Library preparation methodology can influence genomic and functional predictions in human microbiome research

Proceedings of the National Academy of Sciences of the United States of America

Volumn 112, Issue 45, 2015, Pages 14024-14029

  Jones, Marcus B ^a,b   Highlander, Sarah K ^b   Anderson, Ericka L ^a   Li, Weizhong ^a,b   Dayrit, Mark ^a   Klitgord, Niels ^a   Fabani, Martin M ^a   Seguritan, Victor ^a   Green, Jessica ^a   Pride, David T ^c,d   Yooseph, Shibu ^a,b   Biggs, William ^a   Nelson, Karen E ^a,b   Craig Venter, J ^a,b  

^a Human Longevity Inc   (United States)

^b J CRAIG VENTER INSTITUTE   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIAL DNA; GENOMIC DNA;

ACINETOBACTER BAUMANNII; ACTINOMYCES; ACTINOMYCES ODONTOLYTICUS; BACILLUS CEREUS; BACTERIAL GENOME; BACTEROIDES VULGATUS; CLOSTRIDIUM BEIJERINCKII; CONFERENCE PAPER; CONTROLLED STUDY; DEINOCOCCUS RADIODURANS; DNA MICROARRAY KIT; ENTEROCOCCUS FAECALIS; ESCHERICHIA COLI; FECES ANALYSIS; GENE LIBRARY; GENE SEQUENCE; HELICOBACTER PYLORI; HUMAN; LACTOBACILLUS GASSERI; LISTERIA MONOCYTOGENES; METAGENOMICS; METHODOLOGY; MICROBIOME; NEISSERIA MENINGITIDIS; NEXT GENERATION SEQUENCING; NONHUMAN; POLYMERASE CHAIN REACTION SYSTEM; PREDICTION; PRIORITY JOURNAL; PROPIONIBACTERIUM ACNES; PSEUDOMONAS AERUGINOSA; QUALITATIVE ANALYSIS; QUALITY CONTROL; QUANTITATIVE ANALYSIS; RHODOBACTER SPHAEROIDES; STAPHYLOCOCCUS AUREUS; STAPHYLOCOCCUS EPIDERMIDIS; STREPTOCOCCUS AGALACTIAE; STREPTOCOCCUS MUTANS; STREPTOCOCCUS PNEUMONIAE; TAXONOMY; ANALYSIS OF VARIANCE; CHEMISTRY; DNA BASE COMPOSITION; DNA SEQUENCE; FECES; GENETICS; HIGH THROUGHPUT SEQUENCING; MICROFLORA; MOLECULAR GENETICS; NUCLEOTIDE SEQUENCE; POLYMERASE CHAIN REACTION; PROCEDURES; SPECIES DIFFERENCE; TRENDS;

ANALYSIS OF VARIANCE; BASE COMPOSITION; BASE SEQUENCE; FECES; GENE LIBRARY; GENOME, BACTERIAL; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; METAGENOMICS; MICROBIOTA; MOLECULAR SEQUENCE DATA; POLYMERASE CHAIN REACTION; SEQUENCE ANALYSIS, DNA; SPECIES SPECIFICITY;

EID: 84946781151     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1519288112     Document Type: Article

References (20)

1. Gill SR, et al. (2006) Metagenomic analysis of the human distal gut microbiome. Science 312(5778):1355-1359.
2. Flores R, et al. (2012) Assessment of the human faecal microbiota: II. Reproducibility and associations of 16S rRNA pyrosequences. Eur J Clin Invest 42(8):855-863.
3. Kim M, Yu Z (2014) Variations in 16S rRNA-based microbiome profiling between pyrosequencing runs and between pyrosequencing facilities. J Microbiol 52(5):355-365.
4. Ravel J, Wommack KE (2014) All hail reproducibility inmicrobiome research. Microbiome 2(1):8.
5. Schmidt TS, Matias Rodrigues JF, von Mering C (2015) Limits to robustness and reproducibility in the demarcation of operational taxonomic units. Environ Microbiol 17(5):1689-1706.
6. Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS (2014) A taxonomic signature of obesity in the microbiome? Getting to the guts of the matter. PLoS One 9(1):e84689.
7. van Best N, Jansen PL, Rensen SS (2015) The gut microbiota of nonalcoholic fatty liver disease: Current methods and their interpretation. Hepatol Int 9(3):406-415.
8. Wagner Mackenzie B, Waite DW, Taylor MW (2015) Evaluating variation in human gut microbiota profiles due to DNA extraction method and inter-subject differences. Front Microbiol 6:130.
9. DeSantis TZ, Stone CE, Murray SR, Moberg JP, Andersen GL (2005) Rapid quantification and taxonomic classification of environmental DNA from both prokaryotic and eukaryotic origins using a microarray. FEMS Microbiol Lett 245(2):271-278.
10. Franzosa EA, et al. (2015) Sequencing and beyond: Integrating molecular 'omics' for microbial community profiling. Nat Rev Microbiol 13(6):360-372.
11. Franzosa EA, et al. (2014) Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci USA 111(22):E2329-E2338.
12. Eichner CA, Erb RW, Timmis KN, Wagner-Döbler I (1999) Thermal gradient gel electrophoresis analysis of bioprotection from pollutant shocks in the activated sludge microbial community. Appl Environ Microbiol 65(1):102-109.
13. Aryee MJ, Gutiérrez-Pabello JA, Kramnik I, Maiti T, Quackenbush J (2009) An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation). BMC Bioinformatics 10:409.
14. DeAngelis KM, et al. (2011) PCR amplification-independent methods for detection of microbial communities by the high-density microarray PhyloChip. Appl Environ Microbiol 77(18):6313-6322.
15. Xia LC, Cram JA, Chen T, Fuhrman JA, Sun F (2011) Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS One 6(12):e27992.
16. Brooks JP, et al.; Vaginal Microbiome Consortium (2015) The truth about metagenomics: Quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol 15:66.
17. Probst AJ, Weinmaier T, DeSantis TZ, Santo Domingo JW, Ashbolt N (2015) New perspectives on microbial community distortion after whole-genome amplification. PLoS One 10(5):e0124158.
18. Nayfach S, Pollard KS (2015) Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol 16:51.
19. Manor O, Borenstein E (2015) MUSiCC: A marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome. Genome Biol 16:53.
20. Human Microbiome Project Consortium (2012) A framework for human microbiome research. Nature 486(7402):215-221.