| 1. |
- 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. |
- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
- Adcox, K, et al.
(författare)
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Formation of dense partonic matter in relativistic nucleus-nucleus collisions at RHIC: Experimental evaluation by the PHENIX Collaboration
- 2005
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Ingår i: Nuclear Physics, Section A. - : Elsevier BV. - 0375-9474. ; 757:1-2, s. 184-283
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Forskningsöversikt (refereegranskat)abstract
- Extensive experimental data from high-energy nucleus-nucleus collisions were recorded using the PHENIX detector at the Relativistic Heavy Ion Collider (RHIC). The comprehensive set of measurements from the first three years of RHIC operation includes charged particle multiplicities, transverse energy, yield ratios and spectra of identified hadrons in a wide range of transverse momenta (PT), elliptic flow, two-particle correlations, nonstatistical fluctuations, and suppression of particle production at high PT. The results are examined with an emphasis on implications for the formation of a new state of dense matter. We find that the state of matter created at RHIC cannot be described in terms of ordinary color neutral hadrons.
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| 5. |
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| 6. |
- Didion, JP, et al.
(författare)
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R2d2 Drives Selfish Sweeps in the House Mouse
- 2016
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Ingår i: Molecular biology and evolution. - : Oxford University Press (OUP). - 1537-1719 .- 0737-4038. ; 33:6, s. 1381-1395
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Tidskriftsartikel (refereegranskat)
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| 7. |
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| 8. |
- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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| 9. |
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| 10. |
- Adler, SS, et al.
(författare)
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Improved measurement of double helicity asymmetry in inclulsive midrapidity pi(0) production for polarized p+p collisions at root s=200 GeV
- 2006
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Ingår i: Physical Review D (Particles, Fields, Gravitation and Cosmology). - 1550-2368. ; 73:9
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Tidskriftsartikel (refereegranskat)abstract
- We present an improved measurement of the double helicity asymmetry for pi(0) production in polarized proton-proton scattering at root s=200 GeV employing the PHENIX detector at the Relativistic Heavy Ion Collider (RHIC). The improvements to our previous measurement come from two main factors: Inclusion of a new data set from the 2004 RHIC run with higher beam polarizations than the earlier run and a recalibration of the beam polarization measurements for the earlier run, which resulted in reduced uncertainties and increased beam polarizations. The results are compared to a Next to Leading Order (NLO) perturbative Quantum Chromodynamics (pQCD) calculation with a range of polarized gluon distributions.
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