Protein Function Prediction for Omics Era

1. Front Matter

   Pages i-xiii

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2. Computational Protein Function Prediction: Framework and Challenges

   • Meghana Chitale, Daisuke Kihara

   Pages 1-17

3. Enhanced Sequence-Based Function Prediction Methods and Application to Functional Similarity Networks

   • Meghana Chitale, Daisuke Kihara

   Pages 19-34

4. Gene Cluster Prediction and Its Application to Genome Annotation

   • Vikas Rao Pejaver, Heewook Lee, Sun Kim

   Pages 35-54

5. Functional Inference in Microbial Genomics Based on Large-Scale Comparative Analysis

   • Ikuo Uchiyama

   Pages 55-92

6. Predicting Protein Functional Sites with Phylogenetic Motifs: Past, Present and Beyond

   • Dennis R. Livesay, Dukka Bahadur KC, David La

   Pages 93-105

7. Exploiting Protein Structures to Predict Protein Functions

   • Alison Cuff, Oliver Redfern, Benoit Dessailly, Christine Orengo

   Pages 107-123

8. Sequence Order Independent Comparison of Protein Global Backbone Structures and Local Binding Surfaces for Evolutionary and Functional Inference

   • Joe Dundas, Bhaskar DasGupta, Jie Liang

   Pages 125-143

9. Protein Binding Ligand Prediction Using Moments-Based Methods

   • Rayan Chikhi, Lee Sael, Daisuke Kihara

   Pages 145-163

10. Computational Methods for Predicting DNA-Binding Sites at a Genomic Scale

    • Shandar Ahmad

    Pages 165-182

11. Electrostatic Properties for Protein Functional Site Prediction

    • Joslynn S. Lee, Mary Jo Ondrechen

    Pages 183-196

12. Function Prediction of Genes: From Molecular Function to Cellular Function

    • Kengo Kinoshita, Takeshi Obayashi

    Pages 197-214

13. Predicting Gene Function Using Omics Data: From Data Preparation to Data Integration

    • Weidong Tian, Xinran Dong, Yuanpeng Zhou, Ren Ren

    Pages 215-242

14. Protein Function Prediction Using Protein–Protein Interaction Networks

    • Hon Nian Chua, Guimei Liu, Limsoon Wong

    Pages 243-270

15. KEGG and GenomeNet Resources for Predicting Protein Function from Omics Data Including KEGG PLANT Resource

    • Toshiaki Tokimatsu, Masaaki Kotera, Susumu Goto, Minoru Kanehisa

    Pages 271-288

16. Towards Elucidation of the Escherichia coli K-12 Unknowneome

    • Yukako Tohsato, Natsuko Yamamoto, Toru Nakayashiki, Rikiya Takeuchi, Barry L. Wanner, Hirotada Mori

    Pages 289-305

17. Back Matter

    Pages 307-310

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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

• Proteomics

• Dept. Biological Science, Purdue University, West Lafayette, USA

  Daisuke Kihara

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