Microbial community profiling for human microbiome projects: Tools, techniques, and challenges.

By Micah Hamady, Rob Knight. Genome Research, 2009

961 citations recorded by the source corpus.

High-throughput sequencing studies and new software tools are revolutionizing microbial community analyses, yet the variety of experimental and computational methods can be daunting. In this review, we discuss some of the different approaches to community profiling, highlighting strengths and weaknesses of various experimental approaches, sequencing methodologies, and analytical methods. We also address one key question emerging from various Human Microbiome Projects: Is there a substantial core of abundant organisms or lineages that we all share? It appears that in some human body habitats, such as the hand and the gut, the diversity among individuals is so great that we can rule out the possibility that any species is at high abundance in all individuals: It is possible that the focus should instead be on higher-level taxa or on functional genes instead.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9b1d95a706fd70260bc0bf0e05b8d7845f457516

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Citations in the corpus

Beyond the Venn diagram: the hunt for a core microbiome. (2012, background): “Instead of a membership core observed across individuals, ‘clusters’ and ‘enterotype’ cores were discovered in vaginal and gut microbiomes, supporting the Hamady and Knight subpopulation hypothesis (Hamady and Knight, 2009; Arumugam et al., 2011; Ravel et al., 2011).”
Chapter 12: Human Microbiome Analysis (2012, background): “It is difficult to recommend comprehensive literature in an area that is changing so rapidly, but the bioinformatics of microbial community studies are currently best covered by the reviews in [22,56,165].”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, background): “The most common and cost-effective approach for microbiome characterization to date targets the 16S rRNA gene as taxonomic marker [14].”
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq (2013, background): “However, as shown here and discussed elsewhere[43] there are acknowledged challenges that are magnified in the examination of Meta-RNA-Seq datasets.”
A Novel Sparse Compositional Technique Reveals Microbial Perturbations (2019, background): “dimensional sparse data (2, 6, 7, 23, 24), making the values unreliable.”
Identifying biologically relevant differences between metagenomic communities (2010, background): “Detailed assessment of communities requires intensive sequencing effort and careful experimental design to ensure that experimental questions can be adequately addressed (Hamady and Knight, 2009).”
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample (2010, background): “Perhaps because 16S rRNA phylogenies tend to correspond well to trends in overall gene content (8), the ability to relate trends at the species level to host or environmental parameters has proven immensely powerful (9).”
Metagenomic biomarker discovery and explanation (2011, background): “These findings demonstrate that a concept of class explanation including both statistical and biological significance is highly beneficial in tackling the statistical challenges associated with high-dimensional biomarker discovery [28,81,82].”
The Hologenome Concept: Human, Animal and Plant Microbiota (2014, background): “Current research on the diversity of Bacteria and Archaea associated with a particular organism relies primarily on culture-independent DNA-based technology (Hamady and Knight 2009).”