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- Campbell, PJ, et al.
(författare)
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Pan-cancer analysis of whole genomes
- 2020
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
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Tidskriftsartikel (refereegranskat)abstract
- Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10–18.
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| 2. |
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| 3. |
- Yakneen, S, et al.
(författare)
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Butler enables rapid cloud-based analysis of thousands of human genomes
- 2020
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Ingår i: Nature biotechnology. - : Springer Science and Business Media LLC. - 1546-1696 .- 1087-0156. ; 38:3, s. 288-
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Tidskriftsartikel (refereegranskat)abstract
- We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.
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| 4. |
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| 5. |
- Taha, Muhamed-Kheir, et al.
(författare)
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Interlaboratory Comparison of PCR-Based Identification and Genogrouping of Neisseria meningitidis
- 2005
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Ingår i: Journal of Clinical Microbiology. - 0095-1137 .- 1098-660X. ; 43:1, s. 144-149
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Tidskriftsartikel (refereegranskat)abstract
- Twenty clinical samples (18 cerebrospinal fluid samples and 2 articular fluid samples) were sent to 11 meningococcus reference centers located in 11 different countries. Ten of these laboratories are participating in the EU-MenNet program (a European Union-funded program) and are members of the European Monitoring Group on Meningococci. The remaining laboratory was located in Burkina Faso. Neisseria meningitidis was sought by detecting several meningococcus-specific genes (crgA, ctrA, 16S rRNA, and porA). The PCR-based nonculture method for the detection of N. meningitidis gave similar results between participants with a mean sensitivity and specificity of 89.7 and 92.7%, respectively. Most of the laboratories also performed genogrouping assays (siaD and mynB/sacC). The performance of genogrouping was more variable between laboratories, with a mean sensitivity of 72.7%. Genogroup B gave the best correlation between participants, as all laboratories routinely perform this PCR. The results for genogroups A and W135 were less similar between the eight participating laboratories that performed these PCRs.
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