73 datasets

NCL Data2026 · dataset

<p dir="ltr">SLO: Datoteka vsebuje filtrirane genetske različice vključene družine Wye Target x BL2/1 in testnih genotipov v zapisu VCF, ki smo jih pripravili z uporabo orodja TASSEL 5 v okviru doktorske naloge. Variante smo filtrirali glede na naslednja merila: največji dopusten delež manjkajočih podatkov na mestu SNP smo določili pri 35,5 %, prag frekvence redkega alela (MAF) pri 0,05, najmanjšo

NCL Data2026 · dataset

<p dir="ltr">SLO: Datoteka vsebuje preglednico s podatki o sekvenciranju testnih genotipov in rezultatih mapiranja zaporedij na oba haplotipa sorte Apollo.</p><table><tr><td><p dir="ltr">EN: This file contains a table with sequencing data for the test genotypes and sequence-mapping results for both haplotypes of the Apollo cultivar.</p></td></tr></table><p></p>

NCL Data2026 · dataset

UniProt Pan-Proteomes 2026_02<p dir="ltr">This is the Pan-Proteomes dataset published as part as UniProt release 2026_02 (10-Jun-2026).</p><p dir="ltr">It is the first release of the new UniProt Pan-Proteomes.</p><p dir="ltr">Please note that due to upload limitations, the original pp* subfolders have been replaced by a single compressed .tgz archive.</p><p dir="ltr">The latest version (published

NCL Data2026 · dataset

<p dir="ltr">SLO: Datoteka vsebuje tabelo SNP s statistično značilnostjo p < 0,001 za posamezne uporabljene modele in za oba haplotipa, P1 in P2. Vsak zavihek predstavlja en model ter vključuje SNP, ki so v tem modelu dosegli navedeni prag značilnosti. Za primerjavo so ob posameznem SNP prikazane tudi p-vrednosti, pridobljene z drugimi modeli.</p><p dir="ltr">EN: This file contains a table of SNPs

NCL Data2026 · dataset

<p dir="ltr">The Ankole is a long-horned Sanga cattle breed (Bos taurus africanus) native to Uganda and widely distributed across sub-Saharan Africa and beyond. It is valued for disease and parasite tolerance, adaptation to the equatorial environment and its unique cultural significance. This data relates to the generation and annotation of a haplotype-resolved, chromosome-level genome assembly us

NCL Data2026 · dataset

<p dir="ltr">SLO: Datoteka vsebuje Bash skripte, ki smo jih razvili za bioinformatsko obdelavo podatkov v okviru doktorske naloge. Skripte so pripravljene za mapiranje zaporedij na haplotip 1, pri čemer smo identičen postopek izvedli tudi za haplotip 2. Celoten proces vključuje poravnavo na referenčni genom in pridobivanje datotek VCF z zaznanimi genetskimi različicami.</p><table><tr><td><p dir="l

NCL Data2026 · dataset

<p dir="ltr">SLO: Datoteka vsebuje tabelo SNP s statistično značilnostjo p < 0,001 za posamezne uporabljene modele in za oba haplotipa, P1 in P2. Vsak zavihek predstavlja en model ter vključuje SNP, ki so v tem modelu dosegli navedeni prag značilnosti. Za primerjavo so ob posameznem SNP prikazane tudi p-vrednosti, pridobljene z drugimi modeli.</p><p dir="ltr">EN: This file provides lists of genes

NCL Data2026 · dataset

<table><tr><td><p dir="ltr">SLO: Datoteka vsebuje filtrirane genetske različice vključene družine Wye Target x BL2/1 in testnih genotipov v zapisu VCF, ki smo jih pripravili z uporabo orodja TASSEL 5 v okviru doktorske naloge. Variante smo filtrirali glede na naslednja merila: največji dopusten delež manjkajočih podatkov na mestu SNP smo določili pri 35,5 %, prag frekvence redkega alela (MAF) pri

figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p dir="ltr">This dataset provides the processed data supporting the manuscript"LCP modulates hepatic gluconeogenesis and gut microbiota in juvenileMicropterus salmoides (largemouth bass)" (submitted to the EgyptianJournal of Aquatic Research). It contains eight summary tablesextracted from the doctoral dissertation (Fan, UMS 2026) and, fromVersion 2, four full-resolution matrices generated direct

Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Crystal structure

<p dir="ltr">Oomycete plant pathogens threaten global food security, agriculture, and environmental health. Molecular identification of oomycetes typically relies on short high-copy number DNA markers (~500-bp or less), such as ribosomal DNA or mitochondrial cytochrome oxidase subunits, but these markers may lack species- or subspecies-level resolution due to factors like insufficient nucleotide v

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p dir="ltr">Consensus sequences of the four <i>bdcdpk</i> genes and detailed maps of the episomal and knockout plasmids.</p>

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p dir="ltr">Overexpression sequencing results</p>

ZivaHub2026 · dataset · unknown

<p>Intersection of somatic SVs called by three callers from six tumor-normal pairs before and after filtering by HPRC v2 pangenome.</p>

ZivaHub2026 · dataset · unknown

<p>Mosaic SV calling from <i>in silico</i> mix of normal–tumor data. COLO829 tumor HiFi reads were computationally downsampled and mixed with all COLO829BL normal reads to a 10:1 normal-to-tumor ratio. <b>A,</b> The <i>in silico</i> mix was assembled and used for <i>de novo</i> assembly (asm)-based filtering. <b>B,</b> HPRC2 pangenome-based filtering.</p>

ZivaHub2026 · dataset · unknown

<p>Raw SV calls stratified by size</p>

ZivaHub2026 · dataset · unknown

<p>Intersection of somatic SVs called by three callers from six tumor-normal pairs before and after filtering by self-assembly based on the downsampled (50%) datasets.</p>

ZivaHub2026 · dataset · unknown

<p>Assembly quality evaluation metrics of all samples in this study.</p>

ZivaHub2026 · dataset · unknown

<p>Somatic SV calling on matched COLO829 tumor–normal cell lines. <b>A–D,</b> Metrics on <i>de novo</i> assembly-based filtering. <b>E–H,</b> Metrics on pangenome-based filtering. <b>A</b> and <b>E,</b> Number of FP and TP SV calls (<b>C</b> and <b>G</b>) from PacBio HiFi data at different cutoffs on supporting reads. The solid bars correspond to SV calls still observed in the alignment against th

ZivaHub + HKU DataHub + figshare + Loughborough Research Repository + UP Research Data Repository2026 · Astronomical catalogue

<p dir="ltr"><b>Supplementary Dataset 5:</b> List of repeats identified in the mitochondrial assemblies of Rafflesiaceae, using the <i>get_repeats.sh</i> script developed by Gandini et al. (2019) specifying -perc_identity flag 90. </p>

ZivaHub2026 · dataset · unknown

<p>Summary of software source codes and reference genomes.</p>

ZivaHub2026 · dataset · unknown

<p>gnomAD filtering analysis of the somatic SVs in COLO829 HiFi sequencing data. The SV calls were filtered at supported read cutoff of 3. The left column show the performance from HiFi sequencing data, right column shows the nanopore dataset performance.</p>

ZivaHub2026 · dataset · unknown

<p>Intersection of somatic SVs called by three callers from five tumor–normal pairs in the osteosarcoma long-read cohort before and after filtering by pangenome alignment (graph). asm, assembly.</p>

ZivaHub2026 · dataset · unknown

<p>Intersection of somatic SVs called by three callers from six tumor–normal pairs before and after filtering by self-assembly (asm).</p>

ZivaHub2026 · dataset · unknown

<p>Intersection of somatic SVs called by three callers from six tumor-normal pairs before and after filtering by HPRC2 pangenome based on the downsampled (50%) datasets.</p>

ZivaHub2026 · dataset · unknown

<p>Overview of somatic SV calling and filtering workflow. Minisv starts from the existing somatic SV call set. Step (1) Extract reads from somatic SVs in the tumor from the call set; (2) realign SV-containing reads to GRCh38, normal self-assembly (if available), and/or pangenome and extract candidate SVs from the realignment in the read coordinate; (3) compare the candidate SVs resulting from the