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Performance of Hidden Markov Models in Recovering the Standard Classification of Glycoside Hydrolases.

ABSTRACT: Glycoside hydrolases (GHs) are carbohydrate-active enzymes that assist the hydrolysis of glycoside bonds of complex sugars into carbohydrates. The current standard GH family classification is available in the CAZy database, which is based on the similarities of amino acid sequences and curated semi-automatically. However, with the exponential increase in data availability from genome sequences, automated classification methods are required for the fast annotation of coding sequences. Currently, the dbCAN database offers automatic annotations of signature domains from CAZy-defined classifications using a statistical approach, the hidden Markov models (HMMs). However, dbCAN does not contain the entire set of CAZy GH families. Moreover, no evaluation has been conducted so far of the viability of using HMM profiles as a means of automatically assigning GH amino acid sequences to the standard CAZy GH family classification itself. In this work, we performed a meta-analysis in which amino acid sequences from CAZy-defined GH families were used to build HMM family-specific profiles. We then queried a set with ~300 000 GH sequences against our database of HMM profiles estimated from CAZy families. We conducted the same evaluation against the available dbCAN HMM profiles. Our analyses recovered 65% of matches with the standard CAZy classification, whereas dbCAN HMMs resulted in 61% of matches. We also provided an analysis of the types of errors commonly found when HMMs are used to recover CAZy-based classifications. Although the performance of HMM was good, further developments are necessary for a fully automated classification of GH, allowing the standardization of GH classification among protein databases.

SUBMITTER: Rossi MF

PROVIDER: S-EPMC5404901 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Performance of Hidden Markov Models in Recovering the Standard Classification of Glycoside Hydrolases.

Rossi Mariana Fonseca MF Mello Beatriz B Schrago Carlos G CG

Evolutionary bioinformatics online 20170420

Glycoside hydrolases (GHs) are carbohydrate-active enzymes that assist the hydrolysis of glycoside bonds of complex sugars into carbohydrates. The current standard GH family classification is available in the CAZy database, which is based on the similarities of amino acid sequences and curated semi-automatically. However, with the exponential increase in data availability from genome sequences, automated classification methods are required for the fast annotation of coding sequences. Currently, ...[more]

PMID: 28469382

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Project description:BackgroundRiboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding. The various classes of riboswitches discovered so far are differentiated by the ligand, which on binding induces a conformational switch. Every class of riboswitch is characterized by an aptamer domain, which provides the site for ligand binding, and an expression platform that undergoes conformational change on ligand binding. The sequence and structure of the aptamer domain is highly conserved in riboswitches belonging to the same class. We propose a method for fast and accurate identification of riboswitches using profile Hidden Markov Models (pHMM). Our method exploits the high degree of sequence conservation that characterizes the aptamer domain.ResultsOur method can detect riboswitches in genomic databases rapidly and accurately. Its sensitivity is comparable to the method based on the Covariance Model (CM). For six out of ten riboswitch classes, our method detects more than 99.5% of the candidates identified by the much slower CM method while being several hundred times faster. For three riboswitch classes, our method detects 97-99% of the candidates relative to the CM method. Our method works very well for those classes of riboswitches that are characterized by distinct and conserved sequence motifs.ConclusionRiboswitches play a crucial role in controlling the expression of several prokaryotic genes involved in metabolism and transport processes. As more and more new classes of riboswitches are being discovered, it is important to understand the patterns of their intra and inter genomic distribution. Understanding such patterns will enable us to better understand the evolutionary history of these genetic regulatory elements. However, a complete picture of the distribution pattern of riboswitches will emerge only after accurate identification of riboswitches across genomes. We believe that the riboswitch detection method developed in this paper will aid in that process. The significant advantage in terms of speed, of our pHMM-based approach over the method based on CM allows us to scan entire databases (rather than 5'UTRs only) in a relatively short period of time in order to accurately identify riboswitch candidates.

Dataset Information

Performance of Hidden Markov Models in Recovering the Standard Classification of Glycoside Hydrolases.

Publications

Performance of Hidden Markov Models in Recovering the Standard Classification of Glycoside Hydrolases.

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