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3,584 results for “genomic analysis”
Grapegenomics.com: a web portal with genomic data and analysis tools for wild and cultivated grapevines
<p><a href="https://grapegenomics.com">Grapegenomics.com</a> is a web portal that provides public access to genome references for grapevine cultivars (<em>Vitis vinifera</em> ssp. <em>vinifera</em>), wild grapevines (<em>Vitis vinifera</em> ssp. <em>sylvestris</em>), various wild grape species (<em>Vitis</em> spp. and <em>Muscadinia</em> spp.), and major fungal pathogens affecting grapes.</p> <p>All genomes are accessible through dedicated genome browsers, and published genomes are available for complete <a href="https://www.grapegenomics.com/download.php">download</a>.</p> <p>The site hosts all genomes produced by the laboratory of Dario Cantù in the Department of Viticulture and Enology at the University of California, Davis, along with published genome references generated by others, such as PN40024 and Pinot noir ENTAV115. Instructions for genome submission are provided <a href="https://www.grapegenomics.com/submit.php">here</a>. The portal is maintained by Noé Cochetel (ndcochetel[at]ucdavis.edu). In this version 2.0, all genome browsers utilize <a href="https://jbrowse.org/jb2/">jbrowse 2</a>. <br><br>Link to the website: <a href="https://www.grapegenomics.com">https://www.grapegenomics.com</a> </p>
GWAS Summary Statistics from "Sex and statin-related genetic associations at the PCSK9 gene locus – results of genome-wide association meta-analysis"
<p>GWAMA summary statistics of PCSK9 levels stratified by sex and statin useage in Europeans.</p> <p>When using this data, please cite:</p> <p>Pott, J., Kheirkhah, A., Gadin, J.R. <em>et al.</em> Sex and statin-related genetic associations at the <em>PCSK9</em> gene locus: results of genome-wide association meta-analysis. <em>Biol Sex Differ</em> <strong>15</strong>, 26 (2024). https://doi.org/10.1186/s13293-024-00602-6</p> <p>All txt files contain the following columns:</p> <ul> <li>markername (unique SNP ID)</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>EA (effect allele)</li> <li>OA (other allele)</li> <li>EAF (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (in case of double-stratified data: number of studies; in case of single-stratified data: 2, as it is a meta-analysis of the two double-stratified data sets)</li> <li>beta (effect estimate)</li> <li>SE (standard error)</li> <li>pval (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>invalidAssoc (TRUE/FALSE flag if this variant was excluded in our analysis)</li> <li>reason4exclusion (reason why this SNP was excluded)</li> <li>phenotype (phenotyp setting)</li> </ul>
Genome data from Effrenium voratum CCMP421, RCC1521, and rt-383 and their analysis
<p>This dataset represents secondary data generated from the genomic analysis of three isolates of <em>Effrenium voratum</em> (CCMP421, RCC1521, and rt-383), the early-diverging, free-living lineage of Symbiodiniaceae dinoflagellates. Theese data include <strong>(A)</strong> assembled genome sequences, predicted gene models and protein sequences, and <strong>(B)</strong> data and scripts associated with generation of graphs and figures presented in the key genome paper (Shah et al., 2024, Massive genome reduction predates the divergence of Symbiodiniaceae dinoflagellates, under review in <em>ISME Journal</em>). An earlier preprint of this manuscript is available at <em>bioRxiv</em>: <a href="https://doi.org/10.1101/2023.03.24.534093" target="_blank" rel="noopener">https://doi.org/10.1101/2023.03.24.534093</a>.</p> <p><strong>A. Genome assemblies, annotation and gene models. </strong>The dataset includes, for each taxon, (a) the <em>de novo</em> assembled genome sequences in FASTA format (<strong>*genome.fa.tgz</strong>), (b) structural annotation of the assembled genome in GFF3 format (<strong>*.genome.annotation.gff3.tgz</strong>), (c) the predicted protein-coding sequences of gene models in FASTA format (<strong>*genemodel.CDS.fa.tgz</strong>), (d) the predicted protein sequences of gene models in FASTA format (<strong>*genemodel.PROT.fa.tgz</strong>), and (c) the associated sequences and gene annotations of organellar genomic sequences (i.e. mitochondrial and plastid) (<strong>*organellar.tgz</strong>). Functional annotations of all gene models from the three genomes are available in the Excel spreadsheet (<strong>*GeneModels.xlsx</strong>).</p> <p><strong>B. Data and scripts associated with generation of graphs and figures in Shah et al. (2024). </strong>These files are organised based on key analyses specific to main figures and supplementary figures in the paper.</p> <p>See <strong>README.txt</strong> for a more-detailed description of the files.</p>
