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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Genomics

Open Access Research

CTF: a CRF-based transcription factor binding sites finding system

Yupeng He14, Yizhe Zhang1, Guangyong Zheng23 and Chaochun Wei13*

Author Affiliations

1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China

2 Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China

3 Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Pudong District, Shanghai 201203, China

4 Bioinformatics and Systems Biology Program, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA

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BMC Genomics 2012, 13(Suppl 8):S18  doi:10.1186/1471-2164-13-S8-S18

Published: 17 December 2012

Additional files

Additional File 1:

Formulation of TFBSs prediction problem. TFBSs prediction problem can be formulated as a function to map a feature matrix (the above matrix in the figure) to an annotation (the below row vector). In the feature matrix, every row corresponds to one features and every column corresponds to one 200 bp bin in a genome. Feature types contain one real value feature (PWM) and multiple binary features (such as "is the bin within a promoter region" and "is it within the peak of a histone marker"). Note that "TSS" stands for transcription start site proximity.

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Additional File 2:

ROC curves for CTF, Chromia and PWM on the dataset of 13 transcription factors. This figure, similar to Figure 5, contained the ROC curves of CTF, Chromia and PWM on all 13 transcription factors. CTF was the CTF model with all features and its ROC curve was obtained by using a 10-fold cross-validation procedure and changing the threshold. ROC curve of Chromia was calculated by using the same data and model contained in its release. ROC curve of PWM was got by scoring directly.

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Additional File 3:

Supplement tables.

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