Computational Biology and Chemistry 50 (2014) 1–2
Contents lists available at ScienceDirect
Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/compbiolchem
Editorial
Advances in bioinformatics: Selected papers from APBC 2014
In this special issue, ten papers were selected from the papers presented at the Twelfth Asia Pacific Bioinformatics Conference (APBC2014). These ten papers cover diverse topics, including epigenetics, GWAS, essential genes prediction, lncRNAs, drug-induced adverse reaction, piRNA clustering, population genetics, plant evolution, and DNA computing. In “Deciphering Histone Code of Transcriptional Regulation in Malaria Parasites by Large-scale Data Mining,” Haifen Chen, Stefano Lonardi and Jie Zheng proposed a framework based on association rule mining to discover the potential regulatory relations between histone modifications and gene expression in Plasmodium falciparum. The results of this study reveal mechanisms for histone modifications to regulate transcription in large-scale. And in “Identification and characterization of lysine methylation sites on histones and non-histone proteins,” Tzong-Yi Lee, Cheng-Tzung Lu, Tzu-Hsiu Cheng and Tzu-Hao Chang constructed two support vector machine (SVM) models by using lysine-methylated data from histones and non-histone proteins for predictions of lysinemethylated sites. Results showed that their models significantly improve the predictive accuracy of histones compared to previous approaches. In “Fast Detection of High-order Epistatic Interactions in Genome-wide Association Studies Using Information Theoretic Measure,” Sangseob Leem, Hyun-Hwan Jeong, Jungseob Lee, Kyubum Wee and Kyung-Ah Sohn introduced a fast algorithm for detection of high order epistatic interactions in GWAS. It first applies the k-means clustering algorithm to the set of all SNPs, then selects candidates from each cluster. These candidates are checked to find the causative SNPs of k-locus interactions. Mutual information is used the measure of association between genotypes and phenotypes. The proposed algorithm can detect high-order epistatic interactions in GWAS in a matter of hours on a PC. In “Predicting Essential Genes for Identifying Potential Drug Targets In Aspergillus fumigatus,” Yao Lu, Jingyuan Deng, Hui Lu and Long Lu employed a machine learning approach to Eukaryotic fungal species to detect essential genes. A compendium of essential genes is predicted in Aspergillus fumigatus by transferring known essential gene annotations from another filamentous fungus N. crassa. The proposed method was validated by comparing with results predicted by homology mapping, and conducting conditional expressed alleles, and by wet lab knockout experiments. In “lncRNAMap: a Map of Putative Regulatory Functions in the Long Non-coding Transcriptome,” Wen-Ling Chan, HsienDa Huang and Jan-Gowth Chang constructed an integrated and
http://dx.doi.org/10.1016/j.compbiolchem.2014.05.001 1476-9271/© 2014 Published by Elsevier Ltd.
