Proteome Bioinformatics (Methods in Molecular Biology)

The feld of proteomics moves rapidly. New methods, techniques, applications, standards, models and software appear almost on a daily basis. Accompanying this are plenty of texts on the experimental side of the feld and a few appearing on the informatic and data analysis side. This latterly includes one in the Methods in Molecular Biology series tackling the specifc analysis of “Mass spectrometry data in proteomics” in MMB vol. 376. This current collection builds on this, but takes a broader view of proteome data analysis covering data analysis essentials, but also the databases and data models, as well as practical consid- ations for analysing database search results, annotating genomes, and speeding up searches. It also digs deeper into some topics, such as decoy database searching and aspects of signal processing in proteomic mass spectrometry. The aim of the volume is to provide the reader with a mix of reviews and methodology chapters, which build from the essentials of database searching in proteomics, on through specifc data processing challenges to databases, data standards and data models.

The field of proteomics moves rapidly, as new methods, techniques, applications, standards, models and software appear almost on a daily basis. In Proteome Bioinformatics, expert researchers explore this ever-changing field, providing the reader with a mix of review and methodology chapters which address the fundamentals of analysis methods, algorithms, data standards and databases. Chapters include practical considerations for analyzing database search results, annotating genomes, speeding up searches, and responding to specific data processing challenges. As a volume of the highly successful Methods in Molecular Biology series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results.

Comprehensive and ground-breaking, Proteome Bioinformatics is an essential guidebook for the novice and expert alike, covering topics that address a wide range of data analysis and management problems.