Research summary 2022: Our aim is to find interesting and useful information in genetic sequences, and to develop algorithmic and mathematical methods for this purpose.  We recently discovered the oldest ever "protein fossils": segments of formerly protein-coding DNA, by sensitive probability-based analysis.  This revealed a great diversity of transposable elements in vertebrate ancestors of the Paleozoic Era. We also collaborate with medical geneticists to understand complex chromosome rearrangements and tandem repeat expansions / contractions that cause disease.  We discovered the cause of neuronal intranuclear inclusion disease: a tandem repeat expansion in a human-specific gene. In related work, we have detected recombination events between LINE and SINE repeat elements, showing that recombination of repeat elements generates somatic complexity in human genomes.  Another project found significant non-existence of sequences in genomes and proteomes, providing clues about immune recognition and pathogen/host adaption.  Finally, we are developing a mathematically-optimal way to sample big sequence data, so it can be analyzed quickly, based on minimally-overlapping words.

We are not specialists in black box machine learning methods (e.g. deep learning), useful and wonderful though they are, because we aim to understand, not just predict.