This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, ?degenerate? peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein?s presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or estimated the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
2P41RR011823-16
Application #
8365888
Study Section
Special Emphasis Panel (ZRG1-CB-L (40))
Project Start
2011-09-01
Project End
2012-06-30
Budget Start
2011-09-01
Budget End
2012-06-30
Support Year
16
Fiscal Year
2011
Total Cost
$21,369
Indirect Cost
Name
University of Washington
Department
Biochemistry
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Seixas, Adriana; Alzugaray, María Fernanda; Tirloni, Lucas et al. (2018) Expression profile of Rhipicephalus microplus vitellogenin receptor during oogenesis. Ticks Tick Borne Dis 9:72-81
Wang, Zheng; Wu, Catherine; Aslanian, Aaron et al. (2018) Defective RNA polymerase III is negatively regulated by the SUMO-Ubiquitin-Cdc48 pathway. Elife 7:
Xavier, Marina Amaral; Tirloni, Lucas; Pinto, Antônio F M et al. (2018) A proteomic insight into vitellogenesis during tick ovary maturation. Sci Rep 8:4698
Hollmann, Taylor; Kim, Tae Kwon; Tirloni, Lucas et al. (2018) Identification and characterization of proteins in the Amblyomma americanum tick cement cone. Int J Parasitol 48:211-224
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Ju Lee, Hyun; Bartsch, Deniz; Xiao, Cally et al. (2017) A post-transcriptional program coordinated by CSDE1 prevents intrinsic neural differentiation of human embryonic stem cells. Nat Commun 8:1456

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