PathRacer: Racing Profile HMM Paths on Assembly Graph

AbstractMotivationThe first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1,000, and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data.ResultshCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2,500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1,000 genomes requires less than 20mn on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels).Availabilityhttps://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/[email protected], [email protected] informationSupplementary information is available at Bioinformatics online.

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Spider Neurotoxins, Short Linear Cationic Peptides and Venom Protein Classification Improved by an Automated Competition between Exhaustive Profile HMM Classifiers

Toxins ◽

10.3390/toxins9080245 ◽

2017 ◽

Vol 9 (8) ◽

pp. 245 ◽

Cited By ~ 3

Author(s):

Dominique Koua ◽

Lucia Kuhn-Nentwig

Keyword(s):

Protein Classification ◽

Cationic Peptides ◽

Venom Protein ◽

Profile Hmm

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Preliminary results in accelerating profile HMM search on FPGAs

2007 IEEE International Parallel and Distributed Processing Symposium ◽

10.1109/ipdps.2007.370447 ◽

2007 ◽

Cited By ~ 14

Author(s):

Arpith C. Jacob ◽

Joseph M. Lancaster ◽

Jeremy D. Buhler ◽

Roger D. Chamberlain

Keyword(s):

Preliminary Results ◽

Profile Hmm

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COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM

Bioinformatics ◽

10.1093/bioinformatics/btaa871 ◽

2020 ◽

Author(s):

Frédéric Lemoine ◽

Luc Blassel ◽

Jakub Voznica ◽

Olivier Gascuel

Keyword(s):

Daily Basis ◽

Multiple Alignment ◽

Supplementary Information ◽

Summary Statistics ◽

Evolutionary Novelty ◽

Bioinformatics Analyses ◽

Sequencing Quality ◽

User Friendly ◽

Profile Hmm ◽

New Mutations

Abstract Motivation The first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1,000, and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data. Results hCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2,500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1,000 genomes requires ∼50mn on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels). Availability https://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/covid-align Supplementary information Supplementary information is available at Bioinformatics online.

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SHsearch - a method for fast remote homology detection

10.31219/osf.io/sh5gj ◽

2018 ◽

Author(s):

Mohamed Baddar

Keyword(s):

Markov Models ◽

Sequence Similarity ◽

Protein Homology ◽

Homology Detection ◽

Remote Homology ◽

Sensitivity Loss ◽

Computationally Intensive ◽

Profile Hmm ◽

Programming Algorithms ◽

Remote Homology Detection

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.

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SHsearch: a method for fast remote homology detection

10.7287/peerj.preprints.27111 ◽

2018 ◽

Author(s):

Mohamed Baddar

Keyword(s):

Markov Models ◽

Sequence Similarity ◽

Protein Homology ◽

Homology Detection ◽

Remote Homology ◽

Sensitivity Loss ◽

Computationally Intensive ◽

Profile Hmm ◽

Programming Algorithms ◽

Remote Homology Detection

Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. Methods based on profile hidden Markov models (HMM) often exhibit relatively higher sensitivity for detecting remote homologies than commonly used approaches. However, calculating similarity scores in profile HMM methods is computationally intensive as they use dynamic programming algorithms. In this paper we introduce SHsearch: a new method for remote protein homology detection. Our method is implemented as a modification of HHsearch: a remote protein homology detection method based on comparing two profile HMMs. The motivation for modification was to reduce the run time of HHsearch significantly with minimal sensitivity loss. SHsearch focuses on comparing the important submodels of the query and database HMMs instead of comparing the complete models. Hence, SHsearch achieves a significant speedup over HHsearch with minimal loss in sensitivity. On SCOP 1.63, SHsearch achieved 88X speedup with 8.2% loss in sensitivity with respect to HHsearch at error rate of 10%, which deemed to be an acceptable tradeoff.

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