Life Sciences Commons^™

High Sensitivity Rna Pseudoknot Prediction, Xiaolu Huang, Hesham Ali

Information Systems and Quantitative Analysis Faculty Publications

Most ab initio pseudoknot predicting methods provide very few folding scenarios for a given RNA sequence and have low sensitivities. RNA researchers, in many cases, would rather sacrifice the specificity for a much higher sensitivity for pseudoknot detection. In this study, we introduce the Pseudoknot Local Motif Model and Dynamic Partner Sequence Stacking (PLMM_DPSS) algorithm which predicts all PLM model pseudoknots within an RNA sequence in a neighboring-region-interferencefree fashion. The PLM model is derived from the existing Pseudobase entries. The innovative DPSS approach calculates the optimally lowest stacking energy between two partner sequences. Combined with the Mfold, PLMM_DPSS can also …

Narrowed Tcr Repertoire And Viral Escape As A Consequence Of Heterologous Immunity, Markus Cornberg, Alex T. Chen, Lee A. Wilkinson, Michael A. Brehm, Sung-Kwon Kim, Claudia Calcagno, Dario Ghersi, Roberto Puzone, Franco Celada, Raymond M. Welsh, Liisa K. Selin

Interdisciplinary Informatics Faculty Publications

Why some virus-specific CD8 TCR repertoires are diverse and others restricted or “oligoclonal” has been unknown. We show here that oligoclonality and extreme clonal dominance can be a consequence ofTcell crossreactivity. Lymphocytic choriomeningitis virus (LCMV) and Pichinde virus (PV) encode NP205–212 epitopes that induce different but highly cross-reactive diverseTCRrepertoires. Homologous viral challenge ofimmune mice only slightly skewed the repertoire and enriched for predictable TCR motifs. However, heterologous viral challenge resulted in a narrow oligoclonal repertoire with dominant clones with unpredictableTCRsequences.This shift in clonal dominance varied with the private, i.e., unique, specificity of the host’s TCR repertoire and was simulated using …

Method Of Predicting Splice Sites Based On Signal Interactions, Alexander Churbanov, Igor B. Rogozin, Jitender S. Deogun, Hesham Ali

Information Systems and Quantitative Analysis Faculty Publications

Background: Predicting and proper ranking of canonical splice sites (SSs) is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan.

Results: …