Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

This article is part of the supplement: The 2010 International Conference on Bioinformatics and Computational Biology (BIOCOMP 2010): Genomics

Open Access Research article

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Xin Wang12, Liran Juan13, Junjie Lv2, Kejun Wang2, Jeremy R Sanford4 and Yunlong Liu135*

Author Affiliations

1 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, IN 46202, USA

2 College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China

3 Department of Medical and Molecular Genetics, Indiana University School of Medicine, IN 46202, USA

4 Department of Molecular, Cellular and Developmental Biology, University of California Santa Cruz, Santa Cruz, California 95064, USA

5 Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN 46202, USA

For all author emails, please log on.

BMC Genomics 2011, 12(Suppl 5):S8  doi:10.1186/1471-2164-12-S5-S8

Published: 23 December 2011

Additional files

Additional file 1:

Optimal 6nt and 7nt sequence-structural consensus for SRSF1 proteins predicted by RNAMotifModeler. The upper panel (A) and the lower panel (B) show the sequence and structural parameters identified for motif of length 6nt and 7nt, respectively.

Format: PDF Size: 18KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 2:

Prediction results based on RNAMotifModeler excluding the information of RNA secondary structure. (A) ROC curve (B) Accuracy curve, and (C) consensus sequence logo.

Format: PDF Size: 93KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 3:

3D heatmaps illustrating (A) the prediction power and (B) time cost of RNAMotifModeler affected by the number of particles and the Contraction-Expansion coefficient which are two critical parameters of QPSO algorithm.

Format: PDF Size: 103KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data