| 1. |
- Campbell, PJ, et al.
(author)
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Pan-cancer analysis of whole genomes
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
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Journal article (peer-reviewed)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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| 4. |
- Romagnoni, A, et al.
(author)
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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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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Journal article (peer-reviewed)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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| 13. |
- Niemi, MEK, et al.
(author)
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- 2021
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swepub:Mat__t
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| 17. |
- Alcorn, J, et al.
(author)
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Basic instrumentation for Hall A at Jefferson Lab
- 2004
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In: Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment. - : Elsevier BV. - 0167-5087 .- 0168-9002. ; 522:3, s. 294-346
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Journal article (peer-reviewed)abstract
- The instrumentation in Hall A at the Thomas Jefferson National Accelerator Facility was designed to study electro-and photo-induced reactions at very high luminosity and good momentum and angular resolution for at least one of the reaction products. The central components of Hall A are two identical high resolution spectrometers, which allow the vertical drift chambers in the focal plane to provide a momentum resolution of better than 2 x 10(-4). A variety of Cherenkov counters, scintillators and lead-glass calorimeters provide excellent particle identification. The facility has been operated successfully at a luminosity well in excess of 10(38) CM-2 s(-1). The research program is aimed at a variety of subjects, including nucleon structure functions, nucleon form factors and properties of the nuclear medium. (C) 2003 Elsevier B.V. All rights reserved.
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| 18. |
- Bethlehem, RAI, et al.
(author)
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Brain charts for the human lifespan
- 2022
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 604:79057906, s. 525-
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Journal article (peer-reviewed)abstract
- Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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| 21. |
- Didion, JP, et al.
(author)
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R2d2 Drives Selfish Sweeps in the House Mouse
- 2016
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In: Molecular biology and evolution. - : Oxford University Press (OUP). - 1537-1719 .- 0737-4038. ; 33:6, s. 1381-1395
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Journal article (peer-reviewed)
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- Korecka, A, et al.
(author)
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Bidirectional communication between the Aryl hydrocarbon Receptor (AhR) and the microbiome tunes host metabolism
- 2016
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In: NPJ biofilms and microbiomes. - : Springer Science and Business Media LLC. - 2055-5008. ; 2, s. 16014-
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Journal article (peer-reviewed)abstract
- The ligand-induced transcription factor, aryl hydrocarbon receptor (AhR) is known for its capacity to tune adaptive immunity and xenobiotic metabolism—biological properties subject to regulation by the indigenous microbiome. The objective of this study was to probe the postulated microbiome-AhR crosstalk and whether such an axis could influence metabolic homeostasis of the host. Utilising a systems-biology approach combining in-depth 1H-NMR-based metabonomics (plasma, liver and skeletal muscle) with microbiome profiling (small intestine, colon and faeces) of AhR knockout (AhR−/−) and wild-type (AhR+/+) mice, we assessed AhR function in host metabolism. Microbiome metabolites such as short-chain fatty acids were found to regulate AhR and its target genes in liver and intestine. The AhR signalling pathway, in turn, was able to influence microbiome composition in the small intestine as evident from microbiota profiling of the AhR+/+ and AhR−/− mice fed with diet enriched with a specific AhR ligand or diet depleted of any known AhR ligands. The AhR−/− mice also displayed increased levels of corticosterol and alanine in serum. In addition, activation of gluconeogenic genes in the AhR−/− mice was indicative of on-going metabolic stress. Reduced levels of ketone bodies and reduced expression of genes involved in fatty acid metabolism in the liver further underscored this observation. Interestingly, exposing AhR−/− mice to a high-fat diet showed resilience to glucose intolerance. Our data suggest the existence of a bidirectional AhR-microbiome axis, which influences host metabolic pathways.
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