Biomedical subjects

Robin D Dowell

Publications and source records attributed to Robin D Dowell.

5 recordsLinked to original sources

PubMed2026

Functional Variant Discovery Identifies a Novel Genetic Link between SPRY2, Wood Smoke, and Asthma.

As a consequence of climate change and land-use policies, there has been a historic rise in wildfire smoke across the United States and the world. Although the deleterious effects of wildfire smoke and associated air pollution on asthma outcomes are established epidemiologically, genetic risks and molecular mechanisms of how wildfire smoke affects asthma are unknown. This knowledge gap hinders the identification of high-risk individuals and the creation of targeted therapies or recommendations to protect these individuals. We identified 52 genetic risk variants that colocalized with genomic responses to woodsmoke particles (WSPs), a model of wildfire particulate matter, and associated with asthma in the GERA (Genetic Epidemiology Research on Adult Health and Aging) cohort. We used additional filters to prioritize variants for direct testing of allele-dependent transcriptional regulatory function in plasmid reporters. We found that the rs3861144 variant (odds ratioasthma, 1.036) changes SPRY2 responses to WSPs in airway epithelial cells, which are involved in IL-8 secretion, ERK (extracellular signal-related kinase) activation, and mechanical scratch repair in cell culture. These findings provide insights into the molecular pathways through which WSPs may influence asthma risk and propose genetic candidates that warrant further study for their potential as clinical tools for asthma.

Asthma↗

PubMed2006

Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints.

BACKGROUND: We are interested in the problem of predicting secondary structure for small sets of homologous RNAs, by incorporating limited comparative sequence information into an RNA folding model. The Sankoff algorithm for simultaneous RNA folding and alignment is a basis for approaches to this problem. There are two open problems in applying a Sankoff algorithm: development of a good unified scoring system for alignment and folding and development of practical heuristics for dealing with the computational complexity of the algorithm. RESULTS: We use probabilistic models (pair stochastic context-free grammars, pairSCFGs) as a unifying framework for scoring pairwise alignment and folding. A constrained version of the pairSCFG structural alignment algorithm was developed which assumes knowledge of a few confidently aligned positions (pins). These pins are selected based on the posterior probabilities of a probabilistic pairwise sequence alignment. CONCLUSION: Pairwise RNA structural alignment improves on structure prediction accuracy relative to single sequence folding. Constraining on alignment is a straightforward method of reducing the runtime and memory requirements of the algorithm. Five practical implementations of the pairwise Sankoff algorithm - this work (Consan), David Mathews' Dynalign, Ian Holmes' Stemloc, Ivo Hofacker's PMcomp, and Jan Gorodkin's FOLDALIGN - have comparable overall performance with different strengths and weaknesses.

Algorithms↗

PubMed2006

Core transcriptional regulatory circuitry in human hepatocytes.

We mapped the transcriptional regulatory circuitry for six master regulators in human hepatocytes using chromatin immunoprecipitation and high-resolution promoter microarrays. The results show that these regulators form a highly interconnected core circuitry, and reveal the local regulatory network motifs created by regulator-gene interactions. Autoregulation was a prominent theme among these regulators. We found that hepatocyte master regulators tend to bind promoter regions combinatorially and that the number of transcription factors bound to a promoter corresponds with observed gene expression. Our studies reveal portions of the core circuitry of human hepatocytes.

Gene Expression Regulation↗

PubMed2006

High-resolution computational models of genome binding events.

Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBD's spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.

Base Sequence↗

PubMed2004

Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction.

BACKGROUND: RNA secondary structure prediction methods based on probabilistic modeling can be developed using stochastic context-free grammars (SCFGs). Such methods can readily combine different sources of information that can be expressed probabilistically, such as an evolutionary model of comparative RNA sequence analysis and a biophysical model of structure plausibility. However, the number of free parameters in an integrated model for consensus RNA structure prediction can become untenable if the underlying SCFG design is too complex. Thus a key question is, what small, simple SCFG designs perform best for RNA secondary structure prediction? RESULTS: Nine different small SCFGs were implemented to explore the tradeoffs between model complexity and prediction accuracy. Each model was tested for single sequence structure prediction accuracy on a benchmark set of RNA secondary structures. CONCLUSIONS: Four SCFG designs had prediction accuracies near the performance of current energy minimization programs. One of these designs, introduced by Knudsen and Hein in their PFOLD algorithm, has only 21 free parameters and is significantly simpler than the others.

Computational Biology↗