Comparison and analysis of models predicting transcriptional regulatory modules based on different backgrounds

• Published in: 2012 5th International Conference on Biomedical Engineering and Informatics pp. 872 - 875
• Main Authors: Huimin Li, Yu Shi, Dan Chen, Jun Hu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: backgrounds; comparison and analysis; motif; transcriptional regulation
• ISBN: 9781467311830; 1467311839
• DOI: 10.1109/BMEI.2012.6513011

Correct recognition of transcriptional regulatory elements (also named motif) is important for understanding the laws of expression of genes. In silicon analysis, generally, a background or named control set constructed by a set of sequences is necessary in predicting transcriptional regulatory elements. Some studies have suggested that the accuracy of models could be improved when selecting backgrounds according to GC-contents. For further examine control set's influence on models predicting transcriptional regulatory modules, 3 different kinds of transcriptional regulatory element-recognizing control sets, which are a background from given sequences, a background from shuffled sequences and a background from Markov model, are introduced. Then comparison and analysis of module-predicting methods based on the above 3 kinds of control sets are performed. The results suggested that the better accuracy of prediction is obtained when using a background from Markov model which considers the composition bias of the nucleotides in the biological sequences, while the accuracy of models would be significantly improved when combining different backgrounds.