Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,383
datasets available to search
ShareScore release 0.9.0
Dataset results
6,383 results for “isolates”
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 3,076 Campylobacter jejuni isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 2,794-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/4/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 3,076 <em>Campylobacter jejuni </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of Sequence Type [ST]). In total, 476 different STs are represented in this dataset, with ST21, ST50, ST48, ST45 and ST257 being the most represented ones and, together, corresponding to 29.1% of the dataset.</p> <p>File “Cj_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Cj_profiles_wgMLST.tsv” corresponds to a tab separated file with the 2,794-loci wgMLST profiles of each solate presented in the metadata file. The files “profiles/Cj_profiles_cgMLST_95.tsv”, “profiles/Cj_profiles_cgMLST_98.tsv” and “profiles/Cj_profiles_cgMLST_100.tsv” correspond to a 1,012-loci, 987-loci and 29-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>C. jejuni</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://pubmlst.org/organisms/campylobacter-jejunicoli">PubMLST</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 3,539 samples. The majority of them are associated with the INNUENDO project (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>). The remaining ones are associated with five BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB31119">PRJEB31119</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB38253">PRJEB38253</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB40238">PRJEB40238</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB4165">PRJEB4165</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA350537">PRJNA350537</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 3,076 isolates passed this curation step and were included in the final dataset. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 2,794-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/4">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 2,794-loci wgMLST profiles of the 3,076 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 1,012-loci, 987-loci and 29-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,999 Escherichia coli isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 7,601-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/5/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,999 <em>Escherichia coli </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 411 different serotypes are represented in this dataset, with O157:H7 being the most represented one, corresponding to 37.1% of the dataset.</p> <p>File “Ec_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Ec_profiles_wgMLST.tsv” corresponds to a tab separated file with the 7,601-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Ec_profiles_cgMLST_95.tsv”, “profiles/Ec_profiles_cgMLST_98.tsv” and “profiles/Ec_profiles_cgMLST_100.tsv” correspond to a 2,826-loci, 2,704-loci and 465-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>E. coli </em>genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/ecoli">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 2,688 samples associated with three BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA230969">PRJNA230969</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB27020">PRJEB27020</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA248042">PRJNA248042</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,999 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with <a href="https://github.com/B-UMMI/seq_typing">seq_typing</a> v2.2. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 7,601-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/5">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 7,601-loci wgMLST profiles of the 1,999 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 2,826-loci, 2,704-loci and 465-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,434 Salmonella enterica isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 8,558-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/8/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,434 <em>Salmonella enterica </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 125 different serotypes are represented in this dataset, with Typhimurium (including monophasic), Enteritidis and Infantis being the most represented ones and, together, corresponding to 56.2% of the dataset.</p> <p>File “Se_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Se_profiles_wgMLST.tsv” corresponds to a tab separated file with the 8,558-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Se_profiles_cgMLST_95.tsv”, “profiles/Se_profiles_cgMLST_98.tsv” and “profiles/Se_profiles_cgMLST_100.tsv” correspond to a 3,261-loci, 3,179-loci and 874-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>S. enterica</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/senterica">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 1,779 samples associated with four BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB16326">PRJEB16326</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB20997">PRJEB20997</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB30335">PRJEB30335</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB39988">PRJEB39988</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,434 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31540993/">Zhang et al. 2019</a>). wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 8,558-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/8">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31<sup>st</sup>, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 8,558-loci wgMLST profiles of the 1,434 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 3,261-loci, 3,179-loci and 874-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Salmonella enterica serovar Derby isolated from eggs show genomic and phenotypic traits that may be linked to inability to produce human infection.
