Publications by authors named "Wei-Sheng Wu"

56 Publications

YPIBP: A repository for phosphoinositide-binding proteins in yeast.

Comput Struct Biotechnol J 2021 24;19:3692-3707. Epub 2021 Jun 24.

Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan 701, Taiwan.

Phosphoinositides (PIs) are a family of eight lipids consisting of phosphatidylinositol (PtdIns) and its seven phosphorylated forms. PIs have important regulatory functions in the cell including lipid signaling, protein transport, and membrane trafficking. Yeast has been recognized as a eukaryotic model system to study lipid-protein interactions. Hundreds of yeast PI-binding proteins have been identified, but this research knowledge remains scattered. Besides, the complete PI-binding spectrum and potential PI-binding domains have not been interlinked. No comprehensive databases are available to support the lipid-protein interaction research on phosphoinositides. Here we constructed the first knowledgebase of Yeast Phosphoinositide-Binding Proteins (YPIBP), a repository consisting of 679 PI-binding proteins collected from high-throughput proteome-array and lipid-array studies, QuickGO, and a rigorous literature mining. The YPIBP also contains protein domain information in categories of lipid-binding domains, lipid-related domains and other domains. The YPIBP provides search and browse modes along with two enrichment analyses (PI-binding enrichment analysis and domain enrichment analysis). An interactive visualization is given to summarize the PI-domain-protein interactome. Finally, three case studies were given to demonstrate the utility of YPIBP. The YPIBP knowledgebase consolidates the present knowledge and provides new insights of the PI-binding proteins by bringing comprehensive and in-depth interaction network of the PI-binding proteins. YPIBP is available at http://cosbi7.ee.ncku.edu.tw/YPIBP/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.csbj.2021.06.035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261538PMC
June 2021

A tool for analyzing and visualizing ribo-seq data at the isoform level.

BMC Bioinformatics 2021 May 25;22(Suppl 10):271. Epub 2021 May 25.

Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, 701, Taiwan.

Background: Translational regulation is one important aspect of gene expression regulation. Dysregulation of translation results in abnormal cell physiology and leads to diseases. Ribosome profiling (RP), also called ribo-seq, is a powerful experimental technique to study translational regulation. It can capture a snapshot of translation by deep sequencing of ribosome-protected mRNA fragments. Many ribosome profiling data processing tools have been developed. However, almost all tools analyze ribosome profiling data at the gene level. Since different isoforms of a gene may produce different proteins with distinct biological functions, it is advantageous to analyze ribosome profiling data at the isoform level. To meet this need, previously we developed a pipeline to analyze 610 public human ribosome profiling data at the isoform level and constructed HRPDviewer database.

Results: To allow other researchers to use our pipeline as well, here we implement our pipeline as an easy-to-use software tool called RPiso. Compared to Ribomap (a widely used tool which provides isoform-level ribosome profiling analyses), our RPiso (1) estimates isoform abundance more accurately, (2) supports analyses on more species, and (3) provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms.

Conclusions: In this study, we developed RPiso software tool ( http://cosbi7.ee.ncku.edu.tw/RPiso/ ) to provide isoform-level ribosome profiling analyses. RPiso is very easy to install and execute. RPiso also provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. We believe that RPiso is a useful tool for researchers to analyze and visualize their own ribosome profiling data at the isoform level.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-021-04192-7DOI Listing
May 2021

Malignant transformation of oral potentially malignant disorders in Taiwan: An observational nationwide population database study.

Medicine (Baltimore) 2021 Mar;100(9):e24934

Department of Oral and Maxillofacial Surgery, Chi Mei Medical Center, Liouying.

Abstract: Oral cancer is one of the leading causes of cancer death, which are mostly preceded by oral potentially malignant disorders (OPMDs). Taiwanese government launched a free oral cancer screening program. The aim of this study was to analyze the malignant transformation rate of OPMDs.This study was based on national-wide oral screening databases. 3,362,232 people were enrolled. Patients clinically diagnosed with leukoplakia, erythroplakia, oral submucosal fibrosis (OSF), oral verrucous hyperplasia (OVH), and oral lichen planus (OLP), from 2010 to 2013, were identified. We followed up OPMD patients in cancer registry databases to analyze the malignant transformation rate.The malignant transformation rates from the highest to the lowest were: OVH > OSF > erythroplakia > OLP > leukoplakia. The malignant transformation rate was 24.55, 12.76, 9.75, 4.23, and 0.60 per 1000 person-years in the OVH, OSF, erythroplakia, leukoplakia, and comparison cohort. The hazard ratio was 8.19 times higher in the OPMD group compared with comparison cohort group, after age and habit adjustment. Female patients with OPMDs had a high risk of malignant transformation.Nationwide screening is very important for early diagnosis. OVH had the highest malignant transformation possibility. Female OPMD patients are a rare but have a relatively high malignant transformation rate.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/MD.0000000000024934DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939230PMC
March 2021

Elucidating the regulatory mechanism of Swi1 prion in global transcription and stress responses.

Sci Rep 2020 12 14;10(1):21838. Epub 2020 Dec 14.

Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, 60011, USA.

