29 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

<table><tr><td><p dir="ltr">SLO: V datoteki so zbrani posamezni SNP in njihove izračunane vrednosti pomembnosti, določene z modelom naključnih gozdov (Random Forest). Podatki so razdeljeni v dva zavihka glede na obravnavani haplotip: p1 in p2. Vsak zavihek vsebuje seznam SNP ter pripadajoče vrednosti njihove pomembnosti v modelu. Višja vrednost pomembnosti predstavlja večji relativni prispevek pos

NCL Data2026 · dataset

<p dir="ltr">SLO: V podatkovni datoteki so zbrani SNP, identificirani z različnimi statističnimi pristopi. Tabela vključuje SNP, ki pri analizah z metodama BLINK ali FarmCPU in MLM Tassel presegajo raziskovalni prag pomembnosti p < 0,001. Poleg tega so v tabelo vključeni tudi SNP, uvrščeni med deset najpomembnejših SNP po modelu naključnih gozdov (Random Forest, RF)</p><p dir="ltr">EN: The dataset

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">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

<p dir="ltr">SLO: Datoteka vsebuje tabelo SNP in pripadajočih statističnih vrednosti po posameznih modelih, s katerimi smo preučevali povezavo z lastnostjo spol. V tabelo smo vključili vse SNP, ki so vsaj pri enem od uporabljenih modelov presegli prag statistične značilnosti po Bonferronijevi korekciji.</p><table><tr><td>EN: This file contains a table of SNPs and their corresponding statistical va

NCL Data2026 · dataset

<p dir="ltr">SLO: Datoteka vsebuje tabelo SNP za oba haplotipa, P1 in P2, ki so v analizi GWAS za vse genotipe (družina in testni) dosegla statistično značilnost p < 0,0001. </p><p dir="ltr">EN: The file contains a table of SNPs for both haplotypes, P1 and P2, that reached a statistical significance of p < 0.0001 in the GWAS analysis across all genotypes (the mapping family and test genotypes).</p

NCL Data2026 · dataset

<p dir="ltr">SLO: Datoteka vsebuje podatke o napovedovanju indeksa bolezni (DSI) za haplotipa P1 in P2, pridobljene z analizo naključnih gozdov (Random Forest) v programu ranger. V analizo smo vključili 1.000 SNP z najnižjimi p-vrednostmi, določenimi z modeloma FarmCPU in BLINK. Prikazane so dejanske in napovedane vrednosti DSI za posamezne razdelitve podatkov; izvedli smo 10 neodvisnih razdelitev

ZivaHub + Deakin Research Online + HKU DataHub + DaYta Ya Rona + figshare + Loughborough Research Repository + UP Research Data Repository2026 · Astronomical catalogue

<p dir="ltr">Metabolic and proteomic data of engineered rice</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">Early-Onset Colorectal Cancer (EOCRC) is colorectal cancer (CRC) diagnosed before age 50. The incidence of EOCRC is increasing in most countries. It is still rare compared to average-onset CRC, making identification of the causes challenging. Whilst not the cause of the increase in incidence, a significant proportion (15-25%) of EOCRC cases have a known hereditary syndrome or a strong

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

<p dir="ltr">DArT Seq SNP Genotyping Data for <i>Brassica napus</i></p><p><br></p>

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

<p dir="ltr">This repository contains supplementary datasets and the data package required to reproduce the analysis, results, and figures presented in Savino et al. (2026).<br><br>The study leverages CRISPR-based functional genomics to systematically evaluate Variants of Unknown Significance (VUS) in oncology, identifying actionable therapeutic targets and highlighting novel opportunities for dru

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

<p dir="ltr">1.667XianSample.PASS.missing0.9.maf0.01.Biallelic.OnlyGT.WildOutgroup.vcf.gz中包含667新增样本+5份公共数据+3份野生稻外群，包含675个样本的SNP/INDEL数据集，主要用于选择性清除分析、LD分析(不要被missing0.9误导，其实就是missing <=0.1)；</p><p dir="ltr"><br>2.667XianSample.PASS.missing0.9.maf0.01.Biallelic.OnlyGT.WildOutgroup.OnlySNP.LDfilter.vcf.gz则是667XianSample.PASS.missing0.9.maf0.01.Biallelic.OnlyGT.WildOutgroup.vcf.gz数据集经过LD剪枝之后的结果，主要用于群体

figshare + Loughborough Research Repository2026 · Astronomical catalogue

<p dir="ltr">This dataset accompanies the thesis “Comparative Genomics in the genus Caenorhabditis and the Discovery of a Novel Toxin-Antidote System in C. elegans.” It contains 37 files documenting the comparative genomic analyses and experimental work presented in the thesis. Main and supplementary figures cover genetic mapping, inheritance tests, RNA interference, reporter expression, microscop

figshare2026 · Astronomical catalogue

<p dir="ltr">Supplementary tables S1, S2, S3 and S4 of "<i>PRX52</i>, a novel candidate gene in flood tolerance".</p>

figshare2026 · Astronomical catalogue

<pre><pre>Records of human transposable elements across five layers: literature-derived typed entities and directed relationships with PMID provenance; the TE catalogue and classification taxonomy; reference-genome repeat occurrence annotations; normalized expression matrices with context summaries; context-specific co-expression networks; and strict TE-variant overlap plus variant-gene-tissue ass

figshare2026 · RNA sequencing

<p><strong>Data Organization and File Naming Convention</strong></p><p>To ensure maximum computational reproducibility, all validation scripts, input records, and generated output files are contained within a single flat directory structure. This design allows the workflows to be executed immediately without requiring complex relative directory path configurations. Users can easily identify and ut

IISH Dataverse2026 · dataset · unknown

This folder contains the code and data necessary for repeating the analyses in the paper. Data for the GREML analysis were obtained from LifeLines and are not publicly available. More information about how to request Lifelines data and the conditions of use can be found on the website (https://www.lifelines-biobank.com/researchers/working-with-us/step-1-prepare-and-submit-your-application). For th

Borealis2026 · dataset · unknown

Abstract This dataset contains original phenotypic trait data for individual seedlings from a common garden experiment of western larch with two drought treatments and a control. The data includes height growth, bud flush and bud set dates, canopy loss, and fluorescence values collected during the 2021 & 2022 growing season.

data.InDoRES2026 · dataset · unknown

The respository contains all the code and data used in the analysis of the paper. Abstract of the paper : Declining populations can avoid extinction by returning to a positive growth rate through rapid adaptation, a process known as evolutionary rescue. Past theory has shown that rescue is unlikely below a critical genetic variance for adaptive traits. Many threatened populations are structured, w

Teesside University Research Data Repository2024 · dataset

This dataset archives raw data and scripts for GWAS and analysis of metabolite contents in the giant duckweed Spirodela polyrhiza. In our study we aim on identifying the genetic basis controlling free metabolite contents and growth in S. polyrhiza. A total of 42 free metabolites were extracted from 137 genotypes and correlated with fitness parameters. Genetic associations with these metabolic trai

IISH Dataverse2024 · dataset · unknown

The dataset contains the code and data necessary for repeating the analyses found in the paper. The data contains 1822 narrow-sense heritability (h2) and 378 coefficients of additive genetic variance (CVA) estimates from published studies for 68 bird and mammal species used in a comparative analysis study of additive genetic variances.

Teesside University Research Data Repository2022 · dataset

The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results pro

Teesside University Research Data Repository2021 · dataset

Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computational time-consumption and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluate