Browsing by Author "Liu, Baw-Jhiune"

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A Human DNA methylation site predictor base on SVM
(2009)
Sun, Yi-Ming
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Liu, Baw-Jhiune
;
Liao, Wei-Li
;
Chang, Cheng-Wei
;
Huang, Hsien-Da
During gene expression, transcription factors are unable to bind to a transcription binding site (TFBS) involved in regulation if DNA methylation has occurred at the TFBS. Methyl-CpG-binding proteins may also occupy the TFBS and prevent the functioning of a transcription factor. Thus, the methylation status of CpG sites is an important issue when trying to understand gene regulation and shows strong correlation with the TFBS involved. In addition, CpG islands would seem to undergo cell-specific and tissue-specific methylation. Such differential methylation is presented at numerous genetic loci that are essential for development. Current DNA methylation site prediction tools need to be improved so that they include TFBS features and have greater accuracy in terms of the DNA region that is involved in methylation. We developed models that compare the differences across these regions and tissues. The TFBSs, DNA properties and DNA distribution were used as features for this classification. From the results, we found some TFBSs that were able to discriminate whether a sequence was methylated or not. The sensitivity, specificity and accuracy estimated using 10-fold cross validation were 90.8%, 80.54%, and 86.07%, respectively. Thus, for these four regions and twelve tissues, the performance levels (ACC) were all greater than 80%. We propose that the differential features or methylations vary between the different regions because the features common to each DNA region made up only 50% of the top 70 features. An online predictor based on EpiMeP is available at http://140.115.51.41/EpiMeP/. Supplementary file is available at http://140.115.51.41/EpiMeP/supplementary.doc.
Characterization and prediction of mRNA polyadenylation sites in human genes
(2011-04-01)
Chang, Tzu-Hao
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Wu, Li-Ching
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Chen, Yu-Ting
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Huang, Hsien-Da
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Liu, Baw-Jhiune
The accurate identification of potential poly(A) sites has contributed to all many studies with regard to alternative polyadenylation. The aim of this study was the development of a machine-learning methodology that will help to discriminate real polyadenylation signals from randomly occurring signals in genomic sequence. Since previous studies have revealed that RNA secondary structure in certain genes has significant impact, the authors tried to computationally pinpoint common structural patterns around the poly(A) sites and to investigate how RNA secondary structure may influence polyadenylation. This involved an initial study on the impact of RNA structure and it was found using motif search tools that hairpin structures might be important. Thus, it was propose that, in addition to the sequence pattern around poly(A) sites, there exists a widespread structural pattern that is also employed during human mRNA polyadenylation. In this study, the authors present a computational model that uses support vector machines to predict human poly(A) sites. The results show that this predictive model has a comparable performance to the current prediction tool. In addition, it was identified common structural patterns associated with polyadenylation using several motif finding programs and this provides new insight into the role of RNA secondary structure plays in polyadenylation.
Computational identification of riboswitches based on RNA conserved functional sequences and conformations
(2009-07-01)
Chang, Tzu-Hao
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Huang, Hsien-Da
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Wu, Li-Ching
;
Yeh, Chi-Ta
;
Liu, Baw-Jhiune
Riboswitches are cis-acting genetic regulatory elements within a specific mRNA that can regulate both transcription and translation by interacting with their corresponding metabolites. Recently, an increasing number of riboswitches have been identified in different species and investigated for their roles in regulatory functions. Both the sequence contexts and structural conformations are important characteristics of riboswitches. None of the previously developed tools, such as covariance models (CMs), Riboswitch finder, and RibEx, provide a web server for efficiently searching homologous instances of known riboswitches or considers two crucial characteristics of each riboswitch, such as the structural conformations and sequence contexts of functional regions. Therefore, we developed a systematic method for identifying 12 kinds of riboswitches. The method is implemented and provided as a web server, RiboSW, to efficiently and conveniently identify riboswitches within messenger RNA sequences. The predictive accuracy of the proposed method is comparable with other previous tools. The efficiency of the proposed method for identifying riboswitches was improved in order to achieve a reasonable computational time required for the prediction, which makes it possible to have an accurate and convenient web server for biologists to obtain the results of their analysis of a given mRNA sequence. RiboSW is now available on the web at http://RiboSW.mbc.nctu.edu.tw/.
Prediction of small non-coding RNA in bacterial genomes using support vector machines
(2010-08-01)
Chang, Tzu-Hao
;
Wu, Li-Ching
;
Lin, Jun-Hong
;
Huang, Hsien-Da
;
Liu, Baw-Jhiune
Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge, using either an experimental or a computational approach, due to the characteristics of sRNAs, which are that sRNAs are small in size, are not translated into proteins and show variable stability. Most known sRNAs have been identified in Escherichia coli and have been shown to be conserved in closely related organisms. We have developed an integrative approach that searches highly conserved intergenic regions among related bacterial genomes for combinations of characteristics that have been extracted from known E. coli sRNA genes. Support vector machines (SVM) were then used with these characteristics to predict novel sRNA genes. (c) 2010 Elsevier Ltd. All rights reserved.
RiboSW - a web server for identifying RNA riboswitches
(2009)
Chang, Tzu-Hao
;
Wu, Li-Ching
;
Yeh, Chi-Ta
;
Chang, Cheng-Wei
;
Liu, Baw-Jhiune
Riboswitches are cis-acting genetic regulatory elements within a specific mRNA, and can regulate both transcription and translation by interacting with their corresponding metabolites. Recently, more and more riboswitches were identified and investigated about their roles in regulatory functions in different species. Both of the sequence contexts and structural conformations are important characteristics of riboswitches. None of previous developed tools, such Covariance Models (CMs), Riboswitch finder, and RibEx, provides a web server for efficiently searching homologous instances to known riboswitches and considers two crucial characteristics of each riboswitch, such as structural conformations and sequence contexts of functional regions. Therefore, we developed a systematic method to identify twelve kinds of riboswitches. The method is implemented and provided as a web server, RiboSW, to efficiently and conveniently identify riboswitches within messenger RNA sequences. RiboSW is now available on the web at http://bioinfo.csie.ncu.edu.tw/RiboSW/. The predictive accuracy of the proposed method is comparable with other previous tools. The efficiency of the proposed method for identifying riboswitches was improved in order to achieve a reasonable computational time required for the prediction. That makes it possible to have an accurate and convenient web server for biologists to obtain their analyzing results in a given mRNA sequence. The supplementary data is available on the web at http://bioinfo.csie.ncu.edu.tw/RiboSW/sup.pdf.