Sequence-specific error profile of Illumina sequencers

By Kensuke Nakamura, T. Oshima, T. Morimoto, Shun Ikeda, H. Yoshikawa, Yuh Shiwa, S. Ishikawa, Margaret C. Linak, Aki Hirai, Hiroki Takahashi and 3 more. Nucleic Acids Research, 2011

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We identified the sequence-specific starting positions of consecutive miscalls in the mapping of reads obtained from the Illumina Genome Analyser (GA). Detailed analysis of the miscall pattern indicated that the underlying mechanism involves sequence-specific interference of the base elongation process during sequencing. The two major sequence patterns that trigger this sequence-specific error (SSE) are: (i) inverted repeats and (ii) GGC sequences. We speculate that these sequences favor dephasing by inhibiting single-base elongation, by: (i) folding single-stranded DNA and (ii) altering enzyme preference. This phenomenon is a major cause of sequence coverage variability and of the unfavorable bias observed for population-targeted methods such as RNA-seq and ChIP-seq. Moreover, SSE is a potential cause of false single-nucleotide polymorphism (SNP) calls and also significantly hinders de novo assembly. This article highlights the importance of recognizing SSE and its underlying mechanisms in the hope of enhancing the potential usefulness of the Illumina sequencers.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/a41129b52e041d62beebbf9d96361b4d2bd326cb

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Citations in the corpus

Toward better understanding of artifacts in variant calling from high-coverage samples (2014, background): “Most of these SNPs have averaged read depth, non-overlap with known segmental duplications (http://bit.ly/eelabdb) and are not associated with known error-prone motifs in Illumina sequencing (Nakamura et al., 2011).”
Summarizing and correcting the GC content bias in high-throughput sequencing (2012, background): “There have been reports (11) that specific sequences in reads are more prone for errors, for example a GGC sequence.”
Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform (2015, methodology): “Since the study presented by Nakamura et al. (7) on the Illumina Genome Analyzer (GAII) in 2011 there have been major developments with another four Illumina platforms entering the market and improvements regarding the chemistry providing much longer reads with lower error rates.”
Transcriptomics technologies (2017, background): “There are many variants on these methods, each with a different error profile for the resulting data [88].”
Schmutzi: estimation of contamination and endogenous mitochondrial consensus calling for ancient DNA (2015, background): “For sequencing errors, the probability of base substitu-tion can be obtained using the assumption that any given nucleotide is equally likely to be miscalled as any of the remaining three nucleotides: However, studies on Illumina sequencing errors show that this assumption is often incorrect [31].”
Sequencing DNA with nanopores: Troubles and biases (2021, background): “SSEs have been shown to exist in NGS data, with various patterns such as homopolymers [17] or inverted repeats [18].”
LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets (2012, background): “Sequencing error rates are known to be highly variable and differ significantly between technologies, runs, lanes, multiplexes, genomic location as well as substitution types (11) (12) (13) .”
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PEAR: a fast and accurate Illumina Paired-End reAd mergeR (2013, neutral): “If, however, the overlap is known to be short ( (cid:7) 35-bp in our simulations), our statistical test will reject up to 4% (based on our simulations) correctly merged sequences because of low quality scores.”
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