Publications by authors named "Julie Bletz"

3 Publications

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Education and Outreach in Physical Sciences in Oncology.

Trends Cancer 2021 01 7;7(1):3-9. Epub 2020 Nov 7.

Department of Biochemistry and Molecular Biology, Mayo Clinic, Jacksonville, FL, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Jacksonville, FL, USA; Department of Transplantation, Mayo Clinic, Jacksonville, FL, USA; Center for Immunotherapeutic Transport Oncophysics, Houston Methodist Research Institute, Houston, TX, USA. Electronic address:

Physical sciences are often overlooked in the field of cancer research. The Physical Sciences in Oncology Initiative was launched to integrate physics, mathematics, chemistry, and engineering with cancer research and clinical oncology through education, outreach, and collaboration. Here, we provide a framework for education and outreach in emerging transdisciplinary fields.
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January 2021

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas.

Mol Cell Proteomics 2011 Sep 1;10(9):M110.006353. Epub 2011 Jun 1.

Institute for Systems Biology, Seattle, WA 98109, USA.

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.
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September 2011