Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of...
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Gespeichert in:

Autor*in:	Shmelkov, Evgeny [verfasserIn] Tang, Zuojian Aifantis, Iannis Statnikov, Alexander

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Ingenuity Pathway Analysis Pathway Database HUGO Gene Nomenclature Committee Direct Transcriptional Target Pathway Studio

Anmerkung:	© Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: Biology direct - London : BioMed Central, 2006, 6(2011), 1 vom: 28. Feb.
Übergeordnetes Werk:	volume:6 ; year:2011 ; number:1 ; day:28 ; month:02

Links:	Volltext

DOI / URN:	10.1186/1745-6150-6-15

Katalog-ID:	SPR030043212

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520			\|a Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan.
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allfields	10.1186/1745-6150-6-15 doi (DE-627)SPR030043212 (SPR)1745-6150-6-15-e DE-627 ger DE-627 rakwb eng Shmelkov, Evgeny verfasserin aut Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. Ingenuity Pathway Analysis (dpeaa)DE-He213 Pathway Database (dpeaa)DE-He213 HUGO Gene Nomenclature Committee (dpeaa)DE-He213 Direct Transcriptional Target (dpeaa)DE-He213 Pathway Studio (dpeaa)DE-He213 Tang, Zuojian aut Aifantis, Iannis aut Statnikov, Alexander aut Enthalten in Biology direct London : BioMed Central, 2006 6(2011), 1 vom: 28. Feb. (DE-627)507522516 (DE-600)2221028-3 1745-6150 nnns volume:6 year:2011 number:1 day:28 month:02 https://dx.doi.org/10.1186/1745-6150-6-15 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 1 28 02
spelling	10.1186/1745-6150-6-15 doi (DE-627)SPR030043212 (SPR)1745-6150-6-15-e DE-627 ger DE-627 rakwb eng Shmelkov, Evgeny verfasserin aut Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. Ingenuity Pathway Analysis (dpeaa)DE-He213 Pathway Database (dpeaa)DE-He213 HUGO Gene Nomenclature Committee (dpeaa)DE-He213 Direct Transcriptional Target (dpeaa)DE-He213 Pathway Studio (dpeaa)DE-He213 Tang, Zuojian aut Aifantis, Iannis aut Statnikov, Alexander aut Enthalten in Biology direct London : BioMed Central, 2006 6(2011), 1 vom: 28. Feb. (DE-627)507522516 (DE-600)2221028-3 1745-6150 nnns volume:6 year:2011 number:1 day:28 month:02 https://dx.doi.org/10.1186/1745-6150-6-15 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 1 28 02
allfields_unstemmed	10.1186/1745-6150-6-15 doi (DE-627)SPR030043212 (SPR)1745-6150-6-15-e DE-627 ger DE-627 rakwb eng Shmelkov, Evgeny verfasserin aut Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. Ingenuity Pathway Analysis (dpeaa)DE-He213 Pathway Database (dpeaa)DE-He213 HUGO Gene Nomenclature Committee (dpeaa)DE-He213 Direct Transcriptional Target (dpeaa)DE-He213 Pathway Studio (dpeaa)DE-He213 Tang, Zuojian aut Aifantis, Iannis aut Statnikov, Alexander aut Enthalten in Biology direct London : BioMed Central, 2006 6(2011), 1 vom: 28. Feb. (DE-627)507522516 (DE-600)2221028-3 1745-6150 nnns volume:6 year:2011 number:1 day:28 month:02 https://dx.doi.org/10.1186/1745-6150-6-15 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 1 28 02
allfieldsGer	10.1186/1745-6150-6-15 doi (DE-627)SPR030043212 (SPR)1745-6150-6-15-e DE-627 ger DE-627 rakwb eng Shmelkov, Evgeny verfasserin aut Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. Ingenuity Pathway Analysis (dpeaa)DE-He213 Pathway Database (dpeaa)DE-He213 HUGO Gene Nomenclature Committee (dpeaa)DE-He213 Direct Transcriptional Target (dpeaa)DE-He213 Pathway Studio (dpeaa)DE-He213 Tang, Zuojian aut Aifantis, Iannis aut Statnikov, Alexander aut Enthalten in Biology direct London : BioMed Central, 2006 6(2011), 1 vom: 28. Feb. (DE-627)507522516 (DE-600)2221028-3 1745-6150 nnns volume:6 year:2011 number:1 day:28 month:02 https://dx.doi.org/10.1186/1745-6150-6-15 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 1 28 02
allfieldsSound	10.1186/1745-6150-6-15 doi (DE-627)SPR030043212 (SPR)1745-6150-6-15-e DE-627 ger DE-627 rakwb eng Shmelkov, Evgeny verfasserin aut Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. Ingenuity Pathway Analysis (dpeaa)DE-He213 Pathway Database (dpeaa)DE-He213 HUGO Gene Nomenclature Committee (dpeaa)DE-He213 Direct Transcriptional Target (dpeaa)DE-He213 Pathway Studio (dpeaa)DE-He213 Tang, Zuojian aut Aifantis, Iannis aut Statnikov, Alexander aut Enthalten in Biology direct London : BioMed Central, 2006 6(2011), 1 vom: 28. Feb. (DE-627)507522516 (DE-600)2221028-3 1745-6150 nnns volume:6 year:2011 number:1 day:28 month:02 https://dx.doi.org/10.1186/1745-6150-6-15 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 1 28 02
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. 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author	Shmelkov, Evgeny
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topic_title	Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale Ingenuity Pathway Analysis (dpeaa)DE-He213 Pathway Database (dpeaa)DE-He213 HUGO Gene Nomenclature Committee (dpeaa)DE-He213 Direct Transcriptional Target (dpeaa)DE-He213 Pathway Studio (dpeaa)DE-He213
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title	Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale
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title_full	Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale
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title_sort	assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale
title_auth	Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale
abstract	Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases. Results The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer. Conclusions Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. Reviewers This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan. © Shmelkov et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale
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We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation. 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Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

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