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Schirmer, Melanie, D'Amore, Rosalinda, Ijaz, Umer Z., Hall, Neil 
ORCID: https://orcid.org/0000-0003-2808-0009 and Quince, Christopher
(2016)
Illumina error profiles:Resolving fine-scale variation in metagenomic sequencing data.
BMC Bioinformatics, 17 (1).
ISSN 1471-2105
Abstract
Background: Illumina's sequencing platforms are currently the most utilised sequencing systems worldwide. The technology has rapidly evolved over recent years and provides high throughput at low costs with increasing read-lengths and true paired-end reads. However, data from any sequencing technology contains noise and our understanding of the peculiarities and sequencing errors encountered in Illumina data has lagged behind this rapid development. Results: We conducted a systematic investigation of errors and biases in Illumina data based on the largest collection of in vitro metagenomic data sets to date. We evaluated the Genome Analyzer II, HiSeq and MiSeq and tested state-of-the-art low input library preparation methods. Analysing in vitro metagenomic sequencing data allowed us to determine biases directly associated with the actual sequencing process. The position- and nucleotide-specific analysis revealed a substantial bias related to motifs (3mers preceding errors) ending in "GG". On average the top three motifs were linked to 16 % of all substitution errors. Furthermore, a preferential incorporation of ddGTPs was recorded. We hypothesise that all of these biases are related to the engineered polymerase and ddNTPs which are intrinsic to any sequencing-by-synthesis method. We show that quality-score-based error removal strategies can on average remove 69 % of the substitution errors - however, the motif-bias remains. Conclusion: Single-nucleotide polymorphism changes in bacterial genomes can cause significant changes in phenotype, including antibiotic resistance and virulence, detecting them within metagenomes is therefore vital. Current error removal techniques are not designed to target the peculiarities encountered in Illumina sequencing data and other sequencing-by-synthesis methods, causing biases to persist and potentially affect any conclusions drawn from the data. In order to develop effective diagnostic and therapeutic approaches we need to be able to identify systematic sequencing errors and distinguish these errors from true genetic variation.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This work was supported by the Technology Strategy Board. M.S. was supported by Unilever R&D Port Sunlight, Bebington, United Kingdom. C.Q. was funded by an EPSRC Career Acceleration Fellowship - EP/H003851/1 and an MRC fellowship MR/M50161X/1 as part of the CLoud Infrastructure for Microbial Genomics (CLIMB) consortium - MR/L015080/1. U.Z.I. was funded by NERC IRF NE/L011956/1. DNA sequencing was generated by The University of Liverpool, Centre for Genomic Research, UK. Publisher Copyright: © 2016 Schirmer et al. |
| Uncontrolled Keywords: | error profiles,illumina,metagenomics,sequencing errors,sequencing-by-synthesis,transposome bias,structural biology,biochemistry,molecular biology,computer science applications,applied mathematics ,/dk/atira/pure/subjectarea/asjc/1300/1315 |
| Faculty \ School: | Faculty of Science > School of Biological Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 09 Sep 2022 09:31 |
| Last Modified: | 20 Oct 2022 18:31 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/88014 |
| DOI: | 10.1186/s12859-016-0976-y |
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