Evidence map›Paper›PMID 26770195›Full record

ArticleAdvances in bioinformatics2015

Developing of the Computer Method for Annotation of Bacterial Genes.

Mikhail A Golyshev, Eugene V Korotkov

Abstract read
In one paragraph

Article in Advances in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Mikhail A GolyshevBioinformatics Laboratory, Centre of Bioengineering, Russian Academy of Sciences, Prospekt 60-letiya Oktyabrya 7/1, Moscow 117312, Russia.
Eugene V KorotkovBioinformatics Laboratory, Centre of Bioengineering, Russian Academy of Sciences, Prospekt 60-letiya Oktyabrya 7/1, Moscow 117312, Russia; Cybernetics Department, National Research Nuclear University "MEPhI", Kashirskoe shosse 31, Moscow 115409, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the last years a great number of bacterial genomes were sequenced. Now one of the most important challenges of computational genomics is the functional annotation of nucleic acid sequences. In this study we presented the computational method and the annotation system for predicting biological functions using phylogenetic profiles. The phylogenetic profile of a gene was created by way of searching for similarities between the nucleotide sequence of the gene and 1204 reference genomes, with further estimation of the statistical significance of found similarities. The profiles of the genes with known functions were used for prediction of possible functions and functional groups for the new genes. We conducted the functional annotation for genes from 104 bacterial genomes and compared the functions predicted by our system with the already known functions. For the genes that have already been annotated, the known function matched the function we predicted in 63% of the time, and in 86% of the time the known function was found within the top five predicted functions. Besides, our system increased the share of annotated genes by 19%. The developed system may be used as an alternative or complementary system to the current annotation systems.

Identifiers

PMID26770195
PMCPMC4684837

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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.