What do we do?
The Bioinformatics, Data Analytics and Predictive Modeling Core both develops new bioinformatics and systems biology methods and tools and supports Center staff in providing bioinformatic and statistical services. Working with the Center's research collaborators, the Core stores, retrieves and analyzes the vast amount of data from the Center’s experiments. The availability of a responsive and dedicated bioinformatics and statistical support team enables the Core to provide rapid data management and processing support, facilitating the timely and accurate analysis and interpretation of the results. In addition, we develop user-friendly and open-access tools that are made available to the entire research community, including NeuroPred (a prohormone processing and peptide predictor), PepShop (one stop integration of peptide sequence and mass spectra information), and neuroProsight (a top-down proteomics toolset). These resources are complemented with an array of transcriptomics and proteomics workflows for a range of experimental designs developed and tested by the Core. Combining these efforts with the world-class computational and informatics capabilities available at the both the University of Illinois and Northwestern University, the Bioinformatics, Data Analytics and Predictive Modeling Core provides a vast range of technical support services to the neuroscience research community.
Gene Network Dysregulation in the Trigeminal Ganglia and Nucleus Accumbens of a Model of Chronic Migraine-Associated Hyperalgesia, H. Jeong, L.S. Moye, B.R. Southey, A.G. Hernandez, I. Dripps, E.V. Romanova, S.S. Rubakhin, J.V. Sweedler, A.A. Pradhan, S.L. Rodriguez-Zas, Front. Sys. Neurosci. 12, 2018, 63. DOI:10.3389/fnsys.2018.00063
Brain Region-dependent Gene Networks Associated with Selective Breeding for Increased Voluntary Wheel-running Behavior, P. Zhang, J.S. Rhodes, T. Garland Jr, S.D. Perez, B.R. Southey, S.L. Rodriguez-Zas, PLoS One 13, 2018, e0201773. DOI:10.1371/journal.pone.0201773
Bioinformatics for Prohormone and Neuropeptide Discovery, B.R. Southey, E.V. Romanova, S.L. Rodriguez-Zas, J.V. Sweedler, Methods Mol. Biol. 1719, 2018, 71–96. DOI:10.1007/978-1-4939-7537-2_5
Accurate Assignment of Significance to Neuropeptide Identifications Using Monte Carlo K-Permuted Decoy Databases, M.N. Akhtar, B.R. Southey, P.E. Andrén, J.V. Sweedler, S.L. Rodriguez-Zas, PLoS ONE 9, 2014, e111112. DOI:10.1371/journal.pone.0111112
The C-Score: A Bayesian Framework to Sharply Improve Proteoform Scoring in High-Throughput Top Down Proteomics, R.D. LeDuc, R.T. Fellers, B.P. Early, J.B. Greer, P.M. Thomas, N.L. Kelleher, J. Proteome Res. 13, 2014, 3231-3240. DOI: 10.1021/pr401277r
Comparing Label-free Quantitative Peptidomics Approaches to Characterize Diurnal Variation of Peptides in the Rat Suprachiasmatic Nucleus, B.R. Southey, J.E. Lee, L. Zamdborg, N. Atkins, Jr., J.W. Mitchell, M. Li, M.U. Gillette, N.L. Kelleher, J.V. Sweedler, Anal. Chem., 86, 2014, 443–452. DOI:10.1021/ac4023378
Evaluation of Database Search Programs for Accurate Detection of Neuropeptides in Tandem Mass Spectrometry Experiment, M.N. Akhtar, B.R.Southey, P.E.Andrén, J.V. Sweedler, S.L. Rodriguez-Zas, J. Proteome Res. 11, 2012, 6044–6055. DOI:10.1021/pr3007123