Transcriptional regulators are prevalent among identified prions in Saccharomyces cerevisiae, however, it is unclear how prions affect genome-wide transcription. We show here that the prion ([SWI]) and mutant (swi1∆) forms of Swi1, a subunit of the SWI/SNF chromatin-remodeling complex, confer dramatically distinct transcriptomic profiles. In [SWI] cells, genes encoding for 34 transcription factors (TFs) and 24 Swi1-interacting proteins can undergo transcriptional modifications. Several TFs show enhanced aggregation in [SWI] cells. Further analyses suggest that such alterations are key factors in specifying the transcriptomic signatures of [SWI] cells. Interestingly, swi1∆ and [SWI] impose distinct and oftentimes opposite effects on cellular functions. Translation-associated activities, in particular, are significantly reduced in swi1∆ cells. Although both swi1∆ and [SWI] cells are similarly sensitive to thermal, osmotic and drought stresses, harmful, neutral or beneficial effects were observed for a panel of tested chemical stressors. Further analyses suggest that the environmental stress response (ESR) is mechanistically different between swi1∆ and [SWI] cells-stress-inducible ESR (iESR) are repressed by [SWI] but unchanged by swi1∆ while stress-repressible ESR (rESR) are induced by [SWI] but repressed by swi1∆. Our work thus demonstrates primarily gain-of-function outcomes through transcriptomic modifications by [SWI] and highlights a prion-mediated regulation of transcription and phenotypes in yeast.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-020-77993-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736884PMC
December 2020

Systematic Analysis of Phosphatidylinositol-5-phosphate-Interacting Proteins Using Yeast Proteome Microarrays.

Anal Chem 2021 01 11;93(2):868-877. Epub 2020 Dec 11.

Department of Food Safety/Hygiene and Risk Management, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan.

We used yeast proteome microarrays (∼5800 purified proteins) to conduct a high-throughput and systematic screening of PI5P-interacting proteins with PI5P-tagged fluorescent liposomal nanovesicles. Lissamine rhodamine B-dipalmitoyl phosphatidylethanol was incorporated into the liposome bilayer to provide the nanovesicles with fluorescence without any encapsulants, which not only made the liposome fabrication much easier without the need for purification but also improved the chip-probing quality. A special chip assay was washed very gently without the traditional spin-dry step. Forty-five PI5P-interacting proteins were identified in triplicate with this special chip assay. Subsequently, we used flow cytometry to validate these interactions, and a total of 41 PI5P-interacting proteins were confirmed. Enrichment analysis revealed that these proteins have significant functions associated with ribosome biogenesis, rRNA processing, ribosome binding, GTP binding, and hydrolase activity. Their component enrichment is located in the nucleolus. The InterPro domain analysis indicated that PI5P-interacting proteins are enriched in the P-loop containing nucleoside triphosphate hydrolases domain (P-loop). Additionally, using the MEME program, we identified a consensus motif (IVGPAGTGKSTLF) that contains the Walker A sequence, a well-known nucleotide-binding motif. Furthermore, using a quartz crystal microbalance, both the consensus motif and Walker A motif showed strong affinities to PI5P-containing liposomes but not to PI5P-deprived liposomes or PI-containing liposomes. Additionally, the glycine (G6) and lysine (K7) residues of the Walker A motif (-GPAGTGKS-) were found to be critical to the PI5P-binding ability. This study not only identified an additional set of PI5P-interacting proteins but also revealed the strong PI5P-binding affinity ( = 1.81 × 10 M) of the Walker A motif beyond the motif's nucleotide-binding characteristic.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.analchem.0c03463DOI Listing
January 2021

YQFC: a web tool to compare quantitative biological features between two yeast gene lists.

Database (Oxford) 2020 01;2020

Center for Molecular Medicine and Genetics, Wayne State University, School of Medicine, 3127 Scott Hall, 540 East Canfield, Detroit, MI 48201, USA.

Nowadays high-throughput omics technologies are routinely used in biological research. From the omics data, researchers can easily get two gene lists (e.g. stress-induced genes vs. stress-repressed genes) related to their biological question. The next step would be to apply enrichment analysis tools to identify distinct functional/regulatory features between these two gene lists for further investigation. Although various enrichment analysis tools are already available, two challenges remain to be addressed. First, most existing tools are designed to analyze only one gene list, so they cannot directly compare two gene lists. Second, almost all existing tools focus on identifying the enriched qualitative features (e.g. gene ontology [GO] terms, pathways, domains, etc.). Many quantitative features (e.g. number of mRNA isoforms of a gene, mRNA half-life, protein half-life, transcriptional plasticity, translational efficiency, etc.) are available in the yeast, but no existing tools provide analyses on these quantitative features. To address these two challenges, here we present Yeast Quantitative Features Comparator (YQFC) that can directly compare various quantitative features between two yeast gene lists. In YQFC, we comprehensively collected and processed 85 quantitative features from the yeast literature and yeast databases. For each quantitative feature, YQFC provides three statistical tests (t-test, U test and KS test) to test whether this quantitative feature is statistically different between the two input yeast gene lists. The distinct quantitative features identified by YQFC may help researchers to study the underlying molecular mechanisms that differentiate the two input yeast gene lists. We believe that YQFC is a useful tool to expedite the biological research that uses high-throughput omics technologies.

Database Url: http://cosbi2.ee.ncku.edu.tw/YQFC/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/baaa076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805433PMC
January 2020

Systematical Analysis of the Protein Targets of Lactoferricin B and Histatin-5 Using Yeast Proteome Microarrays.

Int J Mol Sci 2019 Aug 28;20(17). Epub 2019 Aug 28.

Graduate Institute of Systems Biology and Bioinformatics, National Central University, Jhongli 32001, Taiwan.

