9 th Annual "Humies" Awards 2012 — Philadelphia, Pennsylvania Uday Kamath, Amarda Shehu,Kenneth A De Jong Department of Computer Science George Mason University Fairfax,VA, {ukamath, amarda, Genetic Programming Based Feature Generation for Automated DNA Sequence Analysis

Bioinformatics and Molecular Biology LarrañagaP et al. Brief Bioinform2006;7:86-112

Promoter Site Identification Copyright 2012 the British Journal of Anaesthesia Background Promoters signal the beginning of a coding region They are important signals for initiation of DNA->RNA transcription. Challenges Complex Gene-specific Many decoys

DNA Splice Site Identification Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, and Gunnar Rätsch TUTORIAL: SUPPORT VECTOR MACHINES AND KERNELS FOR COMPUTATIONAL BIOLOGY [2008] Background Splice sites mark boundaries between exons and introns in a gene Challenges No known sequence pattern i.Diverse sequence length ii.Diverse exon lengths iii.Diverse number and lengths of introns 0.1 to 1% true splice sites, rest decoys

Evolutionary (GP) Approach

Finding Functional Features GP Functional Features Terminals  A,C,T,G  Integers for position/region Basic Non Terminals  Motif (combination of ACTG)  Position based Motifs  Correlation based Motifs  Region based Motifs  Composition based Motifs Complex Non Terminals  Conjuntions  Disjunctions  Negations  Features Evolved combining accuracy/precision

Why Human Competitive ? B) The result >= than a result that was accepted as a new scientific result E) The result >= than the most recent human- created solution to a long-standing problem F) The result >= than a result that was considered an achievement when was first discovered G) The result solves a problem of indisputable difficulty in its field

Why Human Competitive ? B) The result >= than a result that was accepted as a new scientific result Splice Site Prediction Research compares state of the art Enumeration, Iterative, Probabilistic methods, Kernel methods etc. Best Precision with statistical significant improvements on most datasets Promoter Prediction Research compares results with 7 state of the art algorithms ranging from Enumeration, Iterative, Neural Networks, Kernel based etc. Best Precision and with statistical significant improvements on different datasets F) The result >= than a result that was considered an achievement when was first discovered

Why Human Competitive ? F) The result >= than a result that was considered an achievement when was first discovered On Promoter Identification Problem What was considered achievementWhere we stand Uday Kamath, Kenneth A De Jong, and Amarda Shehu. "An Evolutionary-based Approach for Feature Generation: Eukaryotic Promoter Recognition." IEEE Congress on Evolutionary Computation (IEEE CEC), New Orleans, LA, pg , 2011

Why Human Competitive ? On Splice site Identification Problem F) The result >= than a result that was considered an achievement when was first discovered What was considered achievement Where we stand Uday Kamath, Jack Compton, Rezarta Islamaj Dogan, Kenneth A. De Jong, and Amarda Shehu. An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and its Application to DNA Splice-Site Prediction. Trans Comp Biol and Bioinf 2012

Why Human Competitive ? Long Standing Problem(s) Genome Sequence prediction and annotation of Splice sites and Promoters Computational Results >= Around 7 datasets and 10 algorithms compared Advancing Understanding in Genomics Our top features do contain signals painstakingly determined by biologists through decades of wet-lab research. More importantly, new features are found that may help biologists further advance their understanding of DNA architecture All our features are available online for experts to analyze and spur further wet-lab research E) The result >= than the most recent human-created solution to a long-standing problem

Why Human Competitive? G) The result solves a problem of indisputable difficulty in its field Estimated 10-25K human protein-coding genes (only 1.5% of entire genome) Wet-lab models of discovery costly and prone to errors Cannot keep pace with growing genomic sequences Computational models good complements, but Black Box Models – No or Little help to Biologists White Box Models- Lower precision/accuracy and reliant on manual steps Decades of research into DNA function and architecture “Gene finding” on pubmed returns > 80,000 research articles Progress crucial to speed up our understanding of disease and development of targeted treatments

Why is this the Best Entry Addresses central problems to molecular biology and health research Finding functional signals in genome sequences is complex and NP-Hard Improvements over state of the art are statistically significant Extensive statistical analysis validates usefulness of GP features – F-score and Information gain techniques Advances understanding to motivate further research – Features found by GP reproduce results of decades of research by biologists – Novel interesting features also reported – Features, data sets, and software publicly available for community Far reaching implications, spurring research beyond genomics – Example: finding what features determine anti-microbial activity for the purpose of generating novel peptides to combat drug resistance.