title: Coverage bias and sensitivity of variant calling for four whole-genome sequencing technologies.
abstract: The emergence of high-throughput, next-generation sequencing technologies has dramatically altered the way we assess genomes in population genetics and in cancer genomics. Currently, there are four commonly used whole-genome sequencing platforms on the market: Illumina's HiSeq2000, Life Technologies' SOLiD 4 and its completely redesigned 5500xl SOLiD, and Complete Genomics' technology. A number of earlier studies have compared a subset of those sequencing platforms or compared those platforms with Sanger sequencing, which is prohibitively expensive for whole genome studies. Here we present a detailed comparison of the performance of all currently available whole genome sequencing platforms, especially regarding their ability to call SNVs and to evenly cover the genome and specific genomic regions. Unlike earlier studies, we base our comparison on four different samples, allowing us to assess the between-sample variation of the platforms. We find a pronounced GC bias in GC-rich regions for Life Technologies' platforms, with Complete Genomics performing best here, while we see the least bias in GC-poor regions for HiSeq2000 and 5500xl. HiSeq2000 gives the most uniform coverage and displays the least sample-to-sample variation. In contrast, Complete Genomics exhibits by far the smallest fraction of bases not covered, while the SOLiD platforms reveal remarkable shortcomings, especially in covering CpG islands. When comparing the performance of the four platforms for calling SNPs, HiSeq2000 and Complete Genomics achieve the highest sensitivity, while the SOLiD platforms show the lowest false positive rate. Finally, we find that integrating sequencing data from different platforms offers the potential to combine the strengths of different technologies. In summary, our results detail the strengths and weaknesses of all four whole-genome sequencing platforms. It indicates application areas that call for a specific sequencing platform and disallow other platforms. This helps to identify the proper sequencing platform for whole genome studies with different application scopes.
authors.affiliation: Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Nora
authors.initials: N
authors.lastname: Rieber
authors.forename: Marc
authors.initials: M
authors.lastname: Zapatka
authors.forename: Bärbel
authors.initials: B
authors.lastname: Lasitschka
authors.forename: David
authors.initials: D
authors.lastname: Jones
authors.forename: Paul
authors.initials: P
authors.lastname: Northcott
authors.forename: Barbara
authors.initials: B
authors.lastname: Hutter
authors.forename: Natalie
authors.initials: N
authors.lastname: Jäger
authors.forename: Marcel
authors.initials: M
authors.lastname: Kool
authors.forename: Michael
authors.initials: M
authors.lastname: Taylor
authors.forename: Peter
authors.initials: P
authors.lastname: Lichter
authors.forename: Stefan
authors.initials: S
authors.lastname: Pfister
authors.forename: Stephan
authors.initials: S
authors.lastname: Wolf
authors.forename: Benedikt
authors.initials: B
authors.lastname: Brors
authors.forename: Roland
authors.initials: R
authors.lastname: Eils
mesh_terms: D000818:Animals; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods