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Impact of DNA-binding position variants on yeast gene expression

Nucleic Acids Research 2009年第21期37卷 6991-7001页

作者： Swamy, Krishna B.S. Cho, Chung-Yi Chiang, Sufeng Tsai, Zing Tsung-Yeh Tsai, Huai-Kuang Institute of Information Science National Yang-Ming University Taiwan Bioinformatics Program Taiwan International Graduate Program National Yang-Ming University Taiwan Institute of Biomedical Informatics National Yang-Ming University Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei 115 Taiwan

Transcription factors (TFs) regulate gene expression by binding to specific binding sites (TFBSs) in gene promoters. TFBS motifs may contain one or more variable positions. Although the prevailing assumption is that nucleotide variants at such positions are functionally equivalent, there is increasing evidence that such variants play a role in regulation of gene expression. In this article, we propose a method for studying the relationship between the expression of target genes and nucleotide variants in TFBS motifs at a genome-wide scale in Saccharomyces cerevisiae, especially the combinatorial effects of variants at two positions. Our analysis shows that nucleotide variations in more than one-third of variable positions and in 20% of dependent position pairs are highly correlated to gene expression. We define such positions as 'functional'. However, some positions are only functional as dependent pairs, but not individually. In addition, a significant proportion of the functional positions have been well conserved across all yeast-related species studied. We also find that some positions require the presence of co-occurring TFs, while others maintain their functionality in the absence of a co-occurring TF. Our analysis supports the importance of nucleotide variants at variable positions of TFBSs in gene regulation. © The Author(s) 2009. Published by Oxford University Press.

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Predicting helix-helix interactions from residue contacts in membrane proteins

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bioinformatics 2009年第8期25卷 996-1003页

作者： Lo, Allan Chiu, Yi-Yuan Rødland, Einar Andreas Lyu, Ping-Chiang Sung, Ting-Yi Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structural Biology Department of Life Sciences National Tsing Hua University Hsinchu Taiwan Bioinformatics Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Centre for Cancer Biomedicine University of Oslo NO-0027 Oslo Norway SAMBA Norwegian Computing Center Blindern NO-0314 Oslo P.O. Box 114 Norway

Motivation: Helix-helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction. Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins. © 2009 The Author(s).

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Redesign of high-affinity nonspecific nucleases with altered sequence preference

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Journal of the American Chemical Society 2009年第47期131卷 17345-17353页

作者： Wang, Yi-Ting Wright, Jon D. Doudeva, Lyudmila G. Jhang, Hua-Ci Lim, Carmay Yuan, Hanna S. Institute of Molecular Biology Academia Sinica Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan Institute of Chemical Biology and Molecular Biophysics Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structural Biology Department of Chemistry National Tsing Hua University HsinChu Taiwan Institute of Biochemistry and Molecular Biology National Taiwan University Taipei Taiwan

It is of crucial importance to elucidate the underlying principles that govern the binding affinity and selectivity between proteins and DNA. Here we use the nuclease domain of Colicin E7 (nColE7) as a model system to generate redesigned nucleases with improved DNA-binding affinities. ColE7 is a bacterial toxin, bearing a nonspecific endonuclease domain with a preference for hydrolyzing DNA phosphodiester bonds at the 3O-side after thymine and adenine;i.e., it prefers Thy and Ade at the -1 site. Using systematic computational screening, six nColE7 mutants were predicted to bind DNA with high affinity. Five of the redesigned single-point mutants were constructed and purified, and four mutants had a 3- to 5-fold higher DNA binding affinity than wild-type nColE7 as measured by fluorescence kinetic assays. Moreover, three of the designed mutants, D493N, D493Q, and D493R, digested DNA with an increased preference for guanine at +3 sites compared to the wild-type enzyme, as shown by DNA footprint assays. X-ray structure determination of the ColE7 mutant D493Q-DNA complex in conjunction with structural and free energy decomposition analyses provides a physical basis for the improved protein-DNA interactions: Replacing D493 at the protein-DNA interface with an amino acid residue that can maintain the native hydrogen bonds removes the unfavorable electrostatic repulsion between the negatively charged carboxylate and DNA phosphate groups. These results show that computational screening combined with biochemical, structural, and free energy analyses provide a useful means for generating redesigned nucleases with a higher DNA-binding affinity and altered sequence preferences in DNA cleavage. © 2009 American Chemical Society.

关键词： DNA

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The keyword-based and semantic-driven data matching approach for assisting structuralizing the textual clinical documents

The keyword-based and semantic-driven data matching approach...

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international Conference on Biomedical Engineering and Informatics

作者： Chen, Chi-Huang Ping, Xiao-Ou Wang, Zi-Jun Hsieh, Sheau-Ling Chen, Li-Chin Tseng, Yi-Ju Hsu, Ching-Wei Lai, Feipei Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronic and Bioinformatics National Taiwan University Taipei Taiwan EECS International Graduate Program Institute/IT Center National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781424464968

The clinical data stored in the health information system can be categorized as two types including structuralized data and non-structuralized ones. In the paper, a data extraction system is developed to assist data retrieval from the non-structuralized textual clinical documents such as radiology reports, pathology reports, etc. The system provides keyword-based and semantic-driven data matching methodology to extract the specific information from the textual clinical documents. The matching methodology provides the capabilities to recognize the selected keywords and the related semantics in the documents. Through the extraction verification interface, clinicians can extract and verify the matched information semi-automatically. The extracted data can be filled into predefined case-oriented templates. The structuralized data can be stored back into the clinical data warehouse for further analyzing. Moreover, the case-oriented templates can support collecting corresponding extracted data for various researches. ©2010 IEEE.

关键词： Semantics

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Enhanced expression of β3-galactosyltransferase 5 activity is sufficient to induce in vivo synthesis of extended type 1 chains on lactosylceramides of selected human colonic carcinoma cell lines

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Glycobiology 2009年第4期19卷 418-427页

作者： Lin, Chi-Hung Fan, Yao-Yun Chen, Yen-Ying Wang, Shih-Hsin Chen, Chun-I Yu, Lung-Chih Khoo, Kay-Hooi Chemical Biology and Molecular Biophysics Program Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing-Hua University Hsinchu 300 Taiwan Institute of Biological Chemistry Academia Sinica Nankang Taipei 115 Taiwan Taiwan and Institute of Biochemical Science National Taiwan University Taipei 106 Taiwan

In general, an elevated expression of β3-galactosyltrans-ferase (β3GalT) activity contributed by β3GalT5 correlates well with increased biosynthesis and expression of type 1 chain (Galβ1-3GlcNAcβ1-) derivatives such as Lewis A and sialyl Lewis A, which are mostly recognized as terminal epitopes and not further extended. Most known β3-N-acetylglucosaminyltransferases show a higher activity toward extending type 2 chain (Galβ1-4GlcNAcβ1-), and an over-expression of β3GalT5 could suppress the formation of the type 2 chain poly-N- acetyllactosaminoglycans. The potential of extending instead the predominant type 1 chain termini synthesized under such circumstances was, however, not investigated, partly due to technical difficulty in unambiguous identification of extended type 1 chains. Using an advanced mass spectrometry-based glycomic mapping and glycan sequencing approach, we show here that type 1 chains carried on the lacto-series glycosphingolipids of colonic carcinoma cells can be extended when the endogenous β3GalT activity relative to competing β4GalT activity, as defined against a common GlcNAcβ1-3Galβ1-4Glc acceptor, is sufficiently high, as found in Colo205 and SW1116, but not in DLD-1 cells. In support of this positive correlation, the lacto-series glycosphingolipids isolated from stably transfected DLD-1 clones over-expressing β3GalT5 were shown to comprise fucosylated dimeric type 1 chains, whereas a mock transfectant and the DLD-1 parent carried only fucosylated dimeric type 2 chains on their lactosylceramides. It suggests that while the natural expression of extended type 1 chain is likely to be determined by many contributing factors including the relative amounts of competing glycosyltransferases and the UDP-Gal level, the enhanced expression of β3GalT5 is sufficient to promote in vivo extension of type 1 chains by furnishing a significantly higher amount of type 1 chain precursors relative to competing type 2 chains. © The Author 2009. Published by Oxfo

关键词： β3-galactosyltransferase β3GalT5 Extended type 1 chain Glycomics Mass spectrometry

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Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model 1

Chemical-Induced Disease Detection Using Invariance-based Pa...

