Inferring activity changes of transcription factors by binding association with sorted expression profiles

被引:71
作者
Cheng, Chao [1 ]
Yan, Xiting [1 ]
Sun, Fengzhu [1 ,2 ]
Li, Lei M. [1 ,2 ]
机构
[1] Univ So Calif, Dept Biol Sci, Mol & Computat Biol Program, Los Angeles, CA 90089 USA
[2] Univ So Calif, Dept Math, Los Angeles, CA 90089 USA
关键词
D O I
10.1186/1471-2105-8-452
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: The identification of transcription factors (TFs) associated with a biological process is fundamental to understanding its regulatory mechanisms. From microarray data, however, the activity changes of TFs often cannot be directly observed due to their relatively low expression levels, post-transcriptional modifications, and other complications. Several approaches have been proposed to infer TF activity changes from microarray data. In some models, a linear relationship between gene expression and TF-gene binding strength is assumed. In some other models, the target genes of a TF are first determined by a significance cutoff to binding affinity scores, and then expression differentiation is checked between the target and other genes. Results: We propose a novel method, referred to as BASE (binding association with sorted expression), to infer TF activity changes from microarray expression profiles with the help of binding affinity data. It searches the maximum association between bind affinity profile of a TF and expression change profile along the direction of sorted differentiation. The method does not make hard target gene selection, rather, the significances of TF activity changes are evaluated by permutation tests of binding association at the end. To show the effectiveness of this method, we apply it to three typical examples using different kinds of binding affinity data, namely, ChIP-chip data, motif discovery data, and positional weighted matrix scanning data, respectively. The implications obtained from all three examples are consistent with established biological results. Moreover, the inferences suggest new and biological meaningful hypotheses for further investigation. Conclusion: The proposed method makes transcription inference from profiles of expression and binding affinity. The same machinery can be used to deal with various kinds of binding affinity data. The method does not require a linear assumption, and has the desirable property of scale-invariance with respect to TF-specific binding affinity. This method is easy to implement and can be routinely applied for transcriptional inferences in microarray studies.
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页数:12
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共 48 条
[1]   Integrative analysis of genome-scale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription [J].
Alter, O ;
Golub, GH .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2004, 101 (47) :16577-16582
[2]   High functional overlap between MluI cell-cycle box binding factor and Swi4/6 cell-cycle box binding factor in the G1/S transcriptional program in Saccharomyces cerevisiae [J].
Bean, JM ;
Siggia, ED ;
Cross, FR .
GENETICS, 2005, 171 (01) :49-61
[3]   Predicting gene expression from sequence [J].
Beer, MA ;
Tavazoie, S .
CELL, 2004, 117 (02) :185-198
[4]   Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses [J].
Bhattacharjee, A ;
Richards, WG ;
Staunton, J ;
Li, C ;
Monti, S ;
Vasa, P ;
Ladd, C ;
Beheshti, J ;
Bueno, R ;
Gillette, M ;
Loda, M ;
Weber, G ;
Mark, EJ ;
Lander, ES ;
Wong, W ;
Johnson, BE ;
Golub, TR ;
Sugarbaker, DJ ;
Meyerson, M .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2001, 98 (24) :13790-13795
[5]   Interferon regulatory factor-1 (IRF-1) exhibits tumor suppressor activities in breast cancer associated with caspase activation and induction of apoptosis [J].
Bouker, KB ;
Skaar, TC ;
Riggins, RB ;
Harburger, DS ;
Fernandez, DR ;
Zwart, A ;
Wang, A ;
Clarke, R .
CARCINOGENESIS, 2005, 26 (09) :1527-1535
[6]   Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach [J].
Boulesteix, Anne-Laure ;
Strimmer, Korbinian .
THEORETICAL BIOLOGY AND MEDICAL MODELLING, 2005, 2
[7]   Regulatory element detection using correlation with expression [J].
Bussemaker, HJ ;
Li, H ;
Siggia, ED .
NATURE GENETICS, 2001, 27 (02) :167-171
[8]   Inference of transcription modification in long-live yeast strains from their expression profiles [J].
Cheng, Chao ;
Fabrizio, Paola ;
Ge, Huanying ;
Longo, Valter D. ;
Li, Lei M. .
BMC GENOMICS, 2007, 8 (1)
[9]   Identifying transcription factor functions and targets by phenotypic activation [J].
Chua, Gordon ;
Morris, Quaid D. ;
Sopko, Richelle ;
Robinson, Mark D. ;
Ryan, Owen ;
Chan, Esther T. ;
Frey, Brendan J. ;
Andrews, Brenda J. ;
Boone, Charles ;
Hughes, Timothy R. .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2006, 103 (32) :12045-12050
[10]   Integrating regulatory motif discovery and genome-wide expression analysis [J].
Conlon, EM ;
Liu, XS ;
Lieb, JD ;
Liu, JS .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2003, 100 (06) :3339-3344