Separating drivers from passengers in whole genome analysis: identification of combinatorial effects of genes by mining knowledge sources

Enrico W. Coiera; Stephen Anthony; Vitali Sintchenko

Separating drivers from passengers in whole genome analysis

identification of combinatorial effects of genes by mining knowledge sources

Enrico W. Coiera, Stephen Anthony, Vitali Sintchenko

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

Abstract

This study aimed to develop a new informatics platform for the discovery, recovery and multi-level analysis of the effects of individual genes and multiple gene combinations on pathophenotypes of bacteria. Natural language processing algorithms were employed to extract gene-disease associations from PubMed literature and annotated genomes of bacteria with epidemic potential. From these associations gene virulence profiles were generated enabling the comparison of gene signatures within and across genomes. It allowed the identification of virulence genes and construction of their association networks as well as the detection of knowledge gaps. This proof-of-concept study confirmed the feasibility of our original approach for integrating bacterial genome level knowledge with published observations from clinical settings.

Original language	English
Title of host publication	Biotechno 2011
Subtitle of host publication	the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies : proceedings
Editors	Pei-Yuan Qian, Son V. Nghiem
Place of Publication	United States
Publisher	IARIA
Pages	6-11
Number of pages	6
ISBN (Print)	9781612081373
Publication status	Published - 2011
Externally published	Yes
Event	Biotechno 2011 : the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies - Venice/Mestre, Italy Duration: 22 May 2011 → 27 May 2011

Conference

Conference	Biotechno 2011 : the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies
City	Venice/Mestre, Italy
Period	22/05/11 → 27/05/11

Keywords

structural bioinformatics
whole genome analysis
text mining
infectious diseases
knowledge discovery

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Cite this

Coiera, E. W., Anthony, S., & Sintchenko, V. (2011). Separating drivers from passengers in whole genome analysis: identification of combinatorial effects of genes by mining knowledge sources. In P-Y. Qian, & S. V. Nghiem (Eds.), Biotechno 2011: the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies : proceedings (pp. 6-11). United States: IARIA.

Coiera, Enrico W. ; Anthony, Stephen ; Sintchenko, Vitali. / Separating drivers from passengers in whole genome analysis : identification of combinatorial effects of genes by mining knowledge sources. Biotechno 2011: the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies : proceedings. editor / Pei-Yuan Qian ; Son V. Nghiem. United States : IARIA, 2011. pp. 6-11

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title = "Separating drivers from passengers in whole genome analysis: identification of combinatorial effects of genes by mining knowledge sources",

abstract = "This study aimed to develop a new informatics platform for the discovery, recovery and multi-level analysis of the effects of individual genes and multiple gene combinations on pathophenotypes of bacteria. Natural language processing algorithms were employed to extract gene-disease associations from PubMed literature and annotated genomes of bacteria with epidemic potential. From these associations gene virulence profiles were generated enabling the comparison of gene signatures within and across genomes. It allowed the identification of virulence genes and construction of their association networks as well as the detection of knowledge gaps. This proof-of-concept study confirmed the feasibility of our original approach for integrating bacterial genome level knowledge with published observations from clinical settings.",

keywords = "structural bioinformatics, whole genome analysis, text mining, infectious diseases, knowledge discovery",

author = "Coiera, {Enrico W.} and Stephen Anthony and Vitali Sintchenko",

year = "2011",

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isbn = "9781612081373",

pages = "6--11",

editor = "Pei-Yuan Qian and Nghiem, {Son V.}",

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Coiera, EW, Anthony, S & Sintchenko, V 2011, Separating drivers from passengers in whole genome analysis: identification of combinatorial effects of genes by mining knowledge sources. in P-Y Qian & SV Nghiem (eds), Biotechno 2011: the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies : proceedings. IARIA, United States, pp. 6-11, Biotechno 2011 : the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies, Venice/Mestre, Italy, 22/05/11.

Separating drivers from passengers in whole genome analysis : identification of combinatorial effects of genes by mining knowledge sources. / Coiera, Enrico W.; Anthony, Stephen; Sintchenko, Vitali.

Biotechno 2011: the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies : proceedings. ed. / Pei-Yuan Qian; Son V. Nghiem. United States : IARIA, 2011. p. 6-11.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

TY - GEN

T1 - Separating drivers from passengers in whole genome analysis

T2 - identification of combinatorial effects of genes by mining knowledge sources

AU - Coiera, Enrico W.

AU - Anthony, Stephen

AU - Sintchenko, Vitali

PY - 2011

Y1 - 2011

N2 - This study aimed to develop a new informatics platform for the discovery, recovery and multi-level analysis of the effects of individual genes and multiple gene combinations on pathophenotypes of bacteria. Natural language processing algorithms were employed to extract gene-disease associations from PubMed literature and annotated genomes of bacteria with epidemic potential. From these associations gene virulence profiles were generated enabling the comparison of gene signatures within and across genomes. It allowed the identification of virulence genes and construction of their association networks as well as the detection of knowledge gaps. This proof-of-concept study confirmed the feasibility of our original approach for integrating bacterial genome level knowledge with published observations from clinical settings.

AB - This study aimed to develop a new informatics platform for the discovery, recovery and multi-level analysis of the effects of individual genes and multiple gene combinations on pathophenotypes of bacteria. Natural language processing algorithms were employed to extract gene-disease associations from PubMed literature and annotated genomes of bacteria with epidemic potential. From these associations gene virulence profiles were generated enabling the comparison of gene signatures within and across genomes. It allowed the identification of virulence genes and construction of their association networks as well as the detection of knowledge gaps. This proof-of-concept study confirmed the feasibility of our original approach for integrating bacterial genome level knowledge with published observations from clinical settings.

KW - structural bioinformatics

KW - whole genome analysis

KW - text mining

KW - infectious diseases

KW - knowledge discovery

M3 - Conference proceeding contribution

SN - 9781612081373

SP - 6

EP - 11

BT - Biotechno 2011

A2 - Qian, Pei-Yuan

A2 - Nghiem, Son V.

PB - IARIA

CY - United States

ER -

Coiera EW, Anthony S, Sintchenko V. Separating drivers from passengers in whole genome analysis: identification of combinatorial effects of genes by mining knowledge sources. In Qian P-Y, Nghiem SV, editors, Biotechno 2011: the Third International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies : proceedings. United States: IARIA. 2011. p. 6-11