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

By M. Schirmer, Umer Zeeshan Ijaz, Rosalinda D'Amore, N. Hall, William T. Sloan, Christopher Quince. Nucleic Acids Research, 2015

716 citations recorded by the source corpus.

With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illumina's MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e699637bfdce44ebf065d68b1013aa14e9a0ddff

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In Molecular Biology

Citations in the corpus

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin (2018, neutral): “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”
An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar (2018, background): “Measurements of virus intrahost genetic diversity are sensitive to PCR and sequencing errors [12, 14, 16].”
DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, background): “Including mock communities in DNA sequencing efforts can assess technical issues such as incomplete DNA extraction or library preparation, PCR, and sequencing errors (Schirmer et al., 2015) and provide correction factors (Tan et al.”
Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data (2016, methodology): “Here, we extend our work on error profiles in amplicon data sets in connection with the MiSeq platform [1] to metagenomic data sets and included additional Illumina platforms as well as more low-input library preparation methods.”
Benchmarking DNA Metabarcoding for Biodiversity-Based Monitoring and Assessment (2016, background): “…to the fact that longer barcodes can accumulate more errors Frontiers in Marine Science | www.frontiersin.org 7 June 2016 | Volume 3 | Article 96 Frontiers in Marine Science | www.frontiersin.org 8 June 2016 | Volume 3 | Article 96 during the PCR and sequencing processes (Schirmer et al., 2015).”