R Code and Re-analyzed Datasets for: Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses
<p>This submission includes all the scripts and data analyzed in the manuscript "Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses". This manuscript is a technical note on how genome formula data can be analyzed. There are no new experimental data in the manuscript, as published datasets are re-analyzed. Here we reproduce those datasets as formatted for our analysis, for the convenience of the reader. Please consult the README.txt file first.</p> <p>The corresponding paper was published in Viruses <em>16</em>(2): 270. (<a href="https://doi.org/10.3390/v16020270">https://doi.org/10.3390/v16020270</a>).</p> <p>This is the second version of the code, corresponding to the final version of the paper. The intial restricted version for review had a DOI 10.5281/zenodo.10355273.</p> <p> </p>
Analysis of variant-dependent m6A modifications within the Human genome
<p>Interactive and machine-readable results produced by the <a href="https://github.com/cumbof/m6Ad-SNVs" target="_blank" rel="noopener">m6Ad-SNVs</a> tool to asses if m6A-distal SNVs affect DRACH site accessibility, specifically by evaluating the alteration of base-pairing of nucleotides within segments of the DRACH motif.</p> <p>These results contain the predicted m6Ad-SNV candidates with the length of the reference and m6Ad-SNV-containing alternate sequences limited to 250 base pairs. This constraint has been applied to maintain the reliability of the results predicted by RNAFold (<a href="https://www.tbi.univie.ac.at/RNA/">ViennaRNA</a> package). The sequence composition contains up to 100 base pairs from 3'UTRs, with the remaining base pairs limited to the last two exons.</p>
GWAS summary stats in "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."
<p>Summary stats of the genome-wide meta-analysis for dental caries and periodontal diseases in our study (population A and B).</p> <p>Article "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."</p> <p>https://doi.org/10.1186/s12903-024-04799-1<br><br></p>
Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks
<p>Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxE<sub>CCT</sub>, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxE<sub>CCT</sub> to SPAGxEmix<sub>CCT</sub>, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> to analyze time-to-event traits in UK Biobank. For the SPAGxE<sub>CCT</sub> analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included. For the SPAGxEmix<sub>CCT</sub> analyses, 338,044 individuals from all ancestries were included. SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.</p>
Functional genomics analysis to disentangle the role of genetic variants in major depression - Supplementary Tables
<p>This entry contains the data generated by the study "Functional genomics analysis to disentangle the role of genetic variants in major depression" that are part of the Supplementary information of the article describing the study.</p> <p>The entry contains the following data:</p> <p><strong>Supplementary Tables S1-S7</strong></p> <p>Supplementary Table S1. Summary of resources.</p> <p>Supplementary Table S2. Causal GVs for MD.</p> <p>Supplementary Table S3. pGenes functional and disease enrichment analysis.</p> <p>Supplementary Table S4. Fine-mapped MD causal GVs disease enrichment analysis.</p> <p>Supplementary Table S5. Colocalizing GWAS-eQTLs association to disease.</p> <p>Supplementary Table S6. TFBS analysis.</p> <p>Supplementary Table S7. GVs state annotation. </p>
Genome alignments for the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Genome alignments for data generated in the project "<em>Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol – Optimization of the protocol.</em>" Files names indicate unique identifiers of MOIRAI workflow runs, with the following structure: library name, dot, workflow ID (OP-WORKFLOW-CAGEscan-short-reads-v2.0.), dot, timestamp. The raw (FASTQ) data of each library is also deposited in Zenodo (<a href="https://doi.org/10.5281/zenodo.250156">10.5281/zenodo.250156</a>). Library names correspond to the following runs:</p> <ul> <li> NC33: 151007_M00528_0161_000000000-AEBDC</li> <li> NC37: 151204_M00528_0173_000000000-AEBEF</li> <li> NC38: 151211_M00528_0175_000000000-AE9PJ</li> <li> NC39: 160122_M00528_0185_000000000-AEB18</li> <li> NC42: 160302_M00528_0192_000000000-AELYK</li> </ul> <p>This data can be analysed using the "CAGEr" software package available from Bioconductor. The "multiplex_files.zip" file contains tables indicating which samples are biological replicates of each other or negative controls.</p>
Supplementary Materials associated with paper 'Complete linear mitochondrial genomes for Cephea cephea and Mastigias albipunctata (Scyphozoa: Rhizostomeae), with an analysis of phylogenetic relationships'
<p>This is a repository for coverage depth graphs and ML-phylogenetic trees that are associated with the paper 'Complete linear mitochondrial genomes for Cephea cephea and Mastigias albipunctata (Scyphozoa: Rhizostomeae), with an analysis of phylogenetic relationships' by Tan KC, Collins AG and Ames CL.</p>
REPIN population analysis in 42 Pseudomonas chlororaphis genomes
<p>This dataset is the output of RAREFAN (http://rarefan.evolbio.mpg.de/) a webserver to identify REPIN populations across an entire bacterial species. The data was created using the following command "java -jar -Xmx10g rarefan.jar chlororaphis/in/ chlororaphis/out/ chlTAMOak81.fas 55 21 chlororaphis/in/yafM_SBW25.faa chlororaphis.nwk 1e-30 true 1"</p> <p>All input files are located in the folder chlororaphis/in/, all output data is located in chlororaphis/out/.</p> <p>The input files include the 42 fasta formatted <em>P. chlororaphis</em> genome files (*.fas) and a RAYT protein sequence called yafM_SBW25.faa.</p> <p>The output files include the following:</p> <p>A phylogenetic tree "chlororaphis.nwk" of all genomes generated with andi (<a href="http://github.com/evolbioinf/andi/">http://github.com/evolbioinf/andi/</a>) and clustDist (http://guanine.evolbio.mpg.de/problemsBook/node1.html).</p> <p>A file containing the frequencies of all 21bp long sequences found in the TAMOak81 genome: chlTAMOak81.wfr</p> <p>A file containing all 21bp long sequences that occur more frequently than 55 times in the TAMOak81 genome: chlTAMOak81.overrep</p> <p>A file containing information on the RAYTs and their cooccurrence with different REPIN populations: prox.stats</p> <p>A file containing the nucleotide sequences of all yafM_SBW25.faa relatives identified with BLAST+ in the <em>P. chlororaphis</em> species: yafM_relatives.fna</p> <p>maxREPIN_[0-5] Contains the most frequent REPIN identified for each sequence type in each <em>P. chlororaphis</em> strain.</p> <p> presAbs_[0-5].txt Contains for each strain information on the number of RAYTs, the number of REPINs, the master sequence, the number of master sequences, the entire REP/REPIN population size, the number of REPIN clusters that contain more than 10 sequences, all REPINs in the population as well as all all REPINs that differ to the master sequences in at most three nucleotides.</p> <p>rayt_[strain name].tab contains location information for each identified RAYT relative for each strain. The files can be viewed with artemis.</p> <p>results.txt contains for each strain the frequency of the six identified 21bp long seeds.