Mark F. Rogers

Research

Publications

Computational Biology

• A. S. N. Reddy^†, M. F. Rogers^†, D. N. Richardson, M. Hamilton, A. Ben-Hur. 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. (^†Co-first author)
• M. F. Rogers, Julie 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 (designated Highly Accessed article)
• M. F. Rogers, A. Ben-Hur. The Use of Gene Ontology Evidence Codes in Preventing Classifier Assessment Bias. Bioinformatics, Vol. 25, pp. 1173-1177, 2009

Artificial Intelligence

• M. F. Rogers, A. Howe and D. Whitley. Looking for Shortcuts: Infeasible Search Analysis for Oversubscribed Scheduling Problems. Proceedings of ICAPS 2006, June, pp. 314-323 ps.gz, pdf

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.

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 (pdf)
• 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. (pdf)

Computational Biology:

• Alternative Splicing Prediction
  In this project we intend to develop classifiers that are able to predict alternative splicing in DNA sequences. In plants, such as A. thaliana, the most common form of alternative splicing is intron retention, where introns are retained in mature mRNA under some conditions. We are experimenting with classifiers such as Support Vector Machines (SVMs) based on Hidden Markov Models (HMMs), Smith-Waterman alignment scores and other sequence-based scoring schemes.

  SpliceGrapher
  In pursuing these goals we have developed SpliceGrapher a python-based scripting tool that can use annotated gene models, next-generation sequencing data (RNA-Seq) and EST or cDNA alignments to predict alternative splicing events. In our tests we have found that SpliceGrapher produces predictions that are more consistent with annotated gene models than those of other software packages.

  Related articles:

  • SpliceGrapher: Detecting patterns of alternative splicing from RNA-seq data in the context of gene models and EST data. Genome Biology, Vol. 13, 2012
  • Comparative analysis of serine/arginine-rich proteins across 27 eukaryotes: insights into subfamily classification and extent of alternative splicing. (Richardson et al., 2011)
  • Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation (Trapnell et al., 2009)
  • RASE: recognition of alternatively spliced exons in C. elegans (Rätsch et al., 2007)
  • Nuclear Pre-mRNA Splicing in Plants(Reddy, 2001)

• Classifier assessment in protein function prediction
  Researchers have noted that a classifier's performance statistics may be biased if the data used for training and testing includes protein annotations derived through processes similar to the classifier. We conducted a set of controlled experiments using a nearest-neighbor classifier based on PSI-BLAST similarity scores. Our results demonstrate that the source of GO annotations used to assess a protein function predictor can have a significant influence on classifier accuracy.

  Related articles and publications:

  • M. F. Rogers, A. Ben-Hur, The Use of Gene Ontology Evidence Codes in Preventing Classifier Assessment Bias, Bioinformatics, 2009, vol. 25, pp. 1173-1177
  • Modeling the percolation of annotation errors in a database of protein sequences (Gerlt et al., 2002)
  • What we do not know about sequence analysis and sequence databases. (Karp, 1998)

• Motif Finding
  Directed acyclic word graphs (DAWGs) provide a way to recover substring statistics from long strings, or common subsequences between two strings, in linear time. These methods are fast, but provide information only about exact matches between two strings. We are extending this result to establish structures and algorithms that permit a finite number of string mismatches. These mismatches may then yield seed strings for motif-finding algorithms such as Gibbs sampling or Expectation Maximization (EM) to identify new motifs in DNA and amino acid sequences.

  Related articles:

  • Sequence Landscapes
  • DAWGs

Artificial Intelligence:

• Scheduling
  Investigated the role of infeasible solutions in constrained optimization problems (COPs). Many successful algorithms search both feasible and infeasible solutions to achieve superior search performance.  Experimental results suggest that infeasible space adds a quantifiable improvement over feasible space in some circumstances.  Researchers hypothesize two reasons for this behavior:
  1. COPs tend to have optima near the boundary between feasible and infeasible solutions
  2. Infeasible search may yield shortcuts to optima
  Recent research confirms the presence of shortcuts, but we wish to learn how shortcuts depend on specific problems and neighborhood operators. Our research examines infeasible search with the following problems: ROADEF Challenge 2003 , AFSCN Scheduling, other EOS problems.

  Resources:

  • Poster for the 2005 ISTEC Colloquium pdf
  • Poster for the 2006 CS Research Symposium (awarded Best Masters Poster) pdf
  • Poster for the 2006 ICAPS Doctoral Consortium pdf
  • Consortium posters from CSU: Mark Rogers, Mark Roberts, Andrew Sutton
  • AFIT Data Generator gzipped TAR

Data Structures:

• Java implementation of a van Emde Boas heap
• Seminar slides on Mikkel Thorup's On RAM Priority Queues