Measurement Science of Metagenomics
1 Metrology of Metagenomics
1.1 Microbiome
1.2 Measuring Microbiomes
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
- Measurement Error
- 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)
1.5 Outline
- Background
- Metagenomics and microbiome
- Metrology
- Metagenomics and microbiome
- Measurement Process
- summary of individual steps
- Downstream applications
- summary of individual steps
- 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
- subsections for biological and measurement?
- Biological vs. Measurement Sources of error
- Measurement Assessment
- Overview with historical context
- Assessment methods for individual steps in measurement process from a data analysis perspective
- Overview with historical context
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.