69 research outputs found

    ACEseq – allele specific copy number estimation from whole genome sequencing

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    ACEseq is a computational tool for allele-specific copy number estimation in tumor genomes based on whole genome sequencing. In contrast to other tools it features GC-bias correction, unique replication timing-bias correction and integration of structural variant (SV) breakpoints for improved genome segmentation. ACEseq clearly outperforms widely used state-of-the art methods, provides a fully automated estimation of tumor cell content and ploidy, and additionally computes homologous recombination deficiency scores.</jats:p

    The impact of mineral dust on cloud formation during the Saharan dust event in April 2014 over Europe

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    A regional modeling study on the impact of desert dust on cloud formation is presented for a major Saharan dust outbreak over Europe from 2 April to 5 April 2014. The dust event coincided with an extensive and dense cirrus cloud layer, suggesting an influence of dust on atmospheric ice nucleation. Using interactive simulation with the regional dust model COSMO-MUSCAT, we investigate cloud and precipitation representation in the model and test the sensitivity of cloud parameters to dust-cloud and dust-radiation interactions of the simulated dust plume. We evaluate model results with ground-based and space-borne remote sensings of aerosol and cloud properties, as well as the in situ measurements obtained during the ML-CIRRUS aircraft campaign

    A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing.

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    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.We thank the DKFZ Genomics and Proteomics Core Facility and the OICR Genome Technologies Platform for provision of sequencing services. Financial support was provided by the consortium projects READNA under grant agreement FP7 Health-F4-2008-201418, ESGI under grant agreement 262055, GEUVADIS under grant agreement 261123 of the European Commission Framework Programme 7, ICGC-CLL through the Spanish Ministry of Science and Innovation (MICINN), the Instituto de Salud Carlos III (ISCIII) and the Generalitat de Catalunya. Additional financial support was provided by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by German Cancer Aid (109252) and by the German Federal Ministry of Education and Research (BMBF, grants #01KU1201A, MedSys #0315416C and NGFNplus #01GS0883; the Ontario Institute for Cancer Research to PCB and JDM through funding provided by the Government of Ontario, Ministry of Research and Innovation; Genome Canada; the Canada Foundation for Innovation and Prostate Cancer Canada with funding from the Movember Foundation (PCB). PCB was also supported by a Terry Fox Research Institute New Investigator Award, a CIHR New Investigator Award and a Genome Canada Large-Scale Applied Project Contract. The Synergie Lyon Cancer platform has received support from the French National Institute of Cancer (INCa) and from the ABS4NGS ANR project (ANR-11-BINF-0001-06). The ICGC RIKEN study was supported partially by RIKEN President’s Fund 2011, and the supercomputing resource for the RIKEN study was provided by the Human Genome Center, University of Tokyo. MDE, LB, AGL and CLA were supported by Cancer Research UK, the University of Cambridge and Hutchison-Whampoa Limited. SD is supported by the Torres Quevedo subprogram (MI CINN) under grant agreement PTQ-12-05391. EH is supported by the Research Council of Norway under grant agreements 221580 and 218241 and by the Norwegian Cancer Society under grant agreement 71220-PR-2006-0433. Very special thanks go to Jennifer Jennings for administrating the activity of the ICGC Verification Working Group and Anna Borrell for administrative support.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1000
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