Protein Function Prediction

Methods and Protocols

  • Daisuke┬áKihara

Part of the Methods in Molecular Biology book series (MIMB, volume 1611)

Table of contents

  1. Front Matter
    Pages i-x
  2. Qing Wei, Joshua McGraw, Ishita Khan, Daisuke Kihara
    Pages 1-14
  3. Shuji Suzuki, Takashi Ishida, Masahito Ohue, Masanori Kakuta, Yutaka Akiyama
    Pages 15-25
  4. Fatemeh Sharifi, Yuzhen Ye
    Pages 27-34
  5. Genivaldo Gueiros Z. Silva, Fabyano A. C. Lopes, Robert A. Edwards
    Pages 35-44
  6. Ishita Khan, Joshua McGraw, Daisuke Kihara
    Pages 45-57
  7. Henrik Nielsen
    Pages 59-73
  8. Roman A. Laskowski
    Pages 75-95
  9. Ying Yang, Bingjie Hu, Markus A. Lill
    Pages 123-134
  10. Imad Abugessaisa, Shuhei Noguchi, Piero Carninci, Takeya Kasukawa
    Pages 199-217
  11. Satya N. V. Arjunan, Koichi Takahashi
    Pages 219-236
  12. Back Matter
    Pages 237-239

About this book

Introduction

This volume presents established bioinformatics tools and databases for function prediction of proteins. Reflecting the diversity of this active field in bioinformatics, the chapters in this book discuss a variety of tools and resources such as sequence-, structure-, systems-, and interaction-based function prediction methods, tools for functional analysis of metagenomics data, detecting moonlighting-proteins, sub-cellular localization prediction, and pathway and comparative genomics databases. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step instructions of how to use software and web resources, use cases, and tips on troubleshooting and avoiding known pitfalls.

Thorough and cutting-edge, Protein Function Prediction: Methods and Protocols is a valuable and practical guide for using bioinformatics tools for investigating protein function</p>

Keywords

sequence-based function prediction methods MPFit SignalP Gene Ontology Microbial Genome Database bioinformatics seb-cellular localization prediction metagenomics data moonlighting-proteins

Editors and affiliations

  • Daisuke┬áKihara
    • 1
  1. 1.Department of Biological Sciences and Computer SciencePurdue UniversityWest LafayetteUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-7015-5
  • Copyright Information Springer Science+Business Media LLC 2017
  • Publisher Name Humana Press, New York, NY
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-4939-7013-1
  • Online ISBN 978-1-4939-7015-5
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • About this book