Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Errors may be related to sequencing context, with highest rate for nucleotide T, which in our case would be erroneous once in every 1250 nucleotides, and error rates for C, G, and A being much lower.
“Other studies have shown that errors may be related to the sequencing context, where the highest rate of errors was found for nucleotide T, which in our case would result to be erroneous once in every 1250 nucleotides, and the error rates for C, G, and A being much lower39.”
From DNA synthesis for true random number generation (2020) · cites this paper as [39]
Expected error rates increase towards the end of the DNA strand.
“Overall, expected error rates were found to increase towards the end of the DNA strand39.”
From DNA synthesis for true random number generation (2020) · cites this paper as [39]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing
Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M. V., Kulikov, A. S., Lesin, V. M., Nikolenko, S., Pham, S., Prjibelski, A. D., et al. (2012) · Journal of Computational Biology · doi:10.1089/cmb.2012.0021 · Cited by 25,691 · see citing works
Accurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684 · see citing works
Substantial biases in ultra-short read data sets from high-throughput DNA sequencing
Dohm, J. C., Lottaz, C., Borodina, T., & Himmelbauer, H. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn425 · Cited by 1,088 · see citing works
Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform
Schirmer, M., Ijaz, U. Z., D'Amore, R., Hall, N., Sloan, W. T., & Quince, C. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1341 · Cited by 743 · see citing works
Self-citationMinoche, A. E., Dohm, J. C., & Himmelbauer, H. (2011) · Genome biology · doi:10.1186/gb-2011-12-11-r112 · Cited by 620 · see citing works
Sequence-specific error profile of Illumina sequencers
Nakamura, K., Oshima, T., Morimoto, T., Ikeda, S., Yoshikawa, H., Shiwa, Y., Ishikawa, S., Linak, M. C., Hirai, A. M., Takahashi, H., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr344 · Cited by 593 · see citing works
Identification and correction of systematic error in high-throughput sequence dataPreprint
Meacham, F., Boffelli, D., Dhahbi, J. M., Martin, D. I. K., Singer, M., & Pachter, L. S. (2011) · Nature Precedings · doi:10.1186/1471-2105-12-451 · Cited by 240 · see citing works
Shakya, M., Quince, C., Campbell, J. H., Yang, Z. K., Schadt, C. W., & Podar, M. (2013) · Environmental Microbiology · doi:10.1111/1462-2920.12086 · Cited by 214 · see citing works
Self-citationInsertion site preference of Mu, Tn5, and Tn7 transposons
Green, B., Bouchier, C., Fairhead, C., Craig, N. L., & Cormack, B. P. (2012) · Mobile DNA · doi:10.1186/1759-8753-3-3 · Cited by 145 · see citing works
Tn5 as a model for understanding DNA transposition
Reznikoff, W. S. (2003) · Molecular Microbiology · doi:10.1046/j.1365-2958.2003.03382.x · Cited by 138 · see citing works
Li, Y., Mitaxov, V., & Waksman, G. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.17.9491 · Cited by 127 · see citing works
Marine, R. L., Polson, S. W., Ravel, J., Hatfull, G. F., Russell, D. A., Sullivan, M. B., Syed, F., Dumas, M., & Wommack, K. E. (2011) · Applied and Environmental Microbiology · doi:10.1128/aem.05610-11 · Cited by 104 · see citing works
DNA polymerases drive DNA sequencing-by-synthesis technologies: both past and present
Chen, C.-Y. (2014) · Frontiers in Microbiology · doi:10.3389/fmicb.2014.00305 · Cited by 88 · see citing works
Discovering motifs that induce sequencing errors
Allhoff, M., Schönhuth, A., Martin, M., Costa, I. G., Rahmann, S., & Marschall, T. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s5-s1 · Cited by 77 · see citing works
Parkinson, N., Maslau, S., Ferneyhough, B., Zhang, G., Gregory, L., Buck, D., Ragoussis, J., Ponting, C. P., & Fischer, M. D. (2011) · Genome Research · doi:10.1101/gr.124016.111 · Cited by 71 · see citing works