Antimicrobial peptides (AMPs) have potential antifungal activities; however, their intracellular protein targets are poorly reported. Proteome microarray is an effective tool with high-throughput and rapid platform that systematically identifies the protein targets. In this study, we have used yeast proteome microarrays for systematical identification of the yeast protein targets of Lactoferricin B (Lfcin B) and Histatin-5. A total of 140 and 137 protein targets were identified from the triplicate yeast proteome microarray assays for Lfcin B and Histatin-5, respectively. The Gene Ontology (GO) enrichment analysis showed that Lfcin B targeted more enrichment categories than Histatin-5 did in all GO biological processes, molecular functions, and cellular components. This might be one of the reasons that Lfcin B has a lower minimum inhibitory concentration (MIC) than Histatin-5. Moreover, pairwise essential proteins that have lethal effects on yeast were analyzed through synthetic lethality. A total of 11 synthetic lethal pairs were identified within the protein targets of Lfcin B. However, only three synthetic lethal pairs were identified within the protein targets of Histatin-5. The higher number of synthetic lethal pairs identified within the protein targets of Lfcin B might also be the reason for Lfcin B to have lower MIC than Histatin-5. Furthermore, two synthetic lethal pairs were identified between the unique protein targets of Lfcin B and Histatin-5. Both the identified synthetic lethal pairs proteins are part of the Spt-Ada-Gcn5 acetyltransferase (SAGA) protein complex that regulates gene expression via histone modification. Identification of synthetic lethal pairs between Lfcin B and Histatin-5 and their involvement in the same protein complex indicated synergistic combination between Lfcin B and Histatin-5. This hypothesis was experimentally confirmed by growth inhibition assay.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijms20174218DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6747642PMC
August 2019

YHMI: a web tool to identify histone modifications and histone/chromatin regulators from a gene list in yeast.

Database (Oxford) 2018 01 1;2018. Epub 2018 Jan 1.

Department of Life Sciences, National Cheng Kung University, Tainan, Taiwan.

Post-translational modifications of histones (e.g. acetylation, methylation, phosphorylation and ubiquitination) play crucial roles in regulating gene expression by altering chromatin structures and creating docking sites for histone/chromatin regulators. However, the combination patterns of histone modifications, regulatory proteins and their corresponding target genes remain incompletely understood. Therefore, it is advantageous to have a tool for the enrichment/depletion analysis of histone modifications and histone/chromatin regulators from a gene list. Many ChIP-chip/ChIP-seq datasets of histone modifications and histone/chromatin regulators in yeast can be found in the literature. Knowing the needs and having the data motivate us to develop a web tool, called Yeast Histone Modifications Identifier (YHMI), which can identify the enriched/depleted histone modifications and the enriched histone/chromatin regulators from a list of yeast genes. Both tables and figures are provided to visualize the identification results. Finally, the high-quality and biological insight of the identification results are demonstrated by two case studies. We believe that YHMI is a valuable tool for yeast biologists to do epigenetics research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/bay116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6204766PMC
January 2018

piRTarBase: a database of piRNA targeting sites and their roles in gene regulation.

Nucleic Acids Res 2019 01;47(D1):D181-D187

Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, USA.

PIWI-interacting RNAs (piRNAs) are a class of small noncoding RNAs that guard animal genomes against mutation by silencing transposons. In addition, recent studies have reported that piRNAs silence various endogenous genes. Tens of thousands of distinct piRNAs made in animals do not pair well to transposons and currently the functions and targets of piRNAs are largely unexplored. piRTarBase provides a user-friendly interface to access both predicted and experimentally identified piRNA targeting sites in Caenorhabditis elegans. The user can input genes of interest and retrieve a list of piRNA targeting sites on the input genes. Alternatively, the user can input a piRNA and retrieve a list of its mRNA targets. Additionally, piRTarBase integrates published mRNA and small RNA sequencing data, which will help users identify biologically relevant targeting events. Importantly, our analyses suggest that the piRNA sites found by both predictive and experimental approaches are more likely to exhibit silencing effects on their targets than each method alone. Taken together, piRTarBase offers an integrative platform that will help users to identify functional piRNA target sites by evaluating various information. piRTarBase is freely available for academic use at http://cosbi6.ee.ncku.edu.tw/piRTarBase/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gky956DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323935PMC
January 2019

YARG: A repository for arsenic-related genes in yeast.

PLoS One 2018 26;13(7):e0201204. Epub 2018 Jul 26.

Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.

Arsenic is a toxic metalloid. Moderate levels of arsenic exposure from drinking water can cause various human health problems such as skin lesions, circulatory disorders and cancers. Thus, arsenic toxicity is a key focus area for environmental and toxicological investigations. Many arsenic-related genes in yeast have been identified by experimental strategies such as phenotypic screening and transcriptional profiling. These identified arsenic-related genes are valuable information for studying arsenic toxicity. However, the literature about these identified arsenic-related genes is widely dispersed and cannot be easily acquired by researchers. This prompts us to develop YARG (Yeast Arsenic-Related Genes) database, which comprehensively collects 3396 arsenic-related genes in the literature. For each arsenic-related gene, the number and types of experimental evidence (phenotypic screening and/or transcriptional profiling) are provided. Users can use both search and browse modes to query arsenic-related genes in YARG. We used two case studies to show that YARG can return biologically meaningful arsenic-related information for the query gene(s). We believe that YARG is a useful resource for arsenic toxicity research. YARG is available at http://cosbi4.ee.ncku.edu.tw/YARG/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201204PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062094PMC
January 2019

HRPDviewer: human ribosome profiling data viewer.

Database (Oxford) 2018 01;2018

Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, No.1, University Road, Tainan City, Taiwan.

Translational regulation plays an important role in protein synthesis. Dysregulation of translation causes abnormal cell physiology and leads to diseases such as inflammatory disorders and cancers. An emerging technique, called ribosome profiling (ribo-seq), was developed to capture a snapshot of translation. It is based on deep sequencing of ribosome-protected mRNA fragments. A lot of ribo-seq data have been generated in various studies, so databases are needed for depositing and visualizing the published ribo-seq data. Nowadays, GWIPS-viz, RPFdb and TranslatomeDB are the three largest databases developed for this purpose. However, two challenges remain to be addressed. First, GWIPS-viz and RPFdb databases align the published ribo-seq data to the genome. Since ribo-seq data aim to reveal the actively translated mRNA transcripts, there are advantages of aligning ribo-req data to the transcriptome over the genome. Second, TranslatomeDB does not provide any visualization and the other two databases only provide visualization of the ribo-seq data around a specific genomic location, while simultaneous visualization of the ribo-seq data on multiple mRNA transcripts produced from the same gene or different genes is desired. To address these two challenges, we developed the Human Ribosome Profiling Data viewer (HRPDviewer). HRPDviewer (i) contains 610 published human ribo-seq datasets from Gene Expression Omnibus, (ii) aligns the ribo-seq data to the transcriptome and (iii) provides visualization of the ribo-seq data on the selected mRNA transcripts. Using HRPDviewer, researchers can compare the ribosome binding patterns of multiple mRNA transcripts from the same gene or different genes to gain an accurate understanding of protein synthesis in human cells. We believe that HRPDviewer is a useful resource for researchers to study translational regulation in human.Database URL: http://cosbi4.ee.ncku.edu.tw/HRPDviewer/ or http://cosbi5.ee.ncku.edu.tw/HRPDviewer/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/bay074DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6041748PMC
January 2018

pirScan: a webserver to predict piRNA targeting sites and to avoid transgene silencing in C. elegans.

Nucleic Acids Res 2018 07;46(W1):W43-W48

Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, USA.

pirScan is a web-based tool for identifying C. elegans piRNA-targeting sites within a given mRNA or spliced DNA sequence. The purpose of our tool is to allow C. elegans researchers to predict piRNA targeting sites and to avoid the persistent germline silencing of transgenes that has rendered many constructs unusable. pirScan fulfills this purpose by first enumerating the predicted piRNA-targeting sites present in an input sequence. This prediction can be exported in a tabular or graphical format. Subsequently, pirScan suggests silent mutations that can be introduced to the input sequence that would allow the modified transgene to avoid piRNA targeting. The user can customize the piRNA targeting stringency and the silent mutations that he/she wants to introduce into the sequence. The modified sequences can be re-submitted to be certain that any previously present piRNA-targeting sites are now absent and no new piRNA-targeting sites are accidentally generated. This revised sequence can finally be downloaded as a text file and/or visualized in a graphical format. pirScan is freely available for academic use at http://cosbi4.ee.ncku.edu.tw/pirScan/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gky277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030828PMC
July 2018

The piRNA targeting rules and the resistance to piRNA silencing in endogenous genes.

Science 2018 02 1;359(6375):587-592. Epub 2018 Feb 1.

Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, USA.

Piwi-interacting RNAs (piRNAs) silence transposons to safeguard genome integrity in animals. However, the functions of the many piRNAs that do not map to transposons remain unknown. Here, we show that piRNA targeting in can tolerate a few mismatches but prefer perfect pairing at the seed region. The broad targeting capacity of piRNAs underlies the germline silencing of transgenes in Transgenes engineered to avoid piRNA recognition are stably expressed. Many endogenous germline-expressed genes also contain predicted piRNA targeting sites, and periodic An/Tn clusters (PATCs) are an intrinsic signal that provides resistance to piRNA silencing. Together, our study revealed the piRNA targeting rules and highlights a distinct strategy that uses to distinguish endogenous from foreign nucleic acids.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/science.aao2840DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5939965PMC
February 2018

YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules.

Database (Oxford) 2017 01;2017

Center for Molecular Medicine and Genetics, Wayne State University, School of Medicine, Detroit, MI 48201, USA.

Database Url: http://cosbi4.ee.ncku.edu.tw/YGMD/ , http://cosbi5.ee.ncku.edu.tw/YGMD/ or http://cosbi.ee.ncku.edu.tw/YGMD/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/bax085DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5691354PMC
January 2017

CSmiRTar: Condition-Specific microRNA targets database.

PLoS One 2017 13;12(7):e0181231. Epub 2017 Jul 13.

Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, Taiwan.

MicroRNAs (miRNAs) are functional RNA molecules which play important roles in the post-transcriptional regulation. miRNAs regulate their target genes by repressing translation or inducing degradation of the target genes' mRNAs. Many databases have been constructed to provide computationally predicted miRNA targets. However, they cannot provide the miRNA targets expressed in a specific tissue and related to a specific disease at the same time. Moreover, they cannot provide the common targets of multiple miRNAs and the common miRNAs of multiple genes at the same time. To solve these two problems, we construct a database called CSmiRTar (Condition-Specific miRNA Targets). CSmiRTar collects computationally predicted targets of 2588 human miRNAs and 1945 mouse miRNAs from four most widely used miRNA target prediction databases (miRDB, TargetScan, microRNA.org and DIANA-microT) and implements functional filters which allows users to search (i) a miRNA's targets expressed in a specific tissue or/and related to a specific disease, (ii) multiple miRNAs' common targets expressed in a specific tissue or/and related to a specific disease, (iii) a gene's miRNAs related to a specific disease, and (iv) multiple genes' common miRNAs related to a specific disease. We believe that CSmiRTar will be a useful database for biologists to study the molecular mechanisms of post-transcriptional regulation in human or mouse. CSmiRTar is available at http://cosbi.ee.ncku.edu.tw/CSmiRTar/ or http://cosbi4.ee.ncku.edu.tw/CSmiRTar/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181231PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5509330PMC
September 2017

Chronic treatment with cisplatin induces chemoresistance through the TIP60-mediated Fanconi anemia and homologous recombination repair pathways.

