Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data

Abstract

BackgroundIllumina’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.ResultsWe 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.ConclusionSingle-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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0976-y) contains supplementary material, which is available to authorized users.

Optimization of high-throughput sequencing kinetics for determining enzymatic rate constants of thousands of RNA substrates

“…Although we have used data from the Illumina Hi-Seq 2500 in these studies, a similarly high level of fidelity is expected. Systematic miscalling of a particular nucleotide in the cDNA has been investigated and quantified, and there are various approaches to correcting these errors[34, 35]. However, because of the large number of sequence reads (500–1500) obtained for most substrate variants, it is not necessary to apply them in HTS-Kin.…”

Adaptation of A-to-I RNA editing in Drosophila

“…We define . The Illumina HiSeq platform generally has an error rate ε 0 ranging from 0.2% to 0.6% [105–107] and we used ε 0 = 0.5% in the analysis. As we focus on the sites with solely A-to-G but not A-to-C or A-to-T mismatches, the error rate of A-to-G would be scaled to .…”

UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy

“…Recombination events, also called "PCR jumping," create chimeric sequences that may change either the UMI sequence and/or alignment. Miscalling during sequencing is by far the most prevalent error, occurring one to two orders of magnitude more frequently than indels for Illumina sequencing (Marinier et al 2015;Schirmer et al 2015Schirmer et al , 2016. Recombination is common when sequencing amplicons, but much rarer with the shotgun sequencing approaches in which UMIs are utilized (Schloss et al 2011;Waugh et al 2015).…”