Huai-Kuang Tsai

An evolutionary algorithm for large traveling salesman problems

This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each others disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10,000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.

Solving traveling salesman problems by combining global and local search mechanisms

In this paper an evolutionary algorithm for the traveling salesman problem is proposed. The key idea is to enhance the ability of exploration and exploitation by incorporating global search with local search. A new local search, called the neighbor-join (NJ) operator, is proposed to improve the solution quality of the edge assembly crossover (EAX) considered as a global search mechanism in this paper. Our method is applied to 15 well-known traveling salesman problems with numbers of cities ranging from 101 to 3038 cities. The experimental results indicate that the neighbor-join operator is very competitive with related operators surveyed in this paper. Incorporating the NJ into the EAX significantly outperforms the method incorporating 2-opt into the EAX for some hard problems. For each test instance the average value of solution quality stays within 0.03% from the optimum. For the notorious hard problem att532, it is able to find the optimum solution 23 times in 30 independent runs.

Cysteine separations profiles on protein sequences infer disulfide connectivity

MOTIVATION Disulfide bonds play an important role in protein folding. A precise prediction of disulfide connectivity can strongly reduce the conformational search space and increase the accuracy in protein structure prediction. Conventional disulfide connectivity predictions use sequence information, and prediction accuracy is limited. Here, by using an alternative scheme with global information for disulfide connectivity prediction, higher performance is obtained with respect to other approaches. RESULT Cysteine separation profiles have been used to predict the disulfide connectivity of proteins. The separations among oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Through comparisons of their CSPs, the disulfide connectivity of a test protein is inferred from a non-redundant template set. For non-redundant proteins in SwissProt 39 (SP39) sharing less than 30% sequence identity, the prediction accuracy of a fourfold cross-validation is 49%. The prediction accuracy of disulfide connectivity for proteins in SwissProt 43 (SP43) is even higher (53%). The relationship between the similarity of CSPs and the prediction accuracy is also discussed. The method proposed in this work is relatively simple and can generate higher accuracies compared to conventional methods. It may be also combined with other algorithms for further improvements in protein structure prediction. AVAILABILITY The program and datasets are available from the authors upon request. CONTACT cykao@csie.ntu.edu.tw.

MYBS: a comprehensive web server for mining transcription factor binding sites in yeast

Correct interactions between transcription factors (TFs) and their binding sites (TFBSs) are of central importance to gene regulation. Recently developed chromatin-immunoprecipitation DNA chip (ChIP-chip) techniques and the phylogenetic footprinting method provide ways to identify TFBSs with high precision. In this study, we constructed a user-friendly interactive platform for dynamic binding site mapping using ChIP-chip data and phylogenetic footprinting as two filters. MYBS (Mining Yeast Binding Sites) is a comprehensive web server that integrates an array of both experimentally verified and predicted position weight matrixes (PWMs) from eleven databases, including 481 binding motif consensus sequences and 71 PWMs that correspond to 183 TFs. MYBS users can search within this platform for motif occurrences (possible binding sites) in the promoters of genes of interest via simple motif or gene queries in conjunction with the above two filters. In addition, MYBS enables users to visualize in parallel the potential regulators for a given set of genes, a feature useful for finding potential regulatory associations between TFs. MYBS also allows users to identify target gene sets of each TF pair, which could be used as a starting point for further explorations of TF combinatorial regulation. MYBS is available at http://cg1.iis.sinica.edu.tw/~mybs/.

Roles of Trans and Cis Variation in Yeast Intraspecies Evolution of Gene Expression

Both cis and trans mutations contribute to gene expression divergence within and between species. We used Saccharomyces cerevisiae as a model organism to estimate the relative contributions of cis and trans variations to the expression divergence between a laboratory (BY) and a wild (RM) strain of yeast. We examined whether genes regulated by a single transcription factor (TF; single input module, SIM genes) or genes regulated by multiple TFs (multiple input module, MIM genes) are more susceptible to trans variation. Because a SIM gene is regulated by a single immediate upstream TF, the chance for a change to occur in its trans-acting factors would, on average, be smaller than that for a MIM gene. We chose 232 genes that exhibited expression divergence between BY and RM to test this hypothesis. We examined the expression patterns of these genes in a BY-RM coculture system and in a BY-RM diploid hybrid. We found that trans variation is far more important than cis variation for expression divergence between the two strains. However, because in 75% of the genes studied, cis variation has significantly contributed to expression divergence, cis change also plays a significant role in intraspecific expression evolution. Interestingly, we found that the proportion of genes with diverged expression between BY and RM is larger for MIM genes than for SIM genes; in fact, the proportion tends to increase with the number of transcription factors that regulate the gene. Moreover, MIM genes are, on average, subject to stronger trans effects than SIM genes, though the difference between the two types of genes is not conspicuous.

