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Correcting index databases improves metagenomic studies

View ORCID ProfileGuillaume Méric, View ORCID ProfileRyan R. Wick, Stephen C. Watts, View ORCID ProfileKathryn E. Holt, View ORCID ProfileMichael Inouye
doi: https://doi.org/10.1101/712166
Guillaume Méric
1Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne 3004, Victoria, Australia
2Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
3The Milner Centre for Evolution, University of Bath, Claverton Down, Bath, BA2 7AY, UK
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  • For correspondence: guillaume.meric@baker.edu.au rrwick@gmail.com
Ryan R. Wick
2Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
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  • For correspondence: guillaume.meric@baker.edu.au rrwick@gmail.com
Stephen C. Watts
2Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
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Kathryn E. Holt
2Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia
4London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
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Michael Inouye
1Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne 3004, Victoria, Australia
5Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
6Cambridge Substantive Site, Health Data Research UK, Wellcome Genome Campus, Hinxton, UK
7The Alan Turing Institute, London, UK
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Abstract

Assessing the taxonomic composition of metagenomic samples is an important first step in understanding the biology and ecology of microbial communities in complex environments. Despite a wealth of algorithms and tools for metagenomic classification, relatively little effort has been put into the critical task of improving the quality of reference indices to which metagenomic reads are assigned. Here, we inferred the taxonomic composition of 404 publicly available metagenomes from human, marine and soil environments, using custom index databases modified according to two factors: the number of reference genomes used to build the databases, and the monophyletic strictness of species definitions. Index databases built following the NCBI taxonomic system were also compared to others using Genome Taxonomy Database (GTDB) taxonomic redefinitions. We observed a considerable increase in the rate of read classification using modified reference index databases as compared to a default NCBI RefSeq database, with up to a 4.4-, 6.4- and 2.2-fold increase in classified reads per sample for human, marine and soil metagenomes, respectively. Importantly, targeted correction for 70 common human pathogens and bacterial genera in the index database increased their specific detection levels in human metagenomes. We also show the choice of index database can influence downstream diversity and distance estimates for microbiome data. Overall, the study shows a large amount of accessible information in metagenomes remains unexploited using current methods, and that the same data analysed using different index databases could potentially lead to different conclusions. These results have implications for the power and design of individual microbiome studies, and for comparison and meta-analysis of microbiome datasets.

Footnotes

  • https://github.com/rrwick/Metagenomics-Index-Correction

  • https://monash.figshare.com/projects/Metagenomics_Index_Correction/65534

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 23, 2019.
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Correcting index databases improves metagenomic studies
Guillaume Méric, Ryan R. Wick, Stephen C. Watts, Kathryn E. Holt, Michael Inouye
bioRxiv 712166; doi: https://doi.org/10.1101/712166
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Correcting index databases improves metagenomic studies
Guillaume Méric, Ryan R. Wick, Stephen C. Watts, Kathryn E. Holt, Michael Inouye
bioRxiv 712166; doi: https://doi.org/10.1101/712166

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