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Supporting data for "The methylome of Biomphalaria glabrata and other mollusks: enduring modification of epigenetic landscape and phenotypic traits by a new DNA methylation inhibitor"
<p>Methylome of the fresh water snail <em>Biomphalaria glabrata</em>. DNA was extracted from the feet of 10 individuals of <em>B. glabrata</em> originally isolated from Brazil. These snails have been cultivated in the laboratory since 1960. Tissue were grinded at 4°C and incubated in 1 ml volume of lysis buffer (20 mM TRIS pH 8; 1 mM EDTA; 100 mM NaCl; 0.5% SDS), with 0.3 mg of proteinase K at 55°C for 1 night. Afterwards, lysate was purified with phenol-chloroform and DNA was isopropanol precipitated. The extracted DNA (around 138ng/µL) was poled in equivalent amounts and Whole Genome Bisulfite Sequencing was done by GATC-biotech (www.gatc-biotech.com). The principle of this treatment is to convert non-methylated cytosines of gDNA into deoxy-uracil, whereas methylated cytosines remain intact. WGBS was done according to the Lister protocol (sequence 2 forward strands only). The reference genome (Biomphalaria-glabrata-BB02_SCAFFOLDS_BglaB1.fa) and annotation (Biomphalaria-glabrata-BB02_BASEFEATURES_BglaB1.3.gff3) used in this project are available on VectorBase (https://www.vectorbase.org/). To align our short reads, we chose to use two specific bisulfite mapping tools, BSMAP 1.0.0 (https://code.google.com/p/bsmap/) and Bismark 0.10.2 (www.bioinformatics.babraham.ac.uk /projects/bismark/), to compare their efficiency and convenience to finally work with the more suitable one on our datasets. IGV (Interactive Genomics Viewer, https://www.broadinstitute.org/igv/) was used to visualized final alignments.<br> BSMAP performed better than Bismark and was used for downstream analyses. Without default parameters alignement efficiency for BSMAP is 47.1%, allowing for 2 mismatches increases it to 55.6%. Methylation occurs predominantly in CpGs. (C methylated in CpG context: 12.4%, C methylated in CHG context: 0.5%, C methylated in CHH context: 0.5%) The major part of CpG sites, 95.7% were unmethylated, of the remaining 4.3% of CpG sites around 3.8% had low methylation, and 0.5% were completely methylated. Methylation is of the mosaic type. Methylation is relatively low with 1.2% of total cytosines. Our analyses suggested that conserved genes and genes with stable expression are localized in high methylated regions of the genome. Finally, we see that repetitive sequences were predominantly situated in low methylated regions of <em>B. glabrata</em>. </p> <p>Wiggle files were generated for CpG pairs only.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
Data for methylome sequencing: Enriching and Profiling Methylomes for Tumor Classification and Liquid Biopsies
<p>We benchmarked and demonstrated the versatility of FLEXseq (Fragment Ligation EXclusive methylation sequencing) across different sample types: genomic DNA from the K562 (leukemia) cell line, DNA mix-in titrations of four immune cell types (B cells, T cells, monocytes, and neutrophils), DNA titrations of three cancer cell lines (breast invasive carcinoma [BRCA], colon adenocarcinoma [COAD], and glioblastoma [GBM]) mixed with those four immune cell mixtures separately, input titrations of cell-free (cf) DNA from one plasma sample and DNA from formalin-fixed paraffin-embedded (FFPE) tissues, cfDNA from 106 cerebrospinal fluids (CSF) and 42 other body fluids, and DNA from 37 FFPE tissues.</p> <p>We sequenced all the samples mentioned above using FLEXseq. Paired-end reads were quality and length trimmed with cutadapt version 3.5, and all high-quality sequencing reads were then aligned to the hg38 reference genome using Bismark v0.23.0. We then filtered out reads with unmethylated cytosine in the non-CpG context with filter_non_conversion function. Next, we used the bismark_methylation_extractor function to extract the methylation calls (removing single-nucleotide polymorphisms [SNP]).</p> <p>We also used the bam2pat function from wgbs_tools, to convert bam files into .pat files for deconvolution, keeping reads covering at least three CpG sites. The .pat files preserve fragment-level data and were de-identified by removing SNPs using the mask_pat function. </p> <p>We used CNVkit (v0.9.10) to analyze and visualize genome-wide copy numbers. Our inputs into CNVkit were Bismark/Bowtie 2 aligned BAM files deduplicated by Bismark based on end positions and fragment lengths. We then generated log2copy ratio plots for all body fluid and FFPE samples based on the pooled reference and visualized them across all bins using the DNAcopy R package.</p> <p> </p>
Insights from a Methylome-Wide Association Study of Antidepressant Exposure
<p>Results datasets from the paper: </p> <p><strong>Insights from a Methylome-Wide Association Study of Antidepressant Exposure</strong></p> <p><strong>Methylome-wide association study (MWAS) summary statistics: </strong></p> <p><strong>All individuals: </strong></p> <p>MWAS of prescription-derived antidepressant exposure (n = 7,951): </p> <p> <a href="https://zenodo.org/uploads/14203230" target="_blank" rel="noopener noreferrer">GRM_unadjusted_antidep_pheno1_clean_appt_MOA_ORM_residph_standard_06_10.moa</a></p> <p>MWAS of self-reported antidepressant exposure (n = 16,531):</p> <p> <a href="https://zenodo.org/uploads/14203230" target="_blank" rel="noopener noreferrer">GRM_unadjusted_selfrep_pheno3_MOA_ORM_residph_standard_06_10.moa</a></p> <p><strong>Individuals with a lifetime status of Major Depressive Disorder (MDD, MDD-subgroup): </strong></p> <p>MWAS of prescription-derived antidepressant exposure (n = 792)</p> <p><a href="https://zenodo.org/uploads/14203230" target="_blank" rel="noopener noreferrer">GRM_unadjusted_antidep_pheno2_clean_appt_MOA_ORM_residph_standard_06_10.moa</a></p> <p>MWAS of self-reported antidepressant exposure (n = 2,268):</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/GRM_unadjusted_selfrep_pheno4_MOA_ORM_residph_standard_06_10.moa/content" target="_blank" rel="noopener noreferrer">GRM_unadjusted_selfrep_pheno4_MOA_ORM_residph_standard_06_10.moa</a></p> <p><strong>Downstream functional analyses: </strong></p> <p><strong>Differentially methylated region (DMRFF) analysis:</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">antidep_pheno1_clean_appt_dmr_res.tsv</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">selfrep_pheno3_dmr_res.tsv</a></p> <p><strong>GO Biological Pathway (msigdbr) enrichment:</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_PD.txt</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_SR.txt</a></p> <p><strong>SynGo pathway (web-portal) enrichment:</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_annotations_matching_user_input_PD.xlsx</a></p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_ontologies_with_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_ontologies_with_annotations_matching_user_input_PD.xlsx</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_annotations_matching_user_input_SR.xlsx</a></p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/syngo_ontologies_with_annotations_matching_user_input_PD.xlsx/content" target="_blank" rel="noopener noreferrer">syngo_ontologies_with_annotations_matching_user_input_SR.xlsx</a></p> <p><strong>Tissue enrichment (FUMA GENE2FUNC):</strong></p> <p>Prescription-derived antidepressant exposure:</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_PD.txt</a></p> <p>Self-report antidepressant exposure: </p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">gtex_v8_ts_DEG_SR.txt</a></p> <p><strong>Antidepressant exposure methylation profile score (MPS): </strong></p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">GS_AD_MRS_weights.txt</a></p> <p><strong>Source Data:</strong></p> <p>Source data for Figures and Supplementary Figures which do not represent individual-level data.</p> <p><a href="https://zenodo.org/api/records/14203230/draft/files/gtex_v8_ts_DEG_PD.txt/content" target="_blank" rel="noopener noreferrer">source_data.xlsx </a></p> <p> </p>
Identification of age-related CpG sites from longitudinal avian methylomes
<p>Sex chromosomes are thought to play an important role in sex-dependent ageing, yet they are neglected in epigenetic aging research. We identified genome-wide age-related CpG (AR-CpG) sites in two avian species (zebra finch and jackdaw) and found AR-CpG sites to be overrepresented on the haploid, female-specific W chromosome in both species, and on the Z chromosome in the zebra finch. </p>
Summary statistics for: Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans
<p>Summary statistics of single POE-CpG based and POE-CpG co-methylation module based phenome-wide association analyses for the manuscript "Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans"</p>
Dosage compensation and sexual conflict in female heterogametic methylomes
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Identification of age-related CpG sites from longitudinal avian methylomes
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Flongle data for KnowYourCG: Facilitating base-level sparse methylome interpretation
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Quantitative translation of dog-to-human aging by conserved remodeling of the DNA methylome
<p>This repository contains processed data files, custom python scripts and jupyter notebooks containing analyses accompanying the manuscript, <em>Quantitative translation of dog-to-human aging by conserved remodeling of the DNA methylome.</em></p>
Epigenetic Profiling of Social Communication Trajectories and Co-occurring Mental Health Problems: A Prospective, Methylome-wide Association Study
