KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold

AbstractSummaryKofamKOALA is a web server to assign KEGG Orthologs (KOs) to protein sequences by homology search against a database of profile hidden Markov models (KOfam) with pre-computed adaptive score thresholds. KofamKOALA is faster than existing KO assignment tools with its accuracy being comparable to the best performing tools. Function annotation by KofamKOALA helps linking genes to KEGG resources such as the KEGG pathway maps and facilitates molecular network reconstruction.AvailabilityKofamKOALA, KofamScan, and KOfam are freely available from https://www.genome.jp/tools/kofamkoala/[email protected]

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KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold

Bioinformatics ◽

10.1093/bioinformatics/btz859 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2251-2252 ◽

Cited By ~ 65

Author(s):

Takuya Aramaki ◽

Romain Blanc-Mathieu ◽

Hisashi Endo ◽

Koichi Ohkubo ◽

Minoru Kanehisa ◽

...

Keyword(s):

Markov Models ◽

Kegg Pathway ◽

Hidden Markov ◽

Protein Sequences ◽

Homology Search ◽

Supplementary Information ◽

Function Annotation ◽

Ortholog Assignment ◽

Score Threshold ◽

Profile Hmm

Abstract Summary KofamKOALA is a web server to assign KEGG Orthologs (KOs) to protein sequences by homology search against a database of profile hidden Markov models (KOfam) with pre-computed adaptive score thresholds. KofamKOALA is faster than existing KO assignment tools with its accuracy being comparable to the best performing tools. Function annotation by KofamKOALA helps linking genes to KEGG resources such as the KEGG pathway maps and facilitates molecular network reconstruction. Availability and implementation KofamKOALA, KofamScan and KOfam are freely available from GenomeNet (https://www.genome.jp/tools/kofamkoala/). Supplementary information Supplementary data are available at Bioinformatics online.

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Faculty Opinions recommendation of Quasi-consensus-based comparison of profile hidden Markov models for protein sequences.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1027329.329473 ◽

2005 ◽

Author(s):

Nick Grishin

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Protein Sequences ◽

Profile Hidden Markov Models

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Autoregressive and Iterative Hidden Markov Models for Periodicity Detection and Solenoid Structure Recognition in Protein Sequences

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2012.2235852 ◽

2013 ◽

Vol 17 (2) ◽

pp. 436-441 ◽

Cited By ~ 4

Author(s):

N. Y. Song ◽

Hong Yan

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Protein Sequences ◽

Periodicity Detection ◽

Structure Recognition

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Hidden Markov Models and Multiple Alignments of Protein Sequences

Proceedings of the Conference on Applied Mathematics and Scientific Computing ◽

10.1007/1-4020-3197-1_12 ◽

2005 ◽

pp. 187-196

Author(s):

Pavle Goldstein ◽

Maja Karaga ◽

Mate Kosor ◽

Ivana Nižetić ◽

Marija Tadić ◽

...

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Protein Sequences ◽

Multiple Alignments

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OPTIMALLY-CONNECTED HIDDEN MARKOV MODELS FOR PREDICTING MHC-BINDING PEPTIDES

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720006002314 ◽

2006 ◽

Vol 04 (05) ◽

pp. 959-980 ◽

Cited By ~ 18

Author(s):

CHENHONG ZHANG ◽

MIKELIS G. BICKIS ◽

FANG-XIANG WU ◽

ANTHONY J. KUSALIK

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Predictive Accuracy ◽

Hidden Markov ◽

Human Leukocyte ◽

Amino Acid Grouping ◽

Binding Peptides ◽

The Cost ◽

Fully Connected ◽

Profile Hmm

Hidden Markov models (HMMs) are one of various methods that have been applied to prediction of major histo-compatibility complex (MHC) binding peptide. In terms of model topology, a fully-connected HMM (fcHMM) has the greatest potential to predict binders, at the cost of intensive computation. While a profile HMM (pHMM) performs dramatically fewer computations, it potentially merges overlapping patterns into one which results in some patterns being missed. In a profile HMM a state corresponds to a position on a peptide while in an fcHMM a state has no specific biological meaning. This work proposes optimally-connected HMMs (ocHMMs), which do not merge overlapping patterns and yet, by performing topological reductions, a model's connectivity is greatly reduced from an fcHMM. The parameters of ocHMMs are initialized using a novel amino acid grouping approach called "multiple property grouping." Each group represents a state in an ocHMM. The proposed ocHMMs are compared to a pHMM implementation using HMMER, based on performance tests on two MHC alleles HLA (Human Leukocyte Antigen)-A*0201 and HLA-B*3501. The results show that the heuristic approaches can be adjusted to make an ocHMM achieve higher predictive accuracy than HMMER. Hence, such obtained ocHMMs are worthy of trial for predicting MHC-binding peptides.

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OPTIMAL SPACED SEEDS FOR HOMOLOGOUS CODING REGIONS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720004000326 ◽

2004 ◽

Vol 01 (04) ◽

pp. 595-610 ◽

Cited By ~ 34

Author(s):

BROŇA BREJOVÁ ◽

DANIEL G. BROWN ◽

TOMÁŠ VINAŘ

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Homology Search ◽

Genome Comparison ◽

Local Alignment ◽

Coding Regions ◽

Spaced Seeds ◽

Conservation Patterns ◽

Effective Homology

Optimal spaced seeds were developed as a method to increase sensitivity of local alignment programs similar to BLASTN. Such seeds have been used before in the program PatternHunter, and have given improved sensitivity and running time relative to BLASTN in genome–genome comparison. We study the problem of computing optimal spaced seeds for detecting homologous coding regions in unannotated genomic sequences. By using well-chosen seeds, we are able to improve the sensitivity of coding sequence alignment over that of TBLASTX, while keeping runtime comparable to BLASTN. We identify good seeds by first giving effective hidden Markov models of conservation in alignments of homologous coding regions. We give an efficient algorithm to compute the optimal spaced seed when conservation patterns are generated by these models. Our results offer the hope of improved gene finding due to fewer missed exons in DNA/DNA comparison, and more effective homology search in general, and may have applications outside of bioinformatics.

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