Sci Rep 2017 06 20;7(1):3879. Epub 2017 Jun 20.

Department of Life Sciences, National Cheng Kung University, No.1 University Road, Tainan, 701, Taiwan.

The Fanconi anemia pathway in coordination with homologous recombination is essential to repair interstrand crosslinks (ICLs) caused by cisplatin. TIP60 belongs to the MYST family of acetyltransferases and is involved in DNA repair and regulation of gene transcription. Although the physical interaction between the TIP60 and FANCD2 proteins has been identified that is critical for ICL repair, it is still elusive whether TIP60 regulates the expression of FA and HR genes. In this study, we found that the chemoresistant nasopharyngeal carcinoma cells, derived from chronic treatment of cisplatin, show elevated expression of TIP60. Furthermore, TIP60 binds to the promoters of FANCD2 and BRCA1 by using the chromatin immunoprecipitation experiments and promote the expression of FANCD2 and BRCA1. Importantly, the depletion of TIP60 significantly reduces sister chromatid exchange, a measurement of HR efficiency. The similar results were also shown in the FNACD2-, and BRCA1-deficient cells. Additionally, these TIP60-deficient cells encounter more frequent stalled forks, as well as more DNA double-strand breaks resulting from the collapse of stalled forks. Taken together, our results suggest that TIP60 promotes the expression of FA and HR genes that are important for ICL repair and the chemoresistant phenotype under chronic treatment with cisplatin.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-017-04223-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478611PMC
June 2017

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm.

BMC Bioinformatics 2017 Jan 13;18(1):31. Epub 2017 Jan 13.

Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.

Background: Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers' time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework.

Results: MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables.

Conclusions: MVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-016-1429-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237319PMC
January 2017

Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets.

PLoS One 2016 13;11(9):e0162931. Epub 2016 Sep 13.

Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.

In eukaryotic cells, transcriptional regulation of gene expression is usually achieved by cooperative transcription factors (TFs). Therefore, knowing cooperative TFs is the first step toward uncovering the molecular mechanisms of gene expression regulation. Many algorithms based on different rationales have been proposed to predict cooperative TF pairs in yeast. Although various types of rationales have been used in the existing algorithms, functional coherence is not yet used. This prompts us to develop a new algorithm based on functional coherence and similarity of the target gene sets to identify cooperative TF pairs in yeast. The proposed algorithm predicted 40 cooperative TF pairs. Among them, three (Pdc2-Thi2, Hot1-Msn1 and Leu3-Met28) are novel predictions, which have not been predicted by any existing algorithms. Strikingly, two (Pdc2-Thi2 and Hot1-Msn1) of the three novel predictions have been experimentally validated, demonstrating the power of the proposed algorithm. Moreover, we show that the predictions of the proposed algorithm are more biologically meaningful than the predictions of 17 existing algorithms under four evaluation indices. In summary, our study suggests that new algorithms based on novel rationales are worthy of developing for detecting previously unidentifiable cooperative TF pairs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162931PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021274PMC
July 2017

YCRD: Yeast Combinatorial Regulation Database.

PLoS One 2016 8;11(7):e0159213. Epub 2016 Jul 8.

Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.

In eukaryotes, the precise transcriptional control of gene expression is typically achieved through combinatorial regulation using cooperative transcription factors (TFs). Therefore, a database which provides regulatory associations between cooperative TFs and their target genes is helpful for biologists to study the molecular mechanisms of transcriptional regulation of gene expression. Because there is no such kind of databases in the public domain, this prompts us to construct a database, called Yeast Combinatorial Regulation Database (YCRD), which deposits 434,197 regulatory associations between 2535 cooperative TF pairs and 6243 genes. The comprehensive collection of more than 2500 cooperative TF pairs was retrieved from 17 existing algorithms in the literature. The target genes of a cooperative TF pair (e.g. TF1-TF2) are defined as the common target genes of TF1 and TF2, where a TF's experimentally validated target genes were downloaded from YEASTRACT database. In YCRD, users can (i) search the target genes of a cooperative TF pair of interest, (ii) search the cooperative TF pairs which regulate a gene of interest and (iii) identify important cooperative TF pairs which regulate a given set of genes. We believe that YCRD will be a valuable resource for yeast biologists to study combinatorial regulation of gene expression. YCRD is available at http://cosbi.ee.ncku.edu.tw/YCRD/ or http://cosbi2.ee.ncku.edu.tw/YCRD/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0159213PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938206PMC
August 2017

CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.

Database (Oxford) 2016 30;2016. Epub 2016 May 30.

Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan.

In eukaryotic cells, transcriptional regulation of gene expression is usually accomplished by cooperative Transcription Factors (TFs). Therefore, knowing cooperative TFs is helpful for uncovering the mechanisms of transcriptional regulation. In yeast, many cooperative TF pairs have been predicted by various algorithms in the literature. However, until now, there is still no database which collects the predicted yeast cooperative TFs from existing algorithms. This prompts us to construct Cooperative Transcription Factors Database (CoopTFD), which has a comprehensive collection of 2622 predicted cooperative TF pairs (PCTFPs) in yeast from 17 existing algorithms. For each PCTFP, our database also provides five types of validation information: (i) the algorithms which predict this PCTFP, (ii) the publications which experimentally show that this PCTFP has physical or genetic interactions, (iii) the publications which experimentally study the biological roles of both TFs of this PCTFP, (iv) the common Gene Ontology (GO) terms of this PCTFP and (v) the common target genes of this PCTFP. Based on the provided validation information, users can judge the biological plausibility of a PCTFP of interest. We believe that CoopTFD will be a valuable resource for yeast biologists to study the combinatorial regulation of gene expression controlled by cooperative TFs.Database URL: http://cosbi.ee.ncku.edu.tw/CoopTFD/ or http://cosbi2.ee.ncku.edu.tw/CoopTFD/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/baw092DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4885606PMC
January 2017

Investigation of microRNAs in mouse macrophage responses to lipopolysaccharide-stimulation by combining gene expression with microRNA-target information.

BMC Genomics 2015 9;16 Suppl 12:S13. Epub 2015 Dec 9.

Background: Toll-like receptors, which stimulated by pathogen-associated molecular patterns such as lipopolysaccharides (LPS), induces the releasing of many kinds of proinflammatory cytokines to activate subsequent immune responses. Plenty of studies have also indicated the importance of TLR-signalling on the avoidance of excessive inflammation, tissue repairing and the return to homeostasis after infection and tissue injury. The significance of TLR-signalling attracts many attentions on the regulatory mechanisms since several years ago. However, as newly discovered regulators, how and how many different microRNAs (miRNAs) regulate TLR-signalling pathway are still unclear.

Results: By integrating several microarray datasets and miRNA-target information datasets, we identified 431 miRNAs and 498 differentially expressed target genes in bone marrow-derived macrophages (BMDMs) with LPS-stimulation. Cooperative miRNA network were constructed by calculating targets overlap scores, and a sub-network finding algorithm was used to identify cooperative miRNA modules. Finally, 17 and 8 modules are identified in the cooperative miRNA networks composed of miRNAs up-regulate and down-regulate genes, respectively.

Conclusions: We used gene expression data of mouse macrophage stimulated by LPS and miRNA-target information to infer the regulatory mechanism of miRNAs on LPS-induced signalling pathway. Also, our results suggest that miRNAs can be important regulators of LPS-induced innate immune response in BMDMs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2164-16-S12-S13DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4682375PMC
October 2016

Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast.

BMC Genomics 2015 9;16 Suppl 12:S10. Epub 2015 Dec 9.

Background: Transcriptional regulation of gene expression in eukaryotes is usually accomplished by cooperative transcription factors (TFs). Computational identification of cooperative TF pairs has become a hot research topic and many algorithms have been proposed in the literature. A typical algorithm for predicting cooperative TF pairs has two steps. (Step 1) Define the targets of each TF under study. (Step 2) Design a measure for calculating the cooperativity of a TF pair based on the targets of these two TFs. While different algorithms have distinct sophisticated cooperativity measures, the targets of a TF are usually defined using ChIP-chip data. However, there is an inherent weakness in using ChIP-chip data to define the targets of a TF. ChIP-chip analysis can only identify the binding targets of a TF but it cannot distinguish the true regulatory from the binding but non-regulatory targets of a TF.

Results: This work is the first study which aims to investigate whether the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. For this purpose, we propose four simple algorithms, all of which consist of two steps. (Step 1) Define the targets of a TF using (i) ChIP-chip data in the first algorithm, (ii) TF binding data in the second algorithm, (iii) TF perturbation data in the third algorithm, and (iv) the intersection of TF binding and TF perturbation data in the fourth algorithm. Compared with the first three algorithms, the fourth algorithm uses a more biologically relevant way to define the targets of a TF. (Step 2) Measure the cooperativity of a TF pair by the statistical significance of the overlap of the targets of these two TFs using the hypergeometric test. By adopting four existing performance indices, we show that the fourth proposed algorithm (PA4) significantly out performs the other three proposed algorithms. This suggests that the computational identification of cooperative TF pairs is indeed improved when using a more biologically relevant way to define the targets of a TF. Strikingly, the prediction results of our simple PA4 are more biologically meaningful than those of the 12 existing sophisticated algorithms in the literature, all of which used ChIP-chip data to define the targets of a TF. This suggests that properly defining the targets of a TF may be more important than designing sophisticated cooperativity measures. In addition, our PA4 has the power to predict several experimentally validated cooperative TF pairs, which have not been successfully predicted by any existing algorithms in the literature.

Conclusions: This study shows that the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. The main contribution of this study is not to propose another new algorithm but to provide a new thinking for the research of computational identification of cooperative TF pairs. Researchers should put more effort on properly defining the targets of a TF (i.e. Step 1) rather than totally focus on designing sophisticated cooperativity measures (i.e. Step 2). The lists of TF target genes, the Matlab codes and the prediction results of the four proposed algorithms could be downloaded from our companion website http://cosbi3.ee.ncku.edu.tw/TFI/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2164-16-S12-S10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4682405PMC
October 2016

Functional redundancy of transcription factors explains why most binding targets of a transcription factor are not affected when the transcription factor is knocked out.

BMC Syst Biol 2015 9;9 Suppl 6:S2. Epub 2015 Dec 9.

Background: Biologists are puzzled by the extremely low percentage (3%) of the binding targets of a yeast transcription factor (TF) affected when the TF is knocked out, a phenomenon observed by comparing the TF binding dataset and TF knockout effect dataset.

