Browsing by Author "Wu, Li-Ching"

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A biosensor of high-density lipoprotein of human serum on a liquid crystal and polymer composite film
(2013-01-01)
Lin, Yi-Hsin
;
Chang, Kai-Han
;
Chu, Wei-Lin
;
Tsou, Yu-Shih
;
Wu, Li-Ching
A biosensor for the concentration of high-density lipoprotein (HDL) in human serum on a liquid crystal and polymer composite film (LCPCF) is demonstrated. The sensing mechanism is based on a polar-polar interaction between orientation of LC directors and HDL in human serum. The concentration of polar HDL in human serum affects the orientations of LC directors at the interface between LCPCF and the human serum. In addition, the surface free energy of LCPCF changes with the applied voltage due to the electrically tunable orientations of LC directors anchored among the polymer grains of LCPCF. As a result, the droplet motion of human serum on LCPCF under applied voltages can sense the concentration of HDL in human serum.
A biosensor of high-density lipoprotein of human serum on a liquid crystal and polymer composite film
(2013-01-01)
Lin, Yi-Hsin
;
Chang, Kai-Han
;
Chu, Wei-Lin
;
Tsou, Yu-Shih
;
Wu, Li-Ching
A biosensor for the concentration of high-density lipoprotein (HDL) in human serum on a liquid crystal and polymer composite film (LCPCF) is demonstrated. The sensing mechanism is based on a polar-polar interaction between orientation of LC directors and HDL in human serum. The concentration of polar HDL in human serum affects the orientations of LC directors at the interface between LCPCF and the human serum. In addition, the surface free energy of LCPCF changes with the applied voltage due to the electrically tunable orientations of LC directors anchored among the polymer grains of LCPCF. As a result, the droplet motion of human serum on LCPCF under applied voltages can sense the concentration of HDL in human serum.
A Human DNA methylation site predictor base on SVM
(2009)
Sun, Yi-Ming
;
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.
An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data
(2010-03-15)
Wu, Li-Ching
;
Huang, Jhih-Long
;
Horng, Jorng-Tzong
;
Huang, Hsien-Da
;
生物資訊及系統生物研究所
Motivation: The analysis of time series gene expression data can provide us with the opportunity to find co-regulated genes that show a similar expression patterns under a contiguous subset of experimental conditions. However, these co-regulated genes may behave almost independently under other conditions. Furthermore, the similarity in the expression pattern might be time-shifted. In that case, we need to be concerned with grouping genes that share similar expression patterns under a contiguous subset of conditions and where the similarity in expression pattern might have time lags. in addition, to be considered a time-shifted similar pattern, because co-regulated genes in a biological process may show a periodic pattern in their cell cycle expression, we also should group genes with periodic similar patterns over multiple cell cycles. If this is carried out, we can regard such grouped genes as cell-cycle regulated genes. Results: We propose a method that follows the q-cluster concept [Ji, L., & Tan, K. L. (2005). Identifying time-lagged gene clusters using gene expression data. Bioinformatics, 21(4), 509-516] and further advances this approach towards the identification of cell-cycle regulated genes using cell cycle microarray data. We used our method to Cluster a microarray time series of yeast genes to assess the statistically biological significance of the obtained clusters we used the p-value obtained from the hypergeometric distribution. We found that several clusters provided findings suggesting a TF-target relationship. In order to test whether our method could group related genes that other methods have found difficult to group, we compared our method with other measures such as Spearman Rank Correlation and Pearson Correlation. The results of the comparison demonstrate that our method indeed could group known related genes that these measures regard as having only a weak association. (C) 2009 Elsevier Ltd. All rights reserved.
Characterization and prediction of mRNA polyadenylation sites in human genes
(2011-04-01)
Chang, Tzu-Hao
;
Wu, Li-Ching
;
Chen, Yu-Ting
;
Huang, Hsien-Da
;
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
;
Huang, Hsien-Da
;
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/.
