Evidence map›Paper›PMID 19247487›Full record

ArticlePloS one2009

Rapid annotation of anonymous sequences from genome projects using semantic similarities and a weighting scheme in gene ontology.

Paolo Fontana, Alessandro Cestaro, Riccardo Velasco, Elide Formentin, Stefano Toppo

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Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  11. A weighted multipath measurement based on gene ontology for estimating gene products similarity.Journal of computational biology : a journal of computational molecular cell biology · 2014
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Paolo FontanaFEM-IASMA Research Center, San Michele all'Adige (TN), Italy.
Alessandro Cestaro
Riccardo Velasco
Elide Formentin
Stefano Toppo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge-scale sequencing projects have now become routine lab practice and this has led to the development of a new generation of tools involving function prediction methods, bringing the latter back to the fore. The advent of Gene Ontology, with its structured vocabulary and paradigm, has provided computational biologists with an appropriate means for this task. METHODOLOGY: We present here a novel method called ARGOT (Annotation Retrieval of Gene Ontology Terms) that is able to process quickly thousands of sequences for functional inference. The tool exploits for the first time an integrated approach which combines clustering of GO terms, based on their semantic similarities, with a weighting scheme which assesses retrieved hits sharing a certain number of biological features with the sequence to be annotated. These hits may be obtained by different methods and in this work we have based ARGOT processing on BLAST results.

conclusionsThe extensive benchmark involved 10,000 protein sequences, the complete S. cerevisiae genome and a small subset of proteins for purposes of comparison with other available tools. The algorithm was proven to outperform existing methods and to be suitable for function prediction of single proteins due to its high degree of sensitivity, specificity and coverage.

Indexed as

Databases, GeneticGenome, FungalAlgorithmsFungal ProteinsInformation Storage and RetrievalSaccharomyces cerevisiaeFungal Proteins

Identifiers

PMID19247487
PMCPMC2645684

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.