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Institution

Institute for Systems Biology

NonprofitSeattle, Washington, United States
About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.


Papers
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Journal ArticleDOI
TL;DR: Gene fusions represent an important class of somatic alterations in cancer, and integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusion involving tumor suppressors have the opposite effect.

375 citations

Journal ArticleDOI
TL;DR: This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification.
Abstract: Quantitative proteomics employing mass spectrometry is an indispensable tool in life science research. Targeted proteomics has emerged as a powerful approach for reproducible quantification but is limited in the number of proteins quantified. SWATH-mass spectrometry consists of data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, and selectivity) of targeted proteomics at large scale. While previous SWATH-mass spectrometry studies have shown high intra-lab reproducibility, this has not been evaluated between labs. In this multi-laboratory evaluation study including 11 sites worldwide, we demonstrate that using SWATH-mass spectrometry data acquisition we can consistently detect and reproducibly quantify >4000 proteins from HEK293 cells. Using synthetic peptide dilution series, we show that the sensitivity, dynamic range and reproducibility established with SWATH-mass spectrometry are uniformly achieved. This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification. SWATH-mass spectrometry consists of a data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics on the scale of thousands of proteins. Here, using data generated by eleven groups worldwide, the authors show that SWATH-MS is capable of generating highly reproducible data across different laboratories.

372 citations

Book ChapterDOI
TL;DR: A summary of the process and details about the Human, Drosophila and Yeast PeptideAtlas builds are presented and a uniform statistical validation ensures a consistent and high-quality set of peptide and protein identifications.
Abstract: PeptideAtlas is a multi-species compendium of peptides observed with tandem mass spectrometry methods. Raw mass spectrometer output files are collected from the community and reprocessed through a uniform analysis and validation pipeline that continues to advance. The results are loaded into a database and the information derived from the raw data is returned to the community via several web-based data exploration tools. The PeptideAtlas resource is useful for experiment planning, improving genome annotation, and other data mining projects. PeptideAtlas has become especially useful for planning targeted proteomics experiments.

372 citations

Journal ArticleDOI
TL;DR: The unprecedented sensitivity of the mass spectrometric analysis of minimally fractionated plasma samples is the result of the significantly reduced sample complexity of the isolated N-glycosites compared with whole plasma proteome digests and the selectivity of the MRM process.

368 citations


Authors

Showing all 1292 results

NameH-indexPapersCitations
Younan Xia216943175757
Ruedi Aebersold182879141881
David Haussler172488224960
Steven P. Gygi172704129173
Nahum Sonenberg167647104053
Leroy Hood158853128452
Mark H. Ellisman11763755289
Wei Zhang112118993641
John Ralph10944239238
Eric H. Davidson10645447058
James R. Heath10342558548
Alan Aderem9924646682
Anne-Claude Gingras9733640714
Trey Ideker9730672276
Michael H. Gelb9450634714
Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20233
202260
2021216
2020204
2019188
2018168