TY - GEN
T1 - An island-based approach for differential expression analysis
AU - Eteleeb, Abdallah M.
AU - Flight, Robert M.
AU - Harrison, Benjamin J.
AU - Petruska, Jeffrey C.
AU - Rouchka, Eric C.
PY - 2013
Y1 - 2013
N2 - 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 cur- rent RNA-Seq methods is the dependency on annotated bi- ological 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 segmenta- Tion 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 differential is- land 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 perfor- mance 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.
AB - 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 cur- rent RNA-Seq methods is the dependency on annotated bi- ological 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 segmenta- Tion 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 differential is- land 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 perfor- mance 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.
KW - Alternative splicing
KW - Differential expression
KW - RNA-seq
UR - http://www.scopus.com/inward/record.url?scp=84888168328&partnerID=8YFLogxK
U2 - 10.1145/2506583.2506589
DO - 10.1145/2506583.2506589
M3 - Conference contribution
AN - SCOPUS:84888168328
SN - 9781450324342
T3 - 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
SP - 419
EP - 429
BT - 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
T2 - 2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
Y2 - 22 September 2013 through 25 September 2013
ER -