Detecting LTR structures in human genomic sequences using profile hidden Markov models
(2009-01-01)
Wu, Li-Ching
;
Huang, Hsien-Da
;
Chang, Yu-Chung
;
Lee, Ying-Chun
;
Horng, Jorng-Tzong
More than 45% of human genome has been annotated as transposable elements (TEs). The human genome is expanded by the mobilization of these TEs, which they may increase the plasticity and variation of the genome. Long terminal repeat (LTR) retrotransposons are important components in TEs. LTRs include regulatory sites, which the authors believe could be conserved in evolution. Therefore, these significant motifs in the sequence of LTRs are found and are used to train a Hidden Markov Model. These models are used as fingerprints to detect most of the known LTRs detected by RepeatMasker. LTR instances are classified into families using the predictive models proposed. These LTRs can support evolutionary analysis. A new method of detecting LTR is proposed. Analyzing LTR sequences reveals some specific motifs as LTR fingerprints, which can be built into HMM profiles. Experimental results reveal that the proposed experimental approach not only discovers most of the LTRs found by RepeatMasker, but also detects some novel LTRs. Moreover, the novel LTRs may be structurally incomplete or degenerate. (C) 2008 Published by Elsevier Ltd.
EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC
(2013-01-01)
Chang, Tzu-Hao
;
Wu, Li-Ching
;
Lee, Tzong-Yi
;
Chen, Shu-Pin
;
Huang, Hsien-Da
The function of a protein is generally related to its subcellular localization. Therefore, knowing its subcellular localization is helpful in understanding its potential functions and roles in biological processes. This work develops a hybrid method for computationally predicting the subcellular localization of eukaryotic protein. The method is called EuLoc and incorporates the Hidden Markov Model (HMM) method, homology search approach and the support vector machines (SVM) method by fusing several new features into Chou's pseudo-amino acid composition. The proposed SVM module overcomes the shortcoming of the homology search approach in predicting the subcellular localization of a protein which only finds low-homologous or non-homologous sequences in a protein subcellular localization annotated database. The proposed HMM modules overcome the shortcoming of SVM in predicting subcellular localizations using few data on protein sequences. Several features of a protein sequence are considered, including the sequence-based features, the biological features derived from PROSITE, NLSdb and Pfam, the post-transcriptional modification features and others. The overall accuracy and location accuracy of EuLoc are 90.5 and 91.2 %, respectively, revealing a better predictive performance than obtained elsewhere. Although the amounts of data of the various subcellular location groups in benchmark dataset differ markedly, the accuracies of 12 subcellular localizations of EuLoc range from 82.5 to 100 %, indicating that this tool is much more balanced than other tools. EuLoc offers a high, balanced predictive power for each subcellular localization. EuLoc is now available on the web at http://euloc.mbc.nctu.edu.tw/.
Identifying Discriminative Amino Acids Within the Hemagglutinin of Human Influenza A H5N1 Virus Using a Decision Tree
(2008-11-01)
Wu, Li-Ching
;
Horng, Jorng-Tzong
;
Huang, Hsien-Da
;
Chen, Wei-Long
;
生物資訊及系統生物研究所
Recently, the H5N1 virus has had an increasingly important impact on human life. This is because more and more people are becoming infected with this virus, and the possibility of a serious pandemic with human to human transmission is looming. This might occur if the genome of this influenza virus mutates either by antigenic drift or by antigenic shift, especially if there is a mutation of the hemagglutinin (HA) glycoprotein. The HA is the surface glycoprotein, and it binds to sialic acid of the host cell surface receptor. Thus, the combination of HA and sialic acid are central to whether influenza virus infects humans. In this study, we selected 497 HA protein sequences from the National Center for Biotechnology Information (NCBI) Influenza Resource database, and used a decision tree method to identify discriminative amino acids in the HA protein sequences that may possibly influence the binding of HA to sialic acid. Four such amino acid positions at 54, 55, 241, and 281 were identified and these may play an important role in infection by H5N1 influenza virus.
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.