The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
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Experimental design considerations in microbiota/inflammation studiesCytotoxicity of Nanoparticles Contained in Food on Intestinal Cells and the Gut MicrobiotaHeterogeneity of the gut microbiome in mice: guidelines for optimizing experimental designExploring the Gastrointestinal "Nemabiome": Deep Amplicon Sequencing to Quantify the Species Composition of Parasitic Nematode CommunitiesEvaluation of Lysis Methods for the Extraction of Bacterial DNA for Analysis of the Vaginal MicrobiotaQuantifying the biases in metagenome mining for realistic assessment of microbial ecology of naturally fermented foods.An Improved Method for High Quality Metagenomics DNA Extraction from Human and Environmental SamplesmetaBIT, an integrative and automated metagenomic pipeline for analysing microbial profiles from high-throughput sequencing shotgun data.Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing.Changes in vaginal community state types reflect major shifts in the microbiome.Evaluating the accuracy of amplicon-based microbiome computational pipelines on simulated human gut microbial communitiesPerforming Skin Microbiome Research: A Method to the MadnessComparative analysis of gut microbiota associated with body mass index in a large Korean cohort.Library preparation methodology can influence genomic and functional predictions in human microbiome researchStatistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics.Effect of Saccharomyces boulardii and Mode of Delivery on the Early Development of the Gut Microbial Community in Preterm Infants.Pollen DNA barcoding: current applications and future prospects.Pipeline for amplifying and analyzing amplicons of the V1-V3 region of the 16S rRNA gene.Paucibacterial Microbiome and Resident DNA Virome of the Healthy Conjunctiva.High-throughput automated microfluidic sample preparation for accurate microbial genomics.Characterization of bacterial community associated with phytoplankton bloom in a eutrophic lake in South Norway using 16S rRNA gene amplicon sequence analysisHigh-Throughput Single-Cell Cultivation on Microfluidic Streak Plates.Dietary Shifts May Trigger Dysbiosis and Mucous Stools in Giant Pandas (Ailuropoda melanoleuca).Metagenomic sequencing reveals the relationship between microbiota composition and quality of Chinese Rice Wine.Evaluation of 16S rRNA Gene Primer Pairs for Monitoring Microbial Community Structures Showed High Reproducibility within and Low Comparability between Datasets Generated with Multiple Archaeal and Bacterial Primer Pairs.Composition of gut microbiota in infants in China and global comparison.Differential fecal microbiota are retained in broiler chicken lines divergently selected for fatness traits.Diet and Gut Microbial Function in Metabolic and Cardiovascular Disease Risk.Detection and Enumeration of Spore-Forming Bacteria in Powdered Dairy Products.Structural variability and niche differentiation in the rhizosphere and endosphere bacterial microbiome of field-grown poplar treesNovel micelle PCR-based method for accurate, sensitive and quantitative microbiota profiling.Opportunities and challenges in metabarcoding approaches for helminth community identification in wild mammals.Changes in microbiome during and after travellers' diarrhea: what we know and what we do not.Geography, Ethnicity or Subsistence-Specific Variations in Human Microbiome Composition and Diversity.Mechanistic and Technical Challenges in Studying the Human Microbiome and Cancer EpidemiologyHigh-resolution characterization of the human microbiome.Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads.Cyanobacterial harmful algal blooms are a biological disturbance to Western Lake Erie bacterial communities.Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water.Surrogate hosts: Hunting dogs and recolonizing grey wolves share their endoparasites.
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The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
description
2015 nî lūn-bûn
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2015 թուականին հրատարակուած գիտական յօդուած
@hyw
2015 թվականին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@ast
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@en
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@en-gb
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@nl
type
label
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@ast
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@en
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@en-gb
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@nl
prefLabel
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@ast
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@en
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@en-gb
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@nl
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The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
@en
P2093
Bernice Huang
David J Edwards
Gregory A Buck
J Paul Brooks
Jennifer M Fettweis
Jerome F Strauss
Maria C Rivera
Michael D Harwich
Myrna G Serrano
Nihar U Sheth
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P2888
P3181
P356
10.1186/S12866-015-0351-6
P407
P577
2015-01-01T00:00:00Z
P5875
P6179
1001744296