Sequence-specific error profile of Illumina sequencers

Nakamura, Kensuke; Oshima, Taku; Morimoto, Takahiro; Ikeda, Shun; Yoshikawa, Hideki; Shiwa, Yuh; Ishikawa, Shu; Linak, Margaret C.; Hirai, Atsuko; Takahashi, Hiroki; Altaf‐Ul‐Amin, Md.; Ogasawara, Nagahisa; Kanaya, Shigehiko

doi:10.1093/nar/gkr344

Cited by 550 publications

(479 citation statements)

References 39 publications

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“…Only sequences identified to at least genus level were kept for further analyses and grouped according to their taxonomic assignment. Finally, we checked our sequences for the typical error base sequence GGC in Illumina amplicon sequencing (Nakamura et al 2011), but this sequence did not occur as an error in our sequences. As the morphology-based approach always led to at least a genus level identification, the two approaches are thus comparable on the analysed taxonomic level.…”

Section: Genetic Diatom Approachmentioning

confidence: 99%

Sedimentary DNA versus morphology in the analysis of diatom-environment relationships

et al. 2016

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The Arctic treeline ecotone is characterised by a steep vegetation gradient from arctic tundra to northern taiga forests, which is thought to influence the water chemistry of thermokarst lakes in this region. Environmentally sensitive diatoms respond to such ecological changes in terms of variation in diatom diversity and richness, which so far has only been documented by microscopic surveys. We applied next-generation sequencing to analyse the diatom composition of lake sediment DNA extracted from 32 lakes across the treeline in the Khatanga region, Siberia, using a short fragment of the rbcL chloroplast gene as a genetic barcode. We compared diatom richness and diversity obtained from the genetic approach with diatom counts from traditional microscopic analysis. Both datasets were employed to investigate diversity and relationships with environmental variables, using ordination methods. After effective filtering of the raw data, the two methods gave similar results for diatom richness and composition at the genus level (DNA 12 taxa; morphology 19 taxa), even though there was a much higher absolute number of sequences obtained per genetic sample (median 50,278), compared with microscopic counts (median 426). Dissolved organic carbon explained the highest percentage of variance in both datasets (14.2 % DNA; 18.7 % morphology), reflecting the compositional turnover of diatom assemblages along the tundra-taiga transition. Differences between the two approaches are mostly a consequence of the filtering process of genetic data and limitations of genetic references in the database, which restricted the determination of genetically identified sequence types to the genus level. The morphological approach, however, allowed identifications mostly to species level, which permits better ecological interpretation of the diatom data. Nevertheless, because of a rapidly increasing reference database, the genetic approach with sediment DNA will, in the future, enable reliable Electronic supplementary material The online version of this article (doi:10.1007/s10933-016-9926-y) contains supplementary material, which is available to authorized users. 123J Paleolimnol DOI 10.1007/s10933-016-9926-y investigations of diatom composition from lake sediments that will have potential applications in both paleoecology and environmental monitoring.

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Section: Genetic Diatom Approachmentioning

confidence: 99%

Sedimentary DNA versus morphology in the analysis of diatom-environment relationships

et al. 2016

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“…To eliminate or greatly reduce errors it is also possible to apply sophisticated algorithms that filter out errors. This can only be done with errors that exhibit some consistent patterns related to the library preparation and sequencing steps[116] and not feasible for errors that appear at random. Interestingly, a recent approach to the identification of rare variants utilized the PCR amplification step in the (Illumina) library preparation to identify families of templates carrying the same mutation, which then could be inferred to have been present in the original template molecule as a real mutation[115]…”

Section: Mps Applications In Mutation Analysismentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

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DNA mutations are the source of genetic variation within populations. The majority of mutations with observable effects are deleterious. In humans mutations in the germ line can cause genetic disease. In somatic cells multiple rounds of mutations and selection lead to cancer. The study of genetic variation has progressed rapidly since the completion of the draft sequence of the human genome. Recent advances in sequencing technology, most importantly the introduction of massively parallel sequencing (MPS), have resulted in more than a hundred-fold reduction in the time and cost required for sequencing nucleic acids. These improvements have greatly expanded the use of sequencing as a practical tool for mutation analysis. While in the past the high cost of sequencing limited mutation analysis to selectable markers or small forward mutation targets assumed to be representative for the genome overall, current platforms allow whole genome sequencing for less than $5,000. This has already given rise to direct estimates of germline mutation rates in multiple organisms including humans by comparing whole genome sequences between parents and offspring. Here we present a brief history of the field of mutation research, with a focus on classical tools for the measurement of mutation rates. We then review MPS, how it is currently applied and the new insight into human and animal mutation frequencies and spectra that has been obtained from whole genome sequencing. While great progress has been made, we note that the single most important limitation of current MPS approaches for mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells. Such mutations are at the basis of intra-tumor heterogeneity, with important implications for clinical diagnosis, and could also contribute to somatic diseases other than cancer, including aging. Some possible approaches to gain access to low-abundance mutations are discussed, with a brief overview of new sequencing platforms that are currently waiting in the wings to advance this exploding field even further.

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“…It is known that base-call errors are more likely towards the ends of reads and that surrounding sequence motifs influence error frequencies [2,10,15]. For example, errors are more likely at positions preceded by GG or following a number of GGC motifs [10], but regardless of the preceding motif, errors become more likely towards the end of reads [2]. However, we have found that errors at some genomic positions appear with greater frequency than can be explained by these effects, and we refer to this as systematic error.…”

Section: Introductionmentioning

confidence: 99%

Identification and correction of systematic error in high-throughput sequence data

Meacham

Boffelli²,

Dhahbi³

et al. 2011

Nat Prec

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A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations. We characterize and describe systematic errors using overlapping paired reads form high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that quality scores at systematic error sites do not account for the extent of errors. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq). Systematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or in estimates of allele-specific expression from RNA-Seq data. Our characterization of systematic error has allowed us to develop a program, called SysCall, for identifying and correcting such errors. We conclude that correction of systematic errors is important to consider in the design and interpretation of high-throughput sequencing experiments.Availability: Software for correcting systematic errors is freely available from

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Sequence-specific error profile of Illumina sequencers

Cited by 550 publications

References 39 publications

Sedimentary DNA versus morphology in the analysis of diatom-environment relationships

Sedimentary DNA versus morphology in the analysis of diatom-environment relationships

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Identification and correction of systematic error in high-throughput sequence data

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