Measuring and mitigating PCR bias in microbiota datasets

16S rRNA Gene Copy Number Normalization Does Not Provide More Reliable Conclusions in Metataxonomic Surveys

(2020) Robert Starke et al.   MICROBIAL ECOLOGY

Consistent and correctable bias in metagenomic sequencing experiments

(2019) Michael R McLaren et al.   eLife

Understanding PCR Processes to Draw Meaningful Conclusions from Environmental DNA Studies

(2019) Ryan P. Kelly et al.   Scientific Reports

Minimizing polymerase biases in metabarcoding

(2018) Ruth V. Nichols et al.   Molecular Ecology Resources

Microbiology: making the best of PCR bias

(2018) Michael Eisenstein   NATURE METHODS

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

(2018) Justin D. Silverman et al.   Microbiome

Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium

(2017) Rashmi Sinha et al.   NATURE BIOTECHNOLOGY

Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding

(2017) Henrik Krehenwinkel et al.   Scientific Reports

A phylogenetic transform enhances analysis of compositional microbiota data

(2017) Justin D Silverman et al.   eLife

It's all relative: analyzing microbiome data as compositions

(2016) Gregory B. Gloor et al.   ANNALS OF EPIDEMIOLOGY

Bead-beating artefacts in the Bacteroidetes to Firmicutes ratio of the human stool metagenome

(2016) Heidi C. Vebø et al.   JOURNAL OF MICROBIOLOGICAL METHODS

Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation

(2016) Hilary P. Browne et al.   NATURE

Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

(2016) Michael I Love et al.   NATURE BIOTECHNOLOGY

Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies

(2016) Daryl M Gohl et al.   NATURE BIOTECHNOLOGY

DADA2: High-resolution sample inference from Illumina amplicon data

(2016) Benjamin J Callahan et al.   NATURE METHODS

Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples

(2015) Marta Pawluczyk et al.   ANALYTICAL AND BIOANALYTICAL CHEMISTRY

Simple regression for correcting ΔCt bias in RT-qPCR low-density array data normalization

(2015) Xiangqin Cui et al.   BMC GENOMICS

The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies

(2015) J Paul Brooks et al.   BMC MICROBIOLOGY

Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples

(2015) Alma E. Parada et al.   ENVIRONMENTAL MICROBIOLOGY

The Impact of Different DNA Extraction Kits and Laboratories upon the Assessment of Human Gut Microbiota Composition by 16S rRNA Gene Sequencing

(2014) Nicholas A. Kennedy et al.   PLoS One

Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible

(2014) Paul J. McMurdie et al.   PLoS Computational Biology

PCR Biases Distort Bacterial and Archaeal Community Structure in Pyrosequencing Datasets

(2012) Ameet J. Pinto et al.   PLoS One

Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance

(2012) Steven W. Kembel et al.   PLoS Computational Biology

Correction of PCR-bias in quantitative DNA methylation studies by means of cubic polynomial regression

(2011) Evgeny A. Moskalev et al.   NUCLEIC ACIDS RESEARCH

UniFrac: an effective distance metric for microbial community comparison

(2010) Catherine Lozupone et al.   ISME Journal

Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample

(2010) J. G. Caporaso et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Quantitative effects of position and type of single mismatch on single base primer extension

(2009) Jer-Horng Wu et al.   JOURNAL OF MICROBIOLOGICAL METHODS