Development of Computational Methods for Proteomic Analysis As proteome projects come on line and produce data, the need for effective computational tools is paramount. The goal of this project is development of computational resources to address those needs. The focus of development in the first two years will be guided by the proteome analysis project now being undertaken in Dr. Ray Gesteland's laboratories at the University of Utah. The development of computational tools will proceed in stages to best address the needs of the project. Specifically, a basic data handling infrastructure and database system will be implemented first and will be designed with extensibility as a key goal. Second, existing mass spec and protein analysis software tools will be incorporated into the infrastructure for initial analysis work. The third step will then develop software which utilizes database information (sequence, annotations, cDNA's, mass spectra) as well as information from predictive tools (e.g. ORF determination) to identify proteins and their corresponding coding regions, and pinpoint proteins which may have arisen due to RNA coding events such as frameshift, codon redefinition (e.g. secis), and mRNA bypassing. The later stages of the project will then leverage this work to study expression data and intron sequence data (via comparative analysis) and their relation to the proteome.
| Phanstiel, Douglas H; Van Bortle, Kevin; Spacek, Damek et al. (2017) Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development. Mol Cell 67:1037-1048.e6 |
| Reuter, Jason A; Spacek, Damek V; Pai, Reetesh K et al. (2016) Simul-seq: combined DNA and RNA sequencing for whole-genome and transcriptome profiling. Nat Methods 13:953-958 |
| Lehnert, Erik M; Mouchka, Morgan E; Burriesci, Matthew S et al. (2014) Extensive differences in gene expression between symbiotic and aposymbiotic cnidarians. G3 (Bethesda) 4:277-95 |
| Yokoo, Rayka; Zawadzki, Karl A; Nabeshima, Kentaro et al. (2012) COSA-1 reveals robust homeostasis and separable licensing and reinforcement steps governing meiotic crossovers. Cell 149:75-87 |
| Ogando, Diego G; Dahlquist, Kam D; Alizadeh, Mitra et al. (2010) Candidate genes for chromosomes 6 and 10 quantitative trait loci for age-related retinal degeneration in mice. Mol Vis 16:1004-18 |
| Hendrickson, David G; Hogan, Daniel J; McCullough, Heather L et al. (2009) Concordant regulation of translation and mRNA abundance for hundreds of targets of a human microRNA. PLoS Biol 7:e1000238 |
| Yang, Dongmei; Ramkissoon, Kevin; Hamlett, Eric et al. (2008) High-accuracy peptide mass fingerprinting using peak intensity data with machine learning. J Proteome Res 7:62-9 |
| Crayton 3rd, Mack E; Powell, Bradford C; Vision, Todd J et al. (2006) Tracking the evolution of alternatively spliced exons within the Dscam family. BMC Evol Biol 6:16 |
| Wisz, Michael S; Suarez, Melissa Kimball; Holmes, Mark R et al. (2004) GFSWeb: a web tool for genome-based identification of proteins from mass spectrometric samples. J Proteome Res 3:1292-5 |
| Giddings, Michael C; Shah, Atul A; Gesteland, Ray et al. (2003) Genome-based peptide fingerprint scanning. Proc Natl Acad Sci U S A 100:20-5 |
Showing the most recent 10 out of 11 publications
