1 Metrology of Metagenomics

1.1 Microbiome

1.2 Measuring Microbiomes

  • whole metagenomic sequencing
  • marker-gene metagenomics
  • 16S vs. WMS
  • Clooney et al. (2016) compared WMS and 16S rRNA sequence data for environmental samples sequenced using two platforms MiSeq and PGM.
    • WMS clustering seperately form the 16S results (Clooney et al. 2016, fig. 1).

1.3 Metrology

  • No not meteorlogy
  • Biological variability and measurement error
    • Measurement Error
      • Systematic - results in systematic bias
      • Random - increases uncertainty in results but provides an accurate measure of the true mean given sufficient sample size
    • Biological Noise
      • Sources of variability due to biological complexity of the system e.g. difference in results based on 16S rRNA gene region
  • replicate, repeat, and reproduce
    • SI redefining the kilogram example???
    • Uncertainty budgets
      • top-down vs. bottom-up
      • analytical chemsitry text
    • Orthogonal methods
      • Genome in a bottle example
      • Examples of orthogonal methods for 16S metagenomics:
      • Albertsen et al. (2015) 16S, metagenomics, metatranscriptomics, and FISH

1.4 Key references

1.4.1 Measurement Process

Goodrich et al. (2014)

1.4.2 Sample Processing and Sequencing

Brooks et al. (2015)
D’Amore et al. (2016)

1.5 Outline

  • Background
    • Metagenomics and microbiome
    • Metrology
  • Measurement Process
    • summary of individual steps
    • Downstream applications
  • Sources of Error and Bias
    • Biological vs. Measurement Sources of error
    • error and bias for individual steps
      • subsections for biological and measurement?
      • mitigation strategies - wet lab and dry lab
  • Measurement Assessment
    • Overview with historical context
    • Assessment methods for individual steps in measurement process from a data analysis perspective

References

Clooney, Adam G, Fiona Fouhy, Roy D Sleator, Aisling O’ Driscoll, Catherine Stanton, Paul D Cotter, and Marcus J Claesson. 2016. “Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis.” PloS One 11 (2): e0148028. doi:10.1371/journal.pone.0148028.

Albertsen, Mads, Søren M Karst, Anja S Ziegler, Rasmus H Kirkegaard, and Per H Nielsen. 2015. “Back to Basics - The Influence of DNA Extraction and Primer Choice on Phylogenetic Analysis of Activated Sludge Communities.” PloS One 10 (7). Public Library of Science: e0132783. doi:10.1371/journal.pone.0132783.

Goodrich, Julia K., Sara C. Di Rienzi, Angela C. Poole, Omry Koren, William A. Walters, J. Gregory Caporaso, Rob Knight, and Ruth E. Ley. 2014. “Conducting a Microbiome Study.” Cell 158 (2). Elsevier Inc.: 250–62. doi:10.1016/j.cell.2014.06.037.

Brooks, J Paul, David J Edwards, Michael D Harwich, Maria C Rivera, Jennifer M Fettweis, Myrna G Serrano, Robert A Reris, et al. 2015. “The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies.” BMC Microbiology 15 (1): 66. doi:10.1186/s12866-015-0351-6.

D’Amore, Rosalinda, Umer Zeeshan Ijaz, Melanie Schirmer, John G Kenny, Richard Gregory, Alistair C Darby, Christopher Quince, et al. 2016. “A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling.” BMC Genomics 17. BMC Genomics: 1–40. doi:10.1186/s12864-015-2194-9.