Dataset Information

GASS: genome structural annotation for Eukaryotes based on species similarity.

ABSTRACT: With the development of high-throughput sequencing techniques, more and more genomes were sequenced and assembled. However, annotating a genome's structure rapidly and expressly remains challenging. Current eukaryotic genome annotations require various, abundant supporting data, such as: species-specific and cross-species protein sequences, ESTs, cDNA and RNA-Seq data. Collecting those data and merging their analytical results to achieve a consistent complete annotation is a complex, time and cost consuming task.In our study, we proposed a fast and easy-to-use computational tool: GASS (Genome Annotation based on Species Similarity). It annotates a eukaryotic genome based on only the annotations from another similar species. With aligning the exons' sequences of an annotated similar species to the un-annotated genome, GASS detects the optimal transcript annotations with a shortest-path model. In our study, GASS was used to achieve the rhesus annotations based on the human annotations. The produced annotations were evaluated by comparing them to the two existing rhesus annotation databases (RefSeq and Ensembl) directly and being aligned with three RNA-Seq data of rhesus. The experiment results showed that more than 65% RefSeq exons and splicing junctions were exactly found by GASS. GASS's sensitivity was higher than RefSeq's, and was close to Ensembl's. GASS had higher specificities than Ensembl at gene, transcript, exon and splicing junction levels. We also found the mis-assemblies of rheMac3 genome, which led to the 2 bp shifts in annotating position on exons' boundary and then the incomplete splicing canonical sites in Refseq annotations. These detections were further supported by various data sources.GASS quickly produces structural genome annotations in sufficient abundance and accuracy. With simple and rapid running of GASS, small labs can create quick views of genome annotations for an un-annotated species, without the necessity to create, collect, analyze and synthesize extra various data sources, or wait several months for the annotations from professional organizations. GASS can be applied to many study occasions, such as the analysis of RNA-Seq datasets from the unannotated species whose genome drafts are available but the annotations are not.

SUBMITTER: Wang Y

PROVIDER: S-EPMC4352269 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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GASS: genome structural annotation for Eukaryotes based on species similarity.

Wang Ying Y Chen Lina L Song Nianfeng N Lei Xiaoye X

BMC genomics 20150304

<h4>Background</h4>With the development of high-throughput sequencing techniques, more and more genomes were sequenced and assembled. However, annotating a genome's structure rapidly and expressly remains challenging. Current eukaryotic genome annotations require various, abundant supporting data, such as: species-specific and cross-species protein sequences, ESTs, cDNA and RNA-Seq data. Collecting those data and merging their analytical results to achieve a consistent complete annotation is a c ...[more]

PMID: 25764973

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Project description:BackgroundThe Gene Ontology (GO) is a well known controlled vocabulary describing the biological process, molecular function and cellular component aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (i.e. their evidence codes).ResultsWe present here a new semantic similarity measure called IntelliGO which integrates several complementary properties in a novel vector space model. The coefficients associated with each GO term that annotates a given gene or protein include its information content as well as a customized value for each type of GO evidence code. The generalized cosine similarity measure, used for calculating the dot product between two vectors, has been rigorously adapted to the context of the GO graph. The IntelliGO similarity measure is tested on two benchmark datasets consisting of KEGG pathways and Pfam domains grouped as clans, considering the GO biological process and molecular function terms, respectively, for a total of 683 yeast and human genes and involving more than 67,900 pair-wise comparisons. The ability of the IntelliGO similarity measure to express the biological cohesion of sets of genes compares favourably to four existing similarity measures. For inter-set comparison, it consistently discriminates between distinct sets of genes. Furthermore, the IntelliGO similarity measure allows the influence of weights assigned to evidence codes to be checked. Finally, the results obtained with a complementary reference technique give intermediate but correct correlation values with the sequence similarity, Pfam, and Enzyme classifications when compared to previously published measures.ConclusionsThe IntelliGO similarity measure provides a customizable and comprehensive method for quantifying gene similarity based on GO annotations. It also displays a robust set-discriminating power which suggests it will be useful for functional clustering.AvailabilityAn on-line version of the IntelliGO similarity measure is available at: http://bioinfo.loria.fr/Members/benabdsi/intelligo_project/

Dataset Information

GASS: genome structural annotation for Eukaryotes based on species similarity.

Publications

GASS: genome structural annotation for Eukaryotes based on species similarity.

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