Proteomics Data Analysis

Daniela Cecconi

ISBN 10: 1071616404 / ISBN 13: 9781071616406

Editore: Springer US, Springer US Jul 2021, 2021

Riguardo questo articolo

Neuware -This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 344 pp. Englisch.

Codice articolo 9781071616406

Dati bibliografici

• Titolo: Proteomics Data Analysis
• Editore: Springer US, Springer US Jul 2021
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1071616404
• ISBN 13: 9781071616406
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 260 L x 183 A x 25 P (millimetri) 260 millimetri di larghezza per 183 millimetri di altezza per 25 millimetri di profondità
• Peso: 845 grammi

Descrizione articolo

Riassunto

This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. 

Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.