A general integrative genomic feature transcription factor binding site prediction method applied to analysis of USF1 binding in cardiovascular disease
نویسندگان
چکیده
منابع مشابه
A Systems Biology Approach to Transcription Factor Binding Site Prediction
BACKGROUND The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these ...
متن کاملSiTaR: a novel tool for transcription factor binding site prediction
MOTIVATION Prediction of transcription factor binding sites (TFBSs) is crucial for promoter modeling and network inference. Quality of the predictions is spoiled by numerous false positives, which persist as the main problem for all presently available TFBS search methods. RESULTS We suggest a novel approach, which is alternative to widely used position weight matrices (PWMs) and Hidden Marko...
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UNLABELLED Promoter prediction has gained increased attention in studies related to transcriptional regulation of gene expression. We developed a web server named PMSearch (Poly Matrix Search) which utilizes Position Frequency Matrices (PFMs) to predict transcription factor binding sites (TFBSs) in DNA sequences. PMSearch takes PFMs (either user-defined or retrieved from local dataset which cur...
متن کاملThe Next Generation of Transcription Factor Binding Site Prediction
Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make t...
متن کاملConvolutional Kitchen Sinks for Transcription Factor Binding Site Prediction
We present a simple and efficient method for prediction of transcription factor binding sites from DNA sequence. Our method computes a random approximation of a convolutional kernel feature map from DNA sequence and then learns a linear model from the approximated feature map. Our method outperforms state-ofthe-art deep learning methods on five out of six test datasets from the ENCODE consortiu...
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ژورنال
عنوان ژورنال: Human Genomics
سال: 2009
ISSN: 1479-7364
DOI: 10.1186/1479-7364-3-3-221