comprehensive database lncRNAMap for exploring the putative regulatory functions of human lncRNAs with two mechanisms of regulation, by encoding siRNAs and by acting as miRNA decoys. lncRNAMap integrates small RNAs (sRNAs) that are supported by publicly available deep sequencing data from various sRNA libraries and constructs lncRNA-derived siRNA-target interactions. In addition, lncRNAMap demonstrates that lncRNAs can act as targets for miRNAs that would otherwise regulate protein-coding genes. In “Pharmacoepidemiological Characterization of Drug-induced Adverse Reaction Clusters towards Understanding of Their mechanisms,” Sayaka Mizutani, Yousuke Noro, Masaaki Kotera and Susumu Goto applied a biclustering approach to catalogue the relationship between drugs and adverse reactions from a large FDA Adverse Event Reporting System (FAERS) data set, and demonstrated a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients’ conditions. In “piClust: A Density Based piRNA Clustering Algorithm,” Inuk Jung and Sun Kim used a density based clustering approach without assumption of any parametric Distribution, which is robust to noise in the data. In experiments with piRNA data from human, mouse, rat and chicken, piClust is able to detect piRNA clusters from total small RNA-seq data from germ cell lines. piClust outperforms proTRAC in terms of sensitivity and running time (up to 200 folds). In “Effect of Sampling on the Extent and Accuracy of the Inferred Genetic History of Recombining Genome,” Daniel E. Platt, Filippo Utro and Laxmi Parida tried to understand the extent, characteristics (in terms of recent versus ancient genetic events) and reliability of what was resolvable within field samples drawn from modern populations. They concluded that a recombinant phylogenetic reconstruction is necessary to identify which markers are most likely to discriminate ancient events, and to discriminate between populations with lower risk of false positives. Furthermore, on a broader note, this study provides a general methodology for a critical assessment of the inferred common genetic history of populations. In “Practical Halving; the Nelumbo Nucifera Evidence on Early Eudicot Evolution,” Chunfang Zheng and David Sankoff presented a stepwise optimal genome halving algorithm designed for large eukaryote genomes with largely single-copy genes, taking advantage of a signature pattern of paralog distribution in ancient polyploids. They applied the algorithm to the genome of Nelumbo nucifera, the sacred lotus, which is the descendant of a duplicated
2
Editorial / Computational Biology and Chemistry 50 (2014) 1–2
basal eudicot genome. They showed that the duplication of the ancestor of lotus and the triplication of the ancestor of grape were not closely preceded by any additional such event before the divergence of their two lineages. In “Parallel Molecular Computation of Modular-multiplication Using Self-assembly of DNA Tiles,” Yongnan Li, Limin Xiao and Li Ruan poposed to use the parallel tile assembly process for computing the modular-square, modular-multiplication with two same inputs, over finite field GF(2n ). Rigorous theoretical proofs and specific computing instance are given. Time complexity of the proposed system is 3n − 1 and space complexity is 2n2 . Shuigeng Zhou is now a professor of School of Computer Science, Fudan University, Shanghai, China. He received his Bachelor degree from Huazhong University of Science and Technology (HUST) in 1988, his Master degree from University of Electronic Science and Technology of China (UESTC) in 1991, and his PhD of Computer Science from Fudan University in 2000. He served in Shanghai Academy of Spaceflight Technology from 1991 to 1997, as an engineer and a senior engineer (since August 1995) respectively. He was a post-doctoral researcher in State Key Lab of Software Engineering, Wuhan University from 2000 to 2002. His research interests include data management, data mining, machine learning, and bioinformatics. He has published more than 200 papers in domestic and international journals (including IEEE TKDE, IEEE TPDS, IEEE TCBB, Bioinformatics, Plos One, BMC Bioinformatics, DKE, PRE, EPL and EPJB etc.) and conferences (including SIGMOD, VLDB, ICDE, ICDCS, SIGKDD, SIGIR, SODA, IJCAI, RECOMB etc.). Currently he is a member of IEEE, ACM and IEICE. More information about him can be found at http://admis.fudan.edu.cn/∼sgzhou.
Yi-Ping Phoebe Chen received the BInfTech degree with first class honours and the PhD degree in computer science (bioinformatics) from the University of Queensland. She is a professor and chair and director of research in the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. Prof. Chen is the chief investigator of the ARC Centre of Excellence in Bioinformatics. She is currently working on knowledge discovery technologies and is especially interested in their application to genomics and biomedical science. Her research focus is to find the best solutions for mining, integrating, and analyzing complex data structure and functions for scientific and biomedical applications. She has been working in the area of bioinformatics, health informatics, multimedia databases, query system, and systems biology, has coauthored more than 200 research papers, with many published in top journals and conferences. She is steering committee chair of the Asia-Pacific Bioinformatics Conference (founder) and the International Conference on Multimedia Modelling. She has been on the program committees of more than 100 international conferences, including top ranking conferences such as ICDE, ICPR, ISMB, CIKM, etc. More information about her can be found at http://homepage.cs.latrobe.edu.au/ypchen/index.htm.
Shuigeng Zhou Yi-Ping Phoebe Chen
Contents lists available at ScienceDirect
Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/compbiolchem
Editorial
Advances in bioinformatics: Selected papers from APBC 2014
In this special issue, ten papers were selected from the papers presented at the Twelfth Asia Pacific Bioinformatics Conference (APBC2014). These ten papers cover diverse topics, including epigenetics, GWAS, essential genes prediction, lncRNAs, drug-induced adverse reaction, piRNA clustering, population genetics, plant evolution, and DNA computing. In “Deciphering Histone Code of Transcriptional Regulation in Malaria Parasites by Large-scale Data Mining,” Haifen Chen, Stefano Lonardi and Jie Zheng proposed a framework based on association rule mining to discover the potential regulatory relations between histone modifications and gene expression in Plasmodium falciparum. The results of this study reveal mechanisms for histone modifications to regulate transcription in large-scale. And in “Identification and characterization of lysine methylation sites on histones and non-histone proteins,” Tzong-Yi Lee, Cheng-Tzung Lu, Tzu-Hsiu Cheng and Tzu-Hao Chang constructed two support vector machine (SVM) models by using lysine-methylated data from histones and non-histone proteins for predictions of lysinemethylated sites. Results showed that their models significantly improve the predictive accuracy of histones compared to previous approaches. In “Fast Detection of High-order Epistatic Interactions in Genome-wide Association Studies Using Information Theoretic Measure,” Sangseob Leem, Hyun-Hwan Jeong, Jungseob Lee, Kyubum Wee and Kyung-Ah Sohn introduced a fast algorithm for detection of high order epistatic interactions in GWAS. It first applies the k-means clustering algorithm to the set of all SNPs, then selects candidates from each cluster. These candidates are checked to find the causative SNPs of k-locus interactions. Mutual information is used the measure of association between genotypes and phenotypes. The proposed algorithm can detect high-order epistatic interactions in GWAS in a matter of hours on a PC. In “Predicting Essential Genes for Identifying Potential Drug Targets In Aspergillus fumigatus,” Yao Lu, Jingyuan Deng, Hui Lu and Long Lu employed a machine learning approach to Eukaryotic fungal species to detect essential genes. A compendium of essential genes is predicted in Aspergillus fumigatus by transferring known essential gene annotations from another filamentous fungus N. crassa. The proposed method was validated by comparing with results predicted by homology mapping, and conducting conditional expressed alleles, and by wet lab knockout experiments. In “lncRNAMap: a Map of Putative Regulatory Functions in the Long Non-coding Transcriptome,” Wen-Ling Chan, HsienDa Huang and Jan-Gowth Chang constructed an integrated and
http://dx.doi.org/10.1016/j.compbiolchem.2014.05.001 1476-9271/© 2014 Published by Elsevier Ltd.
comprehensive database lncRNAMap for exploring the putative regulatory functions of human lncRNAs with two mechanisms of regulation, by encoding siRNAs and by acting as miRNA decoys. lncRNAMap integrates small RNAs (sRNAs) that are supported by publicly available deep sequencing data from various sRNA libraries and constructs lncRNA-derived siRNA-target interactions. In addition, lncRNAMap demonstrates that lncRNAs can act as targets for miRNAs that would otherwise regulate protein-coding genes. In “Pharmacoepidemiological Characterization of Drug-induced Adverse Reaction Clusters towards Understanding of Their mechanisms,” Sayaka Mizutani, Yousuke Noro, Masaaki Kotera and Susumu Goto applied a biclustering approach to catalogue the relationship between drugs and adverse reactions from a large FDA Adverse Event Reporting System (FAERS) data set, and demonstrated a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients’ conditions. In “piClust: A Density Based piRNA Clustering Algorithm,” Inuk Jung and Sun Kim used a density based clustering approach without assumption of any parametric Distribution, which is robust to noise in the data. In experiments with piRNA data from human, mouse, rat and chicken, piClust is able to detect piRNA clusters from total small RNA-seq data from germ cell lines. piClust outperforms proTRAC in terms of sensitivity and running time (up to 200 folds). In “Effect of Sampling on the Extent and Accuracy of the Inferred Genetic History of Recombining Genome,” Daniel E. Platt, Filippo Utro and Laxmi Parida tried to understand the extent, characteristics (in terms of recent versus ancient genetic events) and reliability of what was resolvable within field samples drawn from modern populations. They concluded that a recombinant phylogenetic reconstruction is necessary to identify which markers are most likely to discriminate ancient events, and to discriminate between populations with lower risk of false positives. Furthermore, on a broader note, this study provides a general methodology for a critical assessment of the inferred common genetic history of populations. In “Practical Halving; the Nelumbo Nucifera Evidence on Early Eudicot Evolution,” Chunfang Zheng and David Sankoff presented a stepwise optimal genome halving algorithm designed for large eukaryote genomes with largely single-copy genes, taking advantage of a signature pattern of paralog distribution in ancient polyploids. They applied the algorithm to the genome of Nelumbo nucifera, the sacred lotus, which is the descendant of a duplicated
2
Editorial / Computational Biology and Chemistry 50 (2014) 1–2
basal eudicot genome. They showed that the duplication of the ancestor of lotus and the triplication of the ancestor of grape were not closely preceded by any additional such event before the divergence of their two lineages. In “Parallel Molecular Computation of Modular-multiplication Using Self-assembly of DNA Tiles,” Yongnan Li, Limin Xiao and Li Ruan poposed to use the parallel tile assembly process for computing the modular-square, modular-multiplication with two same inputs, over finite field GF(2n ). Rigorous theoretical proofs and specific computing instance are given. Time complexity of the proposed system is 3n − 1 and space complexity is 2n2 . Shuigeng Zhou is now a professor of School of Computer Science, Fudan University, Shanghai, China. He received his Bachelor degree from Huazhong University of Science and Technology (HUST) in 1988, his Master degree from University of Electronic Science and Technology of China (UESTC) in 1991, and his PhD of Computer Science from Fudan University in 2000. He served in Shanghai Academy of Spaceflight Technology from 1991 to 1997, as an engineer and a senior engineer (since August 1995) respectively. He was a post-doctoral researcher in State Key Lab of Software Engineering, Wuhan University from 2000 to 2002. His research interests include data management, data mining, machine learning, and bioinformatics. He has published more than 200 papers in domestic and international journals (including IEEE TKDE, IEEE TPDS, IEEE TCBB, Bioinformatics, Plos One, BMC Bioinformatics, DKE, PRE, EPL and EPJB etc.) and conferences (including SIGMOD, VLDB, ICDE, ICDCS, SIGKDD, SIGIR, SODA, IJCAI, RECOMB etc.). Currently he is a member of IEEE, ACM and IEICE. More information about him can be found at http://admis.fudan.edu.cn/∼sgzhou.
Yi-Ping Phoebe Chen received the BInfTech degree with first class honours and the PhD degree in computer science (bioinformatics) from the University of Queensland. She is a professor and chair and director of research in the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. Prof. Chen is the chief investigator of the ARC Centre of Excellence in Bioinformatics. She is currently working on knowledge discovery technologies and is especially interested in their application to genomics and biomedical science. Her research focus is to find the best solutions for mining, integrating, and analyzing complex data structure and functions for scientific and biomedical applications. She has been working in the area of bioinformatics, health informatics, multimedia databases, query system, and systems biology, has coauthored more than 200 research papers, with many published in top journals and conferences. She is steering committee chair of the Asia-Pacific Bioinformatics Conference (founder) and the International Conference on Multimedia Modelling. She has been on the program committees of more than 100 international conferences, including top ranking conferences such as ICDE, ICPR, ISMB, CIKM, etc. More information about her can be found at http://homepage.cs.latrobe.edu.au/ypchen/index.htm.
Shuigeng Zhou Yi-Ping Phoebe Chen