</p> <p>There is one folder called groupSeedSequences, which includes the data for identifying the most common 21 bp long sequences in <em>P. chlororaphis</em> TAMOak81. All 21bp long sequences in the genome that occur more frequently than 55 times are sorted into 6 sequence groups. These sequence groups are stored in the files Group_chlTAMOak81_*.out and .out.fas. There is also a chlTAMOak81_words.tab file, which contains the locations of all overrepresented 21bp long sequences in the TAMOak81 genome. This file can be viewed in artemis (https://www.sanger.ac.uk/tool/artemis/) together with the TAMOak81 genome file. The most common sequence in each group is used as a seed sequence to determine REPIN populations across all 42 genomes.</p> <p> </p> <p>For each genome there are six output folders (ending in _0 to _5), for each sequence group one.</p> <p>Each folder contains the following files:</p> <p>*.dd: Degree distribution of the REPIN network, where each REPIN is a node. A REPIN is connected to another REPIN if they differ in exactly one position. The degree distribution is a histogram of the number of connections of all the nodes. </p> <p>*.hist For the largest sequence cluster determined by mcl that consists of REPINs (two REPs in inverted orientation) this file contains the number of REPINs in each sequence class. Sequence class 0 is the master sequence. By definition the most common REPIN in the sequence population. Sequence class 1 contains all REPINs differing in exactly one position to the master sequence. Sequence class 2 contains REPINs differing in 2 positions etc.</p> <p>*.mcl Contains the clustering output by mcl. Each line contains the member of a cluster. Lines are sorted by cluster size.</p> <p>*.mw Contains the most common 21bp long sequence and its frequency in the genome, which is the basis for identifying first all related REP sequences and from those the REPINs formed by these REP sequences.</p> <p>*.nodes The identity and frequency of all REPINs and REP sequences for either all sequences or only for the largest sequence cluster.</p> <p>*.ss Contains REPINs and REP sequences as well as their positions in fasta format. Position information starts with the location in genome fasta file (first sequence is 0...) followed by the start and end position of the entire REPIN/REP sequence. </p> <p>*.ss.REP REP sequence information in fasta format.</p> <p>*.tab Location in tab format. Can be used to display locations of REPs and REPINs in the genome via artemis.</p> <p>*_[0-9].ss Contains REPIN/REP sequence information for each subcluster separately.</p> <p>*_[0-9].tab Contains the location of REP/REPINs for each subcluster separately for viewing in artemis.</p> <p>*allSeed.nw Contains network connections between nodes of all sequences. Can be used to view network in for example R or cytoscape together with the nodes file.</p> <p>*largestCluster.nodes Information on nodes only from the largest REPIN cluster.</p> <p>*largestCluster.ss *.ss file for the largest REPIN cluster.</p> <p>*largestCluster.tab *.tab file for the largest REPIN cluster.</p> <p>*_rayt_repin_prox.txt shows which REPIN/REP cluster is in proximity to any of the RAYT genes identified in the genome (within 200bp).</p> <p>And a subfolder that contains the complete sequences (including the variable region) for all identified REPs and REPINs.</p> <p><strong>The dataset was generated using the following external tools:</strong></p> <p>andi for tree building:</p> <p>B Haubold, F Klötzl, and P Pfaffelhuber. <strong>andi: fast and accurate estimation of evolutionary distances between closely related genomes.</strong> Bioinformatics, 2015 vol. 31 (8) pp. 1169-1175.</p> <p>MCL for REPIN population clustering:</p> <p>A J Enright, S Van Dongen, and C A Ouzounis. <strong>An efficient algorithm for large-scale detection of protein families.</strong> Nucleic Acids Research, 2002 vol. 30 (7) pp. 1575-1584.</p> <p>BLAST+ for identifying RAYT relatives in the different genomes:</p> <p>C Camacho, G Coulouris, V Avagyan, N Ma, J Papadopoulos, K Bealer, and T L Madden. <strong>BLAST+: architecture and applications.</strong> BMC Bioinformatics, 2009 vol. 10 (1) pp. 421-9.</p>
REPIN population analysis in 130 Neisseria meningitidis and N. gonorrhoeae genomes
<p>This dataset is the output of RAREFAN (http://rarefan.evolbio.mpg.de/) a webserver to identify REPIN populations across an entire bacterial species. The data was created using the following command "java -jar -Xmx10g rarefan.jar neisseria/in/ neisseria/out/ Nmen_2594.fas 55 21 neisseria/in/NMAA_0235.faa neisseria.nwk 1e-80 true 1"</p> <p>All input files are located in the folder neisseria/in/, all output data is located in neisseria/out/.</p> <p>The input files include the 130 fasta formatted <em>Neisseria</em> genome files (*.fas) and a RAYT protein sequence called NMAA_0235.faa.</p> <p>The output files include the following:</p> <p>A phylogenetic tree "neisseria.nwk" of all genomes generated with andi (<a href="http://github.com/evolbioinf/andi/">http://github.com/evolbioinf/andi/</a>) and clustDist (http://guanine.evolbio.mpg.de/problemsBook/node1.html).</p> <p>A file containing the frequencies of all 21bp long sequences found in the Nmen_2594 genome: Nmen_2594.wfr</p> <p>A file containing all 21bp long sequences that occur more frequently than 55 times in the Nmen_2594 genome: Nmen_2594.overrep</p> <p>A file containing information on the RAYTs and their cooccurrence with different REPIN populations: prox.stats</p> <p>A file containing the nucleotide sequences of all NMAA_0235.faa relatives identified with BLAST+ in the <em>Neisseria </em>species: yafM_relatives.fna</p> <p>maxREPIN_[0-5] Contains the most frequent REPIN identified for each sequence type in each <em>Neisseria</em> strain.</p> <p> presAbs_[0-5].txt Contains for each strain information on the number of RAYTs, the number of REPINs, the master sequence, the number of master sequences, the entire REP/REPIN population size, the number of REPIN clusters that contain more than 10 sequences, all REPINs in the population as well as all REPINs that differ to the master sequences in at most three nucleotides.</p> <p>rayt_[strain name].tab contains location information for each identified RAYT relative for each strain. The files can be viewed with artemis.</p> <p>results.txt contains for each strain the frequency of the six identified 21bp long seeds.</p> <p>There is one folder called groupSeedSequences, which includes the data for identifying the most common 21 bp long sequences in <em>Neisseria meningitidis</em> WUE 2594. All 21bp long sequences in the genome that occur more frequently than 55 times are sorted into 6 sequence groups. These sequence groups are stored in the files Group_Nmen_2594_*.out and .out.fas. There is also a Nmen_2594_words.tab file, which contains the locations of all overrepresented 21bp long sequences in the Nmen_2594 genome. This file can be viewed in artemis (https://www.sanger.ac.uk/tool/artemis/) together with the Nmen_2594 genome file. The most common sequence in each group is used as a seed sequence to determine REPIN populations across all 130 genomes.</p> <p> </p> <p>For each genome there are six output folders (ending in _0 to _5), for each sequence group one.</p> <p>Each folder contains the following files:</p> <p>*.dd: Degree distribution of the REPIN network, where each REPIN is a node. A REPIN is connected to another REPIN if they differ in exactly one position. The degree distribution is a histogram of the number of connections of all the nodes. </p> <p>*.hist For the largest sequence cluster determined by mcl that consists of REPINs (two REPs in inverted orientation) this file contains the number of REPINs in each sequence class. Sequence class 0 is the master sequence. By definition the most common REPIN in the sequence population. Sequence class 1 contains all REPINs differing in exactly one position to the master sequence. Sequence class 2 contains REPINs differing in 2 positions etc.</p> <p>*.mcl Contains the clustering output by mcl. Each line contains the member of a cluster. Lines are sorted by cluster size.</p> <p>*.mw Contains the most common 21bp long sequence and its frequency in the genome, which is the basis for identifying first all related REP sequences and from those the REPINs formed by these REP sequences.</p> <p>*.nodes The identity and frequency of all REPINs and REP sequences for either all sequences or only for the largest sequence cluster.</p> <p>*.ss Contains REPINs and REP sequences as well as their positions in fasta format. Position information starts with the location in genome fasta file (first sequence is 0...) followed by the start and end position of the entire REPIN/REP sequence. </p> <p>*.ss.REP REP sequence information in fasta format.