Constructs optimal signal maps between mass spectrometry (MS) experiments and a practical application where these can be used to increase sensitivity and throughput. ChAMS was developed to rely on signal maps that associate raw data between experiments and the deferral of individual feature recognition to the last analysis stage. This approach can be expected to decrease inter- and intra-experiment biases and improve the signal-to-noise ratio, thus improving sensitivity and throughput.
Department of Computer Science, University of Washington, Seattle, WA, USA; Institute for Systems Biology, Seattle, WA, USA; Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Institute for Molecular Systems Biology, Eidgenossische Technische Hochschule and Faculty of Natural Sciences, University of Zurich, Zurich, Switzerland; Department of Molecular Biology and Biochemistry, Wesleyan University, Middletown, CT, USA; Systems Biology Group, Institut Pasteur, Paris, France; CedarsSinai Medical Center, Los Angeles, CA, USA
ChAMS funding source(s)
Supported by federal funds from the NHLBI, National Institutes of Health under Contract Number N01-HV-28179.