Chien-Yu Chen

National Taiwan University, Taipei, Taipei, Taiwan

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Publications (56)127.62 Total impact

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    ABSTRACT: Agarwood is derived from Aquilaria trees, the trade of which has come under strict control with a listing in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora. Many secondary metabolites of agarwood are known to have medicinal value to humans, including compounds that have been shown to elicit sedative effects and exhibit anti-cancer properties. However, little is known about the genome, transcriptome, and the biosynthetic pathways responsible for producing such secondary metabolites in agarwood.
    BMC genomics. 07/2014; 15(1):578.
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    ABSTRACT: Salamanders are unique among vertebrates in their ability to completely regenerate amputated limbs through the mediation of blastema cells located at the stump ends. This regeneration is nerve-dependent because blastema formation and regeneration does not occur after limb denervation. To obtain the genomic information of blastema tissues, de novo transcriptomes from both blastema tissues and denervated stump ends of Ambystoma mexicanum (axolotls) 14 days post-amputation were sequenced and compared using Solexa DNA sequencing. The sequencing done for this study produced 40,688,892 reads that were assembled into 307,345 transcribed sequences. The N50 of transcribed sequence length was 562 bases. A similarity search with known proteins identified 39,200 different genes to be expressed during limb regeneration with a cut-off E-value exceeding 10-5. We annotated assembled sequences by using gene descriptions, gene ontology, and clusters of orthologous group terms. Targeted searches using these annotations showed that the majority of the genes were in the categories of essential metabolic pathways, transcription factors and conserved signaling pathways, and novel candidate genes for regenerative processes. We discovered and confirmed numerous sequences of the candidate genes by using quantitative polymerase chain reaction and in situ hybridization. The results of this study demonstrate that de novo transcriptome sequencing allows gene expression analysis in a species lacking genome information and provides the most comprehensive mRNA sequence resources for axolotls. The characterization of the axolotl transcriptome can help elucidate the molecular mechanisms underlying blastema formation during limb regeneration.
    BMC Genomics 07/2013; 14(1):434. · 4.40 Impact Factor
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    ABSTRACT: The amount of gene expression data of microarray has grown exponentially. To apply them for extensive studies, integrated analysis of cross-laboratory (cross-lab) data becomes a trend, and thus, choosing an appropriate feature selection method is an essential issue. This paper focuses on feature selection for Affymetrix (Affy) microarray studies across different labs. We investigate four feature selection methods: $(t)$-test, significance analysis of microarrays (SAM), rank products (RP), and random forest (RF). The four methods are applied to acute lymphoblastic leukemia, acute myeloid leukemia, breast cancer, and lung cancer Affy data which consist of three cross-lab data sets each. We utilize a rank-based normalization method to reduce the bias from cross-lab data sets. Training on one data set or two combined data sets to test the remaining data set(s) are both considered. Balanced accuracy is used for prediction evaluation. This study provides comprehensive comparisons of the four feature selection methods in cross-lab microarray analysis. Results show that SAM has the best classification performance. RF also gets high classification accuracy, but it is not as stable as SAM. The most naive method is $(t)$-test, but its performance is the worst among the four methods. In this study, we further discuss the influence from the number of training samples, the number of selected genes, and the issue of unbalanced data sets.
    IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 06/2013; 10(3):593-604. · 2.25 Impact Factor
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    Chih-Kang Lin, Chien-Yu Chen
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    ABSTRACT: Predicting binding sites of a transcription factor in the genome is an important, but challenging, issue in studying gene regulation. In the past decade, a large number of protein-DNA co-crystallized structures available in the Protein Data Bank have facilitated the understanding of interacting mechanisms between transcription factors and their binding sites. Recent studies have shown that both physics-based and knowledge-based potential functions can be applied to protein-DNA complex structures to deliver position weight matrices (PWMs) that are consistent with the experimental data. To further use the available structural models, the proposed Web server, PiDNA, aims at first constructing reliable PWMs by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. With PiDNA, the users can easily predict the relative preference of all the DNA sequences with limited mutations from the native sequence co-crystallized in the model in a single run. More predictions on sequences with unlimited mutations can be realized by additional requests or file uploading. Three types of information can be downloaded after prediction: (i) the ranked list of mutated sequences, (ii) the PWM constructed by the favourable mutated structures, and (iii) any mutated protein-DNA complex structure models specified by the user. This study first shows that the constructed PWMs are similar to the annotated PWMs collected from databases or literature. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein-binding microarrays. Finally, PiDNA is shown to be able to select the experimentally validated binding sites from 10 000 random sites with high accuracy. With PiDNA, the users can design biological experiments based on the predicted sequence specificity and/or request mutated structure models for further protein design. As well, it is expected that PiDNA can be incorporated with chromatin immunoprecipitation data to refine large-scale inference of in vivo protein-DNA interactions. PiDNA is available at: http://dna.bime.ntu.edu.tw/pidna.
    Nucleic Acids Research 05/2013; · 8.81 Impact Factor
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    ABSTRACT: By binding to short and highly conserved DNA sequences in genomes, DNA-binding proteins initiate, enhance or repress biological processes. Accurately identifying such binding sites, often represented by position weight matrices (PWMs), is an important step in understanding the control mechanisms of cells. When given coordinates of a DNA-binding domain (DBD) bound with DNA, a potential function can be used to estimate the change of binding affinity after base substitutions, where the changes can be summarized as a PWM. This technique provides an effective alternative when the chromatin immunoprecipitation data are unavailable for PWM inference. To facilitate the procedure of predicting PWMs based on protein-DNA complexes or even structures of the unbound state, the web server, DBD2BS, is presented in this study. The DBD2BS uses an atom-level knowledge-based potential function to predict PWMs characterizing the sequences to which the query DBD structure can bind. For unbound queries, a list of 1066 DBD-DNA complexes (including 1813 protein chains) is compiled for use as templates for synthesizing bound structures. The DBD2BS provides users with an easy-to-use interface for visualizing the PWMs predicted based on different templates and the spatial relationships of the query protein, the DBDs and the DNAs. The DBD2BS is the first attempt to predict PWMs of DBDs from unbound structures rather than from bound ones. This approach increases the number of existing protein structures that can be exploited when analyzing protein-DNA interactions. In a recent study, the authors showed that the kernel adopted by the DBD2BS can generate PWMs consistent with those obtained from the experimental data. The use of DBD2BS to predict PWMs can be incorporated with sequence-based methods to discover binding sites in genome-wide studies. Available at: http://dbd2bs.csie.ntu.edu.tw/, http://dbd2bs.csbb.ntu.edu.tw/, and http://dbd2bs.ee.ncku.edu.tw.
    Nucleic Acids Research 06/2012; 40(Web Server issue):W173-9. · 8.81 Impact Factor
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    ABSTRACT: Gene regulation involves complicated mechanisms such as cooperativity between a set of transcription factors (TFs). Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, this task remains challenging because the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This article aims at improving the accuracy of inferring TF-TF interactions by incorporating motif discovery as a fundamental step when detecting overlapping targets of TFs based on ChIP-chip data. The proposed method, simTFBS, outperforms three naïve methods that adopt fixed thresholds when inferring TF-TF interactions based on ChIP-chip data. In addition, simTFBS is compared with two advanced methods and demonstrates its advantages in predicting TF-TF interactions. By comparing simTFBS with predictions based on the set of available annotated yeast TF binding motifs, we demonstrate that the good performance of simTFBS is indeed coming from the additional motifs found by the proposed procedures. Supplementary data are available at Bioinformatics online.
