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30 results for “Bisulfite sequencing data”
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Data and code for "Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR"
<p>This data set provides data files and R code to accompany the article <em>Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR</em> published by F1000Research.</p> <p>The data consists of Reduced Representation BS-seq methylation profiles of epithelial populations from the mouse mammary gland, with n=2 biological replicates for each of three cell populations.</p> <p>RNA-seq expression profiles of luminal and basal mammary epithelial populations are also provided.</p> <p>The R code undertakes an differential methylation analysis of the BS-seq profiles and demonstrates a strong negative correlation between the differential methylation and differential expression results.</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
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List of known SNP positions (based on SNP chip data) for base quality score recalibration of alignments for whole-genome resequencing and whole-genome bisulfite sequencing data from great tits (Parus major)
<p>The profiling of epigenetic marks like DNA methylation has become a central aspect of studies in evolution and ecology. Bisulfite sequencing is commonly used for assessing genome-wide DNA methylation at single nucleotide resolution but these data can also provide information on genetic variants like single nucleotide polymorphisms (SNPs). However, bisulfite conversion causes unmethylated cytosines to appear as thymines, complicating the alignment and subsequent SNP calling. Several tools have been developed to overcome this challenge, but there is no independent evaluation of such tools for non-model species, which often lack genomic references. Here, we used whole-genome bisulfite sequencing (WGBS) data from four female great tits (<i>Parus major</i>) to evaluate the performance of seven tools for SNP calling from bisulfite sequencing data. We used SNPs from whole-genome resequencing data of the same samples as baseline SNPs to assess common performance metrics like sensitivity, precision, and the number of true positive, false positive, and false negative SNPs for the full range of variant and genotype quality values. We found clear differences between the tools in either optimizing precision (Bis-SNP), sensitivity (biscuit), or a compromise between both (all other tools). Overall, the choice of SNP caller strongly depends on which performance parameter should be maximized and whether ascertainment bias should be minimized to optimize downstream analysis, highlighting the need for studies that assess such differences.</p>
List of known SNP positions (based on SNP chip data) for base quality score recalibration of alignments for whole-genome resequencing and whole-genome bisulfite sequencing data from great tits (Parus major)
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Data from: Detection of somatic epigenetic variation in Norway spruce via targeted bisulfite sequencing
Epigenetic mechanisms represent a possible mechanism for achieving a rapid response of long‐lived trees to changing environmental conditions. However, our knowledge on plant epigenetics is largely limited to a few model species. With increasing availability of genomic resources for many tree species, it is now possible to adopt approaches from model species that permit to obtain single‐base pair resolution data on methylation at a reasonable cost. Here, we used targeted bisulfite sequencing (TBS) to study methylation patterns in the conifer species Norway spruce (Picea abies). To circumvent the challenge of disentangling epigenetic and genetic differences, we focused on four clone pairs, where clone members were growing in different climatic conditions for 24 years. We targeted >26.000 genes using TBS and determined the performance and reproducibility of this approach. We characterized gene body methylation and compared methylation patterns between environments. We found highly comparable capture efficiency and coverage across libraries. Methylation levels were relatively constant across gene bodies, with 21.3 ± 0.3%, 11.0 ± 0.4% and 1.3 ± 0.2% in the CG, CHG, and CHH context, respectively. The variance in methylation profiles did not reveal consistent changes between environments, yet we could identify 334 differentially methylated positions (DMPs) between environments. This supports that changes in methylation patterns are a possible pathway for a plant to respond to environmental change. After this successful application of TBS in Norway spruce, we are confident that this approach can contribute to broaden our knowledge of methylation patterns in natural tree populations.
Melanoma Cell Lines SLC22A17 Bisulfite Sequencing Raw Data
<p>Bisulfite sequencing raw data of SLC22A17 gene obtained in melanoma cell lines. The data will be published in the article entitled "<span>Identification of <em>SLC22A17</em> DNA methylation hotspot as a potential biomarker in cutaneous melanoma</span>" submitted to Journal of Translational Medicine</p>
Data from: Detection of somatic epigenetic variation in Norway spruce via targeted bisulfite sequencing
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Reduced representation bisulfite sequencing data of mouse genome
GEO Series GSE76238. Mus musculus. 4 samples. Type: Methylation profiling by high throughput sequencing.
Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation [Array]
GEO Series GSE169218. Mus musculus; Rattus norvegicus; Homo sapiens. 80 samples. Type: Methylation profiling by genome tiling array.
Genome-wide Regulation of 5hmC, 5mC and Gene Expression by Tet1 Hydroxylase in Mouse Embryonic Stem Cells (bisulfite sequencing data)
GEO Series GSE28533. Mus musculus. 4 samples. Type: Methylation profiling by high throughput sequencing.
Analyzing whole genome bisulfite sequencing data from highly divergent genotypes
GEO Series GSE87101. Mus musculus. 8 samples. Type: Methylation profiling by high throughput sequencing.
methylGrapher: Genome-Graph-Based Processing of DNA Methylation Data from Whole Genome Bisulfite Sequencing
GEO Series GSE261315. Homo sapiens. 10 samples. Type: Methylation profiling by high throughput sequencing.
Methylated DNA is over-represented in whole-genome bisulfite sequencing data
GEO Series GSE58217. Arabidopsis thaliana. 18 samples. Type: Methylation profiling by high throughput sequencing.
ltra-low-input native ChIP-seq and Whole genome bisulfite Sequencing data of four types mouse spermatogenesis
GEO Series GSE137744. Mus musculus. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia: Whole Genome oxidative bisulfite sequencing data
GEO Series GSE140271. Mus musculus. 48 samples. Type: Methylation profiling by high throughput sequencing.
Whole genome bisulfite sequencing data of Russian wheat aphid biotypes SA1 and SAM
GEO Series GSE119504. Diuraphis noxia. 6 samples. Type: Methylation profiling by high throughput sequencing.
Differences in DNA Methylation between Human Neuronal and Glial Cells are Concentrated in Enhancers and non-CpG Sites [Enhanced Reduced Representation Bisulfite Sequencing data]
GEO Series GSE50852. Homo sapiens. 6 samples. Type: Methylation profiling by high throughput sequencing.
Reduced representation bisulfite sequencing (RRBS) data from cattle monocytes, fibroblasts and sperm cells
GEO Series GSE102169. Bos taurus. 6 samples. Type: Methylation profiling by high throughput sequencing.
Whole Genome Bisulfite Sequencing Data of Botrytis cinerea
GEO Series GSE131718. Botrytis cinerea. 12 samples. Type: Methylation profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.