A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology

By A. M. Eren, Joseph H Vineis, Hilary G. Morrison, Mitch L Sogin. PLoS ONE, 2013

317 citations recorded by the source corpus.

Consensus between independent reads improves the accuracy of genome and transcriptome analyses, however lack of consensus between very similar sequences in metagenomic studies can and often does represent natural variation of biological significance. The common use of machine-assigned quality scores on next generation platforms does not necessarily correlate with accuracy. Here, we describe using the overlap of paired-end, short sequence reads to identify error-prone reads in marker gene analyses and their contribution to spurious OTUs following clustering analysis using QIIME. Our approach can also reduce error in shotgun sequencing data generated from libraries with small, tightly constrained insert sizes. The open-source implementation of this algorithm in Python programming language with user instructions can be obtained from https://github.com/meren/illumina-utils.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/217556b7c37d0fd307243136f221f2f76d410699

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Citations in the corpus

Ecology and exploration of the rare biosphere (2015, background): “This error profile can be countered by excluding low‑confidence base calls and incorporating paired‑end sequencing 16,19 , along with enforcing perfect compliance over long paired‑end overlaps, which decreases random sequencing errors 176 .”
Tracking microbial colonization in fecal microbiota transplantation experiments via genome-resolved metagenomics (2017, methodology): “We removed the low-quality reads from the raw sequencing results using the program “iu-filter-quality-minoche” in illumina-utils [33] (available from https://github.”
Linking pangenomes and metagenomes: the Prochlorococcus metapangenome (2018, methodology): “4.1 ( Eren et al., 2013 ) (available from https://github.com/merenlab/illumina-utils), which implements the noise filtering parameters described by Minoche, Dohm & Himmelbauer (2011) .”
TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline (2014, unknown): “Furthermore, the quality scores frequently are not indicative of true quality [45,46].”
Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences (2014, methodology): “Such OTUs aggregate distinct organisms and conceal ecologically important findings (Eren et al. , 2013a).”
Validation and Development of COI Metabarcoding Primers for Freshwater Macroinvertebrate Bioassessment (2017, unknown): “These numbers were potentially inflated by PCR/sequencing errors and pseudogenes (Bensasson et al., 2001; Eren et al., 2013).”
Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform (2015, background): “This has been previously reported for the V6 region of E. coli amplicon sequencing data (18).”