Jakub M Bartoszewicz1,2, Anja Seidel1,2, Robert Rentzsch1, Bernhard Y Renard1. 1. Bioinformatics Unit (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany. 2. Department of Mathematics and Computer Science, Free University of Berlin, 14195 Berlin, Germany.
Abstract
MOTIVATION: We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. Moreover, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, which limits their performance on unknown, unrecognized and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads, even though the biological context is unavailable. RESULTS: We present DeePaC, a Deep Learning Approach to Pathogenicity Classification. It includes a flexible framework allowing easy evaluation of neural architectures with reverse-complement parameter sharing. We show that convolutional neural networks and LSTMs outperform the state-of-the-art based on both sequence homology and machine learning. Combining a deep learning approach with integrating the predictions for both mates in a read pair results in cutting the error rate almost in half in comparison to the previous state-of-the-art. AVAILABILITY AND IMPLEMENTATION: The code and the models are available at: https://gitlab.com/rki_bioinformatics/DeePaC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. Moreover, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, which limits their performance on unknown, unrecognized and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads, even though the biological context is unavailable. RESULTS: We present DeePaC, a Deep Learning Approach to Pathogenicity Classification. It includes a flexible framework allowing easy evaluation of neural architectures with reverse-complement parameter sharing. We show that convolutional neural networks and LSTMs outperform the state-of-the-art based on both sequence homology and machine learning. Combining a deep learning approach with integrating the predictions for both mates in a read pair results in cutting the error rate almost in half in comparison to the previous state-of-the-art. AVAILABILITY AND IMPLEMENTATION: The code and the models are available at: https://gitlab.com/rki_bioinformatics/DeePaC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Laura Uelze; Josephine Grützke; Maria Borowiak; Jens Andre Hammerl; Katharina Juraschek; Carlus Deneke; Simon H Tausch; Burkhard Malorny Journal: One Health Outlook Date: 2020-02-18
Authors: Jing Yang; Mohammed Eslami; Yi-Pei Chen; Mayukh Das; Dongmei Zhang; Shaorong Chen; Alexandria-Jade Roberts; Mark Weston; Angelina Volkova; Kasra Faghihi; Robbie K Moore; Robert C Alaniz; Alice R Wattam; Allan Dickerman; Clark Cucinell; Jarred Kendziorski; Sean Coburn; Holly Paterson; Osahon Obanor; Jason Maples; Stephanie Servetas; Jennifer Dootz; Qing-Ming Qin; James E Samuel; Arum Han; Erin J van Schaik; Paul de Figueiredo Journal: Proc Natl Acad Sci U S A Date: 2022-04-01 Impact factor: 12.779