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

Content Provider	PubMed Central
Author	Schirmer, Melanie Ijaz, Umer Z. D'amore, Rosalinda Hall, Neil Sloan, William T. Quince, Christopher
Copyright Year	2015
Abstract	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%.
Related Links	http://dx.doi.org/10.1093/nar/gku1341
Starting Page	37
File Format	PDF
ISSN	03051048
e-ISSN	13624962
Journal	Nucleic Acids Research
Issue Number	6
Volume Number	43
Language	English
Publisher	Oxford University Press
Publisher Date	2015-03-31
Access Restriction	Open
Rights Holder	Oxford University Press
Subject Keyword	Genetics Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics

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