20 results on '"Fu Jou Lai"'
Search Results
2. Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets.
- Author
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Wei-Sheng Wu and Fu-Jou Lai
- Subjects
Medicine ,Science - Abstract
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.
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- 2016
- Full Text
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3. YCRD: Yeast Combinatorial Regulation Database.
- Author
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Wei-Sheng Wu, Yen-Chen Hsieh, and Fu-Jou Lai
- Subjects
Medicine ,Science - Abstract
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/.
- Published
- 2016
- Full Text
- View/download PDF
4. Identifying functional transcription factor binding sites in yeast by considering their positional preference in the promoters.
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Fu-Jou Lai, Chia-Chun Chiu, Tzu-Hsien Yang, Yueh-Min Huang, and Wei-Sheng Wu
- Subjects
Medicine ,Science - Abstract
Transcription factor binding site (TFBS) identification plays an important role in deciphering gene regulatory codes. With comprehensive knowledge of TFBSs, one can understand molecular mechanisms of gene regulation. In the recent decades, various computational approaches have been proposed to predict TFBSs in the genome. The TFBS dataset of a TF generated by each algorithm is a ranked list of predicted TFBSs of that TF, where top ranked TFBSs are statistically significant ones. However, whether these statistically significant TFBSs are functional (i.e. biologically relevant) is still unknown. Here we develop a post-processor, called the functional propensity calculator (FPC), to assign a functional propensity to each TFBS in the existing computationally predicted TFBS datasets. It is known that functional TFBSs reveal strong positional preference towards the transcriptional start site (TSS). This motivates us to take TFBS position relative to the TSS as the key idea in building our FPC. Based on our calculated functional propensities, the TFBSs of a TF in the original TFBS dataset could be reordered, where top ranked TFBSs are now the ones with high functional propensities. To validate the biological significance of our results, we perform three published statistical tests to assess the enrichment of Gene Ontology (GO) terms, the enrichment of physical protein-protein interactions, and the tendency of being co-expressed. The top ranked TFBSs in our reordered TFBS dataset outperform the top ranked TFBSs in the original TFBS dataset, justifying the effectiveness of our post-processor in extracting functional TFBSs from the original TFBS dataset. More importantly, assigning functional propensities to putative TFBSs enables biologists to easily identify which TFBSs in the promoter of interest are likely to be biologically relevant and are good candidates to do further detailed experimental investigation. The FPC is implemented as a web tool at http://santiago.ee.ncku.edu.tw/FPC/.
- Published
- 2013
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5. PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.
- Author
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Fu-Jou Lai, Hong-Tsun Chang, and Wei-Sheng Wu
- Published
- 2015
- Full Text
- View/download PDF
6. A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.
- Author
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Fu-Jou Lai, Hong-Tsun Chang, Yueh-Min Huang, and Wei-Sheng Wu
- Published
- 2014
- Full Text
- View/download PDF
7. Identifying cooperative transcription factors in yeast using multiple data sources.
- Author
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Fu-Jou Lai, Mei-Huei Jhu, Chia-Chun Chiu, Yueh-Min Huang, and Wei-Sheng Wu
- Published
- 2014
- Full Text
- View/download PDF
8. YCRD: Yeast Combinatorial Regulation Database
- Author
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Yen Chen Hsieh, Fu Jou Lai, and Wei Sheng Wu
- Subjects
0301 basic medicine ,genetic structures ,Gene Identification and Analysis ,lcsh:Medicine ,Gene Expression ,Biologists ,computer.software_genre ,Database and Informatics Methods ,Gene expression ,Databases, Genetic ,Transcriptional regulation ,Cell Cycle and Cell Division ,Database Searching ,lcsh:Science ,Genetics ,Regulation of gene expression ,Multidisciplinary ,Database ,Saccharomyces cerevisiae Proteins ,Professions ,Cell Processes ,Algorithms ,Research Article ,Saccharomyces cerevisiae ,DNA transcription ,Biology ,Research and Analysis Methods ,03 medical and health sciences ,Gene Regulation ,Gene ,Transcription factor ,lcsh:R ,Organisms ,Fungi ,Biology and Life Sciences ,Cell Biology ,biology.organism_classification ,Yeast ,030104 developmental biology ,Genetic Interactions ,Gene Expression Regulation ,People and Places ,lcsh:Q ,Population Groupings ,computer ,Transcription Factors - Abstract
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/.