<p><em><span>Salmonella enterica</span></em><span> serovar Derby causes foodborne disease (FBD) outbreaks worldwide, mainly from contaminated pork but also from chickens. During a major epidemic of FBD in Uruguay due to <em>S</em>. Enteritidis from poultry, we conducted a large survey of commercially available eggs, where we isolated many <em>S.</em> Enteritidis strains but surprisingly also a much larger number (ratio 5:1) of <em>S</em>. Derby strains. No single case of <em>S</em>. Derby infection was detected in that period, suggesting that the <em>S</em>. Derby egg strains were impaired for human infection. We sequenced fourteen of these egg isolates, as well as fifteen isolates from pork or human infection that were isolated in Uruguay before and after that period, and all sequenced strains had the same sequence type <span>(ST40). Phylogenomic genomic analysis was conducted using more than 3500 genomes from the same sequence type (ST), revealing that Uruguayan isolates clustered into four distantly related lineages. Population structure analysis (BAPS) suggested the division of the analyzed genomes into nine different BAPS1 groups, with Uruguayan strains clustering within four of them. </span>All egg isolates clustered together as a monophyletic group and showed marked differences in gene content with the strains in the other clusters. <span>Differences included the absence of a C-terminal fragment of the <em>speF</em> gene, as well as variations in the composition of mobile genetic elements, such as plasmids, insertion sequences, transposons, and phages, between egg isolates and human/pork isolates.</span></span> <span>Egg isolates showed an acid susceptibility phenotype, reduced ability to reach the intestine after oral inoculation of mice, and reduced induction of SPI-2 <em>ssaG</em> gene, compared to human isolates from other monophyletic groups. Mice challenge experiments showed that mice infected intraperitoneally with human/pork isolates died between 1-7 days p.i., while all animals infected with the egg strain survived the challenge. Altogether, our results suggest that loss of gene functions and the absence of plasmids in egg isolates may explain why these <em>S</em>. Derby were not capable of producing human infection despite being at that time, the main serovar recovered from eggs countrywide.</span></p>
Raw Data for the Protocol: Antibody-Assisted Selective Isolation of Purkinje Cell Nuclei
<p>Sun1/sfGFP+, Pcp2-Cre+ and Sun1/sfGFP+, Pcp2-Cre- cryosectioned cerebella immunostained for the Myc tag (files 3037, 3046), which is fused to the GFP protein, Calbindin (files 3038, 3047) and Hoechst (files 3036, 3045). </p> <p> </p> <p>Original uncropped images from western blot analysis of TOM20, Histone H3, and GAPDH.</p>
Variant dataset and code for "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness"
<p>This dataset contains genetic variants (SNPs) of <em>Ascochyta rabiei</em> isolates and the R code used in their analysis to generate the results and figures described in the manuscript "<strong>Population-level whole genome sequencing of <em>Ascochyta rabiei</em> identifies genomic loci associated with isolate aggressiveness</strong>".</p> <div> <div> </div> </div>
On The Design and Application of Thermal Isolation Servers
<p>Dataset.</p> <p>Recently, there has been an increasing trend towards executing real-time applications on multi-core platforms.<br> However, this complicates the design problem, as applications running on different cores can interfere<br> due to shared resources and mediums. In this paper, we focus on thermal interference, where a given task (τ1)<br> heats the processor, resulting in reduced service (due to Dynamic Thermal Management (DTM)) to another<br> task (τ2). In real-time domain, where tasks have deadline constraints, thermal interference is a substantial<br> problem as it directly impacts the Worst Case Execution Time (WCET) of the effected application (τ2). The<br> problem exacerbates as we move to mixed-criticality systems, where the criticality of τ2 may be greater than<br> the criticality of τ1, complicating the certification process.<br> In this paper,we propose a server based strategy (Thermal Isolation Server (TI Server))which can be used to<br> avoid thermal interference of applications.We also present a heuristic to design TI Servers to meet the timing<br> constraints of all tasks and the thermal constraints of the system. TI Servers are time/space composable, and<br> can be applied to a variety of task models. We also evaluate TI Servers on a hardware test-bed for validation<br> purposes.</p>
Supplementary dataset to publication: "Genomic insight into Campylobacter jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing analysis"
<p><em>Campylobacter jejuni </em>is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogeneity of <em>Campylobacter jejuni</em> recovered from commercial turkey farms in Germany using whole-genome sequencing. The Illumina MiSeq<sup>®</sup> technology was used to sequence 66 <em>Campylobacter jejuni </em>isolates obtained between 2010 and 2011 from commercial meat turkey flocks located in ten German federal states. Phenotypic antimicrobial resistance was determined. Phylogeny, resistome, plasmidome and virulome profiles were analyzed using whole-genome sequencing data. Genetic resistancemarkers were identified with bioinformatics tools (AMRFinder, ResFinder, NCBI and ABRicate) and compared with the phenotypic antimicrobial resistance.</p>
Transcriptome assemblies of three diatom and three prymnesiophyte isolates from Station ALOHA and Kaneohe Bay
<p><strong>Culture ID/name</strong></p> <p>AT125A – Pseudo-nitzschia sp.</p> <p>AT125C – Pseudo-nitzschia sp.</p> <p>ATCH2 – Chaetoceros sp.</p> <p>Pn B2 – Pseudo-nitzschia sp.</p> <p>KB-HA01 – Chrysochromulina sp. (also called </p> <p>AL-TEMP-12 – Chrysochromulina sp. (also called </p> <p>NF-H275 – Chrysochromulina sp.<br> <br> </p> <p><strong>Growth Conditions</strong></p> <p>All cultures were grown at 27°C, 12:12 light:dark cycle, and with a light intensity of 100 µmol photons m<sup>-2</sup> sec<sup>-1</sup>. AT125C, AT125A, and Pn B2 were grown with Aquil media. ATCH2 was grown with F/20 media with the phosphate concentration modified to a final concentration of 0.5µM. KB-HA01 was grown with F/2 media and AL-TEMP-12 and NF-H275 were grown with K media. None of the cultures were axenic. All cultures were filtered in “light” and “dark” conditions and were in exponential phase when filtered. (Filter types and volumes filtered listed below.) After filtration, all filters were placed into 2mL screwcap tubes, flash frozen with liquid nitrogen, and stored at -80°C.</p> <p><strong>Growth Conditions</strong></p> <p>All cultures were grown at 27°C, 12:12 light:dark cycle, and with a light intensity of 100 µmol photons m<sup>-2</sup> sec<sup>-1</sup>. AT125C, AT125A, and Pn B2 were grown with Aquil media. ATCH2 was grown with F/20 media with the phosphate concentration modified to a final concentration of 0.5µM. KB-HA01 was grown with F/2 media and AL-TEMP-12 and NF-H275 were grown with K media. None of the cultures were axenic. All cultures were filtered in “light” and “dark” conditions and were in exponential phase when filtered. (Filter types and volumes filtered listed below.) After filtration, all filters were placed into 2mL screwcap tubes, flash frozen with liquid nitrogen, and stored at -80°C.<br> <br> [TRANSCRIPTOME SEQUENCING]<br> <br> [QC AND ASSEMBLY]<br> <br> [POST-ASSEMBLY PROCESSING]<br> Diamond v2.0.5.143 was used to blast (e-value: 1e-5) to a cross-kingdom reference sequence database (as described in Coesel et al., 2021). Diamond v2.0.5.143 was used to find the least common ancestor of each contig based upon the blast results. Contigs that were identified as bacteria, archaea, or viruses were excluded from the assemblies.<br> <br> </p>
Multiple Sequence Alignment of a diverse dataset with 1788 Mycobacterium tuberculosis isolates
