Find research datasets worth reusing

opencc-zeroOct 2015View details →

Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.

BACKGROUND: Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×). RESULTS: We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency. CONCLUSION: This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.

opencc-by-4.0Nov 2024View details →

Imputation panel for low-pass whole genome sequencing (GLIMPSE2 format)

This dataset includes autosomal genotypes from the 1000 Genomes +HGDP project (<a href="https://doi.org/10.1101/2023.01.23.525248" target="_blank" rel="noopener">10.1101/2023.01.23.525248 </a>)  as well as X chromosome genotypes from the NY Genome Center (as of yet, a comparable dataset that includes HGDP is not available for the X; see 10.1016/j.cell.2022.08.004). The genotypes were down-sampled so as to be appropriate for low-pass imputation; uncertain phase calls were removed (any PP tags), and individuals deemed to be outliers or relatives (based on autosomal data, as per the first citation) were also removed. Similarly, singleton polymorphisms were also excluded. Hemizygous genotypes on the X were converted into (quasi) diploid genotypes. These data were then converted into a binary imputation panel format using glimpse v2 (https://odelaneau.github.io/GLIMPSE/; using the static binaries provided). The "chunk" size was doubled from the defaults (which considers a minimum number of snps, genetic length and physical length) so as to be more performant.

opencc-by-4.0Jun 2024View details →

Variant dataset and code for "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness"

This dataset contains genetic variants (SNPs) of Ascochyta rabiei isolates and the R code used in their analysis to generate the results and figures described in the manuscript "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness". <div> <div> </div> </div>

opencc-by-4.0Jan 2023View details →

Whole-genome capture and sequencing of Mycobacterium tuberculosis directly from clinical samples - Design of RNA oligonucleotide baits for Agilent Technologies' SureSelect target enrichment

This dataset comprises the sequence of 44 278 RNA oligonucleotide "baits" (120 bp each) designed to perform whole-genome capture and sequencing of Mycobacterium tuberculosis directly from clinical samples (DNA) using Agilent Technologies’ SureSelect target enrichment system following the Illumina paired-end multiplexed sequencing library protocol.  RNA oligonucleotide “baits” were designed to span the &sim;4.5 Mb of the M. tuberculosis genome. In brief, the reference genome sequence of the MTBC H37Rv strain (Genbank #AL123456) was in silico fragmented into 120 bp sequences twice, to ensure an overlap of 60 bp between sequences. Due to their rich GC content, which could interfere with DNA capture, all MTBC genes of the PE, PPE and PE-PGRS family were also independently fragmented into 120 bp sequences, in order to increase capture sensitivity. All resulting sequences were BLASTn searched against the Human Genomic + Transcript database to excluded homologous sequences to the human genome. Overall, a total of 42,278 RNA probes were generated and this custom bait library was then uploaded to the SureDesign software (https://earray.chem.agilent.com/suredesign) and synthesized by Agilent Technologies. During synthesis, the 2198 sequences complementary to the PE, PPE and PE-PGRS family were unbalanced 8:1 to potentiate capture. More details can be found in the following publication: - Macedo, R., Isidro, J., Ferreira, R., Pinto, M., Borges, V., Duarte, S., Vieira, L., & Gomes, J. P. (2023). Molecular Capture of Mycobacterium tuberculosis Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences. International journal of molecular sciences, 24(3), 2912. https://doi.org/10.3390/ijms24032912

opencc-by-4.0Feb 2023View details →

Supplementary dataset to publication: "Genomic insight into Campylobacter jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing analysis"

Campylobacter jejuni is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogeneity of Campylobacter jejuni recovered from commercial turkey farms in Germany using whole-genome sequencing. The Illumina MiSeq® technology was used to sequence 66 Campylobacter jejuni 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.

opencc-by-4.0Oct 2022View details →

Data For: Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data

Simulation output and Genome-wide scan for nIBD variants in UK10K data as reported in: Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data Johnson KE, Adams CJ, Voight BF. Methods Ecol Evol 2022 Nov;13(11): 2429–2442. Code available at: https://github.com/kelsj/EVICORD

opencc-by-4.0Aug 2020View details →

Whole Genome Sequencing of birches (Betulaceae: Betula) from the Kenai Peninsula, Alaska

We sought to learn more about the identity of Betula kenaica W.H.Evans, the Kenai birch, which has remained somewhat enigmatic since its description in 1899. Tissue samples were collected from two birch specimens on August 12, 2019. Cuttings were taken from a B. kenaica individual ("BK01" in this dataset, <a href="https://arctos.database.museum/guid/KNWR:Herb:11553">https://arctos.database.museum/guid/KNWR:Herb:11553</a>) from Kasilof Beach, Kasilof, Alaska, one of the two type localities of B. kenaica. Cuttings were also taken from a Betula pendula subsp. mandshurica (Regel) Ashburner & McAll. individual ("BP01" in this dataset, <a href="https://arctos.database.museum/guid/KNWR:Herb:11556">https://arctos.database.museum/guid/KNWR:Herb:11556</a>) from Soldotna, Alaska. The cuttings were shipped to SNPsaurus (Eugene, Oregon, USA, <a href="https://www.snpsaurus.com/">https://www.snpsaurus.com/</a>) for whole genome sequencing. This dataset includes specimen collection data, the sample sample submission form, and the three files delivered by SNPsaurus.

opencc-by-4.0May 2022View details →

Whole genome re-sequencing of B. dorsalis in the Indian Ocean

Files containing essential data for the production of the paper: Bactrocera dorsalis in the Indian Ocean: a tale of two invasions (Deschepper et. al, 202X) DOI:####

opencc-by-4.0Sep 2022View details →

Sei whole-genome sequence class annotations

Sei sequence class whole-genome annotations are available in the following files: <ul> <li> sorted.hg38.tiling.bed.ipca_randomized_300.labels.merged.bed - The sorted, merged sequence class assignments from Louvain community clustering of the 30 million sequences, uniformly tiling the whole human genome. The fourth column is the sequence class number, with any sequence classes numbering 40-61 excluded from our analyses in the publication. Sequence classes 0-39 can be mapped to the following labels: <a href="https://github.com/FunctionLab/sei-framework/blob/main/model/seqclass.names">https://github.com/FunctionLab/sei-framework/blob/main/model/seqclass.names</a> </li> <li> sorted.hg19.tiling.bed.ipca_randomized_300.labels.merged.bed - lifted over version of the hg38 BED file. </li> </ul>

Figure 5 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing

Figure 5. Gene ontology enrichment analysis of genes from genome regions with strong selective signals.

Figure 2 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing

Figure 2. Box plots of diversity, Tajima's D of S. japonicum and their FST. (A) Nucleotide diversity estimated in 100-kb windows sliding in 10-kb steps throughout the genome. (B) Tajima's D estimated within a nonoverlapping 100-kb window throughout the genome. (C) pairwise FST computed in 100-kb windows sliding in 10-kb steps throughout the genome.

Figure 4 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing

Figure 4. Distribution of p ratios (pST/pHX) and FST values calculated in 100-kb windows sliding in 10-kb steps throughout the genome. Data points colored red and blue were identified as selected regions in ST (red dots) and in HX (blue dots), respectively. These points correspond to the 5% left and right tails of the empirical p ratio distribution, where the p ratios are 0.092 and 1.643, respectively (vertical dashed lines), and the 5% right tail of the empirical FST distribution, where FST is 0.965 (horizontal dashed line).

Figure 3 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing

Figure 3. Box plots of diversity and relationship of two sample groups. (A) Nucleotide diversity estimated in 100-kb windows sliding in 10-kb steps throughout the genome. (B) Tajima's D estimated within a nonoverlapping 100-kb window throughout the genome. (C) FST between computed in 100-kb windows sliding in 10-kb steps throughout the genome.

opencc-by-4.0Sep 2024View details →

Supplementary data to the African Yam Bean Whole Genome Sequencing Project ENA Project_ID:PRJEB57813

The first chromosome-scale assembly of the African yam bean, Sphenostylis stenocarpa (Hochst. ex. A. Rich.) Harms, an original African tuberous legume producing both pods and protein-rich tubers. ENA Project_ID: PRJEB57813 Genome Assembly Accession: GCA_963425845

opencc-by-4.0Jun 2021View details →

Data release: Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration

Source data used in the publication: Berger et al. (2021) - Provisional title: 'Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration'. These data were used to generate all figures used in the publication and all files are organised and labelled specifically to run with the custom code that uses these data can be found at: http://doi.org/10.5281/zenodo.4975908.  File descriptions: SOURCE DATA.zip - All source data for all figures.  Figure 1b: <ul> <li>supplementary_data_9.txt - Metadata</li> </ul> Figure 2a&b: <ul> <li>207_PCA.eigenvec - PCA eigenvectors</li> <li>207_PCA.eigenval - PCA eigenvalues</li> </ul> Figure 2c: <ul> <li>autosomes.mdist - PLINK distance matrix used to build the neighbour joining phylogeny</li> </ul> Figure 2d: <ul> <li>all.pi.pixy.schools.txt - Nucleotide diversity results for each school subpopulation.</li> </ul> Figure 2e: <ul> <li>autosomes.dxy.5kb.schools.txt - Autosomal DXY results between school subpopulations. </li> <li>autosomes.fst.5kb.schools.txt - Autosomal FST results between school subpopulations.</li> </ul> Figure 2f: <ul> <li>admixture_all.txt - ADMIXTURE results for each sample and population sizes, column 1 represents number of populations (K), columns 3-8 represent admixture values for each population. </li> </ul> Figure 3a, Supplementary figure 10a: <ul> <li>sfs.csv - Site frequency spectra (allelic proportions at each frequency bin) for each school. </li> </ul> Figure 3b: <ul> <li>TD.all.txt - Tajima's D values calculated in 5 kb windows for each school subpopulation. </li> </ul> Figure 4a, Supplementary figures 13-18:  <ul> <li>ALL.MAYUGE.IHS.ihs.out.100bins.norm.txt.zip  - Normalised iHS scores for the Mayuge district parasite populations (Selscan output).</li> </ul> Figure 4b, Supplementary figures 13-18:  <ul> <li>ALL.TORORO.IHS.ihs.out.100bins.norm.txt.zip - - Normalised iHS scores for the Tororo district parasite populations (Selscan output).</li> </ul> Figure 4c, Supplementary figures 13-18:  <ul> <li>ALL.MAYUGEvsTORORO.xpehh.xpehh.out.norm.txt.zip - - Normalised XP-EHH scores between Mayuge and Tororo parasite populations.</li> </ul> Figure 4d, Supplementary figures 13-18: <ul> <li>MAYUGE_TORORO_2000.windowed.weir.txt.zip - FST values calculated between Mayuge and Tororo populations in 2kb windows.  </li> </ul> Figure 4e, Supplementary figures 12a&c: <ul> <li>MAYUGE_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Mayuge populations. </li> <li>TORORO_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Kocoge populations (Tororo district).</li> </ul> Figure 5a: <ul> <li>all.pi.treat.fix.txt.zip - Nucleotide diversity results for each treatment subpopulation</li> </ul> Figure 5b <ul> <li>autosomes.dxy.5kb.treatment.txt -  - Autosomal DXY results between clearance phenotype subpopulations. </li> <li>autosomes.fst.5kb.treatment.txt - Autosomal FST results between clearance phenotype subpopulations. </li> </ul> Figure 5c: <ul> <li>fst.windows.2kb.treatment.txt.zip - FST values for comparisons between different treatment groups (Pre-treatment, post-treatment (good clearers), post-treatment (poor clearers))</li> </ul> Figure 5d:  <ul> <li>assoc_err_binary.txt.zip - Results of binary trait association between miracidia sampled from hosts with good clearance phenotypes (where treatment appeared to be highly effective) and miracidia isolated post-treatment from hosts with poor clearance phenotypes (where miracidia are potentially derived from parasites that survived treatment.</li> </ul> Figure 5e: <ul> <li>assoc_err_linear.txt.zip - - Results of linear regression genome-wide association study with the ERR estimates for all 198 samples, using the mean of the posterior ERR estimates from Crellen et al. (2016) as a quantitative trait.</li> </ul> Supplementary figure 1: <ul> <li>median.coverage.txt - Normalised depth of read coverage (column 4) calculated in 25 kb windows (columns 2&3) across all samples for all chromosomes (column 1).</li> </ul> Supplementary figure 2a-f:  <ul> <li>cohort.genotyped.txt.zip -  - Variant quality site values (used to inform variant site retention or removal). </li> </ul> Supplementary figure 2g: <ul> <li>hard_filtered.imiss.txt -  Per sample variant missingness (used to inform quality control).</li> </ul> Supplementary figure 2h: <ul> <li>hard_filtered_filtindv.lmiss.txt.zip - Per site missingness (used to inform quality control).</li> </ul> Supplementary figure 3a, 4a, 4b: <ul> <li>prunedData.eigenvec - PCA eigenvectors</li> <li>prunedData.eigenval - PCA eigenvalues</li> </ul> Supplementary figure 3b: <ul> <li>pruned_data.mdist.csv - Distance matrix used as the basis for the neighbour joining phylogeny.</li> </ul> Supplementary figure 5: <ul> <li>cv_scores.txt - ADMIXTURE coefficient of variation scores (column 2) for each population size (1).</li> </ul> Supplementary figure 6: <ul> <li>*_SMC_SE.csv - SMC++ results (from 25 subsampled replicates) for each school subpopulation and outgroup samples. </li> </ul> Supplementary Figure 7: <ul> <li>smcpp.csv - SMC++ results for each school subpopulation and outgroup samples. </li> </ul> Supplementary Figure 8a-d <ul> <li>pi.per_host.txt.zip - Nucleotide diversity values for each host infrapopulation. </li> </ul> Supplementary Figure 9: <ul> <li>sexing.csv - inferred sex (based on differential read coverage over pseudoautosomal and Z-specific regions of the Z chromosome). </li> </ul> Supplementary Figure 10b: <ul> <li>sfs_res.csv - residuals for the SFS analysis in 3a/10a.</li> </ul> Supplementary Figure 11: <ul> <li>MAYUGE_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Mayuge population in 2kb windows. </li> <li>Tororo_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Tororo population in 2kb windows. </li> </ul> Supplementary Figures 13-18: <ul> <li>genes.bed - Coordinates of gene models (S. mansoni v7 annotation).</li> <li>KOCOGE_SITE_PI.sites.pi.txt.zip - Per site nucleotide diversity values</li> <li>MAYUGE_TORORO_sites.weir.fst.txt.zip - Per site FST values between Mayuge and Tororo populations. </li> <li>coverage_5kb.windows.txt.zip - Per sample depth of read coverage in 5 kb windows. Columns 4,5,6 represent the median, mean and sstev of coverage for each 5kb window (columns 2&3) along each chromosome (column 1). </li> <li>median.sample.coverage.txt -  Median chromosomal depth of read coverage for each sample. </li> </ul> Supplementary Figure 19: <ul> <li>kocoge_median.ld.txt.zip -  - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Kocoge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> <li>mayuge_median.ld.txt.zip - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Mayuge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> </ul> Misc files: schools.list - List of samples and schools where they were sampled.

opencc-by-4.0May 2024View details →

Results from the revision of MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data

afr47041.zip, lat36378.zip, and eur115620.zip contain All of Us Summary Statistics used in the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Summary statistics for three cohorts are included: Afr47k, Lat36k, and Eur116k. These cohorts have not been downsampled to have equal levels of missingness. pips.tsv contains fine-mapped variants with PIP > 0.01 via MultiSuSiE from the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Subcohorts with the _unmatched suffix have not been downsampled to have equal levels of phenotyped missingness across ancestries.  MultiSuSiE-main.zip contains the MultiSuSiE software packages (corresponds to the Github repo on 10/16/2025). Please cite: Rossen, Jordan, et al. "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data." medRxiv (2024): 2024-05.