Browsing by Author "Sun, Yi-Ming"

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A Human DNA methylation site predictor base on SVM
(2009)
Sun, Yi-Ming
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Liu, Baw-Jhiune
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Liao, Wei-Li
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Chang, Cheng-Wei
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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.
Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data
(2006-07-01)
Tsou, Ann-Ping
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Sun, Yi-Ming
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Liu, Chia-Lin
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Huang, Hsien-Da
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Horng, Jorng-Tzong
Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.
Identifying transcriptional start sites of human microRNAs based on high-throughput sequencing data
(2011-11-01)
Chien, Chia-Hung
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Sun, Yi-Ming
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Chang, Wen-Chi
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Chiang-Hsieh, Pei-Yun
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Lee, Tzong-Yi
MicroRNAs (miRNAs) are critical small non-coding RNAs that regulate gene expression by hybridizing to the 3'-untranslated regions (3'-UTR) of target mRNAs, subsequently controlling diverse biological processes at post-transcriptional level. How miRNA genes are regulated receives considerable attention because it directly affects miRNA-mediated gene regulatory networks. Although numerous prediction models were developed for identifying miRNA promoters or transcriptional start sites (TSSs), most of them lack experimental validation and are inadequate to elucidate relationships between miRNA genes and transcription factors (TFs). Here, we integrate three experimental datasets, including cap analysis of gene expression (CAGE) tags, TSS Seq libraries and H3K4me3 chromatin signature derived from high-throughput sequencing analysis of gene initiation, to provide direct evidence of miRNA TSSs, thus establishing an experimental-based resource of human miRNA TSSs, named miRStart. Moreover, a machine-learning-based Support Vector Machine (SVM) model is developed to systematically identify representative TSSs for each miRNA gene. Finally, to demonstrate the effectiveness of the proposed resource, an important human intergenic miRNA, hsa-miR-122, is selected to experimentally validate putative TSS owing to its high expression in a normal liver. In conclusion, this work successfully identified 847 human miRNA TSSs (292 of them are clustered to 70 TSSs of miRNA clusters) based on the utilization of high-throughput sequencing data from TSS-relevant experiments, and establish a valuable resource for biologists in advanced research in miRNA-mediated regulatory networks.
Integrated analyses to reconstruct microRNA-mediated regulatory networks in mouse liver using high-throughput profiling
(2015-01-21)
Hsu, Sheng-Da
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Huang, Hsi-Yuan
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Chou, Chih-Hung
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Sun, Yi-Ming
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Hsu, Ming-Ta
Background: MicroRNAs (miRNAs) simultaneously target many transcripts through partial complementarity binding, and have emerged as a key type of post-transcriptional regulator for gene expression. How miRNA accomplishes its pleiotropic effects largely depends on its expression and its target repertoire. Previous studies discovered thousands of miRNAs and numerous miRNA target genes mainly through computation and prediction methods which produced high rates of false positive prediction. The development of Argonaute cross-linked immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) provides a system to effectively determine miRNA target genes. Likewise, the accuracy of dissecting the transcriptional regulation of miRNA genes has been greatly improved by chromatin immunoprecipitation of the transcription factors coupled with sequencing (ChIP-Seq). Elucidation of the miRNA target repertoire will provide an in-depth understanding of the functional roles of microRNA pathways. To reliably reconstruct a miRNA-mediated regulatory network, we established a computational framework using publicly available, sequence-based transcription factor-miRNA databases, including ChIPBase and TransmiR for the TF-miRNA interactions, along with miRNA-target databases, including miRTarBase, TarBase and starBase, for the miRNA-target interactions. We applied the computational framework to elucidate the miRNA-mediated regulatory network in the Mir122a(-/-) mouse model, which has an altered transcriptome and progressive liver disease. Results: We applied our computational framework to the expression profiles of miRNA/mRNA of Mir122a(-/-) mutant mice and wild-type mice. The miRNA-mediated network involves 40 curated TFs contributing to the aberrant expression of 65 miRNAs and 723 curated miRNA target genes, of which 56% was found in the differentially-expressed genes of Mir122a(-) mice. Hence, the regulatory network disclosed previously-known and also many previously-unidentified miRNA-mediated regulations in mutant mice. Moreover, we demonstrate that loss of imprinting at the chromosome 12qF1 region is associated with miRNA overexpression in human hepatocellular carcinoma and stem cells, suggesting initiation of precancerous changes in young mice deficient in miR-122. A group of 9 miRNAs was found to share miR-122 target genes, indicating synergy between miRNAs and target genes by way of multiplicity and cooperativity. Conclusions: The study provides significant insight into miRNA-mediated regulatory networks. Based on experimentally verified data, this network is highly reliable and effective in revealing previously-undetermined disease-associated molecular mechanisms. This computational framework can be applied to explore the significant TF-miRNA-miRNA target interactions in any complex biological systems with high degrees of confidence.
MethHC: a database of DNA methylation and gene expression in human cancer
(2015-01-28)
Huang, Wei-Yun
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Hsu, Sheng-Da
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Huang, Hsi-Yuan
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Sun, Yi-Ming
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Chou, Chih-Hung
We present MethHC (http://MethHC.mbc.nctu.edu.tw), a database comprising a systematic integration of a large collection of DNA methylation data and mRNA/microRNA expression profiles in human cancer. DNA methylation is an important epigenetic regulator of gene transcription, and genes with high levels of DNA methylation in their promoter regions are transcriptionally silent. Increasing numbers of DNA methylation and mRNA/microRNA expression profiles are being published in different public repositories. These data can help researchers to identify epigenetic patterns that are important for carcinogenesis. MethHC integrates data such as DNA methylation, mRNA expression, DNA methylation of microRNA gene and microRNA expression to identify correlations between DNA methylation and mRNA/microRNA expression from TCGA (The Cancer Genome Atlas), which includes 18 human cancers in more than 6000 samples, 6548 microarrays and 12 567 RNA sequencing data.
Using a novel sulfonated silica nanoparticles for Nafion (R) membrane for direct methanol fuel cell
(2006-11-20)
Su, Yu-Huei
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Wei, Tung-Yu
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Hsu, Chih-Hao
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Liu, Ying-Ling
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Sun, Yi-Ming