pmid:8877513 fatcat:ek3rtml4xvclvcnj7rjmyzvsaq

Applications of Generalized Pair Hidden Markov Models to Alignment and Gene Finding Problems

Lior Pachter, Marina Alexandersson, Simon Cawley
2002 Journal of Computational Biology  
Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene nding and annotation.  ...  We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene nding and describe applications to DNA-cDNA and DNA-protein alignment.  ...  M.A. was supported by STINT, the Swedish Foundation for International Cooperation in Research and Higher Education.  ... 
doi:10.1089/10665270252935520 pmid:12015888 fatcat:emx73l4vgbhr5k2rgjycout6zi

Applications of generalized pair hidden Markov models to alignment and gene finding problems

Lior Pachter, Marina Alexandersson, Simon Cawley
2001 Proceedings of the fifth annual international conference on Computational biology - RECOMB '01  
Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene nding and annotation.  ...  We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene nding and describe applications to DNA-cDNA and DNA-protein alignment.  ...  M.A. was supported by STINT, the Swedish Foundation for International Cooperation in Research and Higher Education.  ... 
doi:10.1145/369133.369227 dblp:conf/recomb/PachterAC01 fatcat:4ejucaqjhrbmxlspku57uhf2pq
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