Free software for purifying mass spectrometry data

December 8, 2010 at 5:14 am Leave a comment

Researchers at the Samuel Lunenfeld Research Institute of Mount Sinai Hospital and the University of Toronto, as well as colleagues in Michigan and Scotland, have developed an innovative computational approach—the first of its kind worldwide—designed to analyze mass spectrometry data. The software, called SAINT (Significance Analysis of INTeractome), will allow researchers globally to quickly assess the reliability and accuracy of protein binding data helping to further their studies of cancer and other illnesses.

SAINT is described online in the leading international journal Nature Methods.

Dr. Anne-Claude Gingras

The tool was developed by Lunenfeld Principal Investigator Dr. Anne-Claude Gingras (Lea Reichmann Research Chair in Cancer Proteomics and Assistant Professor in the Department of Molecular Genetics at the University of Toronto), and Dr. Alexey Nesvizhskii (Assistant Professor in the Department of Pathology and in the Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor) to meet a key challenge in the field of protein mass spectrometry (a technology that helps researchers separate, identify, and quantify specific proteins): namely, to identify and quantify ‘true’ protein interactions gleaned from mass spectrometry data, and filter them from protein-based contaminants in the sample data. Previously, other approaches to analyze mass spectrometry data have not allowed for a probability-based model to measure and account for errors in a data set.

“SAINT allows researchers to identify the real protein interactions in their sample, and to exclude the false positives generated through contaminants,” said Dr. Gingras. “In effect, the software applies a much needed filter to purify the data and remove ‘noise.’”

SAINT was introduced earlier this year by Drs. Gingras, Nesvizhskii and Mike Tyers, when their teams generated a comprehensive road map of the signaling proteins that control many aspects of cellular behaviour in yeast cells (www.yeastkinome.org)—a discovery reported in a May issue of Science. However, the updated approach can be applied to a wider variety of datasets of various sizes and levels of protein network density.

“The first version of SAINT was intended to help us analyze very large-scale datasets, which is something that only a few laboratories worldwide are generating,” said Dr. Nesvizhskii. “We then realized that, with some modifications, the same approach could be extended to researchers specifically interested in knowing what a few proteins interact with inside the cell. This makes our approach very useful to most cancer biologists using mass spectrometry, as it enables them to quantify their interaction data.”

Drs. Gingras and Nesvizhskii are former research associates and have collaborated on various research projects over the past eight years, and bring together their expertise in biology and computational modeling, respectively.

“We come from completely different directions but have focused on this problem together,” said Dr. Gingras.

Dr. Gingras has encouraged many other scientists to use SAINT, and the software is being implemented at research institutes internationally. Drs. Gingras and Nesvizhskii, with study first author Dr. Hyungwon Choi (a post-doctoral Research Fellow in the Nesvizhskii lab), recently held a workshop at the Lunenfeld, at which almost 100 Toronto-based scientists and industry representatives learned of the advantages offered by SAINT.

“SAINT is a new and important software tool that—for the first time—allows us to assign a confidence value to every protein-protein interaction that we identify in our mass spectrometry studies,” said Dr. Brian Raught, Canada Research Chair in Proteomics and Molecular Medicine, and a scientist at the Ontario Cancer Institute. Dr. Raught attended Dr. Gingras’ workshop and uses the tool in his research.

“It has been truly instrumental in our work on deciphering the protein-protein interactions within complex intracellular regulatory networks, and thus represents a major advance in our field.”

The software is available for downloading at http://sourceforge.net/projects/saint-apms

The development of SAINT was supported by several agencies, including the Canadian Institutes of Health Research and the National Institutes of Health, as well as the Mount Sinai Hospital Foundation.

About the Samuel Lunenfeld Research Institute of Mount Sinai Hospital

The Samuel Lunenfeld Research Institute of Mount Sinai Hospital, a University of Toronto affiliated research centre established in 1985, is one of the world’s premier centres in biomedical research. Thirty-four principal investigators lead research in diabetes, cancer biology, epidemiology, stem cell research, women’s and infants’ health, neurobiology and systems biology. For more information on the Samuel Lunenfeld Research Institute, please visit www.lunenfeld.ca.

Entry filed under: Proteomics, Tools, Web Resources. Tags: , , , , .

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