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"Variations"
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Current status of structural variation studies in plants
Summary Structural variations (SVs) including gene presence/absence variations and copy number variations are a common feature of genomes in plants and, together with single nucleotide polymorphisms and epigenetic differences, are responsible for the heritable phenotypic diversity observed within and between species. Understanding the contribution of SVs to plant phenotypic variation is important for plant breeders to assist in producing improved varieties. The low resolution of early genetic technologies and inefficient methods have previously limited our understanding of SVs in plants. However, with the rapid expansion in genomic technologies, it is possible to assess SVs with an ever‐greater resolution and accuracy. Here, we review the current status of SV studies in plants, examine the roles that SVs play in phenotypic traits, compare current technologies and assess future challenges for SV studies.
Journal Article
Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017
Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05∘ latitude by 0.05∘ longitude and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).
Journal Article
A numerical model study of the main factors contributing to hypoxia and its interannual and short-term variability in the East China Sea
A three-dimensional physical-biological model of the marginal seas of China was used to analyze interannual and intra-seasonal variations in hypoxic conditions and identify the main processes controlling their generation off the Changjiang (or Yangtze River) estuary. The model was compared against available observations and reproduces the observed temporal and spatial variability of physical and biological properties including bottom oxygen. Interannual variations of hypoxic extent in the simulation are partly explained by variations in river discharge but not nutrient load. As riverine inputs of freshwater and nutrients are consistently high, promoting large productivity and subsequent oxygen consumption in the region affected by the river plume, wind forcing is important in modulating interannual and short-term variability. Wind direction is relevant because it determines the spatial extent and distribution of the freshwater plume, which is strongly affected by either upwelling or downwelling conditions. High-wind events can lead to partial reoxygenation of bottom waters and, when occurring in succession throughout the hypoxic season, can effectively suppress the development of hypoxic conditions, thus influencing interannual variability. A model-derived oxygen budget is presented and suggests that sediment oxygen consumption is the dominant oxygen sink below the pycnocline and that advection of oxygen in the bottom waters acts as an oxygen sink in spring but becomes a source during hypoxic conditions in summer, especially in the southern part of the hypoxic region, which is influenced by open-ocean intrusions.
Journal Article
The things that I love about trees
\"[This] picture book ... looks at how a tree changes with the seasons\"--Amazon.com.
Book
Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing
Background
Structural variations (SVs) or copy number variations (CNVs) greatly impact the functions of the genes encoded in the genome and are responsible for diverse human diseases. Although a number of existing SV detection algorithms can detect many types of SVs using whole genome sequencing (WGS) data, no single algorithm can call every type of SVs with high precision and high recall.
Results
We comprehensively evaluate the performance of 69 existing SV detection algorithms using multiple simulated and real WGS datasets. The results highlight a subset of algorithms that accurately call SVs depending on specific types and size ranges of the SVs and that accurately determine breakpoints, sizes, and genotypes of the SVs. We enumerate potential good algorithms for each SV category, among which GRIDSS, Lumpy, SVseq2, SoftSV, Manta, and Wham are better algorithms in deletion or duplication categories. To improve the accuracy of SV calling, we systematically evaluate the accuracy of overlapping calls between possible combinations of algorithms for every type and size range of SVs. The results demonstrate that both the precision and recall for overlapping calls vary depending on the combinations of specific algorithms rather than the combinations of methods used in the algorithms.
Conclusion
These results suggest that careful selection of the algorithms for each type and size range of SVs is required for accurate calling of SVs. The selection of specific pairs of algorithms for overlapping calls promises to effectively improve the SV detection accuracy.
Journal Article
A big garden
\"This wonderfully insightful and brilliantly illustrated book on gardens and gardeners will provide hours of absorbing fun while introducing young readers to the joys of planning, planting, and harvesting. In vibrant watercolors Vincent Grave shows us how there's something happening every month in the Big Garden. Renowned landscape designer Gilles Clement's lyrical text gently teaches young readers not only what's involved in planning a garden, but how plants, insects, and humans interact all year long to make the garden thrive. Along the way, we witness a forest of mushrooms, the miracle of eggs, and the incredible universe found in a single flower. In every picture, tiny gardeners busy themselves among the leaves, seeds, and earth. Fascinating, heartfelt, and elegantly produced, this book celebrates the deep connection between humans and nature\"-- Publisher's description.
Book
Copy number evolution and its relationship with patient outcome—an analysis of 178 matched presentation-relapse tumor pairs from the Myeloma XI trial
Structural chromosomal changes including copy number aberrations (CNAs) are a major feature of multiple myeloma (MM), however their evolution in context of modern biological therapy is not well characterized. To investigate acquisition of CNAs and their prognostic relevance in context of first-line therapy, we profiled tumor diagnosis–relapse pairs from 178 NCRI Myeloma XI (ISRCTN49407852) trial patients using digital multiplex ligation-dependent probe amplification. CNA profiles acquired at relapse differed substantially between MM subtypes: hyperdiploid (HRD) tumors evolved predominantly in branching pattern vs. linear pattern in t(4;14) vs. stable pattern in t(11;14). CNA acquisition also differed between subtypes based on
CCND
expression, with a marked enrichment of acquired del(17p) in
CCND2
over
CCND1
tumors. Acquired CNAs were not influenced by high-dose melphalan or lenalidomide maintenance randomization. A branching evolution pattern was significantly associated with inferior overall survival (OS; hazard ratio (HR) 2.61,
P
= 0.0048). As an individual lesion, acquisition of gain(1q) at relapse was associated with shorter OS, independent of other risk markers or time of relapse (HR = 2.00;
P
= 0.021). There is an increasing need for rational therapy sequencing in MM. Our data supports the value of repeat molecular profiling to characterize disease evolution and inform management of MM relapse.
Journal Article