Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset DAT of patient S1 with the following format: <ol> <li>DAT.raw_read_counts.bed.gz is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li>DAT.phased_snps_counts.pos.gz is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> All the files have been compressed using standard gzip.

opencc-by-4.0May 2020View details →

Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset DAT of patient S0 with the following format: <ol> <li>DAT.raw_read_counts.bed.gz is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li>DAT.phased_snps_counts.pos.gz is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> All the files have been compressed using standard gzip.

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-4.0May 2019View details →

Relate-estimated coalescence rates, allele ages, and selection p-values for the 1000 Genomes Project

Overview Coalescence rates, allele ages, and p-values for evidence of positive selection calculated for 2478 samples of the 1000 Genomes Project using Relate. We estimated the joint genealogy of all 1000 GP populations and then extracted the embedded genealogy for each population. For the genealogy of each population, we jointly estimated the population size history and branch lengths.  Variants segregating in more than one population therefore have correlated but different allele ages in each population. Please refer to <a href="https://www.nature.com/articles/s41588-019-0484-x">Speidel et al. Nature Genetics (2019)</a> for more details or email leo.speidel@outlook.com for any queries. Coalescence rates The zipped directory coalescence_rates.zip contains coalescence rates for 26 populations in the 1000 Genomes Project data set. <ul> <li>The .coal files show the haploid coalescence rates, please refer to the <a href="https://myersgroup.github.io/relate/modules.html#PopulationSizeScript_FileFormats">Relate documentation</a> for the file format.</li> <li>The popsize.RData file is an R data frame storing the diploid population sizes (0.5/coalescence rate) calculated using the .coal files. The columns of this data frame, named "pop_size", are <ul> <li>gens_ago: Time in generations at which epoch starts. (To get years from generations, we multiply by 28.)</li> <li>population_size: Diploid population size in this epoch.</li> <li>population: Name of population </li> <li>region: Name of region (AFR, AMR, EAS, EUR, SAS)</li> </ul> </li> </ul> Allele ages and selection p-values The zipped directories allele_ages_*.zip contain R data frames for each 1000GP population storing allele ages and selection p-values. Please note that only mutations that segregate in the population and map to a unique branch in the Relate-estimated marginal trees are included. Selection p-values are only provided for mutations of DAF > 2 that pass quality filters (see Speidel et al., 2019).  To get an age estimate for a neutral mutation, use 0.5*(lower_age + upper_age). To get years from generations, we multiply by 28. The columns of these data frames, named "allele_ages", are <ul> <li>CHR: chromosome index</li> <li>BP: base-pair position (GRCh37)</li> <li>ID: id of SNP</li> <li>lower_age: Age in generations of coalescence event at the lower end of the branch onto which the mutation maps</li> <li>upper_age: Age in generations of coalescence event at the upper end of the branch onto which the mutation maps</li> <li>ancestral/derived: Ancestral/derived allele</li> <li>upstream: Upstream (5') allele</li> <li>downstream: Downstream (3') allele</li> <li>DAF: Derived-allele frequency</li> <li>pvalue: log10 p-value for selection evidence</li> </ul>

opencc-by-4.0Jul 2017View details →

Data for: Detecting Long-Term Balancing Selection Using Allele Frequency Correlation

Genome-wide and top 1% scores for 1KG project data output from BetaScan reported in: <a href="https://pubmed.ncbi.nlm.nih.gov/28981714/">Detecting Long-Term Balancing Selection Using Allele Frequency Correlation.</a> Siewert KM, Voight BF. Mol Biol Evol. 2017 Nov 1;34(11):2996-3005. doi: 10.1093/molbev/msx209. PMID: 28981714 Code available at: https://github.com/ksiewert/BetaScan

opencc-by-4.0Nov 2020View details →

Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences

This repository consists of minisatellite VNTR genotypes for 2,800 samples (2,770 individuals). The raw VCF files were produced using <a href="https://github.com/yzhernand/VNTRseek">VNTRseek</a> on xxx data sources: <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/">30 high coverage WGS datasets</a> from the 1000 Genomes Project phase 3, <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">2,504 unrelated genomes</a> from New York Genome Center (NYGC), <a href="https://www.internationalgenome.org/data-portal/data-collection/sgdp">253 genomes from Simons Diversity Genome Project</a> (SGDP), <a href="https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/tumor-normal.html">two tumor-normal breast cancer samples</a> from Illumina Basespace, haploid genomes <a href="https://www.ncbi.nlm.nih.gov/sra/SRX652547">CHM1 </a>and <a href="https://www.ncbi.nlm.nih.gov/sra/SRX1009644">CHM13</a>, and seven genomes from the Personal Genome Project from the Genome In A Bottle Consortium (GIAB). Raw VCF files are provided for each data source separately. The raw VCF files were preprocessed (preprocess.sh) to extract genotypes and provided in VNTRseek_preprocessed_data.tar.gz (uncompressed size 10G). The R Markdown code to analyze the preprocessed data and produce figures and tables is also provided (tables_and_figures.Rmd). For more information see the ReadMe file. This work was supported in part by NSF grants IIS-1423022 and DBI-1559829.

opencc-by-4.0Sep 2023View details →

A scalable, accurate, and universal analysis framework using individual-level allele frequency for large-scale genetic association studies in an admixed population

Inclusion of individuals with diverse or admixed genetic ancestries is crucial to discover novel findings that may be missed by genomics analyses rooted solely in Caucasian population. Here, we present an analysis framework, SPAmix, which is scalable to a large-scale biobank data analysis including hundreds of thousands of admixed individuals and is universally applicable to various types of complex traits including binary trait, quantitative trait, time-to-event trait, longitudinal traits, etc. For each genetic variant, SPAmix uses genotype data and genetic principal components (PCs) to estimate individual-level allele frequency, which is subsequently used to calibrate p values via a retrospective analysis. A hybrid strategy including saddlepoint approximation (SPA) can greatly increase the accuracy to analyze rare genetic variants, especially if the phenotypic distribution is unbalanced or extremely unbalanced. Compared to Tractor, SPAmix does not require local ancestry information and can be straightforwardly applicable to a multi-way admixed population. Meanwhile, SPAmix can also be extended to SPAmixlocal in which the local ancestry can be incorporated if available. In addition, we propose SPAmixCCT to combine the p values of SPAmix and SPAmixlocal via Cauchy combination (CCT). SPAmixlocal performs close to Tractor when analyzing quantitative traits and is more accurate when analyzing binary traits with an unbalanced case-control ratio. And SPAmixCCT is an optimal unified approach for various cross-ancestry genetic architectures. Extensive simulation studies and real data analyses of 369,314 UK Biobank individuals from multiple ancestries demonstrated that SPAmix is scalable and can discover novel hits while controlling type I error rates well.

opencc-by-4.0Sep 2020View details →

WorldCOM Deliverable 1: Prevalence of ESBL subtypes in bacterial pathogens and a sequence database of selected alleles

OHEJP Project: WorldCOM, Deliverable 1, Work Package 1. This dataset is connected to Work Package 1, Task1 of the WorldCOM consortium grant within the One Health EJP group. The aim was to analyse publicly available sequences for antimicrobial resistance genes associated with Salmonella, Campylobacter and E. coli. For the initial phase of this work package, we have focused on ESBL-related AMR genes. As these genes are absent from Campylobacter, we have not included this bacterium in these analyses, and have used the important pathogens Klebsiella and Acinetobacter. All types and subtypes of Extended Spectrum β-Lactamases (ESBLs) and plasmid-mediated colistin resistance genes have been analysed for frequency among reported and extracted sequences. High frequency resistant genes subtypes have been highlighted for further sequence analysis to illustrate geographic distribution and geographic-specific single nucleotide polymorphisms (SNPs). The data shown are work in progress.

opencc-by-4.0Mar 2017View details →

Allelic variation in mouse Ticam2 contributes to SARS-CoV pathogenesis

Genotype calls from the MUGA array for an F2 cross between the mouse strains CC003/Unc and CC053/Unc. The results of this F2 cross are in press at G3 Gralinski, L. E., V. D. Menachery, A. P. Morgan, A. Totura, A. Beall et al., 2017 Allelic variation in mouse Ticam2 contributes to SARS-CoV pathogenesis. G3 7: xx-xx.

opencc-zeroFeb 2024View details →

Data from: Only rare classical MHC-I alleles are highly expressed in the European house sparrow

The exceptional polymorphism observed within genes of the major histocompatibility complex (MHC), a core component of the vertebrate immune system, has long fascinated biologists. The highly polymorphic classical MHC class-I (MHC-I) genes are maintained by pathogen-mediated balancing selection (PMBS), as shown by many sites subject to positive selection, while the more monomorphic MHC-I genes show signatures of purifying selection. In line with PMBS, at any point in time, rare classical MHC alleles are more likely than common classical MHC alleles to confer a selective advantage in host-pathogen interactions. Combining genomic and expression data from the blood of wild house sparrows Passer domesticus, we found that only rare classical MHC-I alleles were highly expressed, while common classical MHC-I alleles were lowly expressed or not expressed. Moreover, highly expressed rare classical MHC-I alleles had more positively selected sites, indicating exposure to stronger PMBS, compared with lowly expressed classical alleles. As predicted, the level of expression was unrelated to allele frequency in the monomorphic non-classical MHC-I alleles. Going beyond previous studies, we offer a fine-scale view of selection on classical MHC-I genes in a wild population by revealing differences in the strength of PMBS according to allele frequency and expression level.

opencc-by-4.0Feb 2024View details →

PubMLST allele profiles and sequence alignments of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019-2020

This dataset provides PubMLST allele profiles, sequence alignments, and Mash distance data used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan".

opencc-zeroJul 2022View details →

Data of: Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts

These files are results obtained in Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts by Yoshito Hirata, Arisa H. Oda, Chie Motono, Masanori Shiro & Kunihiro Ohta. There are 33 files for the corresponding each reconstruction of three-dimensional chromosomone structures for each cell. There are 3D structures for 15 GM cells and 18 PBMC cells, which are obtained from the single cell Hi-C data of Tan et al. Science (2018). For each file, there are 6 columns: The first column corresponds to the allele (0: maternal, 1: paternal) The second column corresponds to the chromosome (1-22: chromosome's number, 23: X, 24: Y) The third column corrsponds to the base point. The fourth column, the fifth column and the sixth column correspond to x-, y-, and z-axes of our reconstruction.

opencc-zeroSep 2022View details →

Distinct signals of clinal and seasonal allele frequency change at eQTLs in Drosophila melanogaster

Populations of short-lived organisms can respond to spatial and temporal environmental heterogeneity through local adaptation. However, the comparative signals of local adaptation across space and time remains poorly understood. Here, we examined patterns of allele frequency change across a latitudinal cline and between seasons at previously reported expression quantitative trait loci (eQTLs). We divided eQTLs into groups by utilizing differential expression profiles of fly populations collected across latitudinal clines or exposed to different environmental conditions. In general, we find that eQTLs are enriched for clinally varying polymorphisms, and that these eQTLs change in frequency in concordant ways across the cline and in response to starvation and chill-coma. The enrichment of eQTLs among seasonally varying polymorphisms is more subtle, and the direction of allele frequency change at eQTLs appears to be somewhat idiosyncratic. Taken together, we suggest that clinal adaptation at eQTLs is at least partially distinct from seasonal adaptation.

opencc-by-4.0Jun 2021View details →

Data and code related to publication "Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model" - Aubree et al. 2021

Those data sets and codes are related to the manuscript "Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model" available on BioRXiv. All the information that are necessary to use those data sets and codes are contained in the file "readme.txt".

opencc-zeroMay 2024View details →

Benefits and limits of phasing alleles for network inference of allopolyploid complexes

Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared to haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared to using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical non-identifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

opencc-by-4.0Dec 2010View details →

Fig. 2 in Allelic Diversity Of The Beta-Amylase Gene Bmy1 In Latvian Barley Breeding Lines

Fig. 2. Genotyping on the (1+6) bp Indel. A – (1+6) bp insertion; B – (1+6) bp deletion; C – heterozygote.