<p><strong>Multiple Sequence Alignment of a diverse dataset with 1788 <em>Mycobacterium tuberculosis</em> isolates used for <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> benchmarking</strong></p> <p>The dataset comprises whole-genome sequence data published by <a href="https://doi.org/10.1016/S1473-3099(15)00062-6">Walker et al. 2015</a>. For the multiple sequence analysis, we proceeded as follows:</p> <ol> <li>Reads were downloaded from ENA BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA282721">PRJNA282721</a> (accessed on March 16<sup>th</sup>, 2023) and trimmed using Trimmomatic (<a href="https://pubmed.ncbi.nlm.nih.gov/24695404/">Bolger et al., 2014</a>) with <a href="https://github.com/B-UMMI/INNUca">INNUca</a> default settings;</li> <li>Quality-processed reads were individually mapped against the H37Rv reference genome (Genbank accession: <a href="https://www.ncbi.nlm.nih.gov/nuccore/NC_000962.3/">NC_000962.3</a>) using <a href="https://github.com/tseemann/snippy">Snippy</a> v4.5.1 and SNP-calling was performed on variant sites with the following criteria: a minimum proportion of reads differing from the reference of 70%, a minimum mapping quality of 30 and a minimum coverage for SNP calling of 10;</li> <li>A full alignment was extracted using Snippy’s core module (snippy-core), with masking of SNPs falling within known <em>M. tuberculosis</em> genomic regions with high GC content, repetitive elements and resistance-associated positions (corresponding to ~8% of the genome), as previously described for surveillance purposes (<a href="https://pubmed.ncbi.nlm.nih.gov/30948181/">Macedo et al., 2019</a>);</li> <li><em>M. tuberculosis </em>lineages were determined using tb-profiler v4.4.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31234910/">Phelan et al., 2019</a>), with samples from the <em>M. tuberculosis</em> complex other than <em>M. tuberculosis</em>, representing a mix of multiple lineages, or with less than 95% of mapped positions in the reference, being excluded;</li> <li>A filtered alignment comprising the maximum number of informative sites (88,562 nucleotide sites with at least one mutation in a given sequence) was extracted from the full alignment using the alignment_processing.py v1.1.0 (default settings) of <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a>, and then used as input for the benchmarking.</li> </ol> <p>In this repository, we provide two alignment files:</p> <ul> <li>Core_MTB_1787_strs.full.aln: this corresponds to the full multiple sequence alignment comprising 1787 samples and the reference (corresponding to the point 4 of the methodology).</li> <li>MTb_original_align_profile.fasta: this corresponds to the multiple sequence alignment comprising 1787 samples and the reference and only presenting the alignment informative sites (corresponding to the point 5 of the methodology)</li> </ul>
Picea mariana isolate 40-10-1 genome annotation
<p>Genome annotation of <em>Picea mariana</em> isolate 40-10-1. Gene models were identified using BRAKER v2.1.6 and functionally annotated with EnTAP v0.10.8. </p> <p>pmariana-v1.gff - Genome annotation in GFF3 format<br> pmariana-v1_proteins.faa - FASTA file of translated protein-coding sequences<br> pmariana-v1_transcripts.fa - FASTA file of transcripts (CDS)</p>
Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies
<p>Data from: Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies</p> <p><strong>Abstract</strong></p> <p>Geographic isolation often leads to the emergence of distinct genetic lineages that are at least partially reproductively isolated. Zones of secondary contact between such lineages are natural experiments that allow investigating how reproductive isolation evolves and co-existence is maintained. While temporal isolation through allochrony has been suggested to promote reproductive isolation in sympatry, its potential for isolation upon secondary contact is far less understood. Sampling two contact zones of a pair of mainly allopatric Alpine butterflies over several years and taking advantage of museum samples, we show that the contact zones have remained geographically stable over several decades. Furthermore, they seem to be maintained by the asynchronous life cycles of the two butterflies, with one reaching adulthood primarily in even and the other primarily in odd years. Genomic inferences document that allochrony is leaky and that gene flow from allopatric sites scales with the degree of geographic isolation. Overall, we show that allochrony has the potential to contribute to the maintenance of secondary contact zones of lineages that diverged in allopatry.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Morphology contains the following files:</p> <p>wing_morpho.R<br> R scripts for data transformation of wing shape</p> <p>genital_morpho.R<br> R scripts for data transformation of genital morphology</p> <p><br> Models_used.R:<br> R scripts used to produce the statistical analyses.</p> <p>genital_morpho_master_with_pca.txt<br> Phenotypic data for genital morphology</p> <p>wing_contemporary_morpho_master_with_pca.txt<br> Phenotypic data for contemporary wing patterns</p> <p>wing_historic_morpho_master_with_pca.txt<br> Phenotypic data for wing patterns from museum samples</p> <p>The text files contains the following information:</p> <p>ID = Individual ID<br> genotyped_allopatric = was the individual genotyped<br> latitude<br> longitude<br> DATE = Date of collection<br> DAY = Day of collection<br> MONTH = Month of collection<br> YEAR = Year of collection<br> SPOT = Collection site<br> boxplotID = ID to reproduce boxplot order as used in the paper<br> colory = color code to plot<br> cycle = year cycle (2018/19 or 2020/21)<br> yeartype = even or odd year<br> genital_x_LM1 = linear measure of genital landmark 1 along the x axis<br> genital_y_LM1 = linear measure of genital landmark 1 along the y axis<br> genital_x_LM2 = linear measure of genital landmark 2 along the x axis <br> genital_y_LM2 = linear measure of genital landmark 2 along the y axis <br> genital_x_LM3 = linear measure of genital landmark 3 along the x axis <br> genital_y_LM3 = linear measure of genital landmark 3 along the y axis <br> genital_x_LM4 = linear measure of genital landmark 4 along the x axis <br> genital_y_LM4 = linear measure of genital landmark 4 along the y axis <br> genital_x_LM5 = linear measure of genital landmark 5 along the x axis <br> genital_y_LM5 = linear measure of genital landmark 5 along the y axis <br> v_t1 = length relationship between v and t1<br> v_t2 = length relationship between v and t2 <br> v_t3 = length relationship between v and t3 <br> t3_t1 = length relationship between t3_t1 <br> t3_t2 = length relationship between t3_t2 <br> t2_t1 = length relationship between t2_t1 <br> v_tg = length relationship between v and tg <br> PC1.x = PC1 axis for unprojected morphospace<br> PC2.x = PC2 axis for unprojected morphospace <br> PC3.x = PC3 axis for unprojected morphospace <br> PC4.x = PC4 axis for unprojected morphospace <br> PC5.x = PC5 axis for unprojected morphospace <br> PC6.x = PC6 axis for unprojected morphospace <br> PC7.x = PC7 axis for unprojected morphospace <br> PC1.y = PC1 axis for projected morphospace <br> PC2.y = PC2 axis for projected morphospace <br> PC3.y = PC3 axis for projected morphospace <br> PC4.y = PC4 axis for projected morphospace <br> PC5.y = PC5 axis for projected morphospace <br> PC6.y = PC6 axis for projected morphospace <br> PC7.y = PC7 axis for projected morphospace</p> <p> </p> <p><br> wing_ProcCoord1 = Procrustes coordinate 1<br> wing_ProcCoord2 = Procrustes coordinate 2<br> wing_ProcCoord3 = Procrustes coordinate 3<br> wing_ProcCoord4 = Procrustes coordinate 4<br> wing_ProcCoord5 = Procrustes coordinate 5<br> wing_ProcCoord6 = Procrustes coordinate 6<br> wing_ProcCoord7 = Procrustes coordinate 7<br> wing_ProcCoord8 = Procrustes coordinate 8<br> wing_ProcCoord9 = Procrustes coordinate 9<br> wing_ProcCoord10 = Procrustes coordinate 10<br> wing_ProcCoord11 = Procrustes coordinate 11<br> wing_ProcCoord12 = Procrustes coordinate 12<br> wing_ProcCoord13 = Procrustes coordinate 13<br> wing_ProcCoord14 = Procrustes coordinate 14<br> wing_ProcCoord15 = Procrustes coordinate 15<br> wing_ProcCoord16 = Procrustes coordinate 16<br> wing_ProcCoord17 = Procrustes coordinate 17<br> wing_ProcCoord18 = Procrustes coordinate 18<br> wing_ProcCoord19 = Procrustes coordinate 19<br> wing_ProcCoord20 = Procrustes coordinate 20<br> wing_ProcCoord21 = Procrustes coordinate 21<br> wing_ProcCoord22 = Procrustes