  Prokaryotic gene prediction plays an important role in understanding the
biology of organisms and their function with applications in medicine and
biotechnology. Although the current gene finders are highly sensitive in
finding long genes, their sensitivity decreases noticeably in finding shorter
genes (<180 nts). The culprit is insufficient annotated gene data to identify
distinguishing features in short open reading frames (ORFs). We develop a deep
learning-based method called ProtiGeno, specifically targeting short
prokaryotic genes using a protein language model trained on millions of evolved
proteins. In systematic large-scale experiments on 4,288 prokaryotic genomes,
we demonstrate that ProtiGeno predicts short coding and noncoding genes with
higher accuracy and recall than the current state-of-the-art gene finders. We
discuss the predictive features of ProtiGeno and possible limitations by
visualizing the three-dimensional structure of the predicted short genes. Data,
codes, and models are available at https://github.com/tonytu16/protigeno.
