CoCoNet: an efficient deep learning tool for viral metagenome binning

Author:

Arisdakessian Cédric G1ORCID,Nigro Olivia D2,Steward Grieg F3,Poisson Guylaine1,Belcaid Mahdi14

Affiliation:

1. Department of Information and Computer Sciences, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA

2. Department of Natural Science, Hawai‘i Pacific University, Honolulu, HI 96813, USA

3. Department of Oceanography, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA

4. Hawai‘i Institute of Marine Biology, University of Hawai‘i at Mānoa, Honolulu, HI 96816, USA

Abstract

Abstract Motivation Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It consists of transforming overlapping DNA sequencing reads into sufficiently accurate representations of the community’s genomes. This process is computationally difficult and commonly results in genomes fragmented across many contigs. Computational binning methods are used to mitigate fragmentation by partitioning contigs based on their sequence composition, abundance or chromosome organization into bins representing the community’s genomes. Existing binning methods have been principally tuned for bacterial genomes and do not perform favorably on viral metagenomes. Results We propose Composition and Coverage Network (CoCoNet), a new binning method for viral metagenomes that leverages the flexibility and the effectiveness of deep learning to model the co-occurrence of contigs belonging to the same viral genome and provide a rigorous framework for binning viral contigs. Our results show that CoCoNet substantially outperforms existing binning methods on viral datasets. Availability and implementation CoCoNet was implemented in Python and is available for download on PyPi (https://pypi.org/). The source code is hosted on GitHub at https://github.com/Puumanamana/CoCoNet and the documentation is available at https://coconet.readthedocs.io/en/latest/index.html. CoCoNet does not require extensive resources to run. For example, binning 100k contigs took about 4 h on 10 Intel CPU Cores (2.4 GHz), with a memory peak at 27 GB (see Supplementary Fig. S9). To process a large dataset, CoCoNet may need to be run on a high RAM capacity server. Such servers are typically available in high-performance or cloud computing settings. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Science Foundation Division of Ocean Sciences

Office of Integrative Activities

Securing Hawaii’s Water Future

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference36 articles.

1. Binning metagenomic contigs by coverage and composition;Alneberg;Nat. Methods,2014

2. Htseq-a python framework to work with high-throughput sequencing data;Anders;Bioinformatics,2015

3. The marine viromes of four oceanic regions;Angly;PLoS Biol,2006

4. Assembly-free single-molecule sequencing recovers complete virus genomes from natural microbial communities;Beaulaurier;Genome Res,2020

5. Signature verification using a “siamese” time delay neural network;Bromley;Proceedings of the 6th International Conference on Neural Information Processing Systems, NIPS’93,1993

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