</p> <p>*.tab Location in tab format. Can be used to display locations of REPs and REPINs in the genome via artemis.</p> <p>*_[0-9].ss Contains REPIN/REP sequence information for each subcluster separately.</p> <p>*_[0-9].tab Contains the location of REP/REPINs for each subcluster separately for viewing in artemis.</p> <p>*allSeed.nw Contains network connections between nodes of all sequences. Can be used to view network in for example R or cytoscape together with the nodes file.</p> <p>*largestCluster.nodes Information on nodes only from the largest REPIN cluster.</p> <p>*largestCluster.ss *.ss file for the largest REPIN cluster.</p> <p>*largestCluster.tab *.tab file for the largest REPIN cluster.</p> <p>*_rayt_repin_prox.txt shows which REPIN/REP cluster is in proximity to any of the RAYT genes identified in the genome (within 200bp).</p> <p>And a subfolder that contains the complete sequences (including the variable region) for all identified REPs and REPINs.</p> <p>The dataset was generated using the following external tools:</p> <p>andi for tree building:</p> <p>B Haubold, F Klötzl, and P Pfaffelhuber. <strong>andi: fast and accurate estimation of evolutionary distances between closely related genomes.</strong> Bioinformatics, 2015 vol. 31 (8) pp. 1169-1175.</p> <p>MCL for REPIN population clustering:</p> <p>A J Enright, S Van Dongen, and C A Ouzounis. <strong>An efficient algorithm for large-scale detection of protein families.</strong> Nucleic Acids Research, 2002 vol. 30 (7) pp. 1575-1584.</p> <p>BLAST+ for identifying RAYT relatives in the different genomes:</p> <p>C Camacho, G Coulouris, V Avagyan, N Ma, J Papadopoulos, K Bealer, and T L Madden. <strong>BLAST+: architecture and applications.</strong> BMC Bioinformatics, 2009 vol. 10 (1) pp. 421-9.</p>
Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis
<p><em>Mycobacterium avium</em> is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. <em>M. avium</em> has ~4,500 predicted protein-coding regions on an average, which can be helpful in discovering several variants at the proteome level. Many of them are potentially associated with virulence, thus identifying such proteins can be a helpful feature in the development of panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of <em>M. avium</em> with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using the protein database for <em>Mycobacterium tuberculosis,</em> and a genome six-frame translated database and a variant protein database of <em>M. avium</em>. A search of mass spectrometry data analysis against <em>M. avium</em> protein database resulted in the identification of 2,954 proteins. Further, proteogenomic analyses aided in the identification of 1,301 novel peptide sequences and correction of translation start sites for 15 proteins. At the end, we created a spectral library of <em>M. avium</em> proteins including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the <em>M. avium</em> proteome. Thus, we present a <em>M. avium </em>spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.</p>
Mitochondrial genome sequencing and analysis of the invasive Microstegium vimineum: a resource for systematics, invasion history, and management
<p>Table S1: Accession data for Microstegium samples included in this study.</p> <p>File S1: Alignment of Mitochondrial CDS for Poales mitochondrial sequences.</p> <p>File S2: SNP data for Microstegium vimineum mitochondrial variants.</p> <p>Figure S1: Transposable element content in the Microstegium vimineum mitogenome.</p> <p>Figure S2: Summary of Kraken2 output.</p> <p> </p>
Analysis of the P. lividus sea urchin genome highlights contrasting trends of genomic and regulatory evolution in deuterostomes
