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ABSTRACT:
PROVIDER: PRJEB57249 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Data for published paper
| PRJEB88494 | ENA
Project description:we generated DPHL v2 from 1608 DDA-MS data acquired using Orbitrap mass spectrometers. The data included 586 DDA-MS newly acquired from 17 tissue types, while 1022 files were derived from DPHL v1. DPHL v2 thus comprises data from 24 sample types, including several cancer types . We generated four variants of DPHL v2 to include semi-tryptic peptides and protein isoforms.
2023-01-18 | PXD039313 |
Project description:We describe an optimized microarray method for identifying genome-wide CpG island methylation called Microarray-based Methylation Assessment of Single Samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimised combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared to a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison to previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation. Keywords: Methylation genomic hybridizations.
2006-10-31 | GSE5326 | GEO
Project description:To facilitate new studies in the Amaryllidoideae subfamily, we present AmarylOmicBase, a unified transcriptomic database that integrates assemblies, annotations, and expression profiles from 39 studies, covering 27 species and 4 hybrid cultivars across 13 genera of Amaryllidoideae. The AmarylOmicBase (v2, https://doi.org/10.5281/zenodo.20349158) consists of de novo assemblies generated from published raw data using Trinity or IsoSeq workflows and provides standardized functional annotation and quantitative expression datasets. Preprint - https://doi.org/10.1101/2025.11.24.690262
2026-06-03 | GSE329951 | GEO
Project description:To facilitate new studies in the Amaryllidoideae subfamily, we present AmarylOmicBase, a unified transcriptomic database that integrates assemblies, annotations, and expression profiles from 39 studies, covering 27 species and 4 hybrid cultivars across 13 genera of Amaryllidoideae. The AmarylOmicBase (v2, https://doi.org/10.5281/zenodo.20349158) consists of de novo assemblies generated from published raw data using Trinity or IsoSeq workflows and provides standardized functional annotation and quantitative expression datasets. Preprint - https://doi.org/10.1101/2025.11.24.690262
2026-06-03 | GSE330014 | GEO
Project description:To facilitate new studies in the Amaryllidoideae subfamily, we present AmarylOmicBase, a unified transcriptomic database that integrates assemblies, annotations, and expression profiles from 39 studies, covering 27 Amaryllidoideae species and 4 hybrid cultivars across 13 genera of Amaryllidoideae. The AmarylOmicBase (v2, https://doi.org/10.5281/zenodo.20349158) includes de novo assemblies generated from published raw data using Trinity or IsoSeq workflows and provides standardized functional annotation (trinotate) and quantitative expression datasets. Preprint - https://doi.org/10.1101/2025.11.24.690262
2026-06-03 | GSE331457 | GEO
Project description:To facilitate new studies in the Amaryllidoideae subfamily, we present AmarylOmicBase, a unified transcriptomic database that integrates assemblies, annotations, and expression profiles from 39 studies, covering 27 species and 4 hybrid cultivars across 13 genera of Amaryllidoideae. The AmarylOmicBase (v2, https://doi.org/10.5281/zenodo.20349158) consists of de novo assemblies generated from published raw data using Trinity or IsoSeq workflows and provides standardized functional annotation and quantitative expression datasets. Preprint - https://doi.org/10.1101/2025.11.24.690262
2026-06-03 | GSE329957 | GEO