scholarly journals Drosophila Evolution over Space and Time (DEST) - A New Population Genomics Resource

2021 ◽  
Author(s):  
Martin Kapun ◽  
Joaquin C B Nunez ◽  
María Bogaerts-Márquez ◽  
Jesús Murga-Moreno ◽  
Margot Paris ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Population Genomics ◽  
Natural Populations ◽  
Easy Access ◽  
Data Repository ◽  
Bioinformatics Pipeline ◽  
Space And Time ◽  
Sequencing Technologies ◽  
Spatio Temporal ◽  
Demographic Inference

Abstract Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in > 20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

scholarly journals Drosophila Evolution over Space and Time (DEST) - A New Population Genomics Resource

2021 ◽  
Author(s):  
Martin Kapun ◽  
Joaquin C. B. Nunez ◽  
María Bogaerts-Márquez ◽  
Jesús Murga-Moreno ◽  
Margot Paris ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Population Genomics ◽  
Natural Populations ◽  
Easy Access ◽  
Data Repository ◽  
Bioinformatics Pipeline ◽  
Space And Time ◽  
Sequencing Technologies ◽  
Different Seasons ◽  
Spatio Temporal

Abstract Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last 20 years. A major challenge is the integration of these disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution and population structure of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 population samples from over 100 locations in >20 countries on four continents based on a combination of 121 unpublished and 150 previously published genomic datasets. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

scholarly journals Spatio-temporal ecological and evolutionary dynamics in natural butterfly populations

2017 ◽  
Vol 40 ◽  
pp. 31-36
Author(s):  
Zachariah Gompert ◽  
Lauren Lucas
Keyword(s):  
Evolutionary Dynamics ◽  
Natural Populations ◽  
Distance Sampling ◽  
Spatial And Temporal Variation ◽  
Insect Community ◽  
Long Term Study ◽  
Greater Yellowstone Area ◽  
Genome Wide ◽  
Spatio Temporal

Long term studies of wild populations indicate that natural selection can cause rapid and dramatic changes in traits, with spatial and temporal variation in the strength of selection a critical driver of genetic variation in natural populations. In 2012, we began a long term study of genome-wide molecular evolution in populations of the butterfly Lycaeides ideas in the Greater Yellowstone Area (GYA). We aimed to quantify the role of environment-dependent selection on evolution in these populations. Building on previous work, in 2017 we collected new samples, incorporated distance sampling, and surveyed the insect community at each site. We also defined the habitat boundary at anew, eleventh site. Our preliminary analyses suggest that both genetic drift and selection are important drivers in this system.   Featured photo from Figure 1 in report.

scholarly journals PoPoolationTE2: comparative population genomics of transposable elements using Pool-Seq

2016 ◽  
Author(s):  
Robert Kofler ◽  
Daniel Gomez-Sanchez ◽  
Christian Schloetterer
Keyword(s):  
Transposable Elements ◽  
Computer Simulations ◽  
Evolutionary Dynamics ◽  
Population Genomics ◽  
Population Level ◽  
Sequencing Technologies ◽  
Bioinformatic Tool ◽  
Pooled Samples ◽  
Different Tissues ◽  
Comparative Population

The evolutionary dynamics of transposable elements (TEs) are still poorly understood. One reason is that TE abundance needs to be studied at the population level, and despite recent advances in sequencing technologies, characterizing TE abundance in multiple populations by sequencing individuals separately is still too expensive. While sequencing pools of individuals (Pool-Seq) dramatically reduces sequencing costs, a comparison of TE abundance between pooled samples has been difficult, if not impossible, due to various biases. Here, we introduce a novel bioinformatic tool, PoPoolationTE2, which is specifically tailored for the comparison of TE abundance among pooled population samples or different tissues. Using computer simulations we demonstrate that PoPoolationTE2 not only faithfully recovers TE insertion frequencies and positions but, by homogenizing the power to identify TEs acrosss samples, it provides an unbiased comparison of TE abundance between pooled population samples. We anticipate that PoPoolationTE2 will greatly facilitate the analysis of TE insertion patterns in a broad range of applications.

scholarly journals A comprehensive annotation dataset of intact LTR retrotransposons of 300 plant genomes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Shan-Shan Zhou ◽  
Xue-Mei Yan ◽  
Kai-Fu Zhang ◽  
Hui Liu ◽  
Jie Xu ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Age Determination ◽  
Genome Plasticity ◽  
Ltr Retrotransposons ◽  
Functional Variation ◽  
Plant Genomes ◽  
Sequencing Technologies ◽  
Plant Groups ◽  
Functional Implications ◽  
Classification Information

AbstractLTR retrotransposons (LTR-RTs) are ubiquitous and represent the dominant repeat element in plant genomes, playing important roles in functional variation, genome plasticity and evolution. With the advent of new sequencing technologies, a growing number of whole-genome sequences have been made publicly available, making it possible to carry out systematic analyses of LTR-RTs. However, a comprehensive and unified annotation of LTR-RTs in plant groups is still lacking. Here, we constructed a plant intact LTR-RTs dataset, which is designed to classify and annotate intact LTR-RTs with a standardized procedure. The dataset currently comprises a total of 2,593,685 intact LTR-RTs from genomes of 300 plant species representing 93 families of 46 orders. The dataset is accompanied by sequence, diverse structural and functional annotation, age determination and classification information associated with the LTR-RTs. This dataset will contribute valuable resources for investigating the evolutionary dynamics and functional implications of LTR-RTs in plant genomes.

scholarly journals A robust machine learning approach to SDG data segmentation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kassim S. Mwitondi ◽  
Isaac Munyakazi ◽  
Barnabas N. Gatsheni
Keyword(s):  
Temporal Variations ◽  
Data Repository ◽  
The Sustainable Development ◽  
Complex Interactions ◽  
Data Segmentation ◽  
Technological Advances ◽  
Machine Learning Approach ◽  
Development Goals ◽  
Data Source ◽  
Spatio Temporal

Abstract In the light of the recent technological advances in computing and data explosion, the complex interactions of the Sustainable Development Goals (SDG) present both a challenge and an opportunity to researchers and decision makers across fields and sectors. The deep and wide socio-economic, cultural and technological variations across the globe entail a unified understanding of the SDG project. The complexity of SDGs interactions and the dynamics through their indicators align naturally to technical and application specifics that require interdisciplinary solutions. We present a consilient approach to expounding triggers of SDG indicators. Illustrated through data segmentation, it is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources. The paper treats each SDG as a Big Data source node, with the potential to contribute towards a unified understanding of applications across the SDG spectrum. Data for five SDGs was extracted from the United Nations SDG indicators data repository and used to model spatio-temporal variations in search of robust and consilient scientific solutions. Based on a number of pre-determined assumptions on socio-economic and geo-political variations, the data is subjected to sequential analyses, exploring distributional behaviour, component extraction and clustering. All three methods exhibit pronounced variations across samples, with initial distributional and data segmentation patterns isolating South Africa from the remaining five countries. Data randomness is dealt with via a specially developed algorithm for sampling, measuring and assessing, based on repeated samples of different sizes. Results exhibit consistent variations across samples, based on socio-economic, cultural and geo-political variations entailing a unified understanding, across disciplines and sectors. The findings highlight novel paths towards attaining informative patterns for a unified understanding of the triggers of SDG indicators and open new paths to interdisciplinary research.

scholarly journals Evolutionary dynamics of spore killers.

Genetics ◽  
1993 ◽  
Vol 135 (3) ◽  
pp. 923-930 ◽  
Author(s):  
M J Nauta ◽  
R F Hoekstra
Keyword(s):  
Life Cycle ◽  
Segregation Distortion ◽  
Population Genetic ◽  
Field Data ◽  
Evolutionary Dynamics ◽  
Natural Populations ◽  
Additional Advantage ◽  
Genetic Modeling ◽  
Spore Killing ◽  
Ascospore Formation

Abstract Spore killing in ascomycetes is a special form of segregation distortion. When a strain with the Killer genotype is crossed to a Sensitive type, spore killing is expressed by asci with only half the number of ascospores as usual, all surviving ascospores being of the Killer type. Using population genetic modeling, this paper explores conditions for invasion of Spore killers and for polymorphism of Killers, Sensitives and Resistants (which neither kill, nor get killed), as found in natural populations. The models show that a population with only Killers and Sensitives can never be stable. The invasion of Killers and stable polymorphism only occur if Killers have some additional advantage during the process of spore killing. This may be due to the effects of local sib competition or some kind of "heterozygous" advantage in the stage of ascospore formation or in the short diploid stage of the life cycle. This form of segregation distortion appears to be essentially different from other, well-investigated forms, and more field data are needed for a better understanding of spore killing.