The History and Advances of Reversible Terminators Used in New Generations of Sequencing Technology
Chen, F., Dong, M., Ge, M., Zhu, L., Ren, L., Liu, G., & Mu, R. (2013) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2013.01.003 · Cited by 64 · see citing works
DNA Sequence Bias During Tn5 Transposition
Ason, B. L., & Reznikoff, W. S. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.11.039 · Cited by 36 · see citing works
Syed, F., Grunenwald, H. L., & Caruccio, N. C. (2009) · Nature Methods · doi:10.1038/nmeth.f.272 · Cited by 35 · see citing works
Petsko, G. A. (2011) · Genome biology · doi:10.1186/gb-2011-12-4-112 · Cited by 28 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Olm, M. R., Crits-Christoph, A., Bouma-Gregson, K., Firek, B. A., Morowitz, M. J., & Banfield, J. F. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00797-0 · Cited by 537 · see citing works
Accurate and complete genomes from metagenomes
Chen, L.-X., Anantharaman, K., Shaiber, A., Eren, A. M., & Banfield, J. F. (2020) · Genome Research · doi:10.1101/gr.258640.119 · Cited by 401 · see citing works
Pangenomics enables genotyping of known structural variants in 5202 diverse genomes
Sirén, J., Monlong, J., Chang, X., Novak, A. M., Eizenga, J. M., Markello, C., Sibbesen, J. A., Hickey, G., Chang, P.-C., Carroll, A., et al. (2021) · Science · doi:10.1126/science.abg8871 · Cited by 400 · see citing works
Codiversification of gut microbiota with humans
Suzuki, T. A., Fitzstevens, J. L., Schmidt, V. T., Enav, H., Huus, K. E., Mbong Ngwese, M., Grießhammer, A., Pfleiderer, A., Adegbite, B. R., Zinsou, J. F., et al. (2022) · Science · doi:10.1126/science.abm7759 · Cited by 210 · see citing works
Joint estimates of heterozygosity and runs of homozygosity for modern and ancient samples
Renaud, G., Hanghøj, K., Korneliussen, T. S., Willerslev, E., & Orlando, L. (2019) · Genetics · doi:10.1534/genetics.119.302057 · Cited by 101 · see citing works
Information-Theoretic Foundations of DNA Data Storage
Shomorony, I., & Heckel, R. (2022) · Foundations and Trends® in Communications and Information Theory · doi:10.1561/0100000117 · Cited by 62 · see citing works
DNA synthesis for true random number generation
Meiser, L. C., Koch, J., Antkowiak, P. L., Stark, W. J., Heckel, R., & Grass, R. N. (2020) · Nature Communications · doi:10.1038/s41467-020-19757-y · Cited by 45 · see citing works
A Characterization of the DNA Data Storage Channel
Heckel, R., Mikutis, G., & Grass, R. N. (2019) · Scientific Reports · doi:10.1038/s41598-019-45832-6 · Cited by 9 · see citing works
- Authors
- Schirmer, M., D’Amore, R., Ijaz, U. Z., Hall, N., & Quince, C.
- Affiliations
- Broad Institute (US)
- University of Glasgow (GB)
- University of Liverpool (GB)
- University of Warwick (GB)
- Corresponding author
- Schirmer, Melanie
- Venue
- BMC Bioinformatics
- Published
- 2016-03-11
- Volume
- 17
- Issue
- 1
- Pages
- 125-125
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4787001
- OpenAlex
- W2295828257
- Cited by
- 376
- References
- 21
- Citations by year
- 2026: 23 · 2025: 15 · 2024: 29 · 2023: 29 · 2022: 32 · 2021: 77 · 2020: 38 · 2019: 52 · 2018: 51 · 2017: 20 · 2016: 10
- FWCI
- 23.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Natural Environment Research Council (NE/L011956/1)
- Technology Strategy Board (TS/J000175/1)
- Engineering and Physical Sciences Research Council (EP/H003851/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- sequencing bias, metagenomics, substitution errors, sequencing by synthesis, single nucleotide polymorphisms, bacterial genomes, HiSeq, MiSeq
- MeSH terms
- Archaea, Bacteria, Genome, Bacterial, Sequence Analysis, DNA, Polymorphism, Single Nucleotide, Genome, Archaeal, GC Rich Sequence, Metagenomics, High-Throughput Nucleotide Sequencing, Nucleotide Motifs, Signal-To-Noise Ratio
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
