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6,363 results for “Mutations”

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zenodo40/100

Comprehensive Single Point Mutational Landscape Analysis of the Monkeypox Virus Proteome

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

zenodo40/100

Data for "Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations"

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →

zenodo40/100

Genome streamlining: effect of mutation rate and population size on genome size reduction: simulated data

Lineages data of populations simulated with Aevol (<a href="https://gitlab.inria.fr/aevol/aevol">https://gitlab.inria.fr/aevol/aevol</a>), and the Wild-Types sequences used for that. Conditions: change of mutation rate, population size, or both. Mutational bias: none, insertion bias or deletion bias

opencc-by-4.0Feb 2024View details →

zenodo40/100

10X Single Cell RNA-Seq On WT and Mutation mouse models

Four four-week-old mice, consisting of wild type, Pten mutation, MAP3K3 mutation, and MAP3K3+Pten mutation, were sampled at 4 weeks of age. Endothelial cells were enriched using CD31 magnetic beads, followed by 10X single-cell RNA sequencing.

opencc-by-4.0Nov 2024View details →

zenodo40/100

Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper

Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper

opencc-by-4.0Jul 2021View details →

zenodo40/100

Epigenetic modifications modify the rate of spontaneous mutations in a pathogenic fungus

Mutations are the source of genetic variation and the substrate for evolution. Genome-wide mutation rates appear to be affected by selection and are probably adaptive. Mutation rates are also known to vary along genomes, possibly in response to epigenetic modifications, but causality is only assumed. In this study we determine the direct impact of epigenetic modifications and temperature stress on mitotic mutation rates in a fungal pathogen using a mutation accumulation approach. Deletion mutants lacking epigenetic modifications confirm that histone mark H3K27me3 increases whereas H3K9me3 decreases the mutation rate. Furthermore, cytosine methylation in transposable elements (TE) increases the mutation rate 15‑fold resulting in significantly less TE mobilization. Also accessory chromosomes have significantly higher mutation rates. Finally, we find that temperature stress substantially elevates the mutation rate. Taken together, we find that epigenetic modifications and environmental conditions modify the rate and the location of spontaneous mutations in the genome and alter its evolutionary trajectory.

opencc-by-4.0Sep 2021View details →

zenodo40/100

Replication Data for "How Closely are Common Mutation Operators Coupled to Real Faults?"

# Replication Data for "How Closely are Common Mutation Operators Coupled to Real Faults?" ## Overview In mutation testing, faulty versions of a program are generated through automated modifications of source code. These mutants are used to assess and improve test suite quality, under the assumption that detection of mutants is indicative of a test suite's ability to detect real faults - i.e., that mutants and faults have a semantic relationship. Improving the effectiveness - in both cost and quality - of mutation testing may lie in better understanding this relationship, in particular with regard to how individual mutation operators (types) couple to real faults.   In this study, we examine coupling between 32,002 mutants produced by 31 mutation operators and 144 real faults, using a scale based on number of failing tests and reasons for failure. Ultimately, we observed that 9.92% of the mutants are strongly coupled to real faults, and 51.03% of the faults have at least one strongly coupled mutant. We identify and examine mutation operators with the highest median coupling, as well as the operators that tend to produce non-compiling mutants, undetected mutants, and mutants that cause the most tests to fail outside of the tests that detect the actual fault. We also examine how coupling could be used to filter the set of operators employed, leading to potentially significant cost savings during mutation testing. Our findings could lead to improvements in how mutation testing is applied, improved implementation of specific mutation operators, and inspiration for new mutation operators.  ## Data Contained in This Package - mutant_data.csv This dataset contains the coupling results for all mutants considered in our experiments. It contains the following attributes for each mutant: -- Project name from Defects4J -- Fault number from Defects4J -- Mutation ID -- Mutation operator -- Number of trigger tests for the fault (tests that detect the real fault) -- Number of failing test cases for the mutant (-1 indicates a compilation error) -- The number of failing trigger tests for the mutant -- The number of trigger tests that fail for the same reason the tests failed for the real fault. -- The number of failing non-trigger tests. -- The categorization of coupling. In order: Compile Error, Not Detected, No Substitution, Partial Test Substitution + Additional Tests Fail, Partial Test Substitution, Partial Substitution + Additional Tests Fail, Partial Substitution, Test Substitution + Additional Tests Fail, Test Substitution, Strong Substitution + Additional Tests Fail, Strong Substitution.  - mutant_logs/{Project}/{Project}{Fault Number}output.txt The raw output log that resulted from executing test cases for each mutant for each case example used from Defects4J. Used to generate the dataset discussed above. Scripting for generating the dataset is also included.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Prediction of metabolites associated with somatic mutations in cancers

<ul> <li>GEMs_AML: 16 acute myeloid leukemia (AML) patient-specific genome-scale metabolic models (GEMs) reconstructed using their corresponding RNA-seq data and Recon 2M.2</li> <li>GEMs_PCAWG: 943 cancer patient-specific GEMs for 24 different cancer types reconstructed using the Pan-Cancer Analysis of Whole Genomes (PCAWG) RNA-seq data and generic human GEM 'Recon 2M.2'</li> <li>GEMs_RCC: 20 renal cell carcinoma (RCC) patient-specific GEMs reconstructed using their corresponding RNA-seq data and Recon 2M.2</li> <li>GEMs_TCGA_LAML: 113 AML patient-specific GEMs reconstructed using The Cancer Genome Atlas (TCGA) LAML RNA-seq data and Recon 2M.2</li> </ul>

opencc-by-4.0Aug 2022View details →

zenodo40/100

Characteristic mutations of SARS VOC

This is a database consisting the list of characteristic mutations of the SARS-CoV-2 variants of Concern (VOC) circulating in Germany  during  September 2021 and March 2022.  This database is created by calling all the mutations listed in the Variant surveillance package of the GISAID for each variant. Second step is to count the frequency of a mutation reported for a variant in GISAID clinical data. And, only top 50 mutations for each variants are retained in this database.

opencc-by-4.0Nov 2022View details →

zenodo40/100

Elevated urine BMP phospholipids in LRRK2 and VPS35 mutation carriers with and without Parkinson's disease

Participant demographic and clinical characteristics, and urine BMP phospholipid levels.  For each participant, sample collection site is provided as: BCN (Barcelona), VIE (Vienna), DND (Dundee), or SSB (San Sebastian). Also provided are age at study participation, age at PD diagnosis (where applicable), sex (M, for male, and F, for female), experimental group (control, iPD –idiopathic PD –, LRRK2 G2019S, LRRK2 R1441G/C, VPS35 D620N, GBA, or other), and PD status (NMC for non-manifesting mutation carriers, or PD). Values for all measured BMP species presented as ng of BMP per mg of creatinine are provided. Additionally, urine creatinine (mg/ml) and non-normalized BMP levels are provided. BQL designates BMP levels that were below quantification level and NM designates values that were not measured for a particular individual.

opencc-by-4.0Feb 2023View details →

zenodo40/100

datset related to article "THE NOVEL I213S MUTATION IN PSEN1 GENE IS LOCATED IN A HOTSPOT CODON ASSOCIATED WITH FAMILIAL EARLY-ONSET ALZHEIMER'S DISEASE"

Electropherogram of the proband psen1 exon 7 ngs analysis of causal and risk genes associated to dementia

opencc-by-4.0Feb 2023View details →

zenodo40/100

Datset related to article "G507D MUTATION IN FUS GENE CAUSES FAMILIAL AMYOTROPHIC LATERAL SCLEROSIS WITH A SPECIFIC GENOTYPE-PHENOTYPE CORRELATION"

NGS_analysis performed at Fondazione Besta carried out as part of the study reported at title

opencc-by-4.0Feb 2023View details →

zenodo40/100

Dataset related to article"G507D MUTATION IN FUS GENE CAUSES FAMILIAL AMYOTROPHIC LATERAL SCLEROSIS WITH A SPECIFIC GENOTYPE-PHENOTYPE CORRELATION"

NGS Analyses of patients included in the study reported at title followed at Fondazione Besta

opencc-by-4.0Feb 2023View details →

zenodo40/100

Dataset related to article: "Congenital insensitivity to pain a novel mutation affecting a U12-type intron causes multiple aberrant splicing of SCN9A"

raw data related to article reported at title

opencc-by-4.0Feb 2023View details →

zenodo40/100

Rps19 R67∆ mutation creates a model of Diamond-Blackfan anemia and reveals downstream mediators of p53 pathway.

