Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing

By Nicholas A. Bokulich, Sathish Subramanian, Jeremiah J. Faith, Dirk Gevers, Jeffrey I. Gordon, Rob Knight, David A. Mills, G. Caporaso. Nature Methods, 2012

3,985 citations recorded by the source corpus.

High-throughput sequencing has revolutionized microbial ecology, but read quality remains a considerable barrier to accurate taxonomy assignment and α-diversity assessment for microbial communities. We demonstrate that high-quality read length and abundance are the primary factors differentiating correct from erroneous reads produced by Illumina GAIIx, HiSeq and MiSeq instruments. We present guidelines for user-defined quality-filtering strategies, enabling efficient extraction of high-quality data and facilitating interpretation of Illumina sequencing results.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/42c6fcf006232a9eb90ce14d02caa3aa2dfb72f3

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Citations in the corpus

The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, background): “Filtering low quality reads considerably improves metagenome analyses such as taxonomical classiﬁcation and α and β diversity calculation (Bokulich et al., 2013).”
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin (2018, positive): “38 Study ID* Target gene** Platform Species Strains Citation mock-1 16S HiSeq 46 48 [33] mock-2 16S MiSeq 46 48 [33] mock-3 16S MiSeq 21 21 [33] mock-4 16S MiSeq 21 21 [33] mock-5 16S MiSeq 21 21 [33] mock-7 16S HiSeq 67 67 [34] mock-8 16S HiSeq 67 67 [11] mock-9 ITS HiSeq 13 16 [11] mock-10 ITS HiS”
A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology (2013, methodology): “Varying the stringency of these thresholds changes the sensitivity and specificity of the outcome, without guaranteeing an accurate basecall [5,6].”
Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: Differentiation by geographic features (2015, background): “…of the total filtered sequences was removed to avoid inclusion of erroneous reads that would otherwise lead to inflated estimates of diversity (Bokulich et al., 2013), as were samples represented by less than 28,008 sequences (a cutoff based on inspection of the library) following all…”
The infant airway microbiome in health and disease impacts later asthma development (2015, methodology): “Merged sequences were quality filtered as follows: % 3 low-quality bp (Phred score < 3) allowed before trimming,R 189 consecutive high-quality bp with no uncalled bases (Ns) (Bokulich et al., 2013).”
Sex differences and hormonal effects on gut microbiota composition in mice (2016, background): “0.(29,30) The total number of sequencing reads was 15,010,979 (an average of 19,571 reads per sample) with an average length of 153 base pair reads.”
Exact sequence variants should replace operational taxonomic units in marker-gene data analysis (2017, background): “Aggressive filtering and complete overlap between paired-end reads can reduce the rate at which OTU methods misinterpret sequencing artifacts as biological variation (Bokulich et al., 2013; Kozich et al., 2013; Edgar and Flyvbjerg, 2015).”