Abstract 52: QDNAseq: A bioinformatics pipeline for DNA copy number analysis from shallow whole genome sequencing with noise levels near the probabilistic lower limit imposed by read counting
Authors: Sie Daoud , Scheinin Ilari , Lieshout Stef van , Cordes Martijn , Pinkel Daniel , Albertson Donna G. , Wiel Mark A. van de , Ylstra Bauke 
Published: May 2016

Abstract
The emerging field of precision medicine considers the patient's individual genetic make up, environment, and lifestyle to arrive at individualized treatment. For cancer patients, analysis of the tumour genome can reveal actionable alterations, including mutations (e.g., BRAF mutation in melanoma), translocations (e.g., t(9;22)(q34;q11) in CML) and copy number changes (e.g., HER2 amplification in breast cancer). Formalin-fixed paraffin embedded (FFPE) biopsy or surgical pathology samples are often the only materials available for analysis, highlighting the need for genomic analysis techniques that perform reliably with such materials. We present a complete pipeline for DNA copy number profiling by sequencing called quantitative DNA sequencing (QDNAseq), which offers a cost effective procedure for genome-wide DNA copy number analysis, facilitates correct biological interpretations from samples with a varying range of quality and is suitable for routine pathology diagnostics from fresh, frozen and FFPE materials.
QDNAseq is implemented in R and made available through BioConductor and Galaxy. The pipeline requires as little as ∼0.1× genome coverage (∼6 million single-end 50nt reads per sample) and utilizes a depth of coverage (DOC) approach. A two-dimensional LOESS correction is applied for GC content and mappability and data from the 1000 genomes project are used to compile a blacklist of problematic genome regions. Downstream analyses have been included in QDNAseq by adapting algorithms previously developed for segmentation (DNAcopy) and the calling of gains and losses (CGHcall).
QDNAseq is able to produce profiles with noise levels near the probabilistic lower limit imposed by read counting and yields a higher resolution than is available from microarrays. Some samples, however, have variances clearly above the probabilistic lower limit and are characterized by a wave pattern. This pattern originates in the sample, as it is reproducible, both in magnitude and in shape along the genome. Much of this wave bias can be reduced by regression with profiles from matched normal FFPE DNA samples using the algorithm implemented in NoWaves.
The QDNAseq procedure is highly cost effective and robust. We have processed over 1500 clinical samples obtained from the FFPE archives of more than 25 institutions from Europe and North America.
Citation Format: Daoud Sie, Ilari Scheinin, Stef Lieshout, van, Martijn Cordes, Daniel Pinkel, Donna G. Albertson, Mark A. Wiel, van de, Bauke Ylstra. QDNAseq: A bioinformatics pipeline for DNA copy number analysis from shallow whole genome sequencing with noise levels near the probabilistic lower limit imposed by read counting. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 52.