Life Sciences Commons^™

A Systematic Approach To Rna-Associated Motif Discovery, Tian Gao, Jiang Shu, Juan Cui

School of Computing: Faculty Publications

Background: Sequencing-based large screening of RNA-protein and RNA-RNA interactions has enabled the mechanistic study of post-transcriptional RNA processing and sorting, including exosome-mediated RNA secretion. The downstream analysis of RNA binding sites has encouraged the investigation of novel sequence motifs, which resulted in exceptional new challenges for identifying motifs from very short sequences (e.g., small non-coding RNAs or truncated messenger RNAs), where conventional methods tend to be ineffective. To address these challenges, we propose a novel motif-finding method and validate it on a wide range of RNA applications.

Results: We first perform motif analysis on microRNAs and longer RNA fragments from …

On Mining Biological Signals Using Correlation Networks, Kathryn Dempsey Cooper, Ishwor Thapa, Claudia Cortes, Zack Eriksen, Dhundy Raj Bastola, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Correlation networks have been used in biological networks to analyze and model high-throughput biological data, such as gene expression from microarray or RNA-seq assays. Typically in biological network modeling, structures can be mined from these networks that represent biological functions; for example, a cluster of proteins in an interactome can represent a protein complex. In correlation networks built from high-throughput gene expression data, it has often been speculated or even assumed that clusters represent sets of genes that are coregulated. This research aims to validate this concept using network systems biology and data mining by identification of correlation network clusters …

An Intelligent Data-Centric Approach Toward Identification Of Conserved Motifs In Protein Sequences, Kathryn Dempsey Cooper, Benjamin Currall, Richard Hallworth, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

The continued integration of the computational and biological sciences has revolutionized genomic and proteomic studies. However, efficient collaboration between these fields requires the creation of shared standards. A common problem arises when biological input does not properly fit the expectations of the algorithm, which can result in misinterpretation of the output. This potential confounding of input/output is a drawback especially when regarding motif finding software. Here we propose a method for improving output by selecting input based upon evolutionary distance, domain architecture, and known function. This method improved detection of both known and unknown motifs in two separate case studies. …