<p><br> Supplementary datasets accompanying paper: </p> <p>stage_peaks_anc_sel.xlsx : ATAC peaks with classification, conservation and binding sites<br> Pliv.mfuzz.enrichGO.txt : GO enrichment in MFuzz cluster<br> Pliv_genes_master_filt.xlsx : Gene models with corresponding information<br> bindetect_results_anf.txt : results of TOBIAS<br> hits_pprx_cl0_ord3vrr+Et_red.fa : alignment of homeobox sequences<br> Pliv_aH2p.gn.gtf.gz : annotation in GTF format<br> Pliv_PqN3S_sm.fa.gz : genome of P. livius <br> ansr_*_network.tsv.gz : Stage specific networks from ANANSE analysis<br> ATAC_pks_normcov.tsv : Coverage of unified peaks for ATAC-seq<br> Cttg_pks_normcov.tsv : Coverage of unified peaks for Cut-and-tag H3K27Ac data<br> lncRNA_stgSpe_fpkm.tsv : Expression levels (FPKM) for predicted lncRNAs for available RNA-seq samples <br> Split_Urchin_FPKMs.clean.txt.gz : Expression levels for unified ATAc-seq peaks following direct and reverse orientation</p> <p> </p> <p> </p> <p> </p> <p> </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>
Data from: Genomic analysis reveals limited hybridization among three giraffe species in Kenya
<p>The data deposited here was generated by and reported in Coimbra <em>et al.</em> (2023).</p> <p><em>SNP calling and linkage pruning</em></p> <ul> <li><strong>snp_calling_per_species.tar.gz:</strong> includes a genotype likelihoods (GL) file estimated with ANGSD for each giraffe species.</li> <li><strong>sampled_ld.tar.gz:</strong> contains a random sample of estimated pairwise <em>r<sup>2</sup></em> values for each species used to fit linkage disequilibrium (LD) decay curves.</li> <li><strong>ld_pruned_snps.tar.gz:</strong> contains an LD-pruned ANGSD GL file per species.</li> <li><strong>snp_calling_combined.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all sampled individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Relatedness</em></p> <ul> <li><strong>relatedness.tar.gz:</strong> contains the input and output files used with NGSremix to estimate relatedness among giraffe in the dataset.</li> <li><strong>snp_calling_combined_unrelated.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all unrelated individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Population structure and admixture</em></p> <ul> <li><strong>pcangsd.tar.gz:</strong> contains the covariance matrix generated by PCAngsd.</li> <li><strong>ngsadmix.tar.gz:</strong> includes run likelihood lists for each K value ranging from 1 to 11, as well as the admixture proportions (stored in '.qopt' files) inferred from the run with the highest log-likelihood for each K in NGSadmix.</li> <li><strong>evaladmix.tar.gz:</strong> contains the pairwise correlation of residuals between individuals estimated with evalAdmix for the NGSadmix runs with the highest log-likelihood run for each K.</li> </ul> <p><em>SNP-based phylogenomic inference</em></p> <ul> <li><strong>snp_phylogeny.tar.gz:</strong> contains the input PHYLIP file and the IQ-TREE output tree and log files.</li> </ul> <p><em>Phylogeny of mitochondrial genomes</em></p> <ul> <li><strong>mtdna_phylogeny.tar.gz:</strong> includes the 13 mitochondrial protein-coding gene alignments, the partitions file, and the IQ-TREE output tree and log files.</li> </ul> <p><em>Inference of migration events</em></p> <ul> <li><strong>admixture_graphs.tar.gz:</strong> contains the TreeMix / OrientAGraph input file ('treemix.frq.strat.gz'), the output files for all TreeMix and OrientAGraph runs, and the OptM summary table of TreeMix runs ('optm.tsv').</li> </ul> <p><em>Test for introgression</em></p> <ul> <li><strong>dsuite_introgression.tar.gz:</strong> includes the input VCF, the admixture graph topology reconstructed by OrientAGraph, and the Dsuite output files for the estimation of Patterson's D, f4-ratio, and f-branch statistics.</li> </ul> <p><em>Contemporary migration rates</em></p> <ul> <li><strong>ba3-snps.tar.gz:</strong> contains the input and output files for the BA3-SNPs-autotune and BA3-SNPs runs.</li> </ul> <p><em>Demographic reconstruction</em></p> <ul> <li><strong>demographic_inference.tar.gz:</strong> includes the SFS files generated with ANGSD and realSFS and the StairwayPlot2 blueprint and output files.</li> </ul> <p>Other:</p> <ul> <li><strong>metadata.csv:</strong> a companion file containing sample information used in conjunction with R scripts to plot the figures in the paper.</li> </ul>
Data from: Genome-wide selection components analysis in a fish with male pregnancy
Open the record for dataset details and reuse information.
Genome-wide association summary statistics for sex- and age-specific analysis of chronic back pain
<p>The dataset comprises summary-level statistics for age- and sex-specific genome-wide association study of chronic back pain (cBP) in individuals of European descent from UK Biobank (<a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a>). The study was carried out under UK Biobank approved project #18219. </p> <p><strong>The dataset accompanies the paper (please cite if using the dataset):</strong></p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/33021770/">Freidin, Maxim B.; Tsepilov, Yakov A.; Stanaway, Ian B.; Meng, Weihua; Hayward, Caroline; Smith, Blair H.; Khoury, Samar; Parisien, Marc; Bortsov, Andrey; Diatchenko, Luda; Børte, Sigrid; Winsvold, Bendik S.; Brumpton, Ben M.; Zwart, John-Anker; HUNT All-In Pain; Aulchenko, Yurii S.; Suri, Pradeep; Williams, Frances M.K. Sex- and age-specific genetic analysis of chronic back pain. Pain. 2020. doi:10.1097/j.pain.0000000000002100.</a></p> <p>The phenotype of cBP was defined as back pain for 3+ months. Linear mixed-effects additive model was fitted adjusting for age, genotyping array type, and 10 genetic PCs provided by UK Biobank. The following filters were applied: minor allele frequency >0.001, genotyping and individual call rates >0.98%, imputation quality score (INFO) >0.7. GWAS were carried out in males and females separately in the whole sample (<strong>allages</strong>) as well as in groups of younger than 65 years (<strong>under65</strong>) and 65+ years old (<strong>65plus</strong>) as detailed in the paper. Accordingly, 6 files are deposited here, corresponding to each group. </p> <p><strong>Column headers:</strong></p> <p>SNP, SNP rsID </p> <p>CHR, chromosome</p> <p>BP, genomic position (GRCh37 build)</p> <p>EA, effect allele (coded as "1")</p> <p>OTHER, other allele (coded as "0")</p> <p>A1FREQ, frequency of effect allele</p> <p>INFO, imputation quality</p> <p>BETA, effect size (for effect allele)</p> <p>SE, standard error of effect size</p> <p>PVAL, p-value for association</p>
Analysis of copy number variation in dogs implicates genomic structural variation in the development of anterior cruciate ligament rupture
<p>Anterior cruciate ligament (ACL) rupture is an important condition of the human knee. Second ruptures are common and societal costs are substantial. Canine cranial cruciate ligament (CCL) rupture closely models the human disease. CCL rupture is common in the Labrador Retriever (5.79% prevalence), ~100-fold more prevalent than in humans. Labrador Retriever CCL rupture is a polygenic complex disease, based on genome-wide association study (GWAS) of single nucleotide polymorphism (SNP) markers. Dissection of genetic variation in complex traits can be enhanced by studying structural variation, including copy number variants (CNVs). Dogs are an ideal model for CNV research because of reduced genetic variability within breeds and extensive phenotypic diversity across breeds. We studied the genetic etiology of CCL rupture by association analysis of CNV regions (CNVRs) using 110 case and 164 control Labrador Retrievers. CNVs were called from SNPs using three different programs (PennCNV, CNVPartition, and QuantiSNP). After quality control, CNV calls were combined to create CNVRs using ParseCNV and an association analysis was performed. We found no strong effect CNVRs but found 46 small effect (max(T) permutation P<0.05) CCL rupture associated CNVRs in 22 autosomes; 25 were deletions and 21 were duplications. Of the 46 CCL rupture associated CNVRs, we identified 39 unique regions. Thirty four were identified by a single calling algorithm, 3 were identified by two calling algorithms, and 2 were identified by all three algorithms. For 42 of the associated CNVRs, frequency in the population was <10% while 4 occurred at a frequency in the population ranging from 10-25%. Average CNVR length was 198,872bp and CNVRs covered 0.11 to 0.15% of the genome. All CNVRs were associated with case status. CNVRs did not overlap previous canine CCL rupture risk loci identified by GWAS. Associated CNVRs contained 152 annotated genes; 12 CNVRs did not have genes mapped to CanFam3.1. Using pathway analysis, a cluster of 19 homeobox domain transcript regulator genes was associated with CCL rupture (P=6.6E-13). This gene cluster influences cranial-caudal body pattern formation during embryonic limb development. Clustered genes were found in 3 CNVRs on chromosome 14 (HoxA), 28 (NKX6-2), and 36 (HoxD). When analysis was limited to deletion CNVRs, the association was strengthened (P=8.7E-16). This study suggests a component of the polygenic risk of CCL rupture in Labrador Retrievers is associated with small effect CNVs and may include aspects of stifle morphology regulated by homeobox domain transcript regulator genes.</p>
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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.