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HaMStR: profile hidden markov model based search for orthologs in ESTs.

ABSTRACT: BACKGROUND: EST sequencing is a versatile approach for rapidly gathering protein coding sequences. They provide direct access to an organism's gene repertoire bypassing the still error-prone procedure of gene prediction from genomic data. Therefore, ESTs are often the only source for biological sequence data from taxa outside mainstream interest. The widespread use of ESTs in evolutionary studies and particularly in molecular systematics studies is still hindered by the lack of efficient and reliable approaches for automated ortholog predictions in ESTs. Existing methods either depend on a known species tree or cannot cope with redundancy in EST data. RESULTS: We present a novel approach (HaMStR) to mine EST data for the presence of orthologs to a curated set of genes. HaMStR combines a profile Hidden Markov Model search and a subsequent BLAST search to extend existing ortholog cluster with sequences from further taxa. We show that the HaMStR results are consistent with those obtained with existing orthology prediction methods that require completely sequenced genomes. A case study on the phylogeny of 35 fungal taxa illustrates that HaMStR is well suited to compile informative data sets for phylogenomic studies from ESTs and protein sequence data. CONCLUSION: HaMStR extends in a standardized manner a pre-defined set of orthologs with ESTs from further taxa. In the same fashion HaMStR can be applied to protein sequence data, and thus provides a comprehensive approach to compile ortholog cluster from any protein coding data. The resulting orthology predictions serve as the data basis for a variety of evolutionary studies. Here, we have demonstrated the application of HaMStR in a molecular systematics study. However, we envision that studies tracing the evolutionary fate of individual genes or functional complexes of genes will greatly benefit from HaMStR orthology predictions as well.

SUBMITTER: Ebersberger I

PROVIDER: S-EPMC2723089 | biostudies-other | 2009

REPOSITORIES: biostudies-other

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HaMStR: profile hidden markov model based search for orthologs in ESTs.

Ebersberger Ingo I Strauss Sascha S von Haeseler Arndt A

BMC evolutionary biology 20090708

<h4>Background</h4>EST sequencing is a versatile approach for rapidly gathering protein coding sequences. They provide direct access to an organism's gene repertoire bypassing the still error-prone procedure of gene prediction from genomic data. Therefore, ESTs are often the only source for biological sequence data from taxa outside mainstream interest. The widespread use of ESTs in evolutionary studies and particularly in molecular systematics studies is still hindered by the lack of efficient ...[more]

PMID: 19586527

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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

HaMStR: profile hidden markov model based search for orthologs in ESTs.

Publications

HaMStR: profile hidden markov model based search for orthologs in ESTs.

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