Genome-scale prediction of bacterial promoters

Abstract

A key step in the transcription of RNA is the binding of the RNA polymerase protein complex to a short promoter sequence that is typically upstream of the gene to be expressed. Automated identification of promoters would serve as a valuable complement to experimental validation in determining which genes are likely to be expressed and when; however, promoter sequences are short and highly variable, which makes them very difficult to accurately classify. The many tools developed to identify promoters in DNA have generally been tested on small and balanced subsets of genomic sequence, and the results may not reflect their expected performance on genomes with millions of DNA base pairs where promoters are likely to comprise less than ∼1% of the sequence. Here we introduce Expositor, a neural-network-based method that uses different types of DNA encodings and tunable sensitivity and specificity parameters. Expositor showed higher sensitivity and precision on the E. coli K-12 MG1655 chromosome than other tested approaches. Expositor predictions were more consistent in the homologous subset of sequence from a strain of Salmonella than they were with another strain of E. coli. We also examined the accuracy of Expositor in distinguishing different classes of promoters and found that misclassification between classes was consistent with the biological similarity between promoters.