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1st international Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th international Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Warikoo, Neha Chang, Yung-Chun Hsu, Wen-Lian Institute of Biomedical Informatics National Yang-Ming University Taipei112 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei115 Taiwan Institute of Information Science Academia Sinica Taipei115 Taiwan Graduate Institute of Data Science Taipei Medical University Taipei106 Taiwan

ISBN: (纸本)9781948087070

In this work, we introduce a novel feature engineering approach named "algebraic invariance" to identify discriminative patterns for learning relation pair features for the chemical-disease relation (CDR) task of BioCreative V. Our method exploits the existing structural similarity of the key concepts of relation descriptions from the CDR corpus to generate robust linguistic patterns for SVM tree kernel-based learning. Preprocessing of the training data classifies the entity pairs as either related or unrelated to build instance types for both inter-sentential and intra-sentential scenarios. An invariant function is proposed to process and optimally cluster similar patterns for both positive and negative instances. The learning model for CDR pairs is based on the SVM tree kernel approach, which generates feature trees and vectors and is modeled on suitable invariance based patterns, bringing brevity, precision and context to the identifier features. Results demonstrate that our method outperformed compared approaches, achieved a high recall rate of 85.08%, and averaged an F1-score of 54.34% without the use of any additional knowledge bases. © 2017 AFNLP

关键词： Forestry

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st international Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th international Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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The largest genome-wide association study for breast cancer in taiwanese Han population

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Breast Cancer Research and Treatment 2024年第2期203卷 291-306页

作者： Hsu, Yu-Ching Chen, Hung-Lin Cheng, Chi-Fung Chattopadhyay, Amrita Chen, Pei-Shan Lin, Che-Chen Chiang, Hsiu-Yin Liu, Ting-Yuan Huang, Chi-Hao Kuo, Chin-Chi Chuang, Eric Y. Lu, Tzu-Pin Tsai, Fuu-Jen Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei Taiwan Big Data Center China Medical University Hospital Taichung Taiwan Center for Translational Genomic Research Department of Medical Research China Medical University Hospital Taichung Taiwan Million-Person Precision Medicine Initiative Department of Medical Research China Medical University Hospital Taichung Taiwan Division of Breast Surgery Department of Surgery China Medical University Hospital Taichung Taiwan Division of Nephrology Department of Internal Medicine China Medical University Hospital Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan

Purpose: Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of taiwanese population to fill in the knowledge gap. Methods: A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. Results: The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79–7.66) and 3.55 (2.13–6.14), respectively. Conclusion: In summary, we explored the association of genetic variants with breast cancer in the largest taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in taiwanese women. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Breast cancer Genome-wide association study Polygenic risk score taiwanese population

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Learning to predict expression efficacy of vectors in recombinant protein production

Learning to predict expression efficacy of vectors in recomb...

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8th Asia-Pacific bioinformatics Conference, APBC 2010

作者： Chan, Wen-Ching Liang, Po-Huang Shih, Yan-Ping Yang, Ueng-Cheng Lin, Wen-Chang Hsu, Chun-Nan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biological Chemistry Academia Sinica Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan

Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression. Results: In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production

关键词： Vectors

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Simultaneous identification for synergistic transcription factors and their transcription factor binding sites

Simultaneous identification for synergistic transcription fa...

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5th international Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

作者： Tsai, Zing Tsung-Yeh Huang, Grace Tzu-Wei Tsai, Huai-Kuang Institute of Information Science Academia Sinica Taipei 115 Taiwan Bioinformatics Program Taiwan International Graduate Program Taiwan Institute of Biomedical Informatics National Yang-Ming University Taiwan Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh Carnegie Mellon University Pittsburgh PA United States Research Center for Information Technology Innovation Taipei 115 Taiwan Computer Science and Engineering National Taiwan Ocean University Keelung 202 Taiwan

ISBN: (纸本)9780769543734

Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While traditional approaches can be used to discover binding motifs of a group of co-regulated genes, they often fail to accurately assign motifs to the corresponding TFs. Here, we consider two TFs together to infer their TFBSs and their synergistic relationship simultaneously. The basic idea is that if two TFs interact, their TFBSs, if distinct, would be conserved across species and coincided in the promoter regions of the genes they co-regulated. Applying our method to Saccharomyces cerevisiae chromatin immunoprecipitation data, we predicted 110 TF pairs with statistically significant motif assignments. A majority of these TF pairs have literature support to be synergistic, and the designated motifs to TFs match well with their known consensus. We further examined the synergism of predicted TF pairs in seven experimental conditions using ANOVA, and identified significant interactions. © 2011 IEEE.

关键词： Yeast

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