Keerthikumar / Mathivanan Proteome Bioinformatics

1. Auflage 2017
ISBN: 978-1-4939-6740-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1549, 233 Seiten, eBook

Reihe: Methods in Molecular Biology

This thorough book covers the most recent proteomics techniques, databases, bioinformatics tools, and computational approaches that are used for the identification and functional annotation of proteins and their structure. The most recent proteomic resources widely used in the biomedical scientific community for storage and dissemination of data are discussed. In addition, specific MS/MS spectrum similarity scoring functions and their application in the field of proteomics, statistical evaluation of labeled comparative proteomics using permutation testing, and methods of phylogenetic analysis using MS data are also described in detail. Written for the highly successful Methods in Molecular Biology series, chapters contain the kind of detail and key implementation advice to ensure successful results.  Authoritative and cutting-edge, Proteome Bioinformatics serves as a useful resource for researchers who are beginners as well as advanced investigators in the field of proteomics.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Keerthikumar, Shivakumar

Mathivanan, Suresh

Inhaltsverzeichnis

An Introduction to Proteome Bioinformatics.- Proteomic Data Storage and Sharing.- Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data.- Label-Based and Label-Free Strategies for Protein Quantitation.- TMT One Stop Shop: From Reliable Sample Preparation to Computational Analysis Platform.- Unassigned MS/MS Spectra: Who Am I?.- Methods to Calculate Spectrum Similarity.- Proteotypic Peptides and Their Applications.- Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test.- De Novo Peptide Sequencing: Deep Mining of High-Resolution Mass Spectrometry Data.- Phylogenetic Analysis Using Protein Mass Spectrometry.- Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data.- A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes.- Network Toolsfor the Analysis of Proteomic Data.- Determining the Significance of Protein Network Features and Attributes Using Permutation Testing.- Bioinformatics Tools and Resources for Analyzing Protein Structures.- In Silico Approach to Identify Potential Inhibitors for Axl-Gas6 Signalling.