cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6844917
title: The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus

description: Supplement of the paper “The emergence of high-fitness variants accelerates the slowdown of genome heterogeneity in the coronavirus” Since the outbreak of the COVID-19 pandemic, the SARS-CoV-2 coronavirus accumulated an important amount of genome variability through mutation and recombination. To test evolutionary trends that could inform us on the adaptive process of the virus to its human host, we compute a genome-wide measure of Sequence Compositional Complexity (<em>SCC</em>) in high-quality coronavirus genomes from across the globe, covering the full span of the pandemic. By using phylogenetic ridge regression, a method able to reveal both macro- and microevolutionary trends, we present evidence for a long-term tendency of decreasing genome sequence heterogeneity in SARS-CoV-2. In early samples, we find no statistical support for any trend in <em>SCC</em> values over time, although the virus genome appears to evolve faster than Brownian Motion expectation. However, in samples taken after the emergence of Variants of Concern with higher transmissibility, and controlling for phylogenetic and sampling effects, we detect a declining trend for <em>SCC</em> and an increasing one for its absolute evolutionary rate. This means that the decline in <em>SCC</em> itself accelerated over time, and that increasing fitness of variant genomes lead to a reduction of their genome sequence heterogeneity. Supplementary files <strong>File</strong> <strong>Description</strong> SupplementaryTables S1-S18.xlsx The strain name, the collection date, and the SCC values for each analyzed genome. SupplementaryTableS19.pdf A complete list acknowledging all originating and submitting laboratories for the sequence data in GISAID EpiCoV on which these analyses are based. SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. PhylogeneticTimetrees_NexusFormat.zip Phylogenetic timetrees (Nexus format). PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format).
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.
DOI: 10.5281/zenodo.7547243
title: Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'

description: Supplement of the paper<br> Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”<br> During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus. Supplementary files <strong>File</strong> <strong>Description</strong> SupplementaryTables S1-S19.zip Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome. SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format).
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.
DOI: 10.5281/zenodo.6650870
title: Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'

description: Supplement of the paper<br> "Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”<br> During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus. Supplementary files <strong>File</strong> <strong>Description</strong> SupplementaryTables S1-S19.zip Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome. nextstrain_ncov_open_global_timetree.nwk ML phylodynamic tree for the Nextstrain sample SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. Nextstrain_sample_fasta_3059.zip Nextstrain sample (sequences in Fasta format) PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format).
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.
DOI: 10.5281/zenodo.7555309
title: Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'

description: Supplement of the paper<br> Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”<br> During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus. Supplementary files <strong>File</strong> <strong>Description</strong> SupplementaryTables S1-S19.zip Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome. SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. Nextstrain_sample_fasta_3059.zip Nextstrain sample (sequences in Fasta format) PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format).
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.
DOI: 10.5281/zenodo.7555406
title: Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'

description: Supplement of the paper<br> "Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”<br> During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus. Supplementary files <strong>File</strong> <strong>Description</strong> SupplementaryTables S1-S19.zip Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome. nextstrain_ncov_open_global_timetree.nwk ML phylodynamic tree for the Nextstrain sample SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. Nextstrain_sample_fasta_3059.zip Nextstrain sample (sequences in Fasta format) PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format).
description: This project was funded by grants from the Spanish Minister of Science, Innovation and Universities (former Spanish Minister of Economy and Competitiveness) to J.L.O. (Project AGL2017-88702-C2-2-R) and A.M. (Project PID2019-105969GB-I00), a grant from Generalitat Valenciana to A.M. (Project Prometeo/2018/A/133) and co-financed by the European Regional Development Fund (ERDF). The most time-demanding computations were done on the servers of the Laboratory of Bioinformatics, Dept. of Genetics & Institute of Biotechnology, Center of Biomedical Research, 18100, Granada, Spain.
DOI: 10.6084/m9.figshare.21932334
title: Data and R code from the paper "CHARACTERISATION OF A NEW LIGHTWEIGHT LORAWAN GPS BIO-LOGGER AND DEPLOYMENT ON GRIFFON VULTURES GYPS FULVUS"

description: Data and R code from Data and R code from the paper "CHARACTERISATION OF A NEW LIGHTWEIGHT LORAWAN GPS BIO-LOGGER AND DEPLOYMENT ON GRIFFON VULTURES GYPS FULVUS" submitted to the Journal of Animal Biotelemetry for publication. <br> Contains a preliminary data set of VGriffon Vulture tracking collected using a new type of GPS-LoRa tracking device. <br> the excel file was provided by the tag manufacturer to help inform battery and solar panel requirements for different tag deployments and configurations.
DOI: 10.5066/p9u2u3a1
title: Water chemistry and fish metrics data for adult largemouth bass exposed in outdoor ponds to 17alpha-ethinylestradiol or an estrone-atrazine mixture

description: Physiological endpoints observed in adult largemouth bass in response to an EE2 (17 alpha-ethinylestradiol) or mixture of estrone and atrazine exposure under laboratory pond conditions. Also included are water quality and chemical concentration data. Adult LMB were exposed in outdoor pond mesocosms from post-spawning to early gonad recrudescence to either EE2 (2.4 nanograms per liter), or a mixture of endocrine-active substances commonly detected in the Chesapeake Bay watershed, atrazine (5.4 micrograms per liter) and estrone (47.9 nanograms per liter). Male reproductive condition, sperm count, and sperm motility were assessed.
DOI: 10.5281/zenodo.7466033
title: "Replication package for: Risk Classification in Insurance Markets with Risk and Preference Heterogeneity"

description: This package contains the replication code for the simulations presented in Farinha Luz, Gottardi and Moreira (forthcoming). "Risk classification in insurance markets with risk and preference heterogeneity." Review of Economic Studies.
DOI: 10.5281/zenodo.7259227
title: "Replication package for: Risk Classification in Insurance Markets with Risk and Preference Heterogeneity"

description: This package contains the replication code for the simulations presented in Farinha Luz, Gottardi and Moreira (forthcoming). "Risk classification in insurance markets with risk and preference heterogeneity." Review of Economic Studies.
DOI: 10.6073/pasta/b366f53b23ce086232edf601b80163aa
title: GIS01 GIS Coverages Defining Internal Boundaries of Konza Prairie (1977-present)

description: This dataset defines the internal boundaries of the Konza Prairie Biological Station (KPBS). Data type one (GIS010) is a record of all fenced areas on KPBS with GIS011 providing locations for all gates and type of gate (exterior, bison, and cattle). Data type three (GIS012) represents various large-scale research areas on Konza including bison grazed, cattle grazed, fire reversal, etc. These data are available as zipped (.zip) shapefiles (.shp).
DOI: 10.22002/vn6v0-pfr77
title: Solar Induced Chlorophyll Fluorescence at 683nm

description: Ungridded solar-induced chlorophyll fluorescence (at 683nm) data set in netCDF4 format with self contained explanations (units, etc). Can be opened with standard tools such as Panoply. Publication: Köhler, P., Behrenfeld, M.J., Landgraf, J., Joiner, J., Magney, T.S. and Frankenberg, C., 2020. Global retrievals of solar‐induced chlorophyll fluorescence at red wavelengths with TROPOMI. Geophysical Research Letters, 47(15), p.e2020GL087541. https://doi.org/10.1029/2020GL087541
DOI: 10.6084/m9.figshare.19964549
title: Practical sampling criteria for using delta channel width to estimate paleodischarge in the rock record - Dataset - Table S2

description: The dataset provided is in conjunction with the manuscript submitted to an international journal provided by the same authors. The dataset contains the list of 4459 measured delta channel widths of 114 global river deltas along with their names, types and climate regions.
DOI: 10.6084/m9.figshare.19964549.v4
title: Practical sampling criteria for using delta channel width to estimate paleodischarge in the rock record - Dataset - Table S2

description: The dataset provided is in conjunction with the manuscript submitted to an international journal provided by the same authors. The dataset contains the list of 4459 measured delta channel widths of 114 global river deltas along with their names, types and climate regions.
DOI: 10.6084/m9.figshare.19964549.v3
title: Practical sampling criteria for using delta channel width to estimate paleodischarge in the rock record - Dataset - Table S2

description: The dataset provided is in conjunction with the manuscript submitted to an international journal provided by the same authors. The dataset contains the list of 4459 measured delta channel widths of 114 global river deltas along with their names, types and climate regions.
DOI: 10.6073/pasta/0e99c0f62b995b54916b0fd92047ce7b
title: GIS02 GIS Coverages Defining the Konza Prairie Experimental Watershed Treatments

description: This dataset defines the experimental watershed treatments for the Konza Prairie Biological Station (KPBS). These treatments have changed over time to represent changes in both physical boundaries as well as changes in watershed treatments. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
DOI: 10.5517/ccdc.csd.cc2cpkc0
title: CCDC 2195462: Experimental Crystal Structure Determination

description: Related Article: Vinod Chaudhari|2022|CSD Communication|||
description: Related Article: Yogesh Sharma, Ganesh P. Pawar, Vinod D. Chaudhari|2022|J.Org.Chem.|88|701|doi:10.1021/acs.joc.2c02171
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21648369
title: Cost-based Feature Selection for Network Model Choice

description: Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computational costs of individual features can span several orders of magnitude. We addressed this issue for the network model selection problem using two approaches. First, we adapted nine feature selection methods to account for the cost of features. We show for two classes of network models that the cost can be reduced by two orders of magnitude without considerably affecting classification accuracy (proportion of correctly identified models). Second, we selected features using pilot simulations with smaller networks. This approach reduced the computational cost by a factor of 50 without affecting classification accuracy. To demonstrate the utility of our approach, we applied it to three different yeast protein interaction networks and identified the best-fitting duplication divergence model. Supplementary materials, including computer code to reproduce our results, are available online.
DOI: 10.6084/m9.figshare.21648369.v2
title: Cost-based Feature Selection for Network Model Choice

description: Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computational costs of individual features can span several orders of magnitude. We addressed this issue for the network model selection problem using two approaches. First, we adapted nine feature selection methods to account for the cost of features. We show for two classes of network models that the cost can be reduced by two orders of magnitude without considerably affecting classification accuracy (proportion of correctly identified models). Second, we selected features using pilot simulations with smaller networks. This approach reduced the computational cost by a factor of 50 without affecting classification accuracy. To demonstrate the utility of our approach, we applied it to three different yeast protein interaction networks and identified the best-fitting duplication divergence model. Supplementary materials, including computer code to reproduce our results, are available online.