18 research outputs found
RNAmut: robust identification of somatic mutations in acute myeloid leukemia using RNA-sequencing.
Acute myeloid leukemia (AML) is an aggressive malignancy of haematopoietic stem cells driven by a well-defined set of somatic mutations.1,2 Identifying the mutations driving individual cases is important for assigning the patient to a recognized World Health Organisation category, establishing prognostic risk and tailoring post-consolidation therapy.3 As a result, AML research and diagnostic laboratories apply diverse methodologies to detect important mutations and many are introducing next-generation sequencing (NGS) approaches to study extended panels of genes in order to refine genomic classification and prognostic category.1 Besides the implications of these developments on costs, expertise and reliance on commercial providers, they also do not capture gene expression data, which have independent prognostic value that cannot be inferred from somatic mutation profiles. The ability to detect AML gene mutations as well as gene expression profiles from a single assay, could provide a holistic tool that accelerates research, simplifies diagnostic work-up and helps develop integrated algorithms to refine individual patient prognosis. Here, we show that AML RNA sequencing (RNA-seq) data can be used to reliably detect all types of clinically important mutations and develop a bespoke fast and easy-to-use software (RNAmut) for this purpose that can be readily used by teams/laboratories without in-house bioinformatic expertise
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Vulnerability to climate change of managed stocks in the California Current large marine ecosystem
Introduction: Understanding how abundance, productivity and distribution of individual species may respond to climate change is a critical first step towards anticipating alterations in marine ecosystem structure and function, as well as developing strategies to adapt to the full range of potential changes. Methods: This study applies the NOAA (National Oceanic and Atmospheric Administration) Fisheries Climate Vulnerability Assessment method to 64 federally-managed species in the California Current Large Marine Ecosystem to assess their vulnerability to climate change, where vulnerability is a function of a species’ exposure to environmental change and its biological sensitivity to a set of environmental conditions, which includes components of its resiliency and adaptive capacity to respond to these new conditions. Results: Overall, two-thirds of the species were judged to have Moderate or greater vulnerability to climate change, and only one species was anticipated to have a positive response. Species classified as Highly or Very Highly vulnerable share one or more characteristics including: 1) having complex life histories that utilize a wide range of freshwater and marine habitats; 2) having habitat specialization, particularly for areas that are likely to experience increased hypoxia; 3) having long lifespans and low population growth rates; and/or 4) being of high commercial value combined with impacts from non-climate stressors such as anthropogenic habitat degradation. Species with Low or Moderate vulnerability are either habitat generalists, occupy deep-water habitats or are highly mobile and likely to shift their ranges. Discussion: As climate-related changes intensify, this work provides key information for both scientists and managers as they address the long-term sustainability of fisheries in the region. This information can inform near-term advice for prioritizing species-level data collection and research on climate impacts, help managers to determine when and where a precautionary approach might be warranted, in harvest or other management decisions, and help identify habitats or life history stages that might be especially effective to protect or restore
SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues
Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to
genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility
and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component.
Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci
(eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene),
including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform
genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer
SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the
diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types
Developing an enterprise simulator to support electronic supply chain for B2B electronic business
The application of any e-Solution promises significant returns. In particular, using internet technologies both within enterprises and across the supply (value) chain provides real opportunity, not only for operational improvement but also for innovative strategic positioning. However, significant questions obscure potential investment; how any value will actually be created and, importantly, how this value will be shared across the value chain is not clear. This paper will describe a programme of research that is developing an enterprise simulator that will provide a more fundamental understanding of the impact of e-Solutions across operational supply chains, in terms of both standard operational and financial measures of performance. An efficient supply chain reduces total costs of operations by sharing accurate real-time information and coordinating inter-organizational business processes. This form of electronic link between organizations is known as business-to-business (B2B) e-Business. The financial measures go beyond simple cost calculations to real bottom-line performance by modelling the financial transactions that business processes generate. The paper will show how this enterprise simulator allows for a complete supply chain to be modelled in this way across four key applications: control system design, virtual enterprises, pan-supply-chain performance metrics and supporting e-Supply-chain design methodology
Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework
Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj \u3c 0.0001) and EC GPP (79.6 days, padj \u3c 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam GCC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m− 2 yr− 1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales