Abstract
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.
Highlights
The generation and analysis of high-throughput sequencing data are becoming a major component of many studies in molecular biology and medical research
These data were a mix of genomic reads of B. vulgaris (Bv, 99%) and the bacteriophage PhiX174 (PhiX, 1%) spiked in as standard quality control
One HiSeq flowcell lane of 2 × 100-nucleotide read pairs containing 99% genomic DNA of A. thaliana (At) and 1% PhiX resulted in 71 million read pairs corresponding to 14.3 billion sequenced bases
Summary
The generation and analysis of high-throughput sequencing data are becoming a major component of many studies in molecular biology and medical research. Generation sequencing (NGS) is revolutionizing molecular biology research with a wide and rapidly growing range of applications. These applications include de novo genome sequencing, re-sequencing, detection and profiling of coding and non-coding transcripts, identification of sequence variants, epigenetic profiling, and interaction mapping. Wrong base calls are frequently preceded by base G [2] and frequencies of base substitutions vary by 10- to 11fold, with A to C conversions being the most frequent error [2,7] Such errors might have profound implications on the interpretation of results: a non-random read distribution can bias profiling of transcripts and hamper the detection of sequence polymorphisms in regions of low sequence coverage. Errors in the reads can result in false positive variant calls or wrong consensus sequences
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