Results: This study gives a plausible biological explanation of this counterintuitive phenomenon. Our analyses find that TFs with high functional redundancy show significantly lower percentage than do TFs with low functional redundancy. This suggests that functional redundancy may lead to one TF compensating for another, thus masking the TF knockout effect on the binding targets of the knocked-out TF. In addition, we show that seven classes of genes (lowly expressed genes, TATA box-less genes, genes containing a nucleosome-free region immediately upstream of the transcriptional start site (TSS), genes with low transcriptional plasticity, genes with a low number of bound TFs, genes with a low number of TFBSs, and genes with a short average distance of TFBSs to the TSS) are insensitive to the knockout of their promoter-binding TFs, providing clues for finding other biological explanations of the surprisingly low percentage of the binding targets of a TF affected when the TF is knocked out.

Conclusions: This study shows that one property of TFs (functional redundancy) and seven properties of genes (expression level, TATA box, nucleosome, transcriptional plasticity, the number of bound TFs, the number of TFBSs, and the average distance of TFBSs to the TSS) may be useful for explaining a counterintuitive phenomenon: most binding targets of a yeast transcription factor are not affected when the transcription factor is knocked out.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1752-0509-9-S6-S2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674858PMC
September 2016

YAGM: a web tool for mining associated genes in yeast based on diverse biological associations.

BMC Syst Biol 2015 9;9 Suppl 6:S1. Epub 2015 Dec 9.

Background: Investigating association between genes can be used in understanding the relations of genes in biological processes. STRING and GeneMANIA are two well-known web tools which can provide a list of associated genes of a query gene based on diverse biological associations such as co-expression, co-localization, co-citation and so on. However, the transcriptional regulation association and mutant phenotype association have not been used in these two web tools. Since the comprehensive transcription factor (TF)-gene binding data, TF-gene regulation data and mutant phenotype data are available in yeast, we developed a web tool called YAGM (Yeast Associated Genes Miner) which constructed the transcriptional regulation association, mutant phenotype association and five commonly used biological associations to mine a list of associated genes of a query yeast gene.

Description: In YAGM, we collected seven kinds of datasets including TF-gene binding (TFB) data, TF-gene regulation (TFR) data, mutant phenotype (MP) data, functional annotation (FA) data, physical interaction (PI) data, genetic interaction (GI) data, and literature evidence (LE) data. Then by using the hypergeometric test to calculate the association scores of all gene pairs in yeast, we constructed seven biological associations including two transcriptional regulation associations (TFB association and TFR association), MP association, FA association, PI association, GI association, and LE association. Moreover, the expression profile association from SPELL database was also included in YAGM. When using YAGM, users can input a query gene and choose any possible subsets of the eight biological associations, then a list of associated genes of the query gene will be returned based on the chosen biological associations.

Conclusions: In this study, we presented the YAGM which provides eight biological associations for mining associated genes of a query gene in yeast. Among the eight biological associations constructed in YAGM, three (TFB association, TFR association, and MP association) are novel ones. By comparing the query results of two well-known web tools (STRING and GeneMANIA), we found that YAGM can find out distinct associated genes of a query gene. That is, YAGM can provide alternative candidates of associated genes for biologists to do further experimental investigation. We believe that YAGM will be a useful web tool for yeast biologists. YAGM is available online at http://cosbi3.ee.ncku.edu.tw/yagm/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1752-0509-9-S6-S1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674844PMC
September 2016

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.

BMC Bioinformatics 2015 9;16 Suppl 18:S2. Epub 2015 Dec 9.

Background: Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface.

Results: The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses.

Conclusions: Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2105-16-S18-S2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4682397PMC
July 2016

Combined Interactions of Plant Homeodomain and Chromodomain Regulate NuA4 Activity at DNA Double-Strand Breaks.

Genetics 2016 Jan 12;202(1):77-92. Epub 2015 Nov 12.

Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan

DNA double-strand breaks (DSBs) represent one of the most threatening lesions to the integrity of genomes. In yeast Saccharomyces cerevisiae, NuA4, a histone acetylation complex, is recruited to DSBs, wherein it acetylates histones H2A and H4, presumably relaxing the chromatin and allowing access to repair proteins. Two subunits of NuA4, Yng2 and Eaf3, can interact in vitro with methylated H3K4 and H3K36 via their plant homeodomain (PHD) and chromodomain. However, the roles of the two domains and how they interact in a combinatorial fashion are still poorly characterized. In this study, we generated mutations in the PHD and chromodomain that disrupt their interaction with methylated H3K4 and H3K36. We demonstrate that the combined mutations in both the PHD and chromodomain impair the NuA4 recruitment, reduce H4K12 acetylation at the DSB site, and confer sensitivity to bleomycin that induces DSBs. In addition, the double mutant cells are defective in DSB repair as judged by Southern blot and exhibit prolonged activation of phospho-S129 of H2A. Cells harboring the H3K4R, H3K4R, K36R, or set1Δ set2Δ mutant that disrupts H3K4 and H3K36 methylation also show very similar phenotypes to the PHD and chromodomain double mutant. Our results suggest that multivalent interactions between the PHD, chromodomain, and methylated H3K4 and H3K36 act in a combinatorial manner to recruit NuA4 and regulate the NuA4 activity at the DSB site.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1534/genetics.115.184432DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701104PMC
January 2016

A Computational Method for Identifying Yeast Cell Cycle Transcription Factors.

Authors:
Wei-Sheng Wu

Methods Mol Biol 2016 ;1342:209-19

Department of Electrical Engineering, National Cheng Kung University, No. 1 Daxue Road, East District, Tainan City, 701, Taiwan,

The eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes. Here, we describe a computational method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor-binding site (TFBS), and cell cycle gene expression data. For each identified cell cycle TF, our method also assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. Moreover, our method can identify novel cell cycle-regulated genes as a by-product.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-4939-2957-3_12DOI Listing
May 2016

A regulatory similarity measure using the location information of transcription factor binding sites in Saccharomyces cerevisiae.

