%0 Journal Article
%T Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and Genome Analyzer systems
%A Andr¨¦ E Minoche
%A Juliane C Dohm
%A Heinz Himmelbauer
%J Genome Biology
%D 2011
%I BioMed Central
%R 10.1186/gb-2011-12-11-r112
%X We 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.The 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.Next 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. Compared with microarrays, previously used for many of these applications, NGS offers a higher dynamic range, enabling the detection of rare transcripts and splice variants in the transcriptome as well as rare genomic polymorphisms - for example, somatic mutations present within cancer samples. The challenge remains to distinguish sequence variation from sequencing errors, and a thorough characterization of NGS data is required in order to detect method-inherent errors and biases
%U http://genomebiology.com/2011/12/11/R112