Deep learning approach to determining the type of long reads

Single and metagenome de novo assembly of long reads is still one of the most difficult problems in bioinformatics. Often used paradigm, called Overlap-Layout-Consensus, aims at finding a Hamiltonian path through an assembly graph obtained from overlapping reads in a sample. However, these graphs can be extremely complex due to repetitive regions in genomes and sequencing artifacts such as chimeric reads, which lead to higher fragmentation of the assembly genomes. A popular approach for tackling this problem is based on dividing reads into three categories and processing them appropriately. These three categories of reads are regular, repetitive, and chimeric. A drawback of read classification with heuristic algorithms in existing assemblers is a manual selection of parameters based on just several genomes. In this work, we propose a deep learning approach for classification of reads based on their pile-o-grams, plots of coverage versus base index. The model was trained on a hand-labeled dataset consisting of pile-o-gram images from multiple bacteria, and tested on a different bacteria species not included in the training set. With such a setup, and with classes being balanced, an accuracy of 93% was achieved which opens the possibility of creating more accurate and less contiguous assemblies.

Poster presentation at International Conference on Intelligent Systems for Molecular Biology - MLCSB COSI, 2020