Solving genomic puzzles: computational methods for metagenomic binning

Author:

Mallawaarachchi Vijini1ORCID,Wickramarachchi Anuradha2ORCID,Xue Hansheng3ORCID,Papudeshi Bhavya1ORCID,Grigson Susanna R1ORCID,Bouras George456ORCID,Prahl Rosa E2ORCID,Kaphle Anubhav2ORCID,Verich Andrey27ORCID,Talamantes-Becerra Berenice2ORCID,Dinsdale Elizabeth A1ORCID,Edwards Robert A1ORCID

Affiliation:

1. Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University , Adelaide, SA 5042, Australia

2. Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO) , Westmead, NSW 2145, Australia

3. School of Computing, National University of Singapore , Singapore 119077, Singapore

4. Adelaide Medical School , Faculty of Health and Medical Sciences, , Adelaide, SA 5005, Australia

5. The University of Adelaide , Faculty of Health and Medical Sciences, , Adelaide, SA 5005, Australia

6. The Department of Surgery—Otolaryngology Head and Neck Surgery, University of Adelaide and the Basil Hetzel Institute for Translational Health Research, Central Adelaide Local Health Network , Adelaide, SA 5011, Australia

7. The Kirby Institute, The University of New South Wales , Randwick, Sydney, NSW 2052, Australia

Abstract

Abstract Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Funder

National Institutes of Health

National Institute of Diabetes and Digestive and Kidney Diseases

Australian Research Council

Publisher

Oxford University Press (OUP)

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