title: Prediction of fine-tuned promoter activity from DNA sequence.
abstract: The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems.
authors.affiliation: Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA; IBM TJ Watson Research Center, NY, USA; IBM Research-Africa, Johannesberg, South Africa.
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Siwo
authors.affiliation: Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Rider
authors.affiliation: Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Epicentre, Madison, WI, USA.
authors.forename: Asako
authors.initials: A
authors.lastname: Tan
authors.affiliation: Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Pinapati
authors.affiliation: Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: Emrich
authors.affiliation: Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.
authors.forename: Nitesh
authors.initials: N
authors.lastname: Chawla
authors.affiliation: Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Ferdig
keywords: DNA sequence; DREAM challenges; Expression prediction; Gene expression; Gene regulation; Machine learning; Promoter activity; Transcription modeling