Dataset Information

CutProtFam-Pred: detection and classification of putative structural cuticular proteins from sequence alone, based on profile hidden Markov models.

ABSTRACT: The arthropod cuticle is a composite, bipartite system, made of chitin ?laments embedded in a proteinaceous matrix. The physical properties of cuticle are determined by the structure and the interactions of its two major components, cuticular proteins (CPs) and chitin. The proteinaceous matrix consists mainly of structural cuticular proteins. The majority of the structural proteins that have been described to date belong to the CPR family, and they are identified by the conserved R&R region (Rebers and Riddiford Consensus). Two major subfamilies of the CPR family RR-1 and RR-2, have also been identified from conservation at sequence level and some correlation with the cuticle type. Recently, several novel families, also containing characteristic conserved regions, have been described. The package HMMER v3.0 (http://hmmer.janelia.org/) was used to build characteristic pro?le Hidden Markov Models based on the characteristic regions for 8 of these families, (CPF, CPAP3, CPAP1, CPCFC, CPLCA, CPLCG, CPLCW, Tweedle). In brief, these families can be described as having: CPF (a conserved region with 44 amino acids); CPAP1 and CPAP-3 (analogous to peritrophins, with 1 and 3 chitin-binding domains, respectively); CPCFC (2 or 3 C-x(5)-C repeats); and four of five low complexity (LC) families, each with characteristic domains. Using these models, as well as the models previously created for the two major subfamilies of the CPR family, RR-1 and RR-2 (Karouzou et al., 2007), we developed CutProtFam-Pred, an on-line tool (http://bioinformatics.biol.uoa.gr/CutProtFam-Pred) that allows one to query sequences from proteomes or translated transcriptomes, for the accurate detection and classification of putative structural cuticular proteins. The tool has been applied successfully to diverse arthropod proteomes including a crustacean (Daphnia pulex) and a chelicerate (Tetranychus urticae), but at this taxonomic distance only CPRs and CPAPs were recovered.

SUBMITTER: Ioannidou ZS

PROVIDER: S-EPMC4143468 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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CutProtFam-Pred: detection and classification of putative structural cuticular proteins from sequence alone, based on profile hidden Markov models.

Ioannidou Zoi S ZS Theodoropoulou Margarita C MC Papandreou Nikos C NC Willis Judith H JH Hamodrakas Stavros J SJ

Insect biochemistry and molecular biology 20140627

The arthropod cuticle is a composite, bipartite system, made of chitin ﬁlaments embedded in a proteinaceous matrix. The physical properties of cuticle are determined by the structure and the interactions of its two major components, cuticular proteins (CPs) and chitin. The proteinaceous matrix consists mainly of structural cuticular proteins. The majority of the structural proteins that have been described to date belong to the CPR family, and they are identified by the conserved R&R region (Reb ...[more]

PMID: 24978609

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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.

Project description:Rapid, sensitive, and specific virus detection is an important component of clinical diagnostics. Massively parallel sequencing enables new diagnostic opportunities that complement traditional serological and PCR based techniques. While massively parallel sequencing promises the benefits of being more comprehensive and less biased than traditional approaches, it presents new analytical challenges, especially with respect to detection of pathogen sequences in metagenomic contexts. To a first approximation, the initial detection of viruses can be achieved simply through alignment of sequence reads or assembled contigs to a reference database of pathogen genomes with tools such as BLAST. However, recognition of highly divergent viral sequences is problematic, and may be further complicated by the inherently high mutation rates of some viral types, especially RNA viruses. In these cases, increased sensitivity may be achieved by leveraging position-specific information during the alignment process. Here, we constructed HMMER3-compatible profile hidden Markov models (profile HMMs) from all the virally annotated proteins in RefSeq in an automated fashion using a custom-built bioinformatic pipeline. We then tested the ability of these viral profile HMMs ("vFams") to accurately classify sequences as viral or non-viral. Cross-validation experiments with full-length gene sequences showed that the vFams were able to recall 91% of left-out viral test sequences without erroneously classifying any non-viral sequences into viral protein clusters. Thorough reanalysis of previously published metagenomic datasets with a set of the best-performing vFams showed that they were more sensitive than BLAST for detecting sequences originating from more distant relatives of known viruses. To facilitate the use of the vFams for rapid detection of remote viral homologs in metagenomic data, we provide two sets of vFams, comprising more than 4,000 vFams each, in the HMMER3 format. We also provide the software necessary to build custom profile HMMs or update the vFams as more viruses are discovered (http://derisilab.ucsf.edu/software/vFam).

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Dataset Information

CutProtFam-Pred: detection and classification of putative structural cuticular proteins from sequence alone, based on profile hidden Markov models.

Publications

CutProtFam-Pred: detection and classification of putative structural cuticular proteins from sequence alone, based on profile hidden Markov models.

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