Barthel, F. We collected genome data from 2, donors Extended Data Table 1 , of which were excluded after quality assurance.
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We collected genome data from 2, donors, representing all ICGC and TCGA donors that met these criteria at the time of the final data freeze in autumn Extended Data Table 1. After quality assurance Supplementary Methods 2. Across the 2, white- and grey-listed donors, whole-genome sequences were available from 2, primary tumours and metastases or local recurrences. Matching normal samples were obtained from blood 2, donors , tissue adjacent to the primary tumour 87 donors or from distant sites donors.
Whole-genome sequencing data were available for tumour and normal DNA for the entire cohort. The majority of specimens Of the remaining specimens, 4. The distribution of read lengths by tumour cohort is shown in Supplementary Fig. RNA-sequencing data were collected and re-analysed centrally for 1, donors, including 1, primary tumours, 67 metastases or local recurrences and matched normal tissue samples adjacent to the primary tumour.
We consolidated histopathology descriptions of the tumour samples, using the ICD tumour site controlled vocabulary Overall, the PCAWG dataset comprises 38 distinct tumour types Extended Data Table 1 and Supplementary Table 1. To generate a consistent set of somatic mutation calls that could be used for cross-tumour analyses, we analysed all 6, samples using a uniform set of algorithms for alignment, variant calling and quality control Extended Data Fig.
We used the BWA-MEM algorithm 90 to align each tumour and normal sample to human reference build hs37d5 as used in the Genomes Project Somatic mutations were identified in the aligned data using three established pipelines, which were run independently on each tumour—normal pair.
Two additional variant algorithms , were included to further improve accuracy across a broad range of clonal and subclonal mutations. We tested different merging strategies using validation data, and choses the optimal method for each variant type to generate a final consensus set of mutation calls Supplementary Methods S2.
Somatic retrotransposition events, including Alu and LINE-1 insertions 72 , L1-mediated transductions 73 and pseudogene formation , were called using a dedicated pipeline We removed these retrotransposition events from the somatic SV call-set. Mitochondrial DNA mutations were called using a published algorithm RNA-sequencing data were uniformly processed to quantify normalized gene-level expression, splicing variation and allele-specific expression, and to identify fusion transcripts, alternative promoter usage and sites of RNA editing 8.
The uniform germline data-processing workflow comprised variant identification using six different variant-calling algorithms 96 , , and was orchestrated using the Butler workflow system We performed call-set benchmarking, merging, variant genotyping and statistical haplotype-block phasing 91 Supplementary Methods 3. Using this strategy, we identified We statistically phased this germline variant set using haplotypes from the Genomes Project 91 as a reference panel, yielding an Nphased block length of kb based on haploid chromosomes from donor-matched tumour genomes.
The requirement to uniformly realign and call variants on nearly 5, whole genomes tumour plus normal presented considerable computational challenges, and raised ethical issues owing to the use of data from different jurisdictions Extended Data Table 2. To process the data, we adopted a cloud-computing architecture 26 in which the alignment and variant calling was spread across 13 data centres on 3 continents, representing a mixture of commercial, infrastructure-as-a-service, academic cloud compute and traditional academic high-performance computer clusters Supplementary Table 3.
Together, the effort used 10 million CPU-core hours. To generate reproducible variant calling across the 13 data centres, we built the core pipelines into Docker containers 28 , in which the workflow description, required code and all associated dependencies were packaged together in stand-alone packages. These heavily tested, extensively validated workflows are available for download Box 1. To evaluate the performance of each of the mutation-calling pipelines and determine an integration strategy, we performed a large-scale deep-sequencing validation experiment Supplementary Notes 1.
Sufficient DNA remained for 50 of the 63 cases for validation, which was performed by hybridization of tumour and matched normal DNA to a custom RNA bait set, followed by deep sequencing, as previously described Although performed using the same sequencing chemistry as the original whole-genome sequencing analyses, the considerably greater depth achieved in the validation experiment enabled accurate assessment of sensitivity and precision of variant calls.
Variant calls in repeat-masked regions were not tested, owing to the challenge of designing reliable validation probes in these areas.
To assess the accuracy of SV calls, we therefore used the property that an SV must either generate a copy-number change or be balanced, whereas artefactual calls will not respect this property. Next, we examined multiple methods for merging calls made by several algorithms into a single definitive call-set to be used for downstream analysis. For indels, because methods were less concordant, we used stacked logistic regression , to integrate the calls. Consensus CNA calls were obtained by joining the outputs of six individual CNA-calling algorithms with SV consensus breakpoints to obtain base-pair resolution CNAs Supplementary Methods 2.
Consensus purity and ploidy were derived, and a multi tier system was developed for consensus copy-number calls Supplementary Methods 2. That is, There remains much work to be done to improve indel calling software; we still lack sensitivity for calling even fully clonal complex indels from short-read sequencing data.
In addition, to access somatic single nucleotide variants derived from TCGA donors, researchers will also need to obtain dbGaP authorisation. Beyond the core sequence data and variant call-sets, the analyses in this paper used a number of datasets that were derived from the variant calls Supplementary Table 4.
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Download references. We thank research participants who donated samples and data, the physicians and clinical staff who contributed to sample annotation and collection, and the numerous funding agencies that contributed to the collection and analysis of this dataset. These authors jointly supervised this work: Peter J. Campbell, Gad Getz, Jan O. Korbel, Joshua M. Stuart, Lincoln D. Peter J. Campbell, Keiran M. Raine, Adam P. Butler, Erik Garrison, Jorge Zamora, David C.
Wedge, Maxime Tarabichi, Nicola D. Roberts, Yilong Li, Ludmil B. Alexandrov, Jonathan Hinton, David R. Stratton, Peter Clapham, Jonathan Nicholson, Jon W. Adams, Kevin J. Dawson, Stefan C. Dentro, Henry Lee-Six, Thomas J. Mitchell, Shriram G. Bowen, Elli Papaemmanuil, Mohammed Ghori, Barbara Kremeyer, Daniel A.
Department of Haematology, University of Cambridge, Cambridge, UK. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Gad Getz, Esther Rheinbay, Gordon Saksena, Grace Tiao, Ayellet V. Segre, Kristian Cibulskis, Ignaty Leshchiner, Dimitri Livitz, Yosef E.
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