    Bioinformatics 03/2012; 28(5):701-8. · 5.47 Impact Factor
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    ABSTRACT: Insecticide resistance has recently become a critical concern for control of many insect pest species. Genome sequencing and global quantization of gene expression through analysis of the transcriptome can provide useful information relevant to this challenging problem. The oriental fruit fly, Bactrocera dorsalis, is one of the world's most destructive agricultural pests, and recently it has been used as a target for studies of genetic mechanisms related to insecticide resistance. However, prior to this study, the molecular data available for this species was largely limited to genes identified through homology. To provide a broader pool of gene sequences of potential interest with regard to insecticide resistance, this study uses whole transcriptome analysis developed through de novo assembly of short reads generated by next-generation sequencing (NGS). The transcriptome of B. dorsalis was initially constructed using Illumina's Solexa sequencing technology. Qualified reads were assembled into contigs and potential splicing variants (isotigs). A total of 29,067 isotigs have putative homologues in the non-redundant (nr) protein database from NCBI, and 11,073 of these correspond to distinct D. melanogaster proteins in the RefSeq database. Approximately 5,546 isotigs contain coding sequences that are at least 80% complete and appear to represent B. dorsalis genes. We observed a strong correlation between the completeness of the assembled sequences and the expression intensity of the transcripts. The assembled sequences were also used to identify large numbers of genes potentially belonging to families related to insecticide resistance. A total of 90 P450-, 42 GST-and 37 COE-related genes, representing three major enzyme families involved in insecticide metabolism and resistance, were identified. In addition, 36 isotigs were discovered to contain target site sequences related to four classes of resistance genes. Identified sequence motifs were also analyzed to characterize putative polypeptide translational products and associate them with specific genes and protein functions.
    PLoS ONE 01/2012; 7(8):e40950. · 3.53 Impact Factor
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    ABSTRACT: DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. Accurate prediction of such target sequences, often represented by position weight matrices (PWMs), is an important step to understand many biological processes. Recent studies have shown that knowledge-based potential functions can be applied on protein-DNA co-crystallized structures to generate PWMs that are considerably consistent with experimental data. However, this success has not been extended to DNA-binding proteins lacking co-crystallized structures. This study aims at investigating the possibility of predicting the DNA sequences bound by DNA-binding proteins from the proteins' unbound structures (structures of the unbound state). Given an unbound query protein and a template complex, the proposed method first employs structure alignment to generate synthetic protein-DNA complexes for the query protein. Once a complex is available, an atomic-level knowledge-based potential function is employed to predict PWMs characterizing the sequences to which the query protein can bind. The evaluation of the proposed method is based on seven DNA-binding proteins, which have structures of both DNA-bound and unbound forms for prediction as well as annotated PWMs for validation. Since this work is the first attempt to predict target sequences of DNA-binding proteins from their unbound structures, three types of structural variations that presumably influence the prediction accuracy were examined and discussed. Based on the analyses conducted in this study, the conformational change of proteins upon binding DNA was shown to be the key factor. This study sheds light on the challenge of predicting the target DNA sequences of a protein lacking co-crystallized structures, which encourages more efforts on the structure alignment-based approaches in addition to docking- and homology modeling-based approaches for generating synthetic complexes.
    PLoS ONE 01/2012; 7(2):e30446. · 3.53 Impact Factor
  • 4th RECOMB Conference on Regulatory Genomics, Systems Biology, and DREAM Challenges; 10/2011
  • 4th RECOMB Conference on Regulatory Genomics, Systems Biology, and DREAM Challenges; 10/2011
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    ABSTRACT: A genome scan of Taiwanese schizophrenia families suggested linkage to chromosome 10q22.3. We aimed to find the candidate genes in this region. A total of 476 schizophrenia families were included. Hierarchical clustering method was used for clustering families to homogeneous subgroups according to their performances of sustained attention and executive function. Association analysis was performed using family-based association testing and TRANSMIT. Candidate associated regions were identified using the longest significance run method. The relative messenger RNA expression level was determined using real-time reverse transcriptase polymerase chain reaction. First, we genotyped 18 microsatellite markers between D10S1432 and D10S1239. The maximum nonparametric linkage score was 2.79 on D10S195. Through family clustering, we found the maximum nonparametric linkage score was 3.70 on D10S195 in the family cluster with deficits in attention and executive function. Second, we genotyped 79 single nucleotide polymorphisms between D10S1432 and D10S580 in 90 attention deficit and execution deficit families. Association analysis indicated significant transmission distortion for nine single nucleotide polymorphisms. Using the longest significance run method, we identified a 427-kilobase region as a significant candidate region, which encompasses nine genes. Third, we studied messenger RNA expression of these nine genes in Epstein-Barr virus-transformed lymphoblastic cells. In schizophrenic patients, there was significantly lower expression of ANXA7, PPP3CB, and DNAJC9 and significantly higher expression of ZMYND17. ANXA7, PPP3CB, DNAJC9, and ZMYND17 genes are potential candidate genes for schizophrenia, especially in patients with deficits in sustained attention and executive function. The responsible functional variants remained to be clarified.
