contributed new reagents or analytic tools A.G., H.V., Y.F., E.O., J.A.O., H.B., and F.S.B. performed research Y.F., M.B., H.B., and F.S.B. All mapping results are freely available via the new CSF Proteome Resource ( ), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.Īuthor contributions: A.G., H.V., E.O., K.M., J.A.O., H.B., and F.S.B. An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. The Philosopher toolkit integrates high-performance algorithms and existing tools.In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. Though this method was efficient for packing and sharing resources, we found that chaining different applications with custom implementation of established algorithms in a transparent and dependency-free way was still a challenge for containerization. To address this challenge, we initially built and deployed Docker containers with different applications for proteomics, which in part inspired the creation of the BioContainers resource for different bioinformatics fields 4.
This is particularly true when experiments demand high-performance configurations such as GNU/Linux clusters or cloud computing. Managing these tools can be a daunting task, even for research groups with substantial bioinformatics expertise. While existing proteomics tools such as the Trans-Proteomic Pipeline (TPP) 1, MaxQuant 2 and PeptideShaker 3 are capable of performing high-quality analyses, all require installation and depend on specific operating systems, libraries and other software.
As technologies continue to rapidly advance with respect to throughput and sensitivity, bioinformatics tools must keep pace with large-scale experiments. To the Editor - Here we introduce Philosopher ( ), a free, open-source, versatile and robust data analysis toolkit designed to bring easy access to a powerful and comprehensive set of computational tools for shotgun proteomics data analysis.Ĭomputational analysis is a central component of any modern experiment, and mass-spectrometry-based proteomics is no exception. (2) Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA (1) Department of Pathology, University of Michigan, Ann Arbor, USA Shanmugam 1 2, Dattatreya Mellacheruvu 1 2, Andy T. Author(s): Felipe da Veiga Leprevost 1, Sarah E.
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