- Published
- 2016
9. PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast
- Author
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Wei Sheng Wu, Hong Tsun Chang, and Fu Jou Lai
- Subjects
Performance comparison ,Computer science ,Bar chart ,Saccharomyces cerevisiae ,computer.software_genre ,Web tool ,Biochemistry ,law.invention ,User-Computer Interface ,Structural Biology ,law ,Transcription factor ,Molecular Biology ,computer.programming_language ,Cooperative transcription factor pairs ,Regulation of gene expression ,Internet ,business.industry ,Applied Mathematics ,Research ,Benchmarking ,Construct (python library) ,Python (programming language) ,Computer Science Applications ,Algorithm ,Identification (information) ,Performance index ,The Internet ,Data mining ,User interface ,business ,computer ,Algorithms ,Transcription Factors - Abstract
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.
- Published
- 2015
10. Probability- and curve-based fractal reconstruction on 2D DEM terrain profile
- Author
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Fu-Jou Lai and Yueh-Min Huang
- Subjects
General Mathematics ,Applied Mathematics ,Fractal transform ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,General Physics and Astronomy ,Statistical and Nonlinear Physics ,computer.software_genre ,Data set ,Data point ,Fractal ,Fractal compression ,Data mining ,computer ,Algorithm ,Mathematics ,Data compression ,Image compression ,Interpolation - Abstract
Data compression and reconstruction has been playing important roles in information science and engineering. As part of them, image compression and reconstruction that mainly deal with image data set reduction for storage or transmission and data set restoration with least loss is still a topic deserved a great deal of works to focus on. In this paper we propose a new scheme in comparison with the well-known Improved Douglas–Peucker (IDP) method to extract characteristic or feature points of two-dimensional digital elevation model (2D DEM) terrain profile to compress data set. As for reconstruction in use of fractal interpolation, we propose a probability-based method to speed up the fractal interpolation execution to a rate as high as triple or even ninefold of the regular. In addition, a curve-based method is proposed in the study to determine the vertical scaling factor that much affects the generation of the interpolated data points to significantly improve the reconstruction performance. Finally, an evaluation is made to show the advantage of employing the proposed new method to extract characteristic points associated with our novel fractal interpolation scheme.
- Published
- 2009
11. Extraction of characteristic points and its fractal reconstruction for terrain profile data
- Author
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Fu-Jou Lai, Tzong-Yeang Lee, Ching-Ju Chen, and Yueh-Min Huang
- Subjects
Computer science ,General Mathematics ,Applied Mathematics ,Cumulative distribution function ,Elevation ,General Physics and Astronomy ,Statistical and Nonlinear Physics ,Terrain ,computer.software_genre ,Fractal ,Data point ,Data mining ,Digital elevation model ,computer ,Algorithm ,Interpolation ,Data reduction - Abstract
The main objective of this paper is to study reduction rate of 2D DEM (digital elevation model) data profile after data reduction by the Douglas–Peucker (DP) linear simplification method and by fractal interpolation to show original terrain reconstruction. In this paper, two-dimensional data of measured geographic profiles are taken as the study object, by using the DP method and the improved Douglas–Peucker (IDP) method to reduce data. Its aim is to retain spatial linear characteristics and variations, then take reduced data points as basic points and use the random fractal interpolation approach to add more data points up to the same as the original data points, in order to reconstruct the terrain, and compare the experimental data with the random point extraction method addressed in related literature. This paper uses tolerance calibration to generate different reduction rates and utilizes four types of evaluation factors, statistical measurement, image measurement, spectral analysis and elevation cumulative probability distribution graph, to make a quantitative analysis of profile variation. The study result indicates that real profile elevation data, manipulated with varied reduction approaches, then reconstructed by means of fractal interpolation can produce data points with a higher resolution than those originally observed, thereby the reconstructed profile gets more natural and real details.
- Published
- 2009
12. A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms
- Author
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Yueh-Min Huang, Hong Tsun Chang, Fu Jou Lai, and Wei Sheng Wu
- Subjects
genetic structures ,Computer science ,Research ,Applied Mathematics ,Systems biology ,Computational Biology ,Saccharomyces cerevisiae ,computer.software_genre ,Position weight matrix ,Performance index ,Data resources ,Computer Science Applications ,Identification (information) ,Structural Biology ,Modelling and Simulation ,Modeling and Simulation ,Relevance (information retrieval) ,Data mining ,Molecular Biology ,computer ,Algorithm ,Algorithms ,Transcription Factors - Abstract
Background Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation. Results We collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast. Conclusions In this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new algorithms developed in the future, thus expedite progress in this research field.
- Published
- 2014
13. CoopTFD: a repository for predicted yeast cooperative transcription factor pairs
- Author
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Fu Jou Lai, Wei Sheng Wu, Darby Tien Hao Chang, and Bor Wen Tu
- Subjects
0301 basic medicine ,Genetics ,Regulation of gene expression ,Saccharomyces cerevisiae Proteins ,Common gene ,Computational Biology ,Biology ,Ontology (information science) ,General Biochemistry, Genetics and Molecular Biology ,Yeast ,03 medical and health sciences ,030104 developmental biology ,Gene Expression Regulation, Fungal ,Databases, Genetic ,Transcriptional regulation ,Original Article ,Biological plausibility ,General Agricultural and Biological Sciences ,Transcription factor ,Gene ,Algorithms ,Transcription Factors ,Information Systems - Abstract
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/
- Published
- 2016
14. A sensor-assisted model for estimating the accuracy of learning retention in computer classroom
- Author
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Fu-Jou Lai, Jan-Pan Hwang, Ting-Ting Wu, and Yueh-Min Huang
- Subjects
business.industry ,Computer science ,Active learning (machine learning) ,Decision tree learning ,Decision tree ,Educational technology ,Multi-task learning ,Machine learning ,computer.software_genre ,Robot learning ,Identification (information) ,Unsupervised learning ,Artificial intelligence ,business ,computer - Abstract
Modern lifestyle is closely associated with information technology, and sensor technology is particularly used, especially in learning and education. This study proposed a sensor-assisted learning system using sensor technology, in order to determine the learning retention of learners in the learning process, and further provide assistance or feedback. The identification rule of this system is constructed based on decision tree algorithm ID3 (C4.5). The system determined the learning retention according to the learners' visual attention recognition, sitting position variability, and physiological signals analysis.
- Published
- 2011
15. Functional redundancy of transcription factors explains why most binding targets of a transcription factor are not affected when the transcription factor is knocked out
- Author
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Fu Jou Lai and Wei Sheng Wu
- Subjects
Genetics ,General transcription factor ,Research ,Applied Mathematics ,TATA box ,Response element ,Computational Biology ,Promoter ,Biology ,TATA Box ,Computer Science Applications ,Gene Knockout Techniques ,Gene Expression Regulation ,Sp3 transcription factor ,Structural Biology ,Modelling and Simulation ,Modeling and Simulation ,TAF2 ,E2F1 ,Transcription Initiation Site ,Molecular Biology ,Transcription factor ,Protein Binding ,Transcription Factors - Abstract
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.
- Published
- 2015
16. Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast.
- Author
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Wei-Sheng Wu and Fu-Jou Lai
- Subjects
- *
GENETIC transcription , *BERLEKAMP-Massey algorithm , *GENETIC polymorphisms , *GENETIC recombination , *HETEROGENEITY - Abstract
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 computationalidentification 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. [ABSTRACT FROM AUTHOR]
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- 2015
- Full Text
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17. Functional redundancy of transcription factors explains why most binding targets of a transcription factor are not affected when the transcription factor is knocked out.
- Author
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Wei-Sheng Wu and Fu-Jou Lai
- Subjects
- *
TRANSCRIPTION factors , *CHROMATIN , *YEAST fungi genetics , *GENE expression , *GENETIC transcription , *FUNGI - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
18. A sensor-assisted model for estimating the accuracy of learning retention in computer classroom.
- Author
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Jan-Pan Hwang, Ting-Ting Wu, Fu-Jou Lai, and Huang, Yueh-Min
- Published
- 2011
- Full Text
- View/download PDF
19. Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast
- Author
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Fu Jou Lai and Wei Sheng Wu
- Subjects
Genetics ,Saccharomyces cerevisiae Proteins ,Intersection (set theory) ,Research ,Computational Biology ,Cooperativity ,Saccharomyces cerevisiae ,Biology ,Measure (mathematics) ,Hypergeometric distribution ,Identification (information) ,Databases, Genetic ,DNA microarray ,Algorithm ,Transcription factor ,SIMPLE algorithm ,Algorithms ,Protein Binding ,Transcription Factors ,Biotechnology - Abstract
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/
- Full Text
- View/download PDF
20. Identifying cooperative transcription factors in yeast using multiple data sources
- Author
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Wei Sheng Wu, Chia Chun Chiu, Mei Huei Jhu, Fu Jou Lai, and Yueh-Min Huang
- Subjects
Saccharomyces cerevisiae Proteins ,Transcription, Genetic ,genetic structures ,Systems biology ,Molecular Sequence Data ,transcription factor binding site ,Information Storage and Retrieval ,Cooperativity ,Computational biology ,Saccharomyces cerevisiae ,Biology ,yeast ,Fungal Proteins ,Sequence Analysis, Protein ,Structural Biology ,Modelling and Simulation ,Protein Interaction Mapping ,Transcriptional regulation ,Nucleosome ,Amino Acid Sequence ,Databases, Protein ,Gene ,Molecular Biology ,Genetics ,Fungal protein ,Binding Sites ,Research ,Applied Mathematics ,nucleosome ,Promoter ,Computer Science Applications ,Nucleosomes ,DNA binding site ,Modeling and Simulation ,transcription factor cooperativity ,Algorithms ,Protein Binding ,Transcription Factors - Abstract
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.
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