    Biological psychiatry 07/2011; 70(1):51-8. · 8.93 Impact Factor
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    ABSTRACT: In acute myeloid leukemia (AML), the mixed lineage leukemia (MLL) gene may be rearranged to generate a partial tandem duplication (PTD), or fused to partner genes through a chromosomal translocation (tMLL). In this study, we first explored the differentially expressed genes between MLL-PTD and tMLL using gene expression profiling of our cohort (15 MLL-PTD and 10 tMLL) and one published data set. The top 250 probes were chosen from each set, resulting in 29 common probes (21 unique genes) to both sets. The selected genes include four HOXB genes, HOXB2, B3, B5, and B6. The expression values of these HOXB genes significantly differ between MLL-PTD and tMLL cases. Clustering and classification analyses were thoroughly conducted to support our gene selection results. Second, as MLL-PTD, FLT3-ITD, and NPM1 mutations are identified in AML with normal karyotypes, we briefly studied their impact on the HOXB genes. Another contribution of this study is to demonstrate that using public data from other studies enriches samples for analysis and yields more conclusive results.
    Cancer Genetics 05/2011; 204(5):252-9. · 1.92 Impact Factor
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    Chen-Ming Hsu, Chien-Yu Chen, Baw-Jhiune Liu
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    ABSTRACT: Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost. WildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm. The mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (http://biominer.csie.cyu.edu.tw/wildspan).
    Algorithms for Molecular Biology 03/2011; 6(1):6. · 1.61 Impact Factor
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    Chien-Chih Wang, Chien-Yu Chen
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    ABSTRACT: DNA-binding proteins perform their functions through specific or non-specific sequence recognition. Although many sequence- or structure-based approaches have been proposed to identify DNA-binding residues on proteins or protein-binding sites on DNA sequences with satisfied performance, it remains a challenging task to unveil the exact mechanism of protein-DNA interactions without crystal complex structures. Without information from complexes, the linkages between DNA-binding proteins and their binding sites on DNA are still missing. While it is still difficult to acquire co-crystallized structures in an efficient way, this study proposes a knowledge-based learning method to effectively predict DNA orientation and base locations around the protein's DNA-binding sites when given a protein structure. First, the functionally important residues of a query protein are predicted by a sequential pattern mining tool. After that, surface residues falling in the predicted functional regions are determined based on the given structure. These residues are then clustered based on their spatial coordinates and the resultant clusters are ranked by a proposed DNA-binding propensity function. Clusters with high DNA-binding propensities are treated as DNA-binding units (DBUs) and each DBU is analyzed by principal component analysis (PCA) to predict potential orientation of DNA grooves. More specifically, the proposed method is developed to predict the direction of the tangent line to the helix curve of the DNA groove where a DBU is going to bind. This paper proposes a knowledge-based learning procedure to determine the spatial location of the DNA groove with respect to the query protein structure by considering geometric propensity between protein side chains and DNA bases. The 11 test cases used in this study reveal that the location and orientation of the DNA groove around a selected DBU can be predicted with satisfied errors. This study presents a method to predict the location and orientation of DNA grooves with respect to the structure of a DNA-binding protein. The test cases shown in this study reveal the possibility of imaging protein-DNA binding conformation before co-crystallized structure can be determined. How the proposed method can be incorporated with existing protein-DNA docking tools to study protein-DNA interactions deserve further studies in the near future.
    Proteome Science 01/2011; 9 Suppl 1:S11. · 2.42 Impact Factor
  • Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on; 01/2010
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    ABSTRACT: Sequence motifs are important in the study of molecular biology. Motif discovery tools efficiently deliver many function related signatures of proteins and largely facilitate sequence annotation. As increasing numbers of motifs are detected experimentally or predicted computationally, characterizing the functional roles of motifs and identifying the potential synergetic relationships between them are important next steps. A good way to investigate novel motifs is to utilize the abundant 3D structures that have also been accumulated at an astounding rate in recent years. This article reports the development of the web service seeMotif, which provides users with an interactive interface for visualizing sequence motifs on protein structures from the Protein Data Bank (PDB). Researchers can quickly see the locations and conformation of multiple motifs among a number of related structures simultaneously. Considering the fact that PDB sequences are usually shorter than those in sequence databases and/or may have missing residues, seeMotif has two complementary approaches for selecting structures and mapping motifs to protein chains in structures. As more and more structures belonging to previously uncharacterized protein families become available, combining sequence and structure information gives good opportunities to facilitate understanding of protein functions in large-scale genome projects. Available at: http://seemotif.csie.ntu.edu.tw,http://seemotif.ee.ncku.edu.tw or http://seemotif.csbb.ntu.edu.tw.
