SPAN Semisupervised Peak Analyzer
We propose a novel semi-supervised approach to peak calling.
Fast and effective semi-supervised peaks analyzer SPAN Peak Analyzer is a multipurpose peak caller capable processing both conventional and ULI-Chip-seq tracks. In the semi-supervised approach, user annotates a handful of locations as peaks, valleys or peak shores, and then uses these annotations to train the model that is optimal for a given sample.
"SPAN model improvement" is a follow-up project done by students in the Bioinformatics Institute. Elena Kartysheva had been working on these tasks for two months under mentorship by Aleksei Dievskii. The intern successfully implemented a new SPAN model and compared it with the previous baseline. Even though the comparison of different computational models in the unsupervised environment is a tough question, the proposed approach can improve SPAN performance.
The full presentation is available here: https://drive.google.com/file/d/1sP9cnEcvdo-cerXVAbribWfLmQvlAA0C/view?usp=sharing