Browsing by Author "Tsou, AP"

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Identifying transcriptional regulatory sites in the human genome using an integrated computation
(2004)
Huang, HD
;
Horng, JT
;
Sun, YM
;
Tsou, AP
;
Huang, SL
Recently, biological databases and analytical methods have become available for analyzing gene expression and transcriptional regulatory sequences. However, users must make the complicated analyses to query the data in various databases, and to analyze the gene upstreams using various predictive tools. Beyond methods for predicting transcriptional regulatory sites, new automated and integrated methods for analyzing gene upstream sequences on a higher level are urgently required. We present an integrated system to predict transcriptional regulatory sites and detect co-occurrence of these regulatory sites after a set of genes are input. The system comprises a biological data warehousing system, patient discovery programs, pattern occurrence association detectors and user interfaces. User profiles and history pages enable users to trace the sequence analyses for transcriptional regulatory sites.
Identifying transcriptional regulatory sites in the human genome using an integrated system
(2004-03-01)
Huang, HD
;
Horng, JT
;
Sun, YM
;
Tsou, AP
;
Huang, SL
This work develops an integrated system which, after a set of genes are inputted, is able to predict transcriptional regulatory sites and to detect the co-occurrence of these regulatory sites. The system integrates several site detection methods such as known site matching, over-presented oligonucleotide detection and DNA motif discovery programs. User profiles and history pages enable users to trace the sequence analyses of these transcriptional regulatory sites. Two groups of co-regulated genes were used to test the proposed system. The results predicted by the proposed system consist of known site homologs and putative regulatory sites. By comparing these sites with previously published results, the proposed system is able to help biologists identify possible candidates for the regulatory sites from groups of co-regulated genes. The integrated system is now available at http://rgsminer. csie.ncu.edu.tw/.
Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites
(2005-07-30)
Huang, HD
;
Lee, TY
;
Tzeng, SW
;
Wu, LC
;
Horng, JT
Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. (c) 2005 Wiley Periodicals, Inc.
MultiProtident: Identifying proteins using database search and protein-protein interactions
(2005-05-01)
Huang, HD
;
Lee, TY
;
Wu, LC
;
Lin, FM
;
Juan, HF
Protein identification is important in proteomics. Proteomic analyses based on mass spectra (MS) constitute innovative ways to identify the components of protein complexes. Instruments can obtain the mass spectrum to an accuracy of 0.01 Da or better, but identification errors are inevitable. This study shows a novel tool, MultiProtldent, which can identify proteins using additional information about protein-protein interactions and protein functional associations. Both single and multiple Peptide Mass Fingerprints (PMFs) are input to MultiProtldent, which matches the PMFs to a theoretical peptide mass database. The relationships or interactions among proteins are considered to reduce false positives in PMF matching. Experiments to identify protein complexes reveal that MultiProtldent is highly promising. The website associated with this study is http://dbms104.csie.ncu.edu.tw/.
RgS-Miner: A biological data warehousing, analyzing and mining system for identifying transcriptional regulatory sites in human genome
(2004)
Sun, YM
;
Huang, HD
;
Horng, JT
;
Tsou, AP
;
Huang, SL
Recently, biological databases and. analytical methods have become available for analyzing gene expression and transcriptional regulatory sequences. However, users must make the complicated analyses to query the data in various databases, and then they must analyze the gene upstreams using various predictive tools, before finally converting date among formats. Beyond methods for predicting transcriptional regulatory sites, new automated and integrated methods for analyzing gene upstream sequences on a higher level are urgently required. Efficient and integrated data management methods are essential, too. We present an integrated system, namely RgS-Miner, to predict transcriptional regulatory sites and detect co-occurrence of these regulatory sites. RgS-Miner comprises a biological data,, warehousing system, pattern discovery programs, pattern occurrence association detectors and user interfaces. The system is available at http://rgsminer.csie.ncu.edu.tw/.