This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. DESCRIPTION (provided by applicant): This application seeks support for a center of excellence in computational mass spectrometry and a national and international resource in the broad area of proteomics. It proposes to enlarge the current research activities, to branch into previously unexplored areas of computational proteomics, and to support multiple collaborative efforts. The proposal addresses the computational bottleneck that affects the entire proteomics community and impairs interpretation of data in thousands of experimental labs around the world. The goal is to bring the modern algorithmic technologies to mass-spectrometry and to build a new generation of reliable open access software tools to support both new development in mass-spectrometry instrumentation and the emerging applications of mass-spectrometry. The proposal focuses on four directions: (i) enabling complex mass spectrometry searches, (ii) analyzing unknown proteomes without protein databases, (iii) analyzing altered proteomes, and (iv) constructing proteogenomic annotations and analyzing pathways. These directions cover both well-studied but still inadequately addressed problems (like search for mutations and post-translational modifications) and unexplored problems for which there are no computational tools currently available (like antibody sequencing or analyzing fusion proteins in cancer). These projects require two-way collaborative efforts on a wide range of topics involving biomedical and computational scientists from various institutions. While many collaborations have been already established at San Diego (UCSD and Burnham Institute), sixteen other US universities, hospitals and biotechnology companies, as well as foreign research institutions at Germany, Singapore, Spain, Sweden, and United Kingdom, we propose to further extend these collaborations by developing robust open access mass spectrometry software that will catalyze the exchanges between experimental and computational researchers in proteomics. The biomedical applications addressed in these collaborative projects include but are not limited to (i) discovery of cancer biomarkers, (ii) elucidation of changes in aged cataractous lens, (iii) understanding how bacteria adjust to antibiotics and other harsh conditions, (iv) addressing the need to constantly reformulate the influenza vaccine to make it efficient, and (v) sequencing of snake venoms that proved instrumental in design of blood clotting drugs. Educational activities in the area of computational proteomics will also be developed, including short courses, a seminar program, an annual conference, and concerted education of students and postdocs.
| Beyter, Doruk; Lin, Miin S; Yu, Yanbao et al. (2018) ProteoStorm: An Ultrafast Metaproteomics Database Search Framework. Cell Syst 7:463-467.e6 |
| Bhattacharya, Anindya; Cui, Yan (2017) A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules. Sci Rep 7:4162 |
| Cha, Seong Won; Bonissone, Stefano; Na, Seungjin et al. (2017) The Antibody Repertoire of Colorectal Cancer. Mol Cell Proteomics 16:2111-2124 |
| Safonova, Yana; Bankevich, Anton; Pevzner, Pavel A (2015) dipSPAdes: Assembler for Highly Polymorphic Diploid Genomes. J Comput Biol 22:528-45 |
| Kim, Sangtae; Pevzner, Pavel A (2014) MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun 5:5277 |
| Castellana, Natalie E; Shen, Zhouxin; He, Yupeng et al. (2014) An automated proteogenomic method uses mass spectrometry to reveal novel genes in Zea mays. Mol Cell Proteomics 13:157-67 |
| Woo, Sunghee; Cha, Seong Won; Na, Seungjin et al. (2014) Proteogenomic strategies for identification of aberrant cancer peptides using large-scale next-generation sequencing data. Proteomics 14:2719-30 |
| Lo, Christine; Kakaradov, Boyko; Lokshtanov, Daniel et al. (2014) SeeSite: Characterizing Relationships between Splice Junctions and Splicing Enhancers. IEEE/ACM Trans Comput Biol Bioinform 11:648-56 |
| Prjibelski, Andrey D; Vasilinetc, Irina; Bankevich, Anton et al. (2014) ExSPAnder: a universal repeat resolver for DNA fragment assembly. Bioinformatics 30:i293-301 |
| Mohimani, Hosein; Liu, Wei-Ting; Kersten, Roland D et al. (2014) NRPquest: Coupling Mass Spectrometry and Genome Mining for Nonribosomal Peptide Discovery. J Nat Prod 77:1902-9 |
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