5,054 research outputs found
Current challenges in software solutions for mass spectrometry-based quantitative proteomics
This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.
Mass Spectrometry-Based Label-Free Quantitative Proteomics
In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies
Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects
Mass spectrometry-based proteomics has become the tool of choice for
identifying and quantifying the proteome of an organism. Though recent years
have seen a tremendous improvement in instrument performance and the
computational tools used, significant challenges remain, and there are many
opportunities for statisticians to make important contributions. In the most
widely used "bottom-up" approach to proteomics, complex mixtures of proteins
are first subjected to enzymatic cleavage, the resulting peptide products are
separated based on chemical or physical properties and analyzed using a mass
spectrometer. The two fundamental challenges in the analysis of bottom-up
MS-based proteomics are as follows: (1) Identifying the proteins that are
present in a sample, and (2) Quantifying the abundance levels of the identified
proteins. Both of these challenges require knowledge of the biological and
technological context that gives rise to observed data, as well as the
application of sound statistical principles for estimation and inference. We
present an overview of bottom-up proteomics and outline the key statistical
issues that arise in protein identification and quantification.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS341 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Multiplierz: An Extensible API Based Desktop Environment for Proteomics Data Analysis
BACKGROUND. Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge. RESULTS. We describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines. CONCLUSION. Collectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research.Dana-Farber Cancer Institute; National Human Genome Research Institute (P50HG004233); National Science Foundation Integrative Graduate Education and Research Traineeship grant (DGE-0654108
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