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

2019 ◽  
Vol 36 (7) ◽  
pp. 2251-2252 ◽  
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.

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

2019 ◽  
Author(s):  
Takuya Aramaki ◽  
Romain Blanc-Mathieu ◽  
Hisashi Endo ◽  
Koichi Ohkubo ◽  
Minoru Kanehisa ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Kegg Pathway ◽  
Hidden Markov ◽  
Protein Sequences ◽  
Homology Search ◽  
Function Annotation ◽  
Ortholog Assignment ◽  
Score Threshold ◽  
Profile Hmm

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]

Hidden Markov Models and Multiple Alignments of Protein Sequences

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

Semi-supervised learning of Hidden Markov Models for biological sequence analysis

2018 ◽  
Vol 35 (13) ◽  
pp. 2208-2215 ◽  
Author(s):  
Ioannis A Tamposis ◽  
Konstantinos D Tsirigos ◽  
Margarita C Theodoropoulou ◽  
Panagiota I Kontou ◽  
Pantelis G Bagos
Keyword(s):  
Sequence Analysis ◽  
Supervised Learning ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Transmembrane Protein ◽  
Training Data ◽  
Supplementary Information ◽  
Training Procedure ◽  
Partially Labeled Data

Abstract Motivation Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. Results We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. Supplementary information Supplementary data are available at Bioinformatics online.

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

2006 ◽  
Vol 04 (05) ◽  
pp. 959-980 ◽  
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.

OPTIMAL SPACED SEEDS FOR HOMOLOGOUS CODING REGIONS

2004 ◽  
Vol 01 (04) ◽  
pp. 595-610 ◽  
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.

scholarly journals How sequence alignment scores correspond to probability models

2019 ◽  
Author(s):  
Martin C Frith
Keyword(s):  
Sequence Alignment ◽  
Ad Hoc ◽  
Markov Models ◽  
Hidden Markov ◽  
Sequence Similarity ◽  
Supplementary Information ◽  
Multiple Models ◽  
Partition Functions ◽  
Probability Models ◽  
Temperature Parameter

Abstract Motivation Sequence alignment remains fundamental in bioinformatics. Pair-wise alignment is traditionally based on ad hoc scores for substitutions, insertions and deletions, but can also be based on probability models (pair hidden Markov models: PHMMs). PHMMs enable us to: fit the parameters to each kind of data, calculate the reliability of alignment parts and measure sequence similarity integrated over possible alignments. Results This study shows how multiple models correspond to one set of scores. Scores can be converted to probabilities by partition functions with a ‘temperature’ parameter: for any temperature, this corresponds to some PHMM. There is a special class of models with balanced length probability, i.e. no bias toward either longer or shorter alignments. The best way to score alignments and assess their significance depends on the aim: judging whether whole sequences are related versus finding related parts. This clarifies the statistical basis of sequence alignment. Supplementary information Supplementary data are available at Bioinformatics online.

aphid: an R package for analysis with profile hidden Markov models

2019 ◽  
Vol 35 (19) ◽  
pp. 3829-3830 ◽  
Author(s):  
Shaun P Wilkinson
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
R Package ◽  
Supplementary Information ◽  
Biological Sequence ◽  
Profile Hmms ◽  
Biological Sequence Analysis ◽  
Import And Export ◽  
Computationally Intensive

Abstract Summary Hidden Markov models (HMMs) and profile HMMs form an integral part of biological sequence analysis, supporting an ever-growing list of applications. The aphid R package can be used to derive, train, plot, import and export HMMs and profile HMMs in the R environment. Computationally-intensive dynamic programing recursions, such as the Viterbi, forward and backward algorithms are implemented in C++ and parallelized for increased speed and efficiency. Availability and implementation The aphid package is released under the GPL-3 license, and is freely available for download from CRAN and GitHub (https://github.com/shaunpwilkinson/aphid). Supplementary information Supplementary data are available at Bioinformatics online.

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