  Diagnosis and risk stratification of cancer and many other diseases require
the detection of genomic breakpoints as a prerequisite of calling copy number
alterations (CNA). This, however, is still challenging and requires
time-consuming manual curation. As deep-learning methods outperformed classical
state-of-the-art algorithms in various domains and have also been successfully
applied to life science problems including medicine and biology, we here
propose Deep SNP, a novel Deep Neural Network to learn from genomic data.
Specifically, we used a manually curated dataset from 12 genomic single
nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at
predicting the presence or absence of genomic breakpoints, an indicator of
structural chromosomal variations, in windows of 40,000 probes. We compare our
results with well-known neural network models as well as Rawcopy though this
tool is designed to predict breakpoints and in addition genomic segments with
high sensitivity. We show, that Deep SNP is capable of successfully predicting
the presence or absence of a breakpoint in large genomic windows and
outperforms state-of-the-art neural network models. Qualitative examples
suggest that integration of a localization unit may enable breakpoint detection
and prediction of genomic segments, even if the breakpoint coordinates were not
provided for network training. These results warrant further evaluation of
DeepSNP for breakpoint localization and subsequent calling of genomic segments.
