Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 361545
Title Predicting and understanding transcription factor interactions based on sequence level determinants of combinatorial control
Author(s) Dijk, A.D.J. van; Braak, C.J.F. ter; Immink, G.H.; Angenent, G.C.; Ham, R.C.H.J. van
Source Bioinformatics 24 (2008)1. - ISSN 1367-4803 - p. 26 - 33.
Department(s) PRI BIOS Applied Bioinformatics
Biometris (WU MAT)
PRI Biometris
PRI Bioscience
Publication type Refereed Article in a scientific journal
Publication year 2008
Keyword(s) protein-protein interactions - regulatory networks - interaction datasets - motif pairs - complexes - evolution - database - dna - classification - signatures
Abstract Motivation: Transcription factor interactions are the cornerstone of combinatorial control, which is a crucial aspect of the gene regulatory system. Understanding and predicting transcription factor interactions based on their sequence alone is difficult since they are often part of families of factors sharing high sequence identity. Given the scarcity of experimental data on interactions compared to available sequence data, however, it would be most useful to have accurate methods for the prediction of such interactions. Results: We present a method consisting of a Random Forestbased feature-selection procedure that selects relevant motifs out of a set found using a correlated motif search algorithm. Prediction accuracy for several transcription factor families (bZIP, MADS, homeobox and forkhead) reaches 60¿90%. In addition, we identified those parts of the sequence that are important for the interaction specificity, and show that these are in agreement with available data. We also used the predictors to perform genome-wide scans for interaction partners and recovered both known and putative new interaction partners
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