BMC Syst Biol 2014 12;8 Suppl 5:S9. Epub 2014 Dec 12.

Background: Defining a measure for regulatory similarity (RS) of two genes is an important step toward identifying co-regulated genes. To date, transcription factor binding sites (TFBSs) have been widely used to measure the RS of two genes because transcription factors (TFs) binding to TFBSs in promoters is the most crucial and well understood step in gene regulation. However, existing TFBS-based RS measures consider the relation of a TFBS to a gene as a Boolean (either 'presence' or 'absence') without utilizing the information of TFBS locations in promoters.

Results: Functional TFBSs of many TFs in yeast are known to have a strong positional preference to occur in a small region in the promoters. This biological knowledge prompts us to develop a novel RS measure that exploits the TFBS location information. The performances of different RS measures are evaluated by the fraction of gene pairs that are co-regulated (validated by literature evidence) by at least one common TF under different RS scores. The experimental results show that the proposed RS measure is the best co-regulation indicator among the six compared RS measures. In addition, the co-regulated genes identified by the proposed RS measure are also shown to be able to benefit three co-regulation-based applications: detecting gene co-function, gene co-expression and protein-protein interactions.

Conclusions: The proposed RS measure provides a good indicator for gene co-regulation. Besides, its good performance reveals the importance of the location information in TFBS-based RS measures.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1752-0509-8-S5-S9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305988PMC
September 2015

Identifying cooperative transcription factors in yeast using multiple data sources.

BMC Syst Biol 2014 12;8 Suppl 5:S2. Epub 2014 Dec 12.

Background: Transcriptional regulation of gene expression is usually accomplished by multiple interactive transcription factors (TFs). Therefore, it is crucial to understand the precise cooperative interactions among TFs. Various kinds of experimental data including ChIP-chip, TF binding site (TFBS), gene expression, TF knockout and protein-protein interaction data have been used to identify cooperative TF pairs in existing methods. The nucleosome occupancy data is not yet used for this research topic despite that several researches have revealed the association between nucleosomes and TFBSs.

Results: In this study, we developed a novel method to infer the cooperativity between two TFs by integrating the TF-gene documented regulation, TFBS and nucleosome occupancy data. TF-gene documented regulation and TFBS data were used to determine the target genes of a TF, and the genome-wide nucleosome occupancy data was used to assess the nucleosome occupancy on TFBSs. Our method identifies cooperative TF pairs based on two biologically plausible assumptions. If two TFs cooperate, then (i) they should have a significantly higher number of common target genes than random expectation and (ii) their binding sites (in the promoters of their common target genes) should tend to be co-depleted of nucleosomes in order to make these binding sites simultaneously accessible to TF binding. Each TF pair is given a cooperativity score by our method. The higher the score is, the more likely a TF pair has cooperativity. Finally, a list of 27 cooperative TF pairs has been predicted by our method. Among these 27 TF pairs, 19 pairs are also predicted by existing methods. The other 8 pairs are novel cooperative TF pairs predicted by our method. The biological relevance of these 8 novel cooperative TF pairs is justified by the existence of protein-protein interactions and co-annotation in the same MIPS functional categories. Moreover, we adopted three performance indices to compare our predictions with 11 existing methods' predictions. We show that our method performs better than these 11 existing methods in identifying cooperative TF pairs in yeast. Finally, the cooperative TF network constructed from the 27 predicted cooperative TF pairs shows that our method has the power to find cooperative TF pairs of different biological processes.

Conclusion: Our method is effective in identifying cooperative TF pairs in yeast. Many of our predictions are validated by the literature, and our method outperforms 11 existing methods. We believe that our study will help biologists to understand the mechanisms of transcriptional regulation in eukaryotic cells.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1752-0509-8-S5-S2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305981PMC
September 2015

The Yeast Nucleosome Atlas (YNA) database: an integrative gene mining platform for studying chromatin structure and its regulation in yeast.

BMC Genomics 2014 8;15 Suppl 9:S5. Epub 2014 Dec 8.

Background: Histone modification and remodeling play crucial roles in regulating gene transcription. These post-translational modifications of histones function in a combinatorial fashion and can be recognized by specific histone-binding proteins, thus regulating gene transcription. Therefore, understanding the combinatorial patterns of the histone code is vital to understanding the associated biological processes. However, most of the datasets regarding histone modification and chromatin regulation are scattered across various studies, and no comprehensive search and query tool has yet been made available to retrieve genes bearing specific histone modification patterns and regulatory proteins.

Description: For this reason, we developed the Yeast Nucleosome Atlas database, or the YNA database, which integrates the available experimental data on nucleosome occupancy, histone modifications, the binding occupancy of regulatory proteins, and gene expression data, and provides the genome-wide gene miner to retrieve genes with a specific combination of these chromatin-related datasets. Moreover, the biological significance analyzer, which analyzes the enrichments of histone modifications, binding occupancy, transcription rate, and functionality of the retrieved genes, was constructed to help researchers to gain insight into the correlation among chromatin regulation and transcription.

Conclusions: Compared to previously established genome browsing databases, YNA provides a powerful gene mining and retrieval interface, and is an investigation tool that can assist users to generate testable hypotheses for studying chromatin regulation during transcription. YNA is available online at http://cosbi3.ee.ncku.edu.tw/yna/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2164-15-S9-S5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4290617PMC
August 2015