Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association.
Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high fal...
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2014-02-01
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doaj-7fe71637fe874c1baeb7ea8900983ac12020-11-25T00:02:54ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042014-02-01102e100412210.1371/journal.pgen.1004122Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association.Konrad J KarczewskiMichael SnyderRuss B AltmanNicholas P TatonettiTranscription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.http://europepmc.org/articles/PMC3916285?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Konrad J Karczewski Michael Snyder Russ B Altman Nicholas P Tatonetti |
spellingShingle |
Konrad J Karczewski Michael Snyder Russ B Altman Nicholas P Tatonetti Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. PLoS Genetics |
author_facet |
Konrad J Karczewski Michael Snyder Russ B Altman Nicholas P Tatonetti |
author_sort |
Konrad J Karczewski |
title |
Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. |
title_short |
Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. |
title_full |
Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. |
title_fullStr |
Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. |
title_full_unstemmed |
Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. |
title_sort |
coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Genetics |
issn |
1553-7390 1553-7404 |
publishDate |
2014-02-01 |
description |
Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease. |
url |
http://europepmc.org/articles/PMC3916285?pdf=render |
work_keys_str_mv |
AT konradjkarczewski coherentfunctionalmodulesimprovetranscriptionfactortargetidentificationcooperativitypredictionanddiseaseassociation AT michaelsnyder coherentfunctionalmodulesimprovetranscriptionfactortargetidentificationcooperativitypredictionanddiseaseassociation AT russbaltman coherentfunctionalmodulesimprovetranscriptionfactortargetidentificationcooperativitypredictionanddiseaseassociation AT nicholasptatonetti coherentfunctionalmodulesimprovetranscriptionfactortargetidentificationcooperativitypredictionanddiseaseassociation |
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1725436050620284928 |