Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and Genome Analyzer systems

By André E. Minoche, Juliane C. Dohm, Heinz Himmelbauer. Genome Biology, 2011

648 citations recorded by the source corpus.

BackgroundThe generation and analysis of high-throughput sequencing data are becoming a major component of many studies in molecular biology and medical research. Illumina's Genome Analyzer (GA) and HiSeq instruments are currently the most widely used sequencing devices. Here, we comprehensively evaluate properties of genomic HiSeq and GAIIx data derived from two plant genomes and one virus, with read lengths of 95 to 150 bases.ResultsWe provide quantifications and evidence for GC bias, error rates, error sequence context, effects of quality filtering, and the reliability of quality values. By combining different filtering criteria we reduced error rates 7-fold at the expense of discarding 12.5% of alignable bases. While overall error rates are low in HiSeq data we observed regions of accumulated wrong base calls. Only 3% of all error positions accounted for 24.7% of all substitution errors. Analyzing the forward and reverse strands separately revealed error rates of up to 18.7%. Insertions and deletions occurred at very low rates on average but increased to up to 2% in homopolymers. A positive correlation between read coverage and GC content was found depending on the GC content range.ConclusionsThe errors and biases we report have implications for the use and the interpretation of Illumina sequencing data. GAIIx and HiSeq data sets show slightly different error profiles. Quality filtering is essential to minimize downstream analysis artifacts. Supporting previous recommendations, the strand-specificity provides a criterion to distinguish sequencing errors from low abundance polymorphisms.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/f942cf8a040a874468b84d42b1aa73cec48fe02f

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Citations in the corpus

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, positive): “In a recent study of the Illumina HiSeq and Genome Analyzer systems [72], for example, a positive correlation between read coverage and GC content was observed when GC percentage is within the spectrum of 24 to 47%.”
Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences (2014, methodology): “Most widely used quality-filtering approaches trim low-quality ends of sequencing reads (Minoche et al., 2011; Bokulich et al., 2013); to eliminate artificial read length variation for MED analysis, reads that require trimming should be discarded prior to analysis.”
AdapterRemoval: easy cleaning of next-generation sequencing reads (2012, background): “It is well-known that the quality of a read is lower in the ends [32], with elevated error rates at both the 5’ and – in particular – 3’ end of the reads.”
Scaling accurate genetic variant discovery to tens of thousands of samples (2017, background): “Although high-throughput sequencing platforms currently generate up to four billion paired-end reads per run, low signal-to-noise ratios necessitate the use of data processing algorithms that differentiate true variants from machine-generated artifacts [19, 11].”
Linking pangenomes and metagenomes: the Prochlorococcus metapangenome (2018, methodology): “4.1 ( Eren et al., 2013 ) (available from https://github.com/merenlab/illumina-utils), which implements the noise filtering parameters described by Minoche, Dohm & Himmelbauer (2011) .”
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) .”
Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data (2016, background): “They also observed elevated rates of insertions and deletions in homopolymeric regions [7].”