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Matthew Chambers

dblp:55/7969 · DBLP profile ↗
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5ranked-venue papers
0as first author
0since 2021 · last 2015
0000-0001-8085-373XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 5Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
3 papers
Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
proteomics
0.532015
Correcting systematic bias and instrument measurement drift with mzRefinery · Bioinform. 2015
Supporting tool suite for production proteomics · Bioinform. 2011
Skyline: an open source document editor for creating and analyzing targeted proteomics experiments · Bioinform. 2010
Bioinformatics and computational biology › proteomics
mass spectrometry data analysis
0.212015
Correcting systematic bias and instrument measurement drift with mzRefinery · Bioinform. 2015
Bioinformatics and computational biology › proteomics
peptide-spectrum matching
0.212015
Correcting systematic bias and instrument measurement drift with mzRefinery · Bioinform. 2015
Bioinformatics and computational biology › proteomics › computational proteomics
proteomics data management
0.112011
Supporting tool suite for production proteomics · Bioinform. 2011
Bioinformatics and computational biology › proteomics › quantitative proteomics
targeted proteomics
0.112010
Skyline: an open source document editor for creating and analyzing targeted proteomics experiments · Bioinform. 2010

Methods — techniques the papers use, named apart from their topics

regression-based calibration · 0.2
YearPublicationVenuePosition
2015 Correcting systematic bias and instrument measurement drift with mzRefinery
abstract
MOTIVATION: Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments. RESULTS: We introduce the mzRefinery tool for calibration of mass spectrometry data files. Using confident peptide spectrum matches, three different calibration methods are explored and the optimal transform function is chosen. After calibration, systematic bias is removed and the mass measurement errors are centered at 0 ppm. Because it is part of the ProteoWizard package, mzRefinery can read and write a wide variety of file formats. AVAILABILITY AND IMPLEMENTATION: The mzRefinery tool is part of msConvert, available with the ProteoWizard open source package at http://proteowizard.sourceforge.net/ CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Bryson C. Gibbons, Matthew Chambers, Matthew E. Monroe, David L. Tabb, Samuel H. Payne
Bioinform.2
2014 A Laboratory-Targeted, Data Management and Processing System for the Early Detection Research Network
abstract
The National Institutes of Health (NIH), National Cancer Institute's Early Detection Research Network (EDRN) is a cross-institutional collaborative initiative seeking to accelerate the clinical application of cancer biomarker research. Over the past decade, it has been our role, as EDRN's Informatics Center (IC), to develop a comprehensive information services infrastructure as well as a set of software tools and services to support this overall initiative. We have recently developed a novel application called the Laboratory Catalog and Archive Service (LabCAS) whose focus is to extend EDRN IC data management and processing capabilities to EDRN laboratories. By leveraging the same technologies used to manage and process NASA Earth and Planetary data sets, we offer EDRN researchers an effective way of managing their laboratory data. More specifically, LabCAS enables EDRN researchers to reliably archive their experimental data, to optionally share these data in a controlled manner with other researchers, and to gain insight into these data through highly configurable data analysis pipelines tailored to the broad range of biomarker related experiments. This particular collaboration leverages expertise from NASA's Jet Propulsion Laboratory, Vanderbilt University Medical Center, and Dartmouth Medical School, as well as builds upon existing cross-governmental collaboration between NASA and the NIH.
Rishi Verma, Andrew F. Hart, Chris Mattmann, Daniel J. Crichton, Heather Kincaid, Sean C. Kelly, Michael J. Joyce, Paul Zimdars, David L. Tabb, Jay D. Holman, Matthew Chambers, Kristen Anton, Maureen Colbert, Christos Patriotis, Sudhir Srivastava
CBMS11
2013 Public infrastructure for cancer biomarker data capture, annotation, analysis, and distribution
David L. Tabb, Kristen Anton, Matthew Chambers, Maureen Colbert, Andrew F. Hart, Jerry D. Holman, Sean C. Kelly, Heather Kincaid, Chris Mattmann, Daniel J. Crichton
AMIA3
2011 Supporting tool suite for production proteomics
abstract
Abstract Summary: The large amount of data produced by proteomics experiments requires effective bioinformatics tools for the integration of data management and data analysis. Here we introduce a suite of tools developed at Vanderbilt University to support production proteomics. We present the Backup Utility Service tool for automated instrument file backup and the ScanSifter tool for data conversion. We also describe a queuing system to coordinate identification pipelines and the File Collector tool for batch copying analytical results. These tools are individually useful but collectively reinforce each other. They are particularly valuable for proteomics core facilities or research institutions that need to manage multiple mass spectrometers. With minor changes, they could support other types of biomolecular resource facilities. Availability and Implementation: Source code and executable versions are available under Apache 2.0 License at http://www.vicc.org/jimayersinstitute/data/ Contact: [email protected]
Zeqiang Ma, David L. Tabb, Joseph Burden, Matthew Chambers, Matthew B. Cox, Michael J. Cantrell, Amy-Joan L. Ham, Michael D. Litton, Michael R. Oreto, William C. Schultz, Scott M. Sobecki, Tina Y. Tsui, Gregory R. Wernke, Daniel C. Liebler
Bioinform.4
2010 Skyline: an open source document editor for creating and analyzing targeted proteomics experiments
abstract
SUMMARY: Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. AVAILABILITY: Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.
Brendan MacLean, Daniela M. Tomazela, Nicholas Shulman, Matthew Chambers, Gregory L. Finney, Barbara Frewen, Randall Kern, David L. Tabb, Daniel C. Liebler, Michael J. MacCoss
Bioinform.4