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University of Louisville Bioinformatics

Bioinformatics Research at the University of Louisville





Abdallah M. Eteleeb1, Robert M. Flight2, Benjamin J. Harrison3,4, Jeffrey C. Petruska3,4, Eric C. Rouchka1

  1. Department of Computer Engineering and Computer Science, University of Louisville.
  2. Department of Molecular and Cellular Biochemistry, University of Kentucky.
  3. Deparmtent of Anatomical Sciences and Neurobiology, University of Louisville.
  4. Deparmtent of Neurological Surgery, Kentucky Spinal Cord Injury Research Center, University of Louisville.

High-throughput mRNA sequencing (also known as RNA-Seq) promises to be the technique of choice for studying transcriptome profiles. This technique provides the ability to develop precise methodologies for transcript and gene expression quantification, novel transcript and exon discovery, and splice variant detection. One of the limitations of the current RNA-Seq methods is the dependency on annotated biological features (e.g. exons, transcripts, genes) to detect expression differences across samples. This forces the identification of expression levels and the detection of significant changes to known genomic regions. Any significant changes that occur in unannotated regions will not be captured.
To overcome this limitation, we developed a novel segmentation approach, Island-Based (IB), for analyzing differential expression in RNA-Seq and targeted sequencing (exome capture) data without specific knowledge of an isoform. The IB segmentation determines individual islands of expression based on windowed read counts that can be compared across experimental conditions to determine diffential island expression. In order to detect differentially expressed genes, the significance of islands (p-values) are combined using Fisher's method. We tested and evaluated the performance of our approach by comparing it to the existing differentially expressed gene (DEG) methods: CuffDiff, DESeq, and edgeR using two benchmark MAQC RNA-Seq datasets. The IB algorithm outperforms all three methods in both datasets as illustrated by an increased auROC.


Supported by NIH NCRR P20 RR16481

Eteleeb AM, Flight RM, Harrison BJ, Petruska JC, Eric EC. (2013) An island-based approach for differential expression analysis. In Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB '13), pp. 419-429. September 22-25, 2013, Washington, DC. (PDF).

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