Research

Messenger RNA (mRNA) plays a central role in carrying out the instructions encoded in a gene. A gene’s components may be combined in various ways to generate a diverse range of mRNA molecules, or transcripts, allowing the same gene to produce different products under different conditions. By sequencing mRNA transcripts and aligning them back to a genome, researchers can elucidate the diverse roles a gene plays in different conditions, as in different tissues or under different forms of stress. The latest sequencing technology can produce millions of short sequence reads from mRNA transcripts, providing researchers with unprecedented opportunities to assess how these genetic instructions change under different conditions.

SpliceGrapher prediction for a gene in A. thaliana.

To analyze these data we have developed the SpliceGrapher method that takes a different approach from existing methods that attempt to predict complete mRNA transcripts from patterns of reads aligned to a genomic region. The short length of these reads makes the task difficult because in many cases the reads do not cover a complete transcript. SpliceGrapher addresses this issue by instead predicting splice graphs that capture in a single structure all the ways in which a gene’s components may be assembled. Whereas other methods make predictions primarily from read alignments, SpliceGrapher uses existing gene annotations to guide its predictions.

Our preliminary work on SpliceGrapher demonstrated that its predictions tend to be more consistent with curated annotations than those of other methods. Further, our evaluation has shown that a substantial number of novel predictions made by other methods depend on what may be spurious evidence. To download SpliceGrapher, click the link at the top of the page.

Publications

• M. F. Rogers, C. Boucher and A. Ben-Hur. SpliceGrapherXT: from splice graphs to transcripts using RNA-Seq. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB) 2013. In press
• M. Frenkel-Morgenstern, A. Gorohovski, V. Lacroix, M. F. Rogers, K. Ibanez, C. Boullosa, E. Andres Leon, A. Ben-Hur, A Valencia. ChiTaRS: a database of human, mouse and fruit fly Chimeric Transcripts and RNA-Sequencing data. Nucleic Acids Research, Vol. 41, no. D1, 2013.
• A. S. N. Reddy*, M. F. Rogers*, D. N. Richardson, M. Hamilton, A. Ben-Hur. (* Co-first author) Deciphering the plant splicing code: Experimental and computational approaches for predicting alternative splicing and splicing regulatory elements. Frontiers in Plant Genetics and Genomics, Vol. 3, 2012.
• M. F. Rogers, J. Thomas, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: Detecting patterns of alternative splicing from RNA-seq data in the context of gene models and EST data. Genome Biology, Vol. 13, 2012.
• D. N. Richardson, M. F. Rogers, A. Labadorf, A. Ben-Hur, H. Guo, A. Paterson and A. S. N. Reddy. Comparative analysis of serine/arginine-rich proteins across 27 eukaryotes: insights into subfamily classification and extent of alternative splicing. PLoS ONE, Vol. 6, no. 9, 2011.
• A. Labadorf, A. Link, M. F. Rogers, J. Thomas, A. S. N. Reddy, A. Ben-Hur. Genome-wide analysis of alternative splicing in Chlamydomonas reinhardtii. BMC Genomics, Vol. 11, no. 114, 2010.
• M. F. Rogers, A. Ben-Hur. The Use of Gene Ontology Evidence Codes in Preventing Classifier Assessment Bias. Bioinformatics, Vol. 25, 2009.
• M. F. Rogers, A. Howe and D. Whitley. Looking for Shortcuts: Infeasible Search Analysis for Oversubscribed Scheduling Problems. Proceedings of ICAPS, June 2006, pp. 314-323

Citation Metrics

Conference Talks

• M. F. Rogers, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: predicting splice graphs from diverse evidence. ISMB 2011, Late-Breaking Research Track, July 18, 2011, Vienna, Austria.
• M. F. Rogers, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: predicting splice graphs from diverse evidence. ISMB 2011 Alternative Splicing SIG, July 15, 2011, Vienna, Austria.
• M. F. Rogers, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: predicting splice graphs from diverse evidence. Rocky Mountain Bioinformatics Conference (Rocky ‘10) December 2010, Snowmass, Colorado.
• M. F. Rogers, A. Howe and D. Whitley. Looking for Shortcuts: Infeasible Search Analysis for Oversubscribed Scheduling Problems. ICAPS, June 2006, Cumbria, U.K.

Invited Talks

• Using RNA-Seq to predict alternative splicing. CS580 Guest Lecture, September 27, 2011.
• SpliceGrapher: predicting splice graphs from diverse evidence. CS Colloquium (BMAC), September 12, 2011.

Poster Presentations

• M. F. Rogers, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: predicting splice graphs from diverse evidence. ISMB 2011, July 17-19, 2011, Vienna, Austria
• M. F. Rogers, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: predicting splice graphs from diverse evidence. ISMB 2011 AS SIG, July 15-16, 2011, Vienna, Austria
• M. F. Rogers, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: predicting splice graphs from diverse evidence. ISMB 2011 HiTSEQ SIG, July 15-16, 2011, Vienna, Austria
• M. F. Rogers, A. Ben-Hur, A. S. N. Reddy. SpliceGrapher: predicting splice graphs from diverse evidence. ISCB Rocky ‘10, December 9-11, 2010, Snowmass, Colorado
• A. Sokolov, M. F. Rogers, A. Curtis, A. Ben-Hur, R. M. McConnell. Accurate Initialization of Probabilistic Motif Finders. Graybill Conference, Bioinformatics Workshop, Summer 2007, Colorado State University
• M. F. Rogers, A. Howe and D. Whitley. Looking for Shortcuts: Infeasible Search Analysis for Oversubscribed Scheduling Problems, ICAPS Doctoral Consortium, June 2006, Cumbria, U.K.