Protein Function Prediction for Omics Era -

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Résumé :
Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred

Sommaire:
Preface; 1 Computational protein function prediction: framework and challenges, Meghana Chitale, Daisuke Kihara (Purdue University, USA); 2 Enhanced sequence-based function prediction methods and application to functional similarity networks, Meghana Chitale, Daisuke Kihara (Purdue University, USA); 3 Gene cluster prediction and its application to genome annotation, Vikas Rao Pejaver, Heewook Lee, Sun Kim (Indiana University, USA); 4 Functional inference in microbial genomics based on large-scale comparative analysis, Ikuo Uchiyama (National Institute for Basic Biology, Japan); 5 Predicting protein functional sites with phylogenetic motifs: Past, present and beyond, Dennis R. Livesay, Dukka Bahadur K.C., David La (Univ. North Carolina, USA); 6 Exploiting protein structures to predict protein functions, Alison Cuff, Oliver Redfern, Benoit Dessailly, Christine Orengo (University College London, UK); 7 Sequence order independent comparison of protein global backbone structures and local binding surfaces for evolutionary and functional inference, Joe Dundas, Bhaskar DasGupta, Jie Liang (Univ. Illinois at Chicago, USA); 8 Protein binding ligand prediction using moment-based methods, Rayan Chikhi, Lee Sael, Daisuke Kihara (Purdue University, USA); 9 Computational methods for predicting DNA-binding sites at a genome scale, Shandar Ahmad (Nat. Institute of Biomedical Innovation, Japan); 10 Electrostatic properties for protein functional site annotation, Joslynn S. Lee, Mary Jo Ondrechen (Northeastern University, USA); 11 Function prediction of genes: from molecular function to cellular function, Kengo Kinoshita, Takeshi Obayashi (Tohoku University, Japan); 12 Predicting gene function using omics data: from data preparation to data integration, Weidong Tian, Xinran Dong, Yuanpeng Zhou, Ren Ren (Fudan University, China); 13 Protein function prediction using protein-protein interaction networks, Hon Nian Chua, Guimei Liu, Limsoon Wong (Nat. University of Singapore, Singapore); 14 KEGG and GenomeNet resources for predicting protein function from omics data including KEGG PLANT Resource, Toshiaki Tokimatsu, Masaaki Kotera, Susumu Goto, Minoru Kanehisa (Kyoto University, Japan); 15 Towards elucidation of the Escherichia coli K-12 unknowneome, Yukako Tohsato, Natsuko Yamamoto, Toru Nakayashiki, Rikiya Takeuchi, Barry L. Wanner, Hirotada Mori (Nara Institute of Science and Technology, Japan); Index