Data Mining Techniques for the Life Sciences

ISBN-13: 9781493935703 / Angielski / Twarda / 2016 / 552 str.

Oliviero Carugo; Frank Eisenhaber

Opis

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.
"

Recenzje

"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)

Spis treści

Part I: Data Basses

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

Tatiana Tatusova

2. Protein Structure Databases

Roman A. Laskowski

3. The MIntAct Project and Molecular Interaction Databases

Luana Licata and Sandra Orchard

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

M. Michael Gromiha, P. Anoosha, and Liang-Tsung Huang

5. Classification and Exploration of 3D Protein Domain Interactions using Kbdock

Anisah W. Ghoorah, Marie-Dominique Devignes, Malika Smaïl-Tabbone, David W. Ritchie

6. Data Mining of Macromolecular Structures

Bart van Beusekom, Anastassis Perrakis, and Robbie P. Joosten

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

Oliviero Carugo and Kristina Djinovic-Carugo

 8. Homology-based Annotation of Large Protein Datasets

Marco Punta and Jaina Mistry

 

PART II: Computational Techniques

9. Identification and Correction Of Erroneous Protein Sequences in Public Databases

László Patthy

10. Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps Of Protein Assemblies Using Evolutionary Information From Aligned Homologous Proteins Ramachandran Rakesh and Narayanaswamy Srinivasan

11. Systematic Exploration of an Efficient Amino Acid Substitution Matrix, MIQS

Kentaro Tomii and Kazunori Yamada

12. Promises and Pitfalls of High Throughput Biological Assays

Greg Finak and Raphael Gottardo

13. Optimizing RNA-seq Mapping with STAR

Alexander Dobin and Thomas R. Gingeras

PART III: Prediction Methods

14. Predicting Conformational Disorder

Philippe Lieutaud, François Ferron, and Sonia Longhi

15. Classification of Protein Kinases Influenced By Conservation of Substrate Binding Residues

Chintalapati Janaki, Narayanaswamy Srinivasan, Malini Manoharan

16. Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence

Maria Chaley and Vladimir Kutyrkin

17.Protein Crystallizability

Pawel Smialowski and Philip Wong

18. Analysis and Visualization of ChIP-Seq and RNA-Seq Sequence Alignments using ngs.plot  

Yong-Hwee Eddie Loh, and Li Shen

19. Dataming with ontologies

Robert Hoehndorft, Georgios V. Gkoutos, and Paul N. Schofield

20. Functional Analysis of Metabolomics Data

Mónica Chagoyen, Javier López-Ibáñez, and Florencio Pazos < 

21. Bacterial Genomics Data Analysis in the Next-Generation Sequencing Era

Massimiliano Orsini, Gianmauro Cuccuru, Paolo Uva, and Giorgio Fotia

22. A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-Synonymous Variants

Stefano Castellana, Caterina Fusilli, and Tommaso Mazza

23. Recommendation Techniques for Drug-Target Interaction Prediction and Drug-Repositioning

Salvatore Alaimo, Rosalba Giugno, and Alfredo Pulvirenti   24. Protein Residue Contacts and Prediction Methods

Badri Adhikari and Jianlin Cheng

25. The Recipe for Protein Sequence-Based Function Prediction and its Implementation in the Annotator Software Environment

Birgit Eisenhaber, Durga Kuchibhatla, Westley Sherman, Fernanda L. Sirota, Igor N. Berezovsky, Wing-Cheong Wong, and Frank Eisenhaber

Part IV: Big Data

26. Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles

Maja Fabijanić and Kristian Vlahoviček

27. Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics

George V. Popescu, Christos Noutsos, and Sorina C. Popescu

 

 

Blurb

High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome profiling, to single-cell sequencing. Having such diversity of alternatives, there is a demand for information by research scientists without experience in HTS that need to choose the most suitable  methodology or combination of platforms  and to define their experimental designs to achieve their specific objectives. Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing