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Data Mining Techniques for the Life Sciences

  • Book
  • © 2016
  • Latest edition

Overview

Editors:
  1. Oliviero Carugo

    1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

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    You can also search for this editor in PubMed   Google Scholar

  2. Frank Eisenhaber

    1. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts
  • Includes supplementary material: sn.pub/extras

Part of the book series: Methods in Molecular Biology (MIMB, volume 1415)

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About this book

This volume details several important databases and data mining tools. Data Mining Techniques for the Life Sciences, Second Edition guides readers through archives of macromolecular three-dimensional structures, databases of protein-protein interactions, thermodynamics information on protein and mutant stability, “Kbdock” protein domain structure database, PDB_REDO databank, erroneous sequences, substitution matrices, tools to align RNA sequences, interesting procedures for kinase family/subfamily classifications, new tools to predict protein crystallizability, metabolomics data, drug-target interaction predictions, and a recipe for protein-sequence-based function prediction and its implementation in the latest version of the ANNOTATOR software suite. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Second Edition aims to ensure successful results in the further study of this vital field.


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Table of contents (27 protocols)

  1. Front Matter

    Pages i-xiii
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  2. Databases

    1. Front Matter

      Pages 1-1
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    2. Update on Genomic Databases and Resources at the National Center for Biotechnology Information

      • Tatiana Tatusova
      Pages 3-30

    3. Protein Structure Databases

      • Roman A. Laskowski
      Pages 31-53

    4. The MIntAct Project and Molecular Interaction Databases

      • Luana Licata, Sandra Orchard
      Pages 55-69

    5. Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants

      • M. Michael Gromiha, P. Anoosha, Liang-Tsung Huang
      Pages 71-89

    6. Classification and Exploration of 3D Protein Domain Interactions Using Kbdock

      • Anisah W. Ghoorah, Marie-Dominique Devignes, Malika Smaïl-Tabbone, David W. Ritchie
      Pages 91-105

    7. Data Mining of Macromolecular Structures

      • Bart van Beusekom, Anastassis Perrakis, Robbie P. Joosten
      Pages 107-138

    8. Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users

      • Oliviero Carugo, Kristina Djinović-Carugo
      Pages 139-152

    9. Homology-Based Annotation of Large Protein Datasets

      • Marco Punta, Jaina Mistry
      Pages 153-176

  3. Computational Techniques

    1. Front Matter

      Pages 177-177
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    2. Identification and Correction of Erroneous Protein Sequences in Public Databases

      • László Patthy
      Pages 179-192

    3. Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps of Protein Assemblies Using Evolutionary Information from Aligned Homologous Proteins

      • Ramachandran Rakesh, Narayanaswamy Srinivasan
      Pages 193-209

    4. Systematic Exploration of an Efficient Amino Acid Substitution Matrix: MIQS

      • Kentaro Tomii, Kazunori Yamada
      Pages 211-223

    5. Promises and Pitfalls of High-Throughput Biological Assays

      • Greg Finak, Raphael Gottardo
      Pages 225-243

    6. Optimizing RNA-Seq Mapping with STAR

      • Alexander Dobin, Thomas R. Gingeras
      Pages 245-262

  4. Prediction Methods

    1. Front Matter

      Pages 263-263
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    2. Predicting Conformational Disorder

      • Philippe Lieutaud, François Ferron, Sonia Longhi
      Pages 265-299

    3. Classification of Protein Kinases Influenced by Conservation of Substrate Binding Residues

      • Chintalapati Janaki, Narayanaswamy Srinivasan, Malini Manoharan
      Pages 301-313

    4. Spectral–Statistical Approach for Revealing Latent Regular Structures in DNA Sequence

      • Maria Chaley, Vladimir Kutyrkin
      Pages 315-340
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Reviews

“The style of the book and the assortment of topics which are presented make it accessible to a wide range of audiences, from undergraduates to established researchers, and from a variety of backgrounds, biologists, chemists, bioinformaticians. This collection of articles highlighting the state of the art for protein analyses, can also be used as a brief yet thorough starting point for post-graduate projects.” (Irina Ioana Mohorianu, zbMATH 1353.92002, 2017)

Editors and Affiliations

  • Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  • Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

Bibliographic Information

  • Book Title: Data Mining Techniques for the Life Sciences

  • Editors: Oliviero Carugo, Frank Eisenhaber

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-4939-3572-7

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media New York 2016

  • Hardcover ISBN: 978-1-4939-3570-3Published: 27 April 2016

  • Softcover ISBN: 978-1-4939-8081-9Published: 21 April 2018

  • eBook ISBN: 978-1-4939-3572-7Published: 26 April 2016

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 2

  • Number of Pages: XIII, 552

  • Number of Illustrations: 13 b/w illustrations, 84 illustrations in colour

  • Topics: Bioinformatics

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