Diamond-Blackfan anemia (DBA) is a rare bone marrow failure syndrome accompanied by cardiovascular, skeletal, and urogenital abnormalities. Most of the affected individuals carry mutations in ribosomal proteins, including S19 (Rps19), a part of the 40S ribosomal subunit. We developed a transgenic model harboring deletion of conserved Arg 67 in RPS19, which is the site of post-translational modification by protein-arginine methyl transferase family and could show that the defect in Rps19 causes phenotype in perfect overlap with the DBA including hematologic dysfunctions, hypotrophy, intrinsic anemia, severe craniofacial, skeletal, urogenital, cardiovascular, and cerebral abnormalities leading to premature lethality during the adolescence of the mouse. This DBA mouse model exhibited activation of the Trp53 signaling pathway in hematopoietic stem cells (HSCs) leading to reduced erythroid lineage development. Competitive transplantation assays using Rps19-deficient bone marrow cells confirmed that HSCs and their progeny lineages were affected while their differentiation was rescued after inactivation of the tumor suppressor Trp53 showing that the development of the DBA phenotype significantly involves non-canonical components of the p53 signaling pathway in the etiopathogenesis of DBA with the Rps19R67∆ mutation leading to the disrupted hematopoietic hierarchy starting at the stage of short-term repopulating stem cells. The activated p53 pathway was mediated by downstream molecules Zmat3, Phlda3, and Eda2r, whose overall function in the pathology of DBA involve erythroid differentiation blockade coupled with cell proliferation and survival disruption. To conclude, the new DBA model represents a powerful tool for exploring new therapeutic options for DBA.

opencc-by-4.0Apr 2023View details →

dryad40/100

Dataset for: Ace and ace-like genes of invasive redlegged earth mite: Copy number variation, target-site mutations, and their associations with organophosphate insensitivity

This repository contains the scripts and data required to replicate the analyses in Thia et al.'s, "Evolution of an acetylcholinesterase gene complex and its contribution toward organophosphate insensitivity in an invasive mite pest", submitted to Pest Management Science. In this work, Thia et al. use a combination of experimental selection and pool-seq genomic analyses to understand the genetic mechanisms underpinning organophosphate insensitivity in the redlegged earth mite, Halotydeus destructor. There is a special emphasis on disentangling the roles of copy number variation and target-site mutations in the acetylcholinesterase genes, ace, and radiated ace-like genes. There are three major analyses: (1) an FST genome scan to identify outlier loci between alive (insensitive) and dead (sensitive) mites; (2) an analysis of ace copy number variation between alive and dead mites; and (3) an analysis of candidate target-site mutations in the ace gene.

opencc-zeroMay 2023View details →

dryad40/100

On ageing and age-specific effects of spontaneous mutations

Evolutionary theories of ageing assume causal mutations either have beneficial early-life effects which gradually become deleterious with advancing age (antagonistic pleiotropy: AP) or mutations that only have deleterious effects at old age (mutation accumulation: MA). Mechanistically, ageing is predicted to result from damage accumulating in the soma. While this scenario is compatible with AP, it is not immediately obvious how damage would accumulate under MA. In a modified version of the MA theory, it has been suggested that mutations with weakly deleterious effects at young age can also contribute to ageing, if they generate damage that gradually accumulates with age. Mutations with increasing deleterious effects have recently gained support from theoretical work and studies of large-effect mutations. Here we address if spontaneous mutations also have negative effects that increase with age. We accumulate mutations with early-life effects in Drosophila melanogaster across 27 generations and compare their relative effects on fecundity early and late in life. Our mutation accumulation lines on average have substantially lower early-life fecundity compared to controls. These effects were further maintained throughout life, but they did not increase with age. Our results thus suggest that most spontaneous mutations do not contribute to damage accumulation and ageing.

opencc-zeroMay 2023View details →

zenodo40/100

Haplotype-aware reference genome reveals hidden somatic mutations of sweet orange

Filename: ASE_in_five_fruit_development.txt Description: Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.   Filename: Biallelic_genes_haplogenomes.tsv Description: The biallelic genes were identified using the Genespace program.   Filename: Haplogenomes_CENH3_chip_peaks.bw Description: The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.   Filename: Haplogenomes_Control_chip_peaks.bw Description: The peaks generated from Control chip-seq.   Filename: Haplotype_based_79accessions_somatic_variations.vcf Description: The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).   Filename: HaplotypeA_CuteSV.vcf Description: The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.   Filename: HaplotypeA_gene_function_annotation.tsv Description: The gene annotations of haplotype A.   Filename: HaplotypeA_gene_model.gff3 Description: The gene structure model of haplotype A.   Filename: HaplotypeA_genome.fa Description: The genome sequences of haplotype A.   Filename: HaplotypeA_PEPPER_OUTPUT.zip Description: The small variations of sweet orange using the haplotype A as the reference genome.   Filename: HaplotypeA_TEs_annotation.gff3 Description: The TE annotations of haplotype A.   Filename: HaplotypeB_gene_function_annotation.tsv Description: The gene annotations of haplotype B.   Filename: HaplotypeB_gene_model.gff3 Description: The gene structure model of haplotype B.   Filename: HaplotypeB_genome.fa Filename: ASE_in_five_fruit_development.txt Description: Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.   Filename: Biallelic_genes_haplogenomes.tsv Description: The biallelic genes were identified using the Genespace program.   Filename: Haplogenomes_CENH3_chip_peaks.bw Description: The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.   Filename: Haplogenomes_Control_chip_peaks.bw Description: The peaks generated from Control chip-seq.   Filename: Haplotype_based_79accessions_somatic_variations.vcf Description: The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).   Filename: HaplotypeA_CuteSV.vcf Description: The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.   Filename: HaplotypeA_gene_function_annotation.tsv Description: The gene annotations of haplotype A.   Filename: HaplotypeA_gene_model.gff3 Description: The gene structure model of haplotype A.   Filename: HaplotypeA_genome.fa Description: The genome sequences of haplotype A.   Filename: HaplotypeA_PEPPER_OUTPUT.zip Description: The small variations of sweet orange using the haplotype A as the reference genome.   Filename: HaplotypeA_TEs_annotation.gff3 Description: The TE annotations of haplotype A.   Filename: HaplotypeB_gene_function_annotation.tsv Description: The gene annotations of haplotype B.   Filename: HaplotypeB_gene_model.gff3 Description: The gene structure model of haplotype B.   Filename: HaplotypeB_genome.fa Description: The genome sequences of haplotype B.   Filename: HaplotypeB_TEs_annotation.gff3 Description: The TE annotations of haplotype B.   Filename: Single_reference_87accessions_somatic_variations.vcf Description: The small somatic variations generated based on the single reference genome (Haplotype A).   Filename: Somatic_material_RNA_seq_matrix.txt Description: The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).   Filename: ASE_in_five_fruit_development.txt Description: Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.   Filename: Biallelic_genes_haplogenomes.tsv Description: The biallelic genes were identified using the Genespace program.   Filename: Haplogenomes_CENH3_chip_peaks.bw Description: The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.   Filename: Haplogenomes_Control_chip_peaks.bw Description: The peaks generated from Control chip-seq.   Filename: Haplotype_based_79accessions_somatic_variations.vcf Description: The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).   Filename: HaplotypeA_CuteSV.vcf Description: The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.   Filename: HaplotypeA_gene_function_annotation.tsv Description: The gene annotations of haplotype A.   Filename: HaplotypeA_gene_model.gff3 Description: The gene structure model of haplotype A.   Filename: HaplotypeA_genome.fa Description: The genome sequences of haplotype A.   Filename: HaplotypeA_PEPPER_OUTPUT.zip Description: The small variations of sweet orange using the haplotype A as the reference genome.   Filename: HaplotypeA_TEs_annotation.gff3 Description: The TE annotations of haplotype A.   Filename: HaplotypeB_gene_function_annotation.tsv Description: The gene annotations of haplotype B.   Filename: HaplotypeB_gene_model.gff3 Description: The gene structure model of haplotype B.   Filename: HaplotypeB_genome.fa Description: The genome sequences of haplotype B.   Filename: HaplotypeB_TEs_annotation.gff3 Description: The TE annotations of haplotype B.   Filename: Single_reference_87accessions_somatic_variations.vcf Description: The small somatic variations generated based on the single reference genome (Haplotype A).   Filename: Somatic_material_RNA_seq_matrix.txt Description: The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).   Filename: ASE_in_five_fruit_development.txt Description: Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.   Filename: Biallelic_genes_haplogenomes.tsv Description: The biallelic genes were identified using the Genespace program.   Filename: Haplogenomes_CENH3_chip_peaks.bw Description: The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.   Filename: Haplogenomes_Control_chip_peaks.bw Description: The peaks generated from Control chip-seq.   Filename: Haplotype_based_79accessions_somatic_variations.vcf Description: The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).   Filename: HaplotypeA_CuteSV.vcf Description: The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.   Filename: HaplotypeA_gene_function_annotation.tsv Description: The gene annotations of haplotype A.   Filename: HaplotypeA_gene_model.gff3 Description: The gene structure model of haplotype A.   Filename: HaplotypeA_genome.fa Description: The genome sequences of haplotype A.   Filename: HaplotypeA_PEPPER_OUTPUT.zip Description: The small variations of sweet orange using the haplotype A as the reference genome.   Filename: HaplotypeA_TEs_annotation.gff3 Description: The TE annotations of haplotype A.   Filename: HaplotypeB_gene_function_annotation.tsv Description: The gene annotations of haplotype B.   Filename: HaplotypeB_gene_model.gff3 Description: The gene structure model of haplotype B.   Filename: HaplotypeB_genome.fa Description: The genome sequences of haplotype B.   Filename: HaplotypeB_TEs_annotation.gff3 Description: The TE annotations of haplotype B.   Filename: Single_reference_87accessions_somatic_variations.vcf Description: The small somatic variations generated based on the single reference genome (Haplotype A).   Filename: Somatic_material_RNA_seq_matrix.txt Description: The expression matrix of BT_3 and BT_5 (a set of somatic mutation material). Filename: ASE_in_five_fruit_development.txt Description: Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.   Filename: Biallelic_genes_haplogenomes.tsv Description: The biallelic genes were identified using the Genespace program.   Filename: Haplogenomes_CENH3_chip_peaks.bw Description: The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.   Filename: Haplogenomes_Control_chip_peaks.bw Description: The peaks generated from Control chip-seq.   Filename: Haplotype_based_79accessions_somatic_variations.vcf Description: The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).   Filename: HaplotypeA_CuteSV.vcf Description: The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.   Filename: HaplotypeA_gene_function_annotation.tsv Description: The gene annotations of haplotype A.   Filename: HaplotypeA_gene_model.gff3 Description: The gene structure model of haplotype A.   Filename: HaplotypeA_genome.fa Description: The genome sequences of haplotype A.   Filename: HaplotypeA_PEPPER_OUTPUT.zip Description: The small variations of sweet orange using the haplotype A as the reference genome.   Filename: HaplotypeA_TEs_annotation.gff3 Description: The TE annotations of haplotype A.   Filename: HaplotypeB_gene_function_annotation.tsv Description: The gene annotations of haplotype B.   Filename: HaplotypeB_gene_model.gff3 Description: The gene structure model of haplotype B.   Filename: HaplotypeB_genome.fa Description: The genome sequences of haplotype B.   Filename: HaplotypeB_TEs_annotation.gff3 Description: The TE annotations of haplotype B.   Filename: Single_reference_87accessions_somatic_variations.vcf Description: The small somatic variations generated based on the single reference genome (Haplotype A).   Filename: Somatic_material_RNA_seq_matrix.txt Description: The expression matrix of BT_3 and BT_5 (a set of somatic mutation material). Description: The genome sequences of haplotype B.   Filename: HaplotypeB_TEs_annotation.gff3 Description: The TE annotations of haplotype B.   Filename: Single_reference_87accessions_somatic_variations.vcf Description: The small somatic variations generated based on the single reference genome (Haplotype A).   Filename: Somatic_material_RNA_seq_matrix.txt Description: The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).

opencc-by-4.0Nov 2023View details →

dryad40/100

Data from: Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve

<div class="section abstract"> Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organised as bricklayer's graphs, so called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype (GP) maps for RNA secondary structure and the HP model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer's graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes. </div>

opencc-zeroJul 2023View details →

zenodo40/100

Codeletion of 1p and 19q determines distinct gene methylation and expression profiles in IDH-mutated oligodendroglial tumors _ Dataset

Overall design: Genome-wide DNA methylation profiling of oligodendroglial tumors (OTs) and five non tumoral brain tissue (NTBT) samples. The Illumina Infinium Human DNA methylation 450k Beadchip was used to obtain DNA methylation profiles across approximately 450,000 CpGs in tumoral samples. Samples included 46 OTs and 5 NTBT. Bisulphite converted DNA from the 51 samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip Extracted molecule: genomic DNA Platform: Illumina HumanMethylation450 BeadChip (HumanMethylation450_15017482) Label protocol: Standard Illumina Protocol Hybridization protocol: bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation 450K Beadchip using standard Illumina protocol Scan protocol: Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting  Data processing: BeadStudio software v3.2 Data format: IDAT files

opencc-by-4.0Sep 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record