2012 bioinformatics projects in Kentucky:
Computational Detection of Alternative Splicing Events Using GeneChipTM Arrays and RNASeq Data
Principal Investigator: Eric C. Rouchka, D.Sc.
Associate Professor
Department of Computer Engineering and Computer Science
University of Louisville, Louisville, KY
Eric.rouchka@louisville.edu
Alternative splicing within eukaryotic transcriptomes provides additional levels of gene complexity whereby specific isoforms, or splice variants, may be expressed in response to stressors in the environment or to tissue specificity. A variety of computational approaches have been used to determine the presence of alternative splicing events using microarray data (typically focusing on exon arrays) and next-generation sequencing data. The focus of these approaches has been on the alternative expression of the coding regions of transcripts. However, alternative splicing of the untranslated regions (UTRs) provides the potential of differential expression by removing or incorporating binding patterns within the 5’ or 3’ UTRs which may be used to regulate gene expression through mechanisms such as transcription factors, miRNAs, or various localization signals. This project will explore methodologies for detecting alternative splicing events involving the UTRs within the probes on Affymetrix® GeneChipTM arrays and next-generation RNASeq data
Analysis of Gene Expression in Unannotated Regions in RNASeq Data
Principal Investigator: Eric C. Rouchka, D.Sc.
Associate Professor
Department of Computer Engineering and Computer Science
University of Louisville, Louisville, KY
Eric.rouchka@louisville.edu
Next generation sequencing methodologies have made it possible for researchers to gain additional insight into differential transcriptome expression. The most common approach to analyzing RNASeq data is to map the transcribed regions onto the genome from which they were sequenced and then to overlay these regions with known annotations, such as RefSeq gene identifiers. Such an approach works well for organisms such as human and mouse that have been well studied, but are restrictive when less is known about the annotated gene coding regions. This project will focus on developing methods for gleaning transcriptome information from unannotated regions, particularly in cases where significant differential expression is occurring.