    Nucleic Acids Research 07/2009; 37(Web Server issue):W552-8. · 8.81 Impact Factor
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    ABSTRACT: A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor alpha (ERalpha) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers. the implementation of CID in R codes can be freely downloaded from (http://homepage.ntu.edu.tw/~lyliu/BC/).
    BMC Bioinformatics 04/2009; 10:85. · 3.02 Impact Factor
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    ABSTRACT: Past experiments of the popular Affymetrix (Affy) microarrays have accumulated a huge amount of public data sets. To apply them for more wide studies, the comparability across generations and experimental environments is an important research topic. This paper particularly investigates the issue of cross-generation/laboratory predictions. That is, whether models built upon data of one generation (laboratory) can differentiate data of another. We consider eight public sets of three cancers. They are from different laboratories and are across various generations of Affy human microarrays. Each cancer has certain subtypes, and we investigate if a model trained from one set correctly differentiates another. We propose a simple rank-based approach to make data from different sources more comparable. Results show that it leads to higher prediction accuracy than using expression values. We further investigate normalization issues in preparing training/testing data. In addition, we discuss some pitfalls in evaluating cross-generation/laboratory predictions. To use data from various sources one must be cautious on some important but easily neglected steps.
    Journal of Biomedical Informatics 09/2008; 41(4):570-9. · 2.13 Impact Factor
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    ABSTRACT: Large-scale automatic annotation of protein sequences remains challenging in postgenomics era. E1DS is designed for annotating enzyme sequences based on a repository of 1D signatures. The employed sequence signatures are derived using a novel pattern mining approach that discovers long motifs consisted of several sequential blocks (conserved segments). Each of the sequential blocks is considerably conserved among the protein members of an EC group. Moreover, a signature includes at least three sequential blocks that are concurrently conserved, i.e. frequently observed together in sequences. In other words, a sequence signature is consisted of residues from multiple regions of the protein sequence, which echoes the observation that an enzyme catalytic site is usually constituted of residues that are largely separated in the sequence. E1DS currently contains 5421 sequence signatures that in total cover 932 4-digital EC numbers. E1DS is evaluated based on a collection of enzymes with catalytic sites annotated in Catalytic Site Atlas. When compared to the famous pattern database PROSITE, predictions based on E1DS signatures are considered more sensitive in identifying catalytic sites and the involved residues. E1DS is available at http://e1ds.ee.ncku.edu.tw/ and a mirror site can be found at http://e1ds.csbb.ntu.edu.tw/.
    Nucleic Acids Research 07/2008; 36(Web Server issue):W291-6. · 8.81 Impact Factor
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    Chen-Ming Hsu, Chien-Yu Chen, Baw-Jhiune Liu
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    ABSTRACT: This paper presents a web service named MAGIICPRO,which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-theart data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper,we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein-protein interactions directly from sequences. The web service is availableat http://biominer.bime.ntu.edu.tw/magiicproand a mirror site at http://biominer.cse.yzu.edu.tw/magiicpro.
    Nucleic Acids Research 04/2008; 36(4):1400-6. · 8.81 Impact Factor

Publication Stats

398 Citations
127.62 Total Impact Points

Institutions

  • 2003–2013
    • National Taiwan University
      • • Department of Computer Science and Information Engineering
      • • Department of Bio-Industrial Mechatronics Engineering
      Taipei, Taipei, Taiwan
  • 2009
    • National Cheng Kung University
      • Department of Electrical Engineering
      Tainan, Taiwan, Taiwan
  • 2008
    • Yuan Ze University
      • Department of Computer Science and Engineering
      Taichung, Taiwan, Taiwan