Platforms and Pipelines for Proteomics Data Analysis and Management
Since mass spectrometry was introduced as the core technology for large-scale analysis of the proteome, the speed of data acquisition, dynamic ranges of measurements, and data quality are continuously improving. These improvements are triggered by regular
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Platforms and Pipelines for Proteomics Data Analysis and Management Marius Cosmin Codrea and Sven Nahnsen
Abstract
Since mass spectrometry was introduced as the core technology for largescale analysis of the proteome, the speed of data acquisition, dynamic ranges of measurements, and data quality are continuously improving. These improvements are triggered by regular launches of new methodologies and instruments. Keywords
Bioinformatics • Proteomics data processing • Protein identification • Protein quantification • Data processing pipeline • Trans proteomic pipeline • OpenMS pipeline • The Central Proteomics Facilities Pipeline (CPFP) • MaxQuant pipeline • Scaffold pipeline • Sorcerer pipeline • IPA/ IP2 pipeline
Abbreviations FDR GO GUI I/O iTRAQ M/Z PTM RT
False Discovery Rate Gene Ontology Graphical User Interface input, output Isobaric tags for relative and absolute quantitation mass-to-charge Post-Translational Modification retention time
M.C. Codrea • S. Nahnsen (*) Quantitative Biology Center (QBiC), University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany e-mail: [email protected]
SILAC SRM TB TPP
9.1
Stable isotope labeling by amino acids in cell culture Selected Reaction Monitoring terra byte Trans-Proteomics Pipeline
Introduction
Since mass spectrometry was introduced as the core technology for large-scale analysis of the proteome, the speed of data acquisition, dynamic ranges of measurements, and data quality are continuously improving. These improvements are triggered by regular launches of new methodologies and instruments.
# Springer International Publishing Switzerland 2016 H. Mirzaei and M. Carrasco (eds.), Modern Proteomics – Sample Preparation, Analysis and Practical Applications, Advances in Experimental Medicine and Biology 919, DOI 10.1007/978-3-319-41448-5_9
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A consequence of higher throughput and performance is the increased size and complexity of the data. Mass spectrometry studies using the latest technology can readily generate datasets in the TB range (e.g., [1, 2]). These datasets contain millions of spectra that need to be converted into biological insights, and manual completion of this task is prohibitive. Hence, the importance of bioinformatics tools for the analysis of proteomics data is growing rapidly. Besides efficient implementations of the required features to meet these demands, there is a strong need for flexible and user-friendly interfaces. Software tools in the field of proteomics need to provide a broad operability to allow for a scalable integration for the complete workflow. The processing and analysis software should be usable by biologists, instrument technicians and computer scientists alike. From a computational point of view, algorithms for the identification and quantification of peptides and proteins from a selection of mass spectra are at the core of the workflow. For the remainder of this chapter, these fundamental tasks are summarized as data processing. Following data processing, proteomics studies usually
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