Fuzzy measures on the Gene Ontology for gene product similarity

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.     

Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data

Next‐generation technologies generate an overwhelming amount of gene sequence data. Efficient annotation tools are required to make these data amenable to functional genomics analyses. The Mercator pipeline automatically assigns functional terms to protein or nucleotide sequences. It uses the MapMan ‘BIN’ ontology, which is tailored for functional annotation of plant ‘omics’ data. The classification procedure performs parallel sequence searches against reference databases, compiles the results and computes the most likely MapMan BINs for each query. In the current version, the pipeline relies on manually curated reference classifications originating from the three reference organisms (Arabidopsis, Chlamydomonas, rice), various other plant species that have a reviewed SwissProt annotation, and more than 2000 protein domain and family profiles at InterPro, CDD and KOG. Functional annotations predicted by Mercator achieve accuracies above 90% when benchmarked against manual annotation. In addition to mapping files for direct use in the visualization software MapMan, Mercator provides graphical overview charts, detailed annotation information in a convenient web browser interface and a MapMan‐to‐GO translation table to export results as GO terms. Mercator is available free of charge via http://mapman.gabipd.org/web/guest/app/Mercator .     

A large‐scale proteogenomics study of apicomplexan pathogens—Toxoplasma gondii and Neospora caninum

Proteomics data can supplement genome annotation efforts, for example being used to confirm gene models or correct gene annotation errors. Here, we present a large‐scale proteogenomics study of two important apicomplexan pathogens: Toxoplasma gondii and Neospora caninum. We queried proteomics data against a panel of official and alternate gene models generated directly from RNASeq data, using several newly generated and some previously published MS datasets for this meta‐analysis. We identified a total of 201 996 and 39 953 peptide‐spectrum matches for T. gondii and N. caninum, respectively, at a 1% peptide FDR threshold. This equated to the identification of 30 494 distinct peptide sequences and 2921 proteins (matches to official gene models) for T. gondii, and 8911 peptides/1273 proteins for N. caninum following stringent protein‐level thresholding. We have also identified 289 and 140 loci for T. gondii and N. caninum, respectively, which mapped to RNA‐Seq‐derived gene models used in our analysis and apparently absent from the official annotation (release 10 from EuPathDB) of these species. We present several examples in our study where the RNA‐Seq evidence can help in correction of the current gene model and can help in discovery of potential new genes. The findings of this study have been integrated into the EuPathDB. The data have been deposited to the ProteomeXchange with identifiers PXD000297and PXD000298.     

绿色杜氏藻转录组分析

为了深入了解绿色杜氏藻(Dunaliella viridis)基因信息及功能、耐盐相关通路(甘油脂代谢)及关键酶,本文首次通过Illumina HiSeq^TM 2000高通量测序技术对绿色杜氏藻转录组进行测序,利用Trinity软件将数据组装形成转录本,对所有转录本进行COG(Clusters of Orthologous Groups)、GO(Gene Ontology)和KEGG(Kyoto Encyclopedia of Genes and Genomes)分类和功能注释、Pathway注释以及蛋白编码区(Opening reading fragment,ORF)的预测,并对甘油脂代谢通路关键酶基因进行了分析。转录组测序共获得81 593个转录本,其中ORF共有77 117条,约占所有转录本的94.50%。COG分类结果表明,16 569条转录本被分为24个类别。GO分类结果表明,76 436条转录本被注释。在所有注释分类中,生物学过程转录本数量最多,为30 678条,占总转录本数的40.14%。KEGG分析结果表明,317个标准途径中包含26 428条转录本,含转录本最多的类别是代谢,为9949条(37.65%)。与代谢有关的途径为131条,占所有注释途径的41.32%。在甘油脂代谢通路中仅发现1条关键酶转录本(二羟丙酮激酶),该酶可能与绿色杜氏藻耐盐胁迫中甘油的合成有较大关系。本研究进一步完善了绿色杜氏藻的基因信息,为绿色杜氏藻代谢途径研究奠定了坚实的基础。     

The DOE-JGI Standard Operating Procedure for the Annotations of Microbial Genomes

The DOE-JGI Microbial Annotation Pipeline (DOE-JGI MAP) supports gene prediction and/or functional annotation of microbial genomes towards comparative analysis with the Integrated Microbial Genome (IMG) system. DOE-JGI MAP annotation is applied on nucleotide sequence datasets included in the IMG-ER (Expert Review) version of IMG via the IMG ER submission site. Users can submit the sequence datasets consisting of one or more contigs in a multi-fasta file. DOE-JGI MAP annotation includes prediction of protein coding and RNA genes, as well as repeats and assignment of product names to these genes.     

The IGS Standard Operating Procedure for Automated Prokaryotic Annotation

The Institute for Genome Sciences (IGS) has developed a prokaryotic annotation pipeline that is used for coding gene/RNA prediction and functional annotation of Bacteria and Archaea. The fully automated pipeline accepts one or many genomic sequences as input and produces output in a variety of standard formats. Functional annotation is primarily based on similarity searches and motif finding combined with a hierarchical rule based annotation system. The output annotations can also be loaded into a relational database and accessed through visualization tools.