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37 results for “haplotype sequencing”
Protein haplotype sequences obtained by ProHap from the Haplotype Reference Consortium Release 1.1 dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Haplotype Reference Consortium, Release 1.1 (<a href="https://ega-archive.org/datasets/EGAD00001002729" target="_blank" rel="noopener">https://ega-archive.org/datasets/EGAD00001002729</a>). We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>Release 1.1 of the HRC is provided aligned with the GRCh37 reference genome. We have performed a liftover to the GRCh38 reference using GeneBe (https://genebe.net/tools/liftover). Variants for which the reported alternative allele is considered as reference in GRCh38 were removed. A threshold of 1% minor allele frequency was applied to filter the remaining variants. After translation, a frequency threshold of 0.5% was applied to filter the resulting unique non-canonical sequences. The complete configuration file for the ProHap run is attached to this repository.</p> <p>This dataset contains one compressed directory, contains the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>The file is provided in two formats - full and simplified. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Protein haplotype sequences obtained by ProHap from the Human Pangenome Reference Consotruim dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Human Pangenome Reference Consotruim (HPRC), first release (<a href="https://github.com/human-pangenomics/hpp_pangenome_resources">https://github.com/human-pangenomics/hpp_pangenome_resources</a>), 44 samples. We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>This repository contains one database created using all 43 samples of the HPRC release (the haplotypes of the sample NA21309 did not encode any non-canonical sequences), and then a database for each of the 43 samples separately. No filtering on allele frequency or haplotype frequency was applied in any of the databases. The complete configuration file for the ProHap run is attached to this repository.</p> <p>There is one compressed directory for each of the databases, containing the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>For this dataset, only the simplified format is provided. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones
<p>This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint.</p> <p>Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software.</p> <p>Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.</p>
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"
<p>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 <em>DAT</em> of patient S1 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>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><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>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> <p>All the files have been compressed using standard <em>gzip</em>.</p>
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"
<p>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 <em>DAT</em> of patient S0 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>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><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>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> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Direct chromosome-length haplotyping by single-cell sequencing.
<p>Selected Strand-seq libraries from PMID:27646535 study. Data were originally shared on the European Nucleotide Archive (http://www.ebi.ac.uk/ena) under the accession number: PRJEB14185</p>
Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq
<p>Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.</p>
Protein haplotype sequences obtained by ProHap from the 1000 Genomes Project data set
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the 1000 Genomes Project, aligned with the GRCh38 genome build (<a href="https://www.internationalgenome.org/data-portal/data-collection/grch38">https://www.internationalgenome.org/data-portal/data-collection/grch38</a>). We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts. The complete configuration file for each ProHap run is attached to this repository.</p> <p>This data set contains six compressed directories, five representing the superpopulations included in the 1000 Genomes Project (<a href="https://catalog.coriell.org/1/NHGRI/Collections/1000-Genomes-Project-Collection/1000-Genomes-Project">https://catalog.coriell.org/1/NHGRI/Collections/1000-Genomes-Project-Collection/1000-Genomes-Project</a>), and one created using all the samples included in the 1000 Genomes data set:</p> <ul> <li>AFR - African</li> <li>AMR - American</li> <li>EUR - European</li> <li>SAS - South Asian</li> <li>EAS - East Asian</li> <li>ALL - all participants in the 1000 Genomes Project</li> </ul> <p>Each of the directories contains the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap, using alleles with at least 1 % frequency within the selected population</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>The file is provided in two formats - full and simplified. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the simplified fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 in Genetic diversity of Halyomorpha halys (Hemiptera, Pentatomidae) in Korea and comparison with COI sequence datasets from East Asia, Europe, and North America
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 bp fragment of the COI gene of Halyomorpha halys. Each circle is labeled with haplotype number, and the size of each circle is proportional to the frequency of each haplotype [H3 (n = 353); H1 (n = 285); H22 (n = 43); H8 (n = 34); H33 (n = 23); H2 (n = 16); H32 (n = 8); H7, H9–H13, and H43 (n = 3); H6, H14, H34, H39, and H40 (n = 2); H4–H5, H12, H15–H21, H23, H30–H31, H35–H38, H41, H42, and H44–H51 (n = 1)]. Differing colors indicate countries in which samples were collected.
Fig 4. Median-joining haplotype network for M in Evolutionary relationships of Macaca fascicularis fascicularis (Raffles 1821) (Primates: Cercopithecidae) from Singapore revealed by Bayesian analysis of mitochondrial DNA sequences
Fig 4. Median-joining haplotype network for M. fascicularis. The size of the circular nodes representing haplotypes is proportional to the number of sequences comprising the haplotype. Shading of circular nodes corresponds to general geographic groupings including Sundaic islands (white), mainland Indochina (gray), Malay Peninsula and northern Sumatra (dark gray), and Singapore (black). Haplotype identifications are presented in Table 1.
Figure 2. Bayesian phylogram for cytochrome c oxidase subunit I sequences. Upper Sacramento River basin haplotypes are distributed among clades A–D in Extensive diversification of pebblesnails (Lithoglyphidae: Fluminicola) in the upper Sacramento River basin, northwestern USA
Figure 2. Bayesian phylogram for cytochrome c oxidase subunit I sequences. Upper Sacramento River basin haplotypes are distributed among clades A–D. Posterior probability values ≥ 90% are shown. Upper Sacramento River basin lineages newly discovered in this study are highlighted by the larger font. Specimen codes are from Table 1.
Linked-read sequencing enables haplotype-resolved resequencing at population scale
The feasibility to sequence entire genomes of virtually any organism provides unprecedented insights into the evolutionary history of populations and species. Nevertheless, many population genomic inferences – including the quantification and dating of admixture, introgression and demographic events, and inference of selective sweeps – are still limited by the lack of high-quality haplotype information. The newest generation of sequencing technology now promises significant progress. To establish the feasibility of haplotype-resolved genome resequencing at population scale, we investigated properties of linked-read sequencing data of songbirds of the genus Oenanthe across a range of sequencing depths. Our results based on the comparison of downsampled (25x, 20x, 15x, 10x, 7x, and 5x) with high-coverage data (46-68x) of seven bird genomes mapped to a reference suggest that phasing contiguities and accuracies adequate for most population genomic analyses can be reached already with moderate sequencing effort. At 15x coverage, phased haplotypes span about 90% of the genome assembly, with 50 and 90 percent of phased sequences located in phase blocks longer than 1.25-4.6 Mb (N50) and 0.27-0.72 Mb (N90). Phasing accuracy reaches beyond 99% starting from 15x coverage. Higher coverages yielded higher contiguities (up to about 7 Mb/1Mb (N50/N90) at 25x coverage), but only marginally improved phasing accuracy. Phase block contiguity improved with input DNA molecule length; thus, higher-quality DNA may help keeping sequencing costs at bay. In conclusion, even for organisms with gigabase-sized genomes like birds, linked-read sequencing at moderate depth opens an affordable avenue towards haplotype-resolved genome resequencing at population scale.
Resolution, validation and divergence of heterozygous haplotypes from pooled long read sequencing of the diamondback moth (Lepidoptera: Plutellidae)
<p>This data sets includes the full set of intermediate genome assemblies produced during our analyses. We make these available for researchers who may be interested in the variation between assembly results and variation within the study organism (<em>Plutella xylostella</em>) prior to removal during subsequent genome processing.</p>
Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing - Real and mock datasets
<p>This repository contains the reads, assemblies, and references required to replicate the <strong>real and mock</strong> results presented in the paper: https://doi.org/10.1101/2023.01.31.526521</p>
Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing - Simulated datasets
<p>This repository contains the reads, assemblies, and references required to replicate the <strong>simulated</strong> results presented in the paper: https://doi.org/10.1101/2023.01.31.526521</p>
Linked-read sequencing enables haplotype-resolved resequencing at population scale
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Three haplotype-resolved pentaploid Rosa assemblies with assembled and extracted single copy orthologue (SCO) sequences from Rosa canina genome, diploid Rosa species, and sect. Caninae pollen
Open the record for dataset details and reuse information.
DNA sequences for six chloroplast loci concatenated, representing haplotypes found in Colocasia esculenta, and closely related Araceae
<p><span>As an ancient clonal root and leaf crop, taro (<i>Colocasia esculenta</i>) is highly polymorphic with uncertain genetic and geographic origins. We explored chloroplast DNA variation in wild and cultivated taros and other <i>Colocasia</i> species, and found cultivated taro to be polyphyletic, with tropical and temperate clades originating in Southeast Asia. A third clade was found exclusively in wild populations from Southeast Asia to Australia and Papua New Guinea. Our findings do not support the hypothesis of taro domestication in Papua New Guinea. </span></p>
Data from: Haplotype sequence collection of ABO blood group alleles by long-read sequencing reveals putative A1-diagnostic variants
<p>In the era of blood group genomics, reference collections of complete and fully-resolved blood group gene alleles have gained high importance. For most blood groups, however, such collections are currently lacking, as resolving full-length gene sequences as haplotypes (i.e. separated maternal/paternal origin) remains exceedingly difficult with both Sanger and short-read next-generation sequencing. Using the latest third-generation long-read sequencing, we generated a collection of fully-resolved sequences for all six main <em>ABO</em> allele groups: <em>ABO</em>*<em>A1</em>/<em>A2</em>/<em>B</em>/<em>O.01.01</em>/<em>O.01.02</em>/<em>O.02</em>. We selected 77 samples from an <em>ABO</em> genotype dataset (n=25,200) of serologically-typed Swiss blood donors. The entire <em>ABO</em> gene was amplified in two overlapping long-range PCRs (covering ~23.6 kb) and sequenced by long-read Oxford Nanopore sequencing. For quality validation, two samples per <em>ABO</em> group were re-sequenced using Illumina and PacBio technology. All 154 full-length <em>ABO</em> sequences were resolved as haplotypes. We observed novel, distinct sequence patterns for each <em>ABO</em> group. Most genetic diversity was found between, not within, <em>ABO</em> groups. Phylogenetic tree and haplotype network analyses highlighted distinct clades of each <em>ABO</em> group. Strikingly, our data uncovered four genetic variants putatively specific for <em>ABO</em>*<em>A1</em>, for which direct diagnostic targets are currently lacking. We validated <em>A1</em>-diagnostic potential using whole-genome data (n=4,872) of a multi-ethnic cohort. Overall, our sequencing strategy proved powerful for producing high-quality <em>ABO</em> haplotypes and holds promise for generating similar collections for other blood groups. The publicly available collection of 154 haplotypes will serve as a valuable resource for molecular analyses of <em>ABO</em>, as well as studies about the function and evolutionary history of <em>ABO</em>.</p>
Complete sequences of six major histocompatibility complex haplotypes rev2
<p>Sequences that the analysis in the paper <a title="Complete sequences of six major histocompatibility complex haplotypes, including all the major MHC class II structures" href="https://doi.org/10.1111/tan.15020" target="_blank" rel="noopener">Houwaart et. al. 2023</a> was based on</p>
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