  Computational identification of promoters is notoriously difficult as human
genes often have unique promoter sequences that provide regulation of
transcription and interaction with transcription initiation complex. While
there are many attempts to develop computational promoter identification
methods, we have no reliable tool to analyze long genomic sequences. In this
work we further develop our deep learning approach that was relatively
successful to discriminate short promoter and non-promoter sequences. Instead
of focusing on the classification accuracy, in this work we predict the exact
positions of the TSS inside the genomic sequences testing every possible
location. We studied human promoters to find effective regions for
discrimination and built corresponding deep learning models. These models use
adaptively constructed negative set which iteratively improves the models
discriminative ability. The developed promoter identification models
significantly outperform the previously developed promoter prediction programs
by considerably reducing the number of false positive predictions. The best
model we have built has recall 0.76, precision 0.77 and MCC 0.76, while the
next best tool FPROM achieved precision 0.48 and MCC 0.60 for the recall of
0.75. Our method is available at http://www.cbrc.kaust.edu.sa/PromID/.
