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Daniel McDonald

Researcher at University of California, San Diego

Publications -  157
Citations -  84726

Daniel McDonald is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Microbiome & Biology. The author has an hindex of 46, co-authored 128 publications receiving 64433 citations. Previous affiliations of Daniel McDonald include University of Colorado Boulder & University of California, Berkeley.

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An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

TL;DR: A ‘taxonomy to tree’ approach for transferring group names from an existing taxonomy to a tree topology is developed and used to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences.
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A framework for human microbiome research

Barbara A. Methé, +253 more
- 14 Jun 2012 - 
TL;DR: The Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomics data available to the scientific community as mentioned in this paper.
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A communal catalogue reveals Earth’s multiscale microbial diversity

TL;DR: A meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project is presented, creating both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity.
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Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns

TL;DR: A novel sub-operational-taxonomic-unit (sOTU) approach that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms, Deblur, which substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity.