71dd06ea-cf06-40d4-a963-00aafdd4765e - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001015400 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:DNA WGS Short Read Sequence (Illumina NovaSeq) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"
| EGAD00001015399 | EGA
Project description:Single cell RNA-profiling in tandem with short-read sequencing (SR-scRNA-seq) has revolutionized the field of transcriptomics, permitting a highly granular view on cellular blood and tissue composition and the construction of human cell atlases. However, discrimination between various transcript isoforms remains challenging. Here we developed single cell long-read isoform sequencing (scLIS-seq), a scRNA-seq workflow based on Smart-seq3xpress (SS3X) cDNA generation and Oxford Nanopore Technologies PromethION sequencing. Using scLIS-seq, we profiled the long-read transcriptomes of Jurkat and HEK293T cells and compared its performance to SS3X starting from the identical cDNA. This dataset refers to the raw and processed data of the Smart-seq3xpress and scLIS-seq experiments of the Jurkat and HEK293T cells.
2025-12-31 | E-MTAB-15047 | biostudies-arrayexpress
Project description:The Long-read POG dataset comprises a cohort of 189 patient tumours and 41 matched normal samples sequenced using the Oxford Nanopore Technologies PromethION platform. This dataset from the Personalized Oncogenomics (POG) program and the Marathon of Hope Cancer Centres Network includes accompanying DNA and RNA short-read sequence data, analytics, and clinical information. We show the potential of long-read sequencing for resolving complex cancer-related structural variants, viral integrations, and extrachromosomal circular DNA. Long-range phasing of variants facilitates the discovery of allelically differentially methylated regions (aDMRs) and allele-specific expression, including recurrent aDMRs in the cancer genes RET and CDKN2A. Germline promoter methylation in MLH1 can be directly observed in Lynch syndrome. Promoter methylation in BRCA1 and RAD51C is a likely driver behind patterns of homologous recombination deficiency where no driver mutation was found. This dataset demonstrates applications for long-read sequencing in precision medicine, and is available as a resource for developing analytical approaches using this technology.
2024-10-15 | GSE270257 | GEO
Project description:This longitudinal study analyzed whole-blood DNA methylation profiles in adults classified as normoglycemic, prediabetic, or type 2 diabetes mellitus (T2DM). Oxford Nanopore PromethION long-read sequencing (PCR-free ligation preparation) was used to generate genome-wide CpG methylation data at baseline and at six-year follow-up. The dataset includes raw nanopore reads (available in SRA) and processed methylation outputs, including CpG-wise differential methylation, methylation summaries, promoter/CpG island annotation, HOMER functional annotation, and gene–pathway mapping
2026-04-20 | GSE311226 | GEO
Project description:Purpose: To generate a reference long-read transcriptomic data set for use in developing new analysis pipelines and comparing their performance with existing methods. Synthetic “sequin” RNA standards (Hardwick et al. 2016) were sequenced using the Oxford Nanopore Technologies (ONT) GridION platform.
2020-06-18 | GSE151984 | GEO
Project description:Purpose: Nanopore sequencing is a new generation of Nanopore-based single-molecule real-time electrical signal sequencing technology for full-length transcriptome sequencing. We use Oxford Nanopore Technology (ONT) three-generation full-length transcriptome sequencing to detect all genetic structural changes in the transcriptome of tumor endothelial cells (TECs) 2H-11 and normal endothelial cells (NECs) SVEC4-10. Methods: The mRNA profiles of three samples of mouse tumor endothelial cells (TECs) 2H-11 and three samples of mouse normal endothelial cells (NECs) were generated by Oxford Nanopore Technologies long read processing, using PromethION. Differential expression analysis of six samples was performed using the DESeq2 R package (1.6.3). Genes with a FDR < 0.01 and foldchange ≥ 2 found by DESeq2 were assigned as differentially expressed. Gene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was implemented by the GOseq R packages. KOBAS software was used to test the statistical enrichment of (DEGs) in KEGG pathways. Results: In mouse tumor endothelial cells 2H-11,1847 genes are up-regulated and 1202 genes are down-regulated. According to the Gene ontology (GO) enrichment analysis of differentially expressed genes (DEGs), we found that different functional trends related to metabolic processes, developmental processes, localization, immune system processes, and locomotion are the main reasons for the differences. DEGs are mainly enriched in signal pathways related to cancer, immunity and metabolism, involving Pathways in cancer, Antigen processing and presentation, Proteoglycans in cancer, Focal adhesion, MAPK signaling pathway, Protein digestion and absorption, ECM-receptor interaction, PI3K-Akt signaling pathway and Glutathione metabolism. We also obtained the structural variation of transcripts such as alternative splicing, gene fusion, and alternative polyadenylation and accurately quantified the expression of the transcript. Conclusions: Differentially expressed genes (DEGs) in key pathways may be potential diagnostic markers or therapeutic targets of TECs. Our study could help better understand the molecular mechanisms of tumor endothelial cells (TECs) involved in tumorigenesis and development. And our data also provide useful genetic resources for improving the genome and transcriptome annotations of TECs and normal endothelial cells (NECs).
2021-12-19 | GSE191094 | GