O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 1 Identifying Regulatory Transcriptional Elements on Functional Gene Groups Using Computer- based Algorithms Jason C. McKay Morehouse College, Atlanta, Ga Research Alliance for Minorities Oak Ridge National Laboratory, Oak Ridge, TN Andrey Gorin, Ph.D. - Mentor

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 2 Objectives  Explain processes involved with gene regulation  Provide brief depiction of computer-based algorithms, which can detect new DNA binding sequences using given parameters  Uncover new DNA transcription binding sequences (motifs) for a set of transcription factors

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 3 Microbiology: Protein Synthesis  DNA - replication in cell nucleus - more DNA produced for tissue growth  DNA is transcribed using a group of cellular enzymes (polymerases) that adhere to the DNA  RNA messenger leaves the nucleus and binds to a ribosomal complex, creating polypeptides (protein monomers)

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 4  Research focuses in on the production of the mRNA transcript (transcription)  Transcription factors act on cellular DNA by binding to specific sequences  By acting on the nucleic acid sequences, these transcription factors will affect RNA transcripts Sequence of Transcription

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 5 Computer-based Algorithm Applied to Genome Research  Computer algorithms search regions upstream for repeating patterns  Parameters are established by way of mathematical formulas  Input genes are tested with the algorithm  Information from the input set is returned based on how well it fit the parameters

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 6 Methods  Identify set of characteristics for basis of algorithm  Design sampling algorithm  Test known DNA sequences from gene databases - online databases - research articles

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 7 Parameters  Positional bias - a number sites upstream from some reading frame - a 50bp window containing highest number of the sites found  Group Specificity - comparison made between reading frames and group used to locate motif - a target list is selected and probabilities made with that target  MAP scoring - judges motifs during algorithm - tightly-packed information sequences; greater number of aligned sites  Group clustering - average is done involving nucleotide bp frequencies - motifs are automatically grouped

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 8 Sequence Trials  Enter from the online database  Apply to algorithm based on likenesses in consensus and position  Determine whether or not the sequence is a potential transcription factor binding site

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 9 Summary  Genes in our cells contain DNA and they are expressed, revealing heritable traits  DNA transcribes messenger RNA, which translates into polypeptides  Transcription factors serve as regulatory elements acting at the level of transcription  Algorithms are created with certain parameters in mind Input sets of gene sequences applied to the algorithm may prove to be novel binding sites due to their similarity to known motifs

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 10 What does all of this mean? Transcription factors bind to DNA and influence the mRNA transcript directly and polypeptide production indirectly Understanding which proteins are synthesized and what genes are expressed provides information regarding cellular control

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 11 Applications  Cancer cell viability?  Suppression of genetic diseases?

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 12 Accomplishments  Learned PERL programming  Discussions with mentors and ORNL faculty regarding research  Became familiar with algorithms and Gibbs- sampling  Consolidated DNA sequence catalogs

O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 13 Acknowledgements Andrey Gorin, Ph.D. Mike Leuze, Ph.D. Ja’Nera Mitchom Debbie McCoy Cheryl Hamby This research was performed under the Research Alliance for Minorities Program administered through the Computer Science and Mathematics Division, Oak Ridge National Laboratory. This Program is sponsored by the Mathematical, Information, and Computational Sciences Division; Office of Advanced Scientific Computing Research; U.S. Department of Energy. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725.