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

[dan]

http://genomebiology.com/2011/12/11/R112/abstract

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

Andre E Minoche, Juliane C Dohm and Heinz Himmelbauer

Genome Biology 2011, 12:R112 doi:10.1186/gb-2011-12-11-r112
Published: 8 November 2011

Abstract (provisional)
Background
The generation and analysis of high-throughput sequencing data is 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 95-150 bases.

Results
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 strand 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.

Conclusions
The errors and biases we report have implications on 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.

[bioBob]

This is an interesting article. I am wondering, however, how the statistics on the GA would have changed if the input dataset was a bit more typical. With only 9M unfiltered reads for the PhiX, would this have passed the platform quality tests? We expect >10M reads to pass the quality filters and typically get 25-40M.