Data Mining Techniques for the Life Sciences

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.

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

Klappentext
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

Inhalt

Update on Genomic Databases and Resources at the National Center for Biotechnology Information.- Protein Structure Databases.- The MIntAct Project and Molecular Interaction Databases.- Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.- Classification and Exploration of 3D Protein Domain Interactions using Kbdock.- Data Mining of Macromolecular Structures.- Criteria to Extract High Quality Protein Data Bank Subsets for Structure Users.- Homology-based Annotation of Large Protein Datasets.- Identification and Correction Of Erroneous Protein Sequences in Public Databases.- Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps Of Protein Assemblies Using Evolutionary Information From Aligned Homologous Proteins.- Systematic Exploration of an Efficient Amino Acid Substitution Matrix, MIQS.- Promises and Pitfalls of High Throughput Biological Assays.- Optimizing RNA-seq Mapping with STAR.- Predicting Conformational Disorder.- Classification of Protein Kinases Influenced By Conservation of Substrate Binding Residues.- Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence.- Protein Crystallizability.- Analysis and Visualization of ChIP-Seq and RNA-Seq Sequence Alignments using ngs.plot.- Data Mining with ontologies.- Functional Analysis of Metabolomics Data.- Bacterial Genomics Data Analysis in the Next-Generation Sequencing Era.- A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-Synonymous Variants.- Recommendation Techniques for Drug-Target Interaction Prediction and Drug-Repositioning .- Protein Residue Contacts and Prediction Methods.- The Recipe for Protein Sequence-Based Function Prediction and its Implementation in the Annotator Software Environment.- Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles.- Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.