DETECT—a Density Estimation Tool for Enzyme ClassificaTion and its application to <i>Plasmodium falciparum</i>

Hung, Stacy; Wasmuth, James D.; Sanford, Christopher; Parkinson, John

doi:10.1093/bioinformatics/btq266

Cited by 34 publications

(32 citation statements)

References 52 publications

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“…Further functional insight was obtained by BLAST searches for similar genes in the GenBank nr database, and putative signal peptides were identified by SignalP [91]. To investigate H. contortus metabolism, a total of 828 ECs, covering 2,853 proteins, were assigned using KAAS [92], DETECT [93] and EFICAz [94]. Of these, 563 ECs covering 1,246 proteins were assigned to a metabolic pathway; the others are non-metabolic enzymes.…”

Section: Methodsmentioning

confidence: 99%

The genome and transcriptome of Haemonchus contortus, a key model parasite for drug and vaccine discovery

et al. 2013

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BackgroundThe small ruminant parasite Haemonchus contortus is the most widely used parasitic nematode in drug discovery, vaccine development and anthelmintic resistance research. Its remarkable propensity to develop resistance threatens the viability of the sheep industry in many regions of the world and provides a cautionary example of the effect of mass drug administration to control parasitic nematodes. Its phylogenetic position makes it particularly well placed for comparison with the free-living nematode Caenorhabditis elegans and the most economically important parasites of livestock and humans.ResultsHere we report the detailed analysis of a draft genome assembly and extensive transcriptomic dataset for H. contortus. This represents the first genome to be published for a strongylid nematode and the most extensive transcriptomic dataset for any parasitic nematode reported to date. We show a general pattern of conservation of genome structure and gene content between H. contortus and C. elegans, but also a dramatic expansion of important parasite gene families. We identify genes involved in parasite-specific pathways such as blood feeding, neurological function, and drug metabolism. In particular, we describe complete gene repertoires for known drug target families, providing the most comprehensive understanding yet of the action of several important anthelmintics. Also, we identify a set of genes enriched in the parasitic stages of the lifecycle and the parasite gut that provide a rich source of vaccine and drug target candidates.ConclusionsThe H. contortus genome and transcriptome provide an essential platform for postgenomic research in this and other important strongylid parasites.

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Section: Methodsmentioning

confidence: 99%

The genome and transcriptome of Haemonchus contortus, a key model parasite for drug and vaccine discovery

et al. 2013

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“…Enzyme classification (EC) assignments were assigned by performing a BLASTP (E-value<e −10 ) search of the 246,538 transcripts against a database of 127,478 enzyme proteins annotated by SwissProt Version 57.0 [66]. A slightly more stringent E-value is used here to reduce the number of false positives that arise when sequence similarity is used for enzyme classification purposes [67]. Metabolic networks were constructed as previously described [68]: enzymes (EC numbers) are represented as nodes and substrates connecting two enzymes are represented as edges in the network.…”

Section: Methodsmentioning

confidence: 99%

Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing

et al. 2012

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With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.

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“…The gene model for O. ulmi , containing 8, 639 genes, was searched against the SwissProt-Uniprot protein database (v 58.0) using the following homology-based enzyme prediction tools: (i) DETECT [54] (cutoff ILS > 0.2, at least 5 positive hits), (ii) BLAST (E-value > 1e-10), (iii) PRIAM [55] (E-value > 1e-10), and (iv) ortholog mappings to Yeast based on OrthoMCL [56,57]. No pathway data for Ophiostoma was available from KEGG.…”

Section: Methodsmentioning

confidence: 99%

Sequencing and annotation of the Ophiostoma ulmigenome

et al. 2013

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BackgroundThe ascomycete fungus Ophiostoma ulmi was responsible for the initial pandemic of the massively destructive Dutch elm disease in Europe and North America in early 1910. Dutch elm disease has ravaged the elm tree population globally and is a major threat to the remaining elm population. O. ulmi is also associated with valuable biomaterials applications. It was recently discovered that proteins from O. ulmi can be used for efficient transformation of amylose in the production of bioplastics.ResultsWe have sequenced the 31.5 Mb genome of O.ulmi using Illumina next generation sequencing. Applying both de novo and comparative genome annotation methods, we predict a total of 8639 gene models. The quality of the predicted genes was validated using a variety of data sources consisting of EST data, mRNA-seq data and orthologs from related fungal species. Sequence-based computational methods were used to identify candidate virulence-related genes. Metabolic pathways were reconstructed and highlight specific enzymes that may play a role in virulence.ConclusionsThis genome sequence will be a useful resource for further research aimed at understanding the molecular mechanisms of pathogenicity by O. ulmi. It will also facilitate the identification of enzymes necessary for industrial biotransformation applications.

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DETECT—a Density Estimation Tool for Enzyme ClassificaTion and its application to Plasmodium falciparum

Abstract: A standalone application is available from the website: http://www.compsysbio.org/projects/DETECT/

Cited by 34 publications

References 52 publications

The genome and transcriptome of Haemonchus contortus, a key model parasite for drug and vaccine discovery

The genome and transcriptome of Haemonchus contortus, a key model parasite for drug and vaccine discovery

Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing

Sequencing and annotation of the Ophiostoma ulmigenome

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