<p>While previous studies suggest that both genetic and environmental factors play an important role in the development of autism-related traits, little is known about potential biological mechanisms underlying these associations. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we examined prospective associations between DNA methylation (DNAm: N-birth=804, N-age7=877) and trajectories of social communication deficits (8-17 years). Methylomic variation at three loci across the genome (false discovery rate=0.048) differentiated children following high (n=80) versus low (n=724) trajectories of social communication deficits. This differential DNAm was specific to the neonatal period and not observed at age 7. Associations between DNAm and trajectory membership remained robust after controlling for co-occurring mental health problems (i.e., hyperactivity/inattention, conduct problems). The three loci identified at birth were not replicated in the Generation R Study. However, to the best of our knowledge, ALSPAC is the only study to date that is prospective enough to examine DNAm in relation to longitudinal trajectories of social communication deficits from late childhood to late adolescence. Although the present findings might point to potentially novel sites that differentiate between a high versus low trajectory of social communication deficits, the results should be considered tentative until further replicated.</p> <p>This dataset contains summary statistics for the methylome-wide association study using DNAm data collected from individuals at birth.</p> <p>Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a zenodo DOI generated for you go to ewascatalog.org/upload</p>
Mappability of the mouse and human genomes and methylomes with Umap and Bismap
<p>This dataset consists of single-read mappability (Bed files) and multi-read mappability (Wiggle files) of human and mouse genomes and methylomes (bisulfite-converted genome). We provide mappability information for the two most recent assemblies of each organism, and for four different read lengths (24 bp, 36 bp, 50 bp, and 100 bp).</p> <p> </p>
Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health
<p>This repository houses fully-adjusted methylome-wide association study (MWAS) summary statistics for 4,231 SomaScan protein measurements. These were generated as part of the study titled ‘Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health’ by Gadd <em>et al</em>. The Stratifying Resilience and Depression Longitudinally (STRADL) cohort used in this study is a subset of individuals from Generation Scotland: The Scottish Family Health Study. There were 744 individuals with complete protein and DNA methylation measurements available at 772,619 CpG probes. MWAS were performed with protein residuals as the outcome and DNA methylation as the exposure, using the Omics-data-based complex trait analysis (OSCA) software.</p> <p>Fully-adjusted models were run using M-values that were adjusted for age, sex, DNA methylation-derived immune cell estimates, depression status, DNA methylation batch and set, body mass index and a DNA methylation-derived smoking score. Protein levels were rank-based inverse normalised and scaled to have a mean of 0 and standard deviation of 1. Protein levels were residualised by age, sex, available pQTLs, technical covariates and 20 genetic principal components.</p> <p>Four of the 4,235 protein MWAS models did not converge (15509-2 - NAGLU, 15584-9 - CFHR2, 4407-10 - MST1 and 6402-8 - PILRA). Therefore, summary statistics are provided for 4,231 protein levels.</p> <p>Each protein MWAS summary statistics file has been saved with the following naming system: "MWAS_SeqId_Protein_gene.csv". For example, the protein with gene name CRYBB2 and SeqId 10000-28 has the following file name: "MWAS_10000-28_CRYBB2.csv".</p> <p>The SeqIds, UniProt codes, gene names and full UniProt names can be found in "annotation_formatted_for_paper.csv" and the full summary statistics are found within "compressed-protein-ewas.tar.gz".</p> <p>Please contact either <a href="mailto:riccardo.marioni@ed.ac.uk">riccardo.marioni@ed.ac.uk</a> or <a href="mailto:danni.gadd@ed.ac.uk">danni.gadd@ed.ac.uk</a> for any queries. All code is available at the following Github repository: <a href="https://github.com/DanniGadd/Epigenome-and-phenome-wide-study-of-brain-health-outcomes">https://github.com/DanniGadd/Epigenome-and-phenome-wide-study-of-brain-health-outcomes</a>.</p>
Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors
<p><span><strong>Purpose</strong>:</span><span> To explore pituitary tumors by methylome and transcriptome signatures in a heterogeneous ethnic population. </span></p> <p><span><strong>Design</strong>: Retrospective cross-sectional study.</span></p> <p><span><strong>Patients and Methods</strong>: Clinicopathological features, methylome, and transcriptome were evaluated in pituitary tumors from 77 patients (61% women, age: 12-72 years)followed due to functioning (FPT: GH-secreting n=18, ACTH-secreting n=14) and non-functioning pituitary tumors (NFPT, n=45) at Ribeirao Preto Medical School, University of Sao Paulo. </span></p> <p><span><strong>Results</strong>: </span><span>U</span><span>nsupervised hierarchical clustering analysis (UHCA) of methylome </span><span>(n=77) </span><span>and transcriptome </span><span>(n=65 out of 77)</span><span> revealed three clusters each: one enriched by FPT, other by NFPT, and </span><span>another by </span><span>ACTH-secreting</span><span> and NFPT. Comparison between each omics-derived cluster identified 3,568 and 5,994 </span><span>differentially methylated and </span><span>expressed genes, respectively, </span><span>which were associated with each other, with tumor clinical presentation, and with 2017 and 2022 WHO classifications. UHCA considering 11 transcripts related to pituitary development/differentiation also supported three clusters: <em>POU1F1</em>-driven somatotroph, <em>TBX19</em>-driven </span><span>corticotroph, and</span><span> <em>NR5A1</em>-driven gonadotroph adenomas, with rare exceptions (</span><em><span>NR5A1</span></em><span> expressed in few GH-secreting and corticotroph-silent adenomas; <em>POU1F1</em> in few ACTH-secreting adenomas; and <em>TBX19</em> in few NFPTs). </span></p> <p><span><strong>Conclusions</strong>: This large heterogenic ethnic Brazilian cohort confirms that integrated methylome and transcriptome signatures classify FPT and NFPT, which are associated with clinical presentation and tumor invasiveness. Moreover, the cluster NFPT/ACTH-secreting adenomas raises interest regarding tumor heterogeneity, supporting the challenge raised by the 2017 and 2022 WHO definitions regarding the discrepancy, in rare cases, between clinical presentation and pituitary lineage markers. Finally, making our data publicly available enables further studies to validate genes/pathways involved in pituitary tumor pathogenesis and prognosis.</span></p>
Data for "Brain cell-type shifts in Alzheimer's disease, autism and schizophrenia interrogated using methylomics and genetics"
<p>Data for "Genetic and methylomic interrogation of brain cell-type shifts in autism, schizophrenia, and Alzheimer’s disease" (Yap et al. 2023).</p> <p>Source data from ROSMAP, LIBD and UCLA_ASD post-mortem brain datasets.</p> <p>This data repository contains 3 files:</p> <p><strong>220819_Supplementary_Tables.xlsx</strong></p> <p>Supplementary Tables for the manuscript:</p> <ol> <li>Supplementary Table 1: Comparison of CTP deconvolution methods in the ROSMAP dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 2: Comparison of CTP deconvolution methods in the LIBD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 3: Comparison of CTP deconvolution methods in the UCLA_ASD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 4: Deconvolved brain CTPs (raw), with covariates.</li> <li>Supplementary Table 5: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and raw PGS for all ancestries. Comp* indicates compositionally-aware principal components of the CTP data; *pgs_raw indicates PGS calculated for using genotyping across all ancestries. This table has a total of n=1,098 across all ancestries, including n=885 EUR. After applying a rel<0.05 threshold on the n=885 EUR, there were n=878 EUR which were used in the PGS analysis so that population stratification PCs did not simply capture family structure.</li> <li>Supplementary Table 6: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and standardised PGS for n=878 Europeans. *pgs_raw indicates unstandardised PGS, PC* indicates genotyping PCs within the European dataset, *_PGS indicates standardised PGS within the European subset.</li> <li>Supplementary Table 7: Deconvolved brain CTPs (clr-transform, offset 1e-3), adjusted for oligodendrocyte proportions, with covariates.</li> <li>Supplementary Table 8: Deconvolved brain CTPs (raw), adjusted for oligodendrocyte proportions, with covariates.</li> </ol> <p><strong>20220108_maf05_gwas_ctp.tar.gz</strong></p> <p>GWAS summary statistics for the 7 brainCTPs (clr-transformed): Exc, Inh, Astro, Endo, Micro, Oligo, OPC</p> <p>METAL output format:<br> MarkerName: SNP<br> Allele1<br> Allele2<br> Freq1: Allele1 frequency<br> FreqSE: frequency standard error<br> MinFreq: minimum Allele1 frequency in meta-analysis<br> MaxFreq: maximum Allele1 frequency in meta-analysis<br> Effect: effect size<br> StdErr: standard error of effect size<br> P-value: calculated in inverse variance weighted meta-analysis<br> Direction: directions of effects across the 3 datasets<br> HetISq: heterozygosity I-squared<br> HetChiSq: heterozygosity chi-squared<br> HetDf: heterozygosity degress of freedom<br> HetPVal: heterozygosity test p-value</p> <p><strong>20220108_maf05_gwas_ctp_pc.tar.gz</strong></p> <p>GWAS summary statistics for the 5 CTP_PCs</p> <p>METAL output format (see above)</p> <p> </p> <p><strong>Source data</strong>:</p> <p>ROSMAP: Raw methylation .idat files were obtained from Synapse accession syn7357283. Whole genome sequencing .vcf files (variants jointly called with MSBB and Mayo studies) were obtained from Synapse accession syn11707420.</p> <p>LIBD: Raw methylation .idat files were obtained from GEO accession GSE74193. SNP genotypes were downloaded from dbGaP accession phs000417.v2.p1.</p> <p>UCLA-ASD: The processed methylation beta matrix was downloaded from Synapse accession syn8263588. SNP genotypes were downloaded from Synapse accession syn10537134.</p> <p>GWAS summary statistics are available at: 10.5281/zenodo.7604231</p> <p>Code is available on GitHub: gandallab/brain_CTP_deconv</p>
Multi-Omic Approach Associates Blood Methylome with Bronchodilator Drug Response in Pediatric Asthma: Summary statistics
<p>We conducted an epigenome-wide association study of bronchodilator drug response (BDR) in a discovery and validation design. The discovery phase was focused on 221 African American children with asthma. The association between DNA methylation and BDR was conducted using the limma package correcting for age, sex, ancestry, and tissue heterogeneity. Summary statistics include the output from toptable limma function and CpG annotation (based on Illumina EPIC Manifest file v 1.0 B4) organized in the following columns:</p> <ul> <li>Probe: Probe ID</li> <li>Chr: chromosome</li> <li>Pos: genomic position based on GRCh37/hg19</li> <li>Gene: Gene annotation based on Illumina EPIC Manifest file v 1.0 B4</li> <li>logFC: estimate of the log2-fold-change corresponding to the effect or contrast</li> <li>SE: standard error</li> <li>AveExpr: average log2-expression for the probe over all arrays and channels</li> <li>t: moderated t-statistic</li> <li>P.value: raw p-value</li> <li>FDR: adjusted p-value by false discovery rate</li> <li>B: log-odds that the gene is differentially expressed</li> <li>Problem: Flagged potentially problematic probes</li> </ul>
MIMOSA: A resource consisting of improved methylome imputation models increases power to identify CpG site-phenotype associations
<p>MIMOSA DNA methylation prediction models, set up for MWAS. To run MWAS with this resource, see the tutorial here: <a href="https://github.com/ChongWuLab/MIMOSA">https://github.com/ChongWuLab/MIMOSA</a></p>
Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors
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Different DNA methylome, transcriptome and histological features in uterine fibroids with and without MED12 mutations
<p><span><span><span><span>Somatic mutations in Mediator complex subunit 12 (MED12m) have been reported as a biomarker of uterine fibroids (UFs). However, the role of MED12m is still unclear in the pathogenesis of UFs. Therefore, we investigated the differences in DNA methylome, transcriptome, and histological features between MED12m-positive and -negative UFs. </span></span></span></span><span><span><span><span>DNA methylomes and transcriptomes were obtained from MED12m-positive and -negative UFs and myometrium, and hierarchically clustered. Differentially expressed genes in comparison with the myometrium and co-expressed genes detected by weighted gene co-expression network analysis were subjected to gene ontology enrichment analyses. The amounts of collagen fibers and the number of blood vessels and smooth muscle cells were histologically evaluated. </span></span></span></span><span><span><span><span>Hierarchical clustering based on DNA methylation clearly separated the myometrium, MED12m-positive, and MED12m-negative UFs. MED12m-positive UFs showed the increased activities of extracellular matrix formation, whereas MED12m-negative UFs had the increased angiogenic activities and smooth muscle cell proliferation. </span></span></span></span><span><span><span><span>The MED12m-positive and -negative UFs had different DNA methylation, gene expression, and histological features.</span></span></span></span> <span><span><span><span>The MED12m-positive UFs form the tumor with a rich extracellular matrix and poor blood vessels and smooth muscle cells compared to the MED12m-negative UFs, suggesting MED12 mutations affect the tissue composition of UFs.</span></span></span></span></p>
Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
First Live Birth Rate With eSET After Preimplantation Methylome Screening (PIMS) Versus Conventional In-vitro Fertilization
ClinicalTrials.gov study NCT05442125. IPD Sharing: NO. Countries: 2. Publications: 14.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
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.
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.
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.
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.