GEM: A Gaussian evolutionary method for predicting protein side-chain conformations

We have developed an evolutionary approach to predicting protein side‐chain conformations. This approach, referred to as the Gaussian Evolutionary Method (GEM), combines both discrete and continuous global search mechanisms. The former helps speed up convergence by reducing the size of rotamer space, whereas the latter, integrating decreasing‐based Gaussian mutations and self‐adaptive Gaussian mutations, continuously adapts dihedrals to optimal conformations. We tested our approach on 38 proteins ranging in size from 46 to 325 residues and showed that the results were comparable to those using other methods. The average accuracies of our predictions were 80% for χ1, 66% for χ1 + 2, and 1.36 Å for the root mean square deviation of side‐chain positions. We found that if our scoring function was perfect, the prediction accuracy was also essentially perfect. However, perfect prediction could not be achieved if only a discrete search mechanism was applied. These results suggest that GEM is robust and can be used to examine the factors limiting the accuracy of protein side‐chain prediction methods. Furthermore, it can be used to systematically evaluate and thus improve scoring functions.

An evolutionary approach for gene expression patterns

This study presents an evolutionary algorithm, called a heterogeneous selection genetic algorithm (HeSGA), for analyzing the patterns of gene expression on microarray data. Microarray technologies have provided the means to monitor the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression data. The proposed method simultaneously solves gene clustering and gene-ordering problems by integrating global and local search mechanisms. Clustering and ordering information is used to identify functionally related genes and to infer genetic networks from immense microarray expression data. HeSGA was tested on eight test microarray datasets, ranging in size from 147 to 6221 genes. The experimental clustering and visual results indicate that HeSGA not only ordered genes smoothly but also grouped genes with similar gene expressions. Visualized results and a new scoring function that references predefined functional categories were employed to confirm the biological interpretations of results yielded using HeSGA and other methods. These results indicate that HeSGA has potential in analyzing gene expression patterns.

Some issues of designing genetic algorithms for traveling salesman problems

This paper demonstrates that a robust genetic algorithm for the traveling salesman problem (TSP) should preserve and add good edges efficiently, and at the same time, maintain the population diversity well. We analyzed the strengths and limitations of several well-known genetic operators for TSPs by the experiments. To evaluate these factors, we propose a new genetic algorithm integrating two genetic operators and a heterogeneous pairing selection. The former can preserve and add good edges efficiently and the later will be able to keep the population diversity. The proposed approach was evaluated on 15 well-known TSPs whose numbers of cities range from 101 to 13509. Experimental results indicated that our approach, somewhat slower, performs very robustly and is very competitive with other approaches in our best surveys. We believe that a genetic algorithm can be a stable approach for TSPs if its operators can preserve and add edges efficiently and it maintains population diversity.

Genome-wide analysis of enhancer RNA in gene regulation across 12 mouse tissues

Enhancers play a crucial role in gene regulation but the participation of enhancer transcripts (i.e. enhancer RNA, eRNAs) in regulatory systems remains unclear. We provide a computational analysis on eRNAs using genome-wide data across 12 mouse tissues. The expression of genes targeted by transcribing enhancer is positively correlated with eRNA expression and significantly higher than expression of genes targeted by non-transcribing enhancers. This result implies eRNA transcription indicates a state of enhancer that further increases gene expression. This state of enhancer is tissue-specific, as the same enhancer differentially transcribes eRNAs across tissues. Therefore, the presence of eRNAs describes a tissue-specific state of enhancer that is generally associated with higher expressed target genes, surmising as to whether eRNAs have gene activation potential. We further found a large number of eRNAs contain regions in which sequences and secondary structures are similar to microRNAs. Interestingly, an increasing number of recent studies hypothesize that microRNAs may switch from their general repressive role to an activating role when targeting promoter sequences. Collectively, our results provide speculation that eRNAs may be associated with the selective activation of enhancer target genes.

Discovering gapped binding sites of yeast transcription factors

A gapped transcription factor-binding site (TFBS) contains one or more highly degenerate positions. Discovering gapped motifs is difficult, because allowing highly degenerate positions in a motif greatly enlarges the search space and complicates the discovery process. Here, we propose a method for discovering TFBSs, especially gapped motifs. We use ChIP-chip data to judge the binding strength of a TF to a putative target promoter and use orthologous sequences from related species to judge the degree of evolutionary conservation of a predicted TFBS. Candidate motifs are constructed by growing compact motif blocks and by concatenating two candidate blocks, allowing 0–15 degenerate positions in between. The resultant patterns are statistically evaluated for their ability to distinguish between target and nontarget genes. Then, a position-based ranking procedure is proposed to enhance the signals of true motifs by collecting position concurrences. Empirical tests on 32 known yeast TFBSs show that the method is highly accurate in identifying gapped motifs, outperforming current methods, and it also works well on ungapped motifs. Predictions on additional 54 TFs successfully discover 11 gapped and 38 ungapped motifs supported by literature. Our method achieves high sensitivity and specificity for predicting experimentally verified TFBSs.