| 1. |
- Testor, Pierre, et al.
(författare)
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OceanGliders: A component of the integrated GOOS
- 2019
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Ingår i: Frontiers in Marine Science. - : Frontiers Media SA. - 2296-7745. ; 6
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Forskningsöversikt (refereegranskat)abstract
- The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in real-time and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintian and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.
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| 2. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 3. |
- Schumacher, Fredrick R., et al.
(författare)
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Genome-wide association study identifies new prostate cancer susceptibility loci
- 2011
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Ingår i: Human Molecular Genetics. - London : IRL Press. - 0964-6906 .- 1460-2083. ; 20:19, s. 3867-3875
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Tidskriftsartikel (refereegranskat)abstract
- Prostate cancer (PrCa) is the most common non-skin cancer diagnosed among males in developed countries and the second leading cause of cancer mortality, yet little is known regarding its etiology and factors that influence clinical outcome. Genome-wide association studies (GWAS) of PrCa have identified at least 30 distinct loci associated with small differences in risk. We conducted a GWAS in 2782 advanced PrCa cases (Gleason grade >= 8 or tumor stage C/D) and 4458 controls with 571 243 single nucleotide polymorphisms (SNPs). Based on in silico replication of 4679 SNPs (Stage 1, P < 0.02) in two published GWAS with 7358 PrCa cases and 6732 controls, we identified a new susceptibility locus associated with overall PrCa risk at 2q37.3 (rs2292884, P = 4.3 x 10(-8)). We also confirmed a locus suggested by an earlier GWAS at 12q13 (rs902774, P = 8.6 x 10(-9)). The estimated per-allele odds ratios for these loci (1.14 for rs2292884 and 1.17 for rs902774) did not differ between advanced and non-advanced PrCa (case-only test for heterogeneity P = 0.72 and P = 0.61, respectively). Further studies will be needed to assess whether these or other loci are differentially associated with PrCa subtypes.
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| 4. |
- Viallon, Vivian, et al.
(författare)
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A new pipeline for the normalization and pooling of metabolomics data
- 2021
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Ingår i: Metabolites. - : MDPI. - 2218-1989 .- 2218-1989. ; 11:9
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Tidskriftsartikel (refereegranskat)abstract
- Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.
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