Experimental and analytical tools for studying the human microbiome

By J. Kuczynski, C. Lauber, William Walters, Laura Wegener Parfrey, José Carlos Clemente, Dirk Gevers, Rob Knight. Nature reviews genetics, 2011

690 citations recorded by the source corpus.

The human microbiome substantially affects many aspects of human physiology, including metabolism, drug interactions and numerous diseases. This realization, coupled with ever-improving nucleotide sequencing technology, has precipitated the collection of diverse data sets that profile the microbiome. In the past 2 years, studies have begun to include sufficient numbers of subjects to provide the power to associate these microbiome features with clinical states using advanced algorithms, increasing the use of microbiome studies both individually and collectively. Here we discuss tools and strategies for microbiome studies, from primer selection to bioinformatics analysis.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c0bb716df98049c42fc5c1314942ce373bd7b5bc

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Citations in the corpus

BugBase predicts organism-level microbiome phenotypes (2017, background): “Background The association of microbiota with a growing number of acute and chronic human diseases, together with decreasing costs of next-generation sequencing, has resulted in an exponential increase in the number of host-associated microbiome studies [1,2].”
Gut Microbes and the Brain: Paradigm Shift in Neuroscience (2014, background): “Increasingly, these datasets also involve time series components, especially as the data acquisition methods decrease in cost (for example, 16S rRNA amplicon profiling has decreased in cost by almost a factor of a million over the past decade) (Kuczynski et al., 2012).”
The infant microbiome development: mom matters (2014, neutral): “A caveat in this study, however, was the use of primers in the V1V2 region that discriminate against bifidobacteria [26].”
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Host remodeling of the gut microbiome and metabolic changes during pregnancy. (2012, methodology): “(It is important to note that the V1V2 region primers used in this study are biased against Biﬁdobacteria [Kuczynski et al., 2012], an important component of the developing infant microbiota [Koenig et al., 2011], although this bias has been shown not to impact the diversity of other taxa [Sim et…”
From Animalcules to an Ecosystem: Application of Ecological Concepts to the Human Microbiome (2012, background): “Reviews of the methodological advances in human microbiome research (Hamady & Knight 2009, Kuczynski et al. 2012) point out that it is increasingly feasible for researchers to rapidly characterize microbial communities in thousands of samples.”
Advancing our understanding of the human microbiome using QIIME (2013, methodology): “1 Demultiplexing and quality-filtering As mentioned earlier, high-throughput sequencing allows multiple samples to be combined in a single sequencing run (Kuczynski et al., 2012).”
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Conducting a Microbiome Study (2014, background): “Archaeal diversity can be characterized using the commonly employed 515F/806R primer set (and others), and their diversity can be analyzed in the same way as bacterial diversity.”
Fecal Microbiota and Metabolome of Children with Autism and Pervasive Developmental Disorder Not Otherwise Specified (2013, background): “The human GI microbiota is a complex consortium of 1014 microbes, whose collective genomes (microbiome) contain at least 100 times as many genes as our own eukaryote genome [16].”
Diversity and heritability of the maize rhizosphere microbiome under field conditions (2013, methodology): “16S rRNA genes were amplified using four different primer sets (27F-338R: AGAGTTTGATCCTGGCTCAG–TGCTGCCTCCCGTAGGAGT; 515F-806R: GTGCCAGCMGCCGCGGTAA–GGACTACHVGGGTWTCTAAT; 804F-1392R: AGATTAGATACCCDRGTAGTC–ACGGGCGGTGTGTRC; and 926F-1392R: AAACTYAAAKGAATTGACGG–ACGGGCGGTGTGTRC) (2), including barcodes an”
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