MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites

• Published in: BMC bioinformatics Vol. 20; no. Suppl 7; pp. 200 - 48
• Main Authors: Hu, Jialu, Wang, Jingru, Lin, Jianan, Liu, Tianwei, Zhong, Yuanke, Liu, Jie, Zheng, Yan, Gao, Yiqun, He, Junhao, Shang, Xuequn
• Format: Journal Article
• Language: English
• Published: London BioMed Central 01.05.2019; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Binding site preference; Binding Sites; Binding sites (Biochemistry); Biochemistry; Bioinformatics; Biological effects; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer applications; Datasets; Deoxyribonucleic acid; DNA; DNA binding proteins; Functionals; Gene expression; Gene sequencing; Genes; Genomes; Humans; Identification; Identification and classification; International conferences; Life Sciences; Microarrays; Multiple instance learning; Novels; Nucleotide Motifs; Optimization; Physiological aspects; Protein Binding; Proteins; Support Vector Machine; Support vector machines; Transcription factor; Transcription factors; Transcription Factors - metabolism; China; Binding site preference; Support vector machine; Transcription factor; Multiple instance learning
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-019-2735-3

Background Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these binding preference patterns is of central significance in the understanding of molecular regulation mechanism. Many algorithms have been proposed for the identification of transcription factor binding sites. However, it remains a challengeable problem. Results Here, we proposed a novel motif discovery algorithm based on support vector machine (MD-SVM) to learn a discriminative model for TF binding sites. MD-SVM firstly obtains position weight matrix (PWM) from a set of training datasets. Then it translates the MD problem into a computational framework of multiple instance learning (MIL). It was applied to several real biological datasets. Results show that our algorithm outperforms MI-SVM in terms of both accuracy and specificity. Conclusions In this paper, we modeled the TF motif discovery problem as a MIL optimization problem. The SVM algorithm was adapted to discriminate positive and negative bags of instances. Compared to other svm-based algorithms, MD-SVM show its superiority over its competitors in term of ROC AUC. Hopefully, it could be of benefit to the research community in the understanding of molecular functions of DNA functional elements and transcription factors.