Sensitivity of shotgun metagenomics to host DNA: abundance estimates depend on bioinformatic tools and contamination is the main issue

References

1. Escobar-Zepeda A, Vera-Ponce de León A, Sanchez-Flores A, León V-Pde. The road to Metagenomics: from microbiology to DNA sequencing technologies and bioinformatics. Front Genet 2015; 6:348 [View Article]
   [Google Scholar]
2. Ranjan R, Rani A, Metwally A, McGee HS, Perkins DL. Analysis of the microbiome: advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem Biophys Res Commun 2016; 469:967–977 [View Article]
   [Google Scholar]
3. Eisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol 2019; 27:105–117 [View Article]
   [Google Scholar]
4. Weyrich LS, Farrer AG, Eisenhofer R, Arriola LA, Young J et al. Laboratory contamination over time during low‐biomass sample analysis. Mol Ecol Resour 2019; 19:982–996 [View Article]
   [Google Scholar]
5. Karstens L, Asquith M, Davin S, Fair D, Gregory WT et al. Controlling for contaminants in Low-Biomass 16S rRNA gene sequencing experiments. mSystems 2019; 4: [View Article]
   [Google Scholar]
6. Marotz CA, Sanders JG, Zuniga C, Zaramela LS, Knight R et al. Improving saliva shotgun metagenomics by chemical host DNA depletion. Microbiome 2018; 6:42 [View Article]
   [Google Scholar]
7. Feehery GR, Yigit E, Oyola SO, Langhorst BW, Schmidt VT et al. A method for selectively enriching microbial DNA from contaminating vertebrate host DNA. PLoS One 2013; 8:e76096 [View Article]
   [Google Scholar]
8. Hasan MR, Rawat A, Tang P, Jithesh PV, Thomas E et al. Depletion of human DNA in spiked clinical specimens for improvement of sensitivity of pathogen detection by next-generation sequencing. J Clin Microbiol 2016; 54:919–927 [View Article]
   [Google Scholar]
9. Nelson MT, Pope CE, Marsh RL, Wolter DJ, Weiss EJ et al. Human and extracellular DNA depletion for metagenomic analysis of complex clinical infection samples yields optimized viable microbiome profiles. Cell Rep 2019; 26:2227–2240 [View Article]
   [Google Scholar]
10. Oechslin CP, Lenz N, Liechti N, Ryter S, Agyeman P et al. Limited correlation of shotgun Metagenomics following host depletion and routine diagnostics for viruses and bacteria in low concentrated surrogate and clinical samples. Front Cell Infect Microbiol 2018; 8: [View Article]
    [Google Scholar]
11. Pereira-Marques J, Hout A, Ferreira RM, Weber M, Pinto-Ribeiro I et al. Impact of host DNA and sequencing depth on the taxonomic resolution of whole metagenome sequencing for microbiome analysis. Front Microbiol 2019; 10: [View Article]
    [Google Scholar]
12. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 2012; 9:811–814 [View Article]
    [Google Scholar]
13. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15:R46 [View Article]
    [Google Scholar]
14. Lindgreen S, Adair KL, Gardner PP. An evaluation of the accuracy and speed of metagenome analysis tools. Sci Rep 2016; 6:19233 [View Article]
    [Google Scholar]
15. Ye SH, Siddle KJ, Park DJ, Sabeti PC. Benchmarking metagenomics tools for taxonomic classification. Cell 2019; 178:779–794
    [Google Scholar]
16. Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci 2017; 3:e104 [View Article]
    [Google Scholar]
17. Glassing A, Dowd SE, Galandiuk S, Davis B, Chiodini RJ. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog 2016; 8:24 [View Article]
    [Google Scholar]
18. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018; 6:226 [View Article]
    [Google Scholar]
19. Knights D, Kuczynski J, Charlson ES, Zaneveld J, Mozer MC et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods 2011; 8:761–763 [View Article]
    [Google Scholar]
20. Walsh AM, Crispie F, O'Sullivan O, Finnegan L, Claesson MJ et al. Species classifier choice is a key consideration when analysing low-complexity food microbiome data. Microbiome 2018; 6:50 [View Article]
    [Google Scholar]