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Nucleic Acids Res. 2015 Mar 31;43(6):e37. doi: 10.1093/nar/gku1341. Epub 2015 Jan 13.

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

Author information

1
School of Engineering, University of Glasgow, Glasgow, UK mail@melanieschirmer.com.
2
School of Engineering, University of Glasgow, Glasgow, UK.
3
Functional and Comparative Genomics, University of Liverpool, Liverpool, UK.

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%.

PMID:
25586220
PMCID:
PMC4381044
DOI:
10.1093/nar/gku1341
[Indexed for MEDLINE]
Free PMC Article

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