scholarly journals Spatio-temporal variation of male sterile frequencies in two natural populations of Beta maritima

Heredity ◽  
1989 ◽  
Vol 63 (3) ◽  
pp. 395-400 ◽  
Author(s):  
V Boutin-Stadler ◽  
P Saumitou-Laprade ◽  
M Valero ◽  
R Jean ◽  
Ph Vernet
Keyword(s):  
Temporal Variation ◽  
Natural Populations ◽  
Male Sterile ◽  
Beta Maritima ◽  
Spatio Temporal

Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework

2021 ◽  
Author(s):  
Micha Eisele ◽  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Pearson Correlation ◽  
Urban Catchment ◽  
Space And Time ◽  
Geostatistical Interpolation ◽  
Primary Network ◽  
Ground Measurement ◽  
Catchment Areas ◽  
Important Input ◽  
Spatio Temporal

<p>Precipitation - highly variable in space and time - is the most important input for many hydrological models. As these models become more and more detailed in space and time, high-resolution input data are required. Especially for modeling and prediction in fast reacting catchments, such as urban catchment areas, a higher space-time resolution is needed than the current ground measurement networks operated by national weather services usually provide. With the increasing number and availability of opportunistic sensors such as commercial microwave links (CMLs) and personal weather stations (PWS) in recent years, new opportunities for measuring meteorological data are emerging.</p><p>We developed a geostatistical interpolation framework which allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g. point and line information. In this framework, a combined kriging approach is introduced, taking into account not only the point information of a reliable primary network, e.g., from national weather services, but also the higher uncertainty of the PWS- and CML-based precipitation. The path-averaged information of the CMLs is included through a block kriging-type approach.</p><p>The methodology was applied for two 7-months periods in Germany using an hourly temporal and a 1x1 km spatial resolution. By incorporating CMLs and PWS, the Pearson correlation could be increased from 0.56 to 0.73 compared to using only primary network for interpolation. The resulting precipitation maps also provided good agreement compared to gauge adjusted radar products.</p>

scholarly journals Evolution to alternative levels of stable diversity leaves areas of niche space unexplored

2021 ◽  
Author(s):  
Ilan N. Rubin ◽  
Iaroslav Ispolatov ◽  
Michael Doebeli
Keyword(s):  
Evolutionary Dynamics ◽  
Natural Populations ◽  
Metastable States ◽  
Community Diversity ◽  
Limited Resources ◽  
Niche Space ◽  
Stable States ◽  
Low Diversity ◽  
Population Sizes ◽  
Adaptive Radiations

AbstractOne of the oldest and most persistent questions in ecology and evolution is whether natural communities tend to evolve toward saturation and maximal diversity. Robert MacArthur’s classical theory of niche packing and the theory of adaptive radiations both imply that populations will diversify and fully partition any available niche space. However, the saturation of natural populations is still very much an open area of debate and investigation. Additionally, recent evolutionary theory suggests the existence of alternative evolutionary stable states (ESSs), which implies that some stable communities may not be fully saturated. Using models with classical Lokta-Volterra ecological dynamics and three formulations of evolutionary dynamics (a model using adaptive dynamics, an individual-based model, and a partial differential equation model), we show that following an adaptive radiation, communities can often get stuck in low diversity states when limited by mutations of small phenotypic effect. These low diversity metastable states can also be maintained by limited resources and finite population sizes. When small mutations and finite populations are considered together, it is clear that despite the presence of higher-diversity stable states, natural populations are likely not fully saturating their environment and leaving potential niche space unfilled. Additionally, within-species variation can further reduce community diversity from levels predicted by models that assume species-level homogeneity.Author summaryUnderstanding if and when communities evolve to saturate their local environments is imperative to our understanding of natural populations. Using computer simulations of classical evolutionary models, we study whether adaptive radiations tend to lead toward saturated communities in which no new species can invade or remain trapped in alternative, lower diversity stable states. We show that with asymmetric competition and small effect mutations, evolutionary Red Queen dynamics can trap communities in low diversity metastable states. Moreover, limited resources not only reduces community population sizes, but also reduces community diversity, denying the formation of saturated communities and stabilizing low diversity, non-stationary evolutionary dynamics. Our results are directly relevant to the longstanding questions important to both ecological empiricists and theoreticians on the species packing and saturation of natural environments. Also, by showing the ease evolution can trap communities in low diversity metastable stats, we demonstrate the potential harm in relying solely on ESSs to answer questions of biodiversity.

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