Статья

Clustering Millions of Tandem Mass Spectra

Ari FrankDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093-0404, Department of Biology, University of California, San Diego, La Jolla, California 92093-0346, Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352Nuno BandeiraDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093-0404, Department of Biology, University of California, San Diego, La Jolla, California 92093-0346, Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352Zhouxin ShenDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093-0404, Department of Biology, University of California, San Diego, La Jolla, California 92093-0346, Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352Stephen TannerDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093-0404, Department of Biology, University of California, San Diego, La Jolla, California 92093-0346, Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352Steven P. BriggsDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093-0404, Department of Biology, University of California, San Diego, La Jolla, California 92093-0346, Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352Richard SmithDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093-0404, Department of Biology, University of California, San Diego, La Jolla, California 92093-0346, Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352Pavel A. PevznerDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093-0404, Department of Biology, University of California, San Diego, La Jolla, California 92093-0346, Bioinformatics Program, University of California, San Diego, La Jolla, California 92093-0419, and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352

2007fr

ABI

Аннотация

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.

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Идентификаторы

DOI: 10.1021/pr070361e

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar