TS Alioto, I Buchhalter, S Derdak, B Hutter, MD Eldridge, E Hovig, LE Heisler, TA Beck, JT Simpson, L Tonon, A-S Sertier, A-M Patch, N Jäger, P Ginsbach, R Drews, N Paramasivam, R Kabbe, S Chotewutmontri, N Diessl, C Previti, S Schmidt, B Brors, L Feuerbach, M Heinold, S Gröbner, A Korshunov, PS Tarpey, AP Butler, J Hinton, D Jones, A Menzies, K Raine, R Shepherd, L Stebbings, JW Teague, P Ribeca, FC Giner, S Beltran, E Raineri, M Dabad, SC Heath, M Gut, RE Denroche, NJ Harding, TN Yamaguchi, A Fujimoto, H Nakagawa, V Quesada, R Valdés-Mas, S Nakken, D Vodák, L Bower, AG Lynch, CL Anderson, N Waddell, JV Pearson, SM Grimmond, M Peto, P Spellman, M He, C Kandoth, S Lee, J Zhang, L Létourneau, S Ma, S Seth, D Torrents, L Xi, DA Wheeler, C López-Otín, E Campo, PJ Campbell, PC Boutros, XS Puente, DS Gerhard, SM Pfister, JD McPherson, TJ Hudson, M Schlesner, P Lichter, R Eils, DTW Jones, IG Gut
As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼ 100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.