Methods Inf Med 2017; 56(06): 442-447
DOI: 10.3414/ME17-01-0036
Focus Theme – Personal Science Reports
Schattauer GmbH

Tracking Human Gut Microbiome Changes Resulting from a Colonoscopy

Larry Smarr
1   University of California San Diego, Calit2, La Jolla, CA, USA
2   University of California San Diego, Center for Microbiome Innovation, La Jolla, CA, USA
5   University of California San Diego, Computer Science and Engineering, La Jolla, CA USA
,
Embriette R. Hyde
4   University of California San Diego, Pediatrics, La Jolla, CA, USA
,
Daniel McDonald
4   University of California San Diego, Pediatrics, La Jolla, CA, USA
,
William J. Sandborn
2   University of California San Diego, Center for Microbiome Innovation, La Jolla, CA, USA
3   University of California San Diego, Gastroenterology, San Diego, CA, USA
,
Rob Knight
2   University of California San Diego, Center for Microbiome Innovation, La Jolla, CA, USA
4   University of California San Diego, Pediatrics, La Jolla, CA, USA
5   University of California San Diego, Computer Science and Engineering, La Jolla, CA USA
› Author Affiliations
We would like to thank members of the Knight Lab and Calit2’s Qualcomm Institute for helpful discussions on this project.
Further Information

Publication History

received: 07 April 2017

accepted: 08 September 2017

Publication Date:
10 February 2018 (online)

 

 
  • References

  • 1 Smarr L. Quantifying your body: a how-to guide from a systems biology perspective. Biotechnol J 2012; 07 (08) 980-991.
  • 2 Smarr L. Quantified Health: A 10-year Detective Story of Digitally Enabled Genomic Medicine. The Strategic News Service Newsletter, Special Letter. 2011 14. (36). Available from: https://www.stratnews.com/recent/mode/show/issue/2011–09–29/.
  • 3 Smarr L. Can You Coordinate the Dance of Your Body’s 100 Trillion Microorganisms?. TEDMED. 2013 Available from: http://www.tedmed.com/talks/show?id=18018.
  • 4 Wu S. et al. Large memory high performance computing enables comparison across human gut microbiome of patients with autoimmune diseases and healthy subjects. Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery (XSEDE ‘13). Article 25, 2013
  • 5 Yazdani M. et al. Using Machine Learning to Identify Major Shifts in Human Gut Microbiome Protein Family Abundance in Disease. IEEE International Conference on Big Data 2016; 1272-1280.
  • 6 Huttenhower C. et al. Structure, function and diversity of the healthy human microbiome. Nature 2012; 486 (7402): 207-214.
  • 7 Human Microbiome Project Consortium. A framework for human microbiome research. Nature 2012; 486 (7402) 215-221.
  • 8 Caporaso JG. et al. Moving pictures of the human microbiome. Genome Biol 2011; 12 (05) R50.
  • 9 David LA. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol 2014; 15: R89.
  • 10 Drago L, Toscano M, De Grandi R, Casini V, Pace F. Persisting changes of intestinal microbiota after bowel lavage and colonoscopy. Eur J Gastroenterol Hepatol 2016; 28 (05) 532-537.
  • 11 O’Brien CL, Allison GE, Grimpen F, Pavli P. Impact of colonoscopy bowel preparation on intestinal microbiota. PLoS One 2013; 08 (05) e62815.
  • 12 Jalanka J. et al. Effects of bowel cleansing on the intestinal microbiota. Gut 2015; 64 (10) 1562-1568.
  • 13 Caporaso JG. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 2012; 06 (08) 1621-1624.
  • 14 Caporaso JG. et al. QIIME allows analysis of highthroughput community sequencing data. Nat Methods 2010; 07 (05) 335-336.
  • 15 Mirarab S, Nguyen N, Warnow T. SEPP: SATé-enabled phylogenetic placement. Pac Symp Biocomput 2012; 247-258.
  • 16 McDonald D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 2012; 06 (03) 610-618.
  • 17 McDonald D. et al. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Gigascience 2012; 01 (01) 7.