coordinate 22<br> wing_ProcCoord23 = Procrustes coordinate 23<br> wing_ProcCoord24 = Procrustes coordinate 24<br> wing_ProcCoord25 = Procrustes coordinate 25<br> wing_ProcCoord26 = Procrustes coordinate 26<br> wing_ProcCoord27 = Procrustes coordinate 27<br> wing_ProcCoord28 = Procrustes coordinate 28<br> wing_ProcCoord29 = Procrustes coordinate 29<br> wing_ProcCoord30 = Procrustes coordinate 30<br> wing_ProcCoord31 = Procrustes coordinate 31<br> wing_ProcCoord32 = Procrustes coordinate 32<br> wing_ProcCoord33 = Procrustes coordinate 33<br> wing_ProcCoord34 = Procrustes coordinate 34<br> wing_ProcCoord35 = Procrustes coordinate 35<br> wing_ProcCoord36 = Procrustes coordinate 36<br> wing_ProcCoord37 = Procrustes coordinate 37<br> wing_ProcCoord38 = Procrustes coordinate 38<br> wing_ProcCoord39 = Procrustes coordinate 39<br> wing_ProcCoord40 = Procrustes coordinate 40<br> wing_ProcCoord41 = Procrustes coordinate 41<br> wing_ProcCoord42 = Procrustes coordinate 42<br> wing_ProcCoord43 = Procrustes coordinate 43<br> wing_ProcCoord44 = Procrustes coordinate 44<br> wing_ProcCoord45 = Procrustes coordinate 45<br> wing_ProcCoord46 = Procrustes coordinate 46<br> wing_ProcCoord47 = Procrustes coordinate 47<br> wing_ProcCoord48 = Procrustes coordinate 48<br> wing_ProcCoord49 = Procrustes coordinate 49<br> wing_ProcCoord50 = Procrustes coordinate 50<br> wing_ProcCoord51 = Procrustes coordinate 51<br> wing_ProcCoord52 = Procrustes coordinate 52<br> wing_ProcCoord53 = Procrustes coordinate 53<br> wing_ProcCoord54 = Procrustes coordinate 54<br> PC1.x = PC1 unprojected<br> PC2.x = PC2 unprojected<br> PC3.x = PC3 unprojected<br> PC4.x = PC4 unprojected<br> PC5.x = PC5 unprojected<br> PC6.x = PC6 unprojected<br> PC7.x = PC7 unprojected<br> PC8.x = PC8 unprojected<br> PC9.x = PC9 unprojected<br> PC10.x = PC10 unprojected<br> PC11.x = PC11 unprojected<br> PC12.x = PC12 unprojected<br> PC13.x = PC13 unprojected<br> PC14.x = PC14 unprojected<br> PC15.x = PC15 unprojected<br> PC16.x = PC16 unprojected<br> PC17.x = PC17 unprojected<br> PC18.x = PC18 unprojected<br> PC19.x = PC19 unprojected<br> PC20.x = PC20 unprojected<br> PC21.x = PC21 unprojected<br> PC22.x = PC22 unprojected<br> PC23.x = PC23 unprojected<br> PC24.x = PC24 unprojected<br> PC25.x = PC25 unprojected<br> PC26.x = PC26 unprojected<br> PC27.x = PC27 unprojected<br> PC28.x = PC28 unprojected<br> PC29.x = PC29 unprojected<br> PC30.x = PC30 unprojected<br> PC31.x = PC31 unprojected<br> PC32.x = PC32 unprojected<br> PC33.x = PC33 unprojected<br> PC34.x = PC34 unprojected<br> PC35.x = PC35 unprojected<br> PC36 = PC36 unprojected<br> PC37 = PC37 unprojected<br> PC38 = PC38 unprojected<br> PC39 = PC39 unprojected<br> PC40 = PC40 unprojected<br> PC41 = PC41 unprojected<br> PC42 = PC42 unprojected<br> PC43 = PC43 unprojected<br> PC44 = PC44 unprojected<br> PC45 = PC45 unprojected<br> PC46 = PC46 unprojected<br> PC47 = PC47 unprojected<br> PC48 = PC48 unprojected<br> PC49 = PC49 unprojected<br> PC50 = PC50 unprojected<br> PC51 = PC51 unprojected<br> PC52 = PC52 unprojected<br> PC53 = PC53 unprojected<br> PC54 = PC54 unprojected<br> PC1.y = PC1 projected<br> PC2.y = PC2 projected<br> PC3.y = PC3 projected<br> PC4.y = PC4 projected<br> PC5.y = PC5 projected<br> PC6.y = PC6 projected<br> PC7.y = PC7 projected<br> PC8.y = PC8 projected<br> PC9.y = PC9 projected<br> PC10.y = PC10 projected<br> PC11.y = PC11 projected<br> PC12.y = PC12 projected<br> PC13.y = PC13 projected<br> PC14.y = PC14 projected<br> PC15.y = PC15 projected<br> PC16.y = PC16 projected<br> PC17.y = PC17 projected<br> PC18.y = PC18 projected<br> PC19.y = PC19 projected<br> PC20.y = PC20 projected<br> PC21.y = PC21 projected<br> PC22.y = PC22 projected<br> PC23.y = PC23 projected<br> PC24.y = PC24 projected<br> PC25.y = PC25 projected<br> PC26.y = PC26 projected<br> PC27.y = PC27 projected<br> PC28.y = PC28 projected<br> PC29.y = PC29 projected<br> PC30.y = PC30 projected<br> PC31.y = PC31 projected<br> PC32.y = PC32 projected<br> PC33.y = PC33 projected<br> PC34.y = PC34 projected<br> PC35.y = PC35 projected</p> <p> </p> <p> </p> <p> </p> <p>Genomics contains the following files (Genomic data is available from NCBI BioProject: PRJNA1019795):</p> <p>all_euryale_calls.vcf.gz<br> The unfiltered VCF file</p> <p>euryale_V2.sh<br> Shell script for the genomic data analysis</p> <p>introgress.R<br> R script for running Introgress</p> <p>introgress_all_east2.txt<br> Output of Introgress for the Eastern contact zone</p> <p>introgress_all_west2.txt<br> Output of Introgress for the Western contact zone</p> <p>Admixture_output.txt<br> Output of Admixture assuming either 2 or 3 genomic clusters (K) with the respective population and ID</p> <p>Outliers2BombyxMori.txt<br> BLAST summary of outlier regions against Bombyx Mori</p> <p>Outliers2ManjolaJurtina.txt<br> BLAST summary of outlier regions against Manjola jurtina</p> <p>Outliers2ParargeAegeria.txt<br> BLAST summary of outlier regions against Pararge aegeria</p> <p> </p>
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: growth and enzyme activity traits of soil fungi isolated from CCASE in July 2017, grown under a common garden experiment in the laboratory that mimicked CCASE soil temperature treatments
Projections for the northeastern U.S. indicate that mean air temperatures will rise and snowfall will become less frequent, causing more frequent soil freezing. To test fungal responses to these combined chronic and extreme soil temperature changes, we conducted a laboratory-based common garden experiment with soil fungi that had been subjected to different combinations of growing season soil warming, winter soil freeze/thaw cycles, and ambient conditions for four years in the field. We found that fungi originating from field plots experiencing a combination of growing season warming and winter freeze/thaw cycles had inherently lower activity of acid phosphatase, but higher cellulase activity, that could not be reversed in the lab. In addition, fungi quickly adjusted their physiology to freeze/thaw cycles in the laboratory, reducing growth rate and potentially reducing their carbon use efficiency. Our findings suggest that less than four years of new soil temperature conditions in the field can lead to physiological shifts by some soil fungi, as well as irreversible loss or acquisition of extracellular enzyme activity traits by other fungi. These findings could explain field observations of shifting soil carbon and nutrient cycling under simulated climate change. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Genome assemblies of four MDR B. fragilis isolates using PacBio data - supporting the PhD Thesis
<p>Unicycler and Canu assemblies using PacBio data of four MDR B. fragilis isolates.</p> <p>Data supporting the PhD Thesis <em>Epidemiology and Genomics of antimicrobial resistance in the Bacteroides fragilis group </em>by Thomas Vognbjerg Sydenham, The research unit of Clinical Microbiology, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark September 2019.</p> <p> </p>
Fig. 1 in Nesterenkonia salmonea sp. nov. and Nesterenkonia sphaerica sp. nov., isolated from the Southern Atlantic Ocean
Fig. 1. Phylogenetic analysis of strains GY074T and GY239T and closely related members of the genus Nesterenkonia based on 16S rRNA gene sequences. GenBank accession numbers are given in parentheses. Multiple alignments, distance calculations and clustering with the neighbour-joining, maximum-likelihood and maximum-parsimony methods were performed by using the software package MEGA version 7. Bootstrap values based on 1000 replications are shown as percentages at the branching points. Bar, 0.005 nucleotide substitutions per nucleotide position. Filled circles (●) indicate that these branches cluster similarly using the different test algorithms.
Fig. 1 in Lysinimonas yzui sp. nov., isolated from cattail root soil from mine tailings
Fig. 1. Neighbour-joining phylogenetic tree based on 16S rRNA gene sequences, showing positions of N7XX-4T and related taxa within the family Microbacteriaceae. Bootstrap values of over 60% (based on 1000 replicates) are shown at branching points. Dots indicate that the corresponding branches were also recovered in the maximum parsimony tree. Bar, 0.01 substitutions per nucleotide.
Fig. 1 in Streptomyces aquilus sp. nov., a novel actinomycete isolated from a Chinese medicinal plant
Fig. 1. Optical micrograph (a) and scanning electron micrograph (b) of GGCR-6T grown on Gause's synthetic medium at 28 °C after incubation for 14 days.
COSMOS isolated galaxy images (parametric models generated with GalSim)
<p>Isolated galaxy images generated with GalSim from parametric models extracted from the Hubble COSMOS catalog.</p> <p>These files contain images and data for 10 000 images of isolated galaxy:</p> <ul> <li><strong>galaxies_isolated_10000_images.npy: </strong>numpy array of shape (10 000, 10, 64, 64), 10 000 images of size 64x64 pixels, in 10 filters (4 Euclid filters and 6 <em>ugrizy</em> LSST filters, in that order). Images contain Poissonian noise.</li> <li><strong>galaxies_isolated_10000_data.csv: </strong>corresponding parameters: <ul> <li>SNR: signal-to-noise ratio</li> <li>redshift: redshift of the galaxy</li> <li>e1: e1 parameter of ellipticity (e = e1 + i.e2)</li> <li>e2: e2 parameter of ellipticity (e = e1 + i.e2)</li> <li>mag: magnitude</li> </ul> </li> </ul>
Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries
<p>These are the results from:</p> <p>"Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries"<br> Authors: L.A.C. van Son, S. E. de Mink, F. S. Broekgaarden, M. Renzo, S. Justham, E. Laplace, J. Moran-Fraile, D. D. Hendriks, and R. Farmer</p> <p>ADS: https://ui.adsabs.harvard.edu/abs/2020arXiv200405187V/abstract<br> arXiv: https://arxiv.org/abs/2004.05187</p> <p>If you use (part of) these results in a scientific publication, we would greatly appreciate it if you would cite the source paper.</p> <p>This work uses <a href="https://compas.science/">COMPAS</a> to compute binary population properties (<a href="http://https://github.com/TeamCOMPAS/COMPAS/tree/master/docs">https://github.com/TeamCOMPAS/COMPAS/tree/master/docs</a>).</p> <p>*****************************</p> <p>For each of our 4 model variations (0. Fiducial, 1. Stable accretion, 2. Common envelope accretion and 3. Combined) we provide 2 files:</p> <p>1.) pythonSubmit.py file describing the initial conditions that were used to run the simulations</p> <p>2.) COMPASOutput.h5 file, which contains the following datasets resulting from our simulations :<br> ['systems',<br> 'doubleCompactObjects',<br> 'commonEnvelopes',<br> ]</p> <p>Detailed descriptions of these groups can be found in the accompanying README file.</p>
Pathogenic Leptospira isolated from rodents in New Orleans, Louisiana USA, and associated site information
<p>Land use change can elevate disease risk by creating conditions beneficial to species that carry zoonotic pathogens. Observations of concordant global trends in pathogen prevalence and disease incidence have engendered concerns that urbanization could increase transmission risk of some pathogens. Yet host-pathogen relationships underlying transmission risk have not been well characterized within cities, even where contact between humans and species capable of transmitting pathogens of concern occur. We addressed this deficit by testing the hypothesis that areas in cities experiencing greater population loss and infrastructure decline (i.e., counter-urbanization) can support a greater diversity of host species and a larger and more diverse pool of pathogens. We did so by characterizing pathogenic <em>Leptospira</em> infection relative to rodent host richness and abundance across a mosaic of abandonment in post-Katrina New Orleans (Louisiana, USA). We found that <em>Leptospira</em> infection loads were highest in areas that harbored higher rodent species richness. Areas with greater host co-occurrence also harbored a greater number of hosts, including the most competent hosts, indicating that <em>Leptospira </em>infection is amplified by increases in overall and relative host abundance. Evidence of shared infection among rodent hosts indicates that cross-species transmission of <em>Leptospira </em>likely increases infection at sites with greater host syntopy. Additionally, evidence that rodent co-occurrence and abundance and <em>Leptospira</em> infection load parallel abandonment suggests that counter-urbanization can elevate zoonotic disease risk within cities, particularly in underserved communities that are burdened with disproportionate concentrations of derelict properties.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.