U.S. Department of Energy

Pacific Northwest National Laboratory

Sam Payne

Sam.Payne's picture

Dr. Payne's research interests are focused on algorithms for proteomics data analysis and subsequent interpretation and integration. He is PI of a cancer informatics proposal through the National Cancer Institute in collaboration with NYU and Washington University Stl. Prior work inclused a DOE Early Career Investigator award for algorithmic research in metaproteomics and an NSF Microbial Genome Sequencing grant to develop proteogenomic software. Before joining PNNL, Dr. Payne was an Assistant Professor of Informatics at the J. Craig Venter Institute in Rockville, MD.

Dr. Payne received a B.S. of computer science at Brigham Young University. He earned his Ph.D. in Bioinformatics from UC, San Diego working with Dr. Vineet Bafna. His research focused on computational proteomics and algorithm design, making a phosphorylation specific scoring method for mass spectrometry data which improved both sensitivity and specificity compared to current methods. He also helped lead the Arabidopsis proteogenomics project.


Research Focus: 

Integrative Omics

Working with proteomically and genomically characterized samples opens new opportunities to understand the interplay and regulation involved in cellular function. In the CPTAC consortium, we analyze tumor samples with both proteomic and a variety of genomic data. One important finding in ovariant cancer is the trans-effects that copy number alterations have in perturbing protein abundance. Using these genomic hotspots and their affect trans-genes, we were able to create a signature of survival that strongly out performed previous work with just one data type (mRNA).

Active Data

Active Data is a visualization platform that helps people browse and explore Big Data. I am creating Active Data Biology, which is adapted to the specific needs of biological data analysis. ADBio has three visual interfaces to project data: an interactive heatmap, a pathway browser, and the Canvas. As a user explores their data, their analysis is stored and versioned at GitHub so that progress on the project is not lost. Using GitHub also allows for easy collaboration on projects. More information can be found at adbio.pnnl.gov


Metaproteomics Data Analysis

In natural environments, microbes are constantly regulating protein function and metabolic activity. Metaproteomics, or analysis of the proteins from organisms in microbial communities, assays active metabolic functions and other biological processes to promote understanding the dynamic relationships between microbes and their environment. Recent advances in mass spectrometry and biological separations have dramatically increased the depth of proteomic discovery.  Unfortunately, traditional computational workflows are in many cases preventing researchers from realizing these benefits for microbial communities. Current metaproteomics algorithms have a dramatically reduced sensitivity, identifying too few proteins to make the technique truly useful. 

To restore sensitivity, we propose to develop a multi-stage workflow for metaproteomics. Our algorithms will be built on the spectral networks paradigm and will use information from commonly identified proteins to identify their counterparts in metaproteomics sample.  We start with the observation that orthologous versions of the same protein are in numerous members of a microbial community. This is true for many core proteins and also much of the larger pangenome. This improved sensitivity will allow us to elucidate much of the physical and functional architecture of the microbial community.



Research Interests:
  • Bioinformatics
  • Data visualization
  • Computational mass spectrometry
  • Ph.D Bioinformatics, University of California San Diego
  • B.S. Computer Science, Brigham Young University
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