Deep learning framework SHADES distinguishes among multiple evolutionary processes

[bioRxiv] A bioRxiv preprint by Khan, DeGiorgio and co-authors introduces SHADES, a deep learning framework that uses information on sequence differences and their frequencies at multiple spatial resolutions to capture evolutionary signatures across genomic scales. According to the authors, SHADES can distinguish among neutrality, selective sweeps, balancing selection and adaptive introgression, and outperformed several contemporary methods evaluated for classification accuracy and power. The framework remained robust to a number of confounding factors, with only modest changes in classification performance, the authors said. Applied to empirical haplotypic variation from European humans, SHADES recovered previously reported signals for selective sweeps, balancing selection and adaptive introgression while also identifying novel genes broadly involved in immune defense, inflammatory regulation and epithelial barrier biology. The work is a preprint and has not been peer-reviewed.