Browsing by Author "Shien, Dray-Ming"

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GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group
(2012-01-17)
Lee, Tzong-Yi
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Chang, Wen-Chi
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Hsu, Justin Bo-Kai
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Chang, Tzu-Hao
;
Shien, Dray-Ming
Background: Sequence features in promoter regions are involved in regulating gene transcription initiation. Although numerous computational methods have been developed for predicting transcriptional start sites (TSSs) or transcription factor (TF) binding sites (TFBSs), they lack annotations for do not consider some important regulatory features such as CpG islands, tandem repeats, the TATA box, CCAAT box, GC box, over-represented oligonucleotides, DNA stability, and GC content. Additionally, the combinatorial interaction of TFs regulates the gene group that is associated with same expression pattern. To investigate gene transcriptional regulation, an integrated system that annotates regulatory features in a promoter sequence and detects co-regulation of TFs in a group of genes is needed. Results: This work identifies TSSs and regulatory features in a promoter sequence, and recognizes co-occurrence of cis-regulatory elements in co-expressed genes using a novel system. Three well-known TSS prediction tools are incorporated with orthologous conserved features, such as CpG islands, nucleotide composition, over-represented hexamer nucleotides, and DNA stability, to construct the novel Gene Promoter Miner (GPMiner) using a support vector machine (SVM). According to five-fold cross-validation results, the predictive sensitivity and specificity are both roughly 80%. The proposed system allows users to input a group of gene names/symbols, enabling the co-occurrence of TFBSs to be determined. Additionally, an input sequence can also be analyzed for homogeneity of experimental mammalian promoter sequences, and conserved regulatory features between homologous promoters can be observed through cross-species analysis. After identifying promoter regions, regulatory features are visualized graphically to facilitate gene promoter observations. Conclusions: The GPMiner, which has a user-friendly input/output interface, has numerous benefits in analyzing human and mouse promoters. The proposed system is freely available at http://GPMiner.mbc.nctu.edu.tw/.
Incorporating Structural Characteristics for Identification of Protein Methylation Sites
(2009-07-15)
Shien, Dray-Ming
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Lee, Tzong-Yi
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Chang, Wen-Chi
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Hsu, Justin Bo-Kai
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Horng, Jorng-Tzong
Studies over the last few years have identified protein methylation on histones and other proteins that are involved in the regulation of gene transcription. Several works have developed approaches to identify computationally the potential methylation sites on lysine and arginine. Studies of protein tertiary structure have demonstrated that the sites of protein methylation are preferentially in regions that are easily accessible. However, previous studies have not taken into account the solvent-accessible surface area (ASA) that surrounds the methylation sites. This work presents a method named MASA that combines the support vector machine with the sequence and structural characteristics of proteins to identify methylation sites on lysine, arginine, glutamate, and asparagine. Since most experimental methylation sites are not associated with corresponding protein tertiary structures in the Protein Data Bank, the effective solvent-accessible prediction tools have been adopted to determine the potential ASA values of amino acids in proteins. Evaluation of predictive performance by cross-validation indicates that the ASA values around the methylation sites can improve the accuracy of prediction. Additionally, an independent test reveals that the prediction accuracies for methylated lysine and arginine are 80.8 and 85.0%, respectively. Finally, the proposed method is implemented as an effective system for identifying protein methylation sites. The developed web server is freely available at http:/IMASA.mbc.nctu.edu.tw/. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 30: 1532-1543, 2009
Incorporating Support Vector Machine for Identifying Protein Tyrosine Sulfation Sites
(2009-11-30)
Chang, Wen-Chi
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Lee, Tzong-Yi
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Shien, Dray-Ming
;
Hsu, Justin Bo-Kai
;
Horng, Jorng-Tzong
Tyrosine sulfation is a post-translational modification of many secreted and membrane-bound proteins. It governs protein-protein interactions that are involved in leukocyte adhesion, hemostasis, and chemokine signaling. However, the intrinsic feature of sulfated protein remains elusive and remains to be delineated. This investigation presents SulfoSite, which is a computational method based on a support vector machine (SVM) for predicting protein sulfotyrosine sites. The approach was developed to consider structural information such as concerning the secondary structure and solvent accessibility of amino acids that surround the sulfotyrosine sites. One hundred sixty-two experimentally verified tyrosine sulfation sites were identified using UniProtKB/SwissProt release 53.0. The results of a five-fold cross-validation evaluation suggest that the accessibility of the solvent around the sulfotyrosine sites contributes substantially to predictive accuracy. The SVM classifier can achieve an accuracy of 94.2% in fivefold cross validation when sequence positional weighted matrix (PWM) is coupled with values of the accessible surface area (ASA). The proposed method significantly outperforms previous methods for accurately predicting the location of tyrosine sulfation sites. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 30: 2526-2537, 2009
RNAMST: efficient and flexible approach for identifying RNA structural homologs
(2006-07-01)
Chang, Tzu-Hao
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Huang, Hsien-Da
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Chuang, Tzu-Neng
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Shien, Dray-Ming
;
Horng, Jorng-Tzong
RNA molecules fold into characteristic secondary structures for their diverse functional activities such as post- translational regulation of gene expression. Searching homologs of a pre-defined RNA structural motif, which may be a known functional element or a putative RNA structural motif, can provide useful information for deciphering RNA regulatory mechanisms. Since searching for the RNA structural homologs among the numerous RNA sequences is extremely time- consuming, this work develops a data preprocessing strategy to enhance the search efficiency and presents RNAMST, which is an efficient and flexible web server for rapidly identifying homologs of a pre-definedRNAstructuralmotifamongnumerous RNA sequences. Intuitive user interface are provided on the web server to facilitate the predictive analysis. By comparing the proposed web server to other tools developed previously, RNAMST performs remarkably more efficiently and provides more effective and flexible functions. RNAMST is now available on the web at http://bioinfo.csie.ncu.edu.tw/similar to rnamst/.