M
Michael J. Rosen
Researcher at Stanford University
Publications - 7
Citations - 15400
Michael J. Rosen is an academic researcher from Stanford University. The author has contributed to research in topics: Population & Ecological niche. The author has an hindex of 6, co-authored 7 publications receiving 7904 citations.
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Journal ArticleDOI
DADA2: High-resolution sample inference from Illumina amplicon data
Benjamin J. Callahan,Paul J. McMurdie,Michael J. Rosen,Andrew W. Han,Amy Jo A. Johnson,Susan Holmes +5 more
TL;DR: The open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors is presented, revealing a diversity of previously undetected Lactobacillus crispatus variants.
Journal ArticleDOI
Statics and Dynamics of Single DNA Molecules Confined in Nanochannels
Walter Reisner,Keith Morton,Robert Riehn,Yan Mei Wang,Zhaoning Yu,Michael J. Rosen,James C. Sturm,Stephen Y. Chou,Erwin Frey,Robert H. Austin +9 more
TL;DR: Measurements of DNA extended in nanochannels are presented and it is shown that below a critical width roughly twice the persistence length there is a crossover in the polymer physics.
Journal ArticleDOI
Fine-scale diversity and extensive recombination in a quasisexual bacterial population occupying a broad niche
TL;DR: Deep sequencing of a thermophilic cyanobacterial population and analysis of the statistics of synonymous single-nucleotide polymorphisms revealed a high rate of homologous recombination and departures from neutral drift consistent with the effects of genetic hitchhiking.
Posted ContentDOI
DADA2: High resolution sample inference from amplicon data
Benjamin J. Callahan,Paul J. McMurdie,Michael J. Rosen,Andrew W. Han,Amy Jo A. Johnson,Susan Holmes +5 more
TL;DR: DADA2 analysis of vaginal samples revealed a diversity of Lactobacillus crispatus strains undetected by OTU methods, and identified more real variants than other methods in Illumina-sequenced mock communities, some differing by a single nucleotide, while outputting fewer spurious sequences.
Journal ArticleDOI
Denoising PCR-amplified metagenome data
TL;DR: A new denoising algorithm that is more accurate and over an order of magnitude faster than AmpliconNoise is introduced, which eliminates the need for training data to establish error parameters, fully utilizes sequence-abundance information, and enables inclusion of context-dependent PCR error rates.