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The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.

ABSTRACT: BACKGROUND: Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand. RESULTS: The support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL). CONCLUSION: The SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.

SUBMITTER: Zheng G

PROVIDER: S-EPMC2440765 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.

Zheng Guangyong G Qian Ziliang Z Yang Qing Q Wei Chaochun C Xie Lu L Zhu Yangyong Y Li Yixue Y

BMC bioinformatics 20080616

<h4>Background</h4>Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some ...[more]

PMID: 18554421

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Project description:BackgroundAtomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models.ResultsWe present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models.ConclusionsA learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models.

Project description:BackgroundHeat shock response in eukaryotes is transcriptionally regulated by conserved heat shock transcription factors (Hsfs). Hsf genes are represented by a large multigene family in plants and investigation of the Hsf gene family will serve to elucidate the mechanisms by which plants respond to stress. In recent years, reports of genome-wide structural and evolutionary analysis of the entire Hsf gene family have been generated in two model plant systems, Arabidopsis and rice. Maize, an important cereal crop, has represented a model plant for genetics and evolutionary research. Although some Hsf genes have been characterized in maize, analysis of the entire Hsf gene family were not completed following Maize (B73) Genome Sequencing Project.ResultsA genome-wide analysis was carried out in the present study to identify all Hsfs maize genes. Due to the availability of complete maize genome sequences, 25 nonredundant Hsf genes, named ZmHsfs were identified. Chromosomal location, protein domain and motif organization of ZmHsfs were analyzed in maize genome. The phylogenetic relationships, gene duplications and expression profiles of ZmHsf genes were also presented in this study. Twenty-five ZmHsfs were classified into three major classes (class A, B, and C) according to their structural characteristics and phylogenetic comparisons, and class A was further subdivided into 10 subclasses. Moreover, phylogenetic analysis indicated that the orthologs from the three species (maize, Arabidopsis and rice) were distributed in all three classes, it also revealed diverse Hsf gene family expression patterns in classes and subclasses. Chromosomal/segmental duplications played a key role in Hsf gene family expansion in maize by investigation of gene duplication events. Furthermore, the transcripts of 25 ZmHsf genes were detected in the leaves by heat shock using quantitative real-time PCR. The result demonstrated that ZmHsf genes exhibit different expression levels in heat stress treatment.ConclusionsOverall, data obtained from our investigation contributes to a better understanding of the complexity of the maize Hsf gene family and provides the first step towards directing future experimentation designed to perform systematic analysis of the functions of the Hsf gene family.

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Dataset Information

The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.

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The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.

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