Substantial biases in ultra-short read data sets from high-throughput DNA sequencing

By Juliane C. Dohm, Claudio Lottaz, T. Borodina, Heinz Himmelbauer. Nucleic Acids Research, 2008

1,181 citations recorded by the source corpus.

Novel sequencing technologies permit the rapid production of large sequence data sets. These technologies are likely to revolutionize genetics and biomedical research, but a thorough characterization of the ultra-short read output is necessary. We generated and analyzed two Illumina 1G ultra-short read data sets, i.e. 2.8 million 27mer reads from a Beta vulgaris genomic clone and 12.3 million 36mers from the Helicobacter acinonychis genome. We found that error rates range from 0.3% at the beginning of reads to 3.8% at the end of reads. Wrong base calls are frequently preceded by base G. Base substitution error frequencies vary by 10- to 11-fold, with A > C transversion being among the most frequent and C > G transversions among the least frequent substitution errors. Insertions and deletions of single bases occur at very low rates. When simulating re-sequencing we found a 20-fold sequencing coverage to be sufficient to compensate errors by correct reads. The read coverage of the sequenced regions is biased; the highest read density was found in intervals with elevated GC content. High Solexa quality scores are over-optimistic and low scores underestimate the data quality. Our results show different types of biases and ways to detect them. Such biases have implications on the use and interpretation of Solexa data, for de novo sequencing, re-sequencing, the identification of single nucleotide polymorphisms and DNA methylation sites, as well as for transcriptome analysis.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/331d11ea1e7861207411df8810c9d35e6841a1d0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012, neutral): “Copy number analysis by NGS has the following potential advantages compared with array-based techniques: (i) estimation of integer copy numbers from NGS data is more accurate for large copy numbers, since depths of coverage scale linearly with copy numbers (6).”
High-throughput genotyping by whole-genome resequencing. (2009, background): “Given the estimated average per-base error rate of 0.3% at the 59 end of Illumina GA reads (Dohm et al. 2008), the sequence error rate for the first two bases of the index combined is 0.6%.”
Extracellular miRNAs: From Biomarkers to Mediators of Physiology and Disease (2019, background): “RT-qPCR is more sensitive and not limited by sequence-abundance bias (Dohm et al., 2008; Hansen et al., 2010; Zheng et al., 2011), but the technique requires optimization, proper standard curves, and ideally the inclusion of spiked-in exogenous miRNAs (Huggett et al.”
Evaluation of next generation sequencing platforms for population targeted sequencing studies (2009, result): “depth coverage decreases with increasing AT content, which is consistent with previous studies [17,18] (Supplemental Figure 1 in Additional data file 3).”
SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data (2010, background): “However, the error profiles of highthroughput short read sequencing technologies differ markedly from traditional Sanger sequencing [2]; they tend to exhibit a steep, exponential increase in error rates along the read length, and are susceptible to a wider range of chemistry and machine failures (su”
High-Throughput Sequencing Technologies (2015, background): “The exact percentage is a source of significant debate (Doolittle, 2013), highlighting the importance of further experimental evidence to assign function to genomic elements.”
Detection and quantification of rare mutations with massively parallel sequencing (2011, background): “05% (33, 34), depending on factors such as the read length (35), use of improved base-calling algorithms (36–38), and the type of variants detected (39).”
Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture (2011, background): “A known major source of bias in sequencing experiments is the nucleotide composition of the DNA under stud”
Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects (2013, methodology): “- 44 GC content and specific sequence structure [7-9].”
Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms (2008, background): “Thus, the GC bias in DGE seems to be limited, but needs further investigation, also in the light of a recently published study where considerable overrepresentation of GC-rich sequences was observed in Solexa/Illumina-based resequencing experiments (49).”
Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood (2008, background): “We strongly suspect that it is an artifact because we also observe GC bias on genomic DNA control, and such bias on the Solexa sequencing platform has recently been reported (38, 39).”
RNA-Seq gene expression estimation with read mapping uncertainty (2009, background): “Such non-uniformity may be due to a variety of factors, including fragmentation protocols and composition biases (Dohm et al., 2008).”
High-resolution mapping of copy-number alterations with massively parallel sequencing (2008, background): “We anticipate that each massively parallel sequencing platform may be susceptible to particular biase”
Characterizing and measuring bias in sequence data (2013, background): “Illumina sequencing [18] has been shown to lose coverage in regions of high or low GC [19-22], a phenomenon also seen in other ‘ next-generation ’ technologies [3,6].”