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Open AccessJournal ArticleDOI

DADA2: High-resolution sample inference from Illumina amplicon data

TLDR
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.
Abstract
We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

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Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Evan Bolyen, +123 more
- 01 Aug 2019 - 
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.

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Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

TL;DR: The results illustrate the importance of parameter tuning for optimizing classifier performance, and the recommendations regarding parameter choices for these classifiers under a range of standard operating conditions are made.
Journal ArticleDOI

Exact sequence variants should replace operational taxonomic units in marker-gene data analysis.

TL;DR: It is argued that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of marker-gene analysis and reporting.
Journal ArticleDOI

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data.

TL;DR: The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants.
References
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Journal ArticleDOI

Removing noise from pyrosequenced amplicons.

TL;DR: AmpliconNoise is a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors and a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data.
Journal ArticleDOI

Error filtering, pair assembly and error correction for next-generation sequencing reads

TL;DR: This work demonstrates large reductions in error frequencies, especially for high-error-rate reads, by three independent means: filtering reads according to their expected number of errors, assembling overlapping read pairs and by exploiting unique sequence abundances to perform error correction.
Journal ArticleDOI

Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions.

TL;DR: A fast method for denoising pyrosequencing for community 16S rRNA analysis is developed and a 2–4 fold reduction in the number of observed OTUs is observed comparing denoised with non-denoised data.
Journal ArticleDOI

Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform

TL;DR: A large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data is conducted and it is shown that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns.
Journal ArticleDOI

Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences.

TL;DR: Minimum Entropy Decomposition (MED) provides a computationally efficient means to partition marker gene datasets into ‘MED nodes’, which represent homogeneous operational taxonomic units and enables sensitive discrimination of closely related organisms in marker gene amplicon datasets without relying on extensive computational heuristics and user supervision.
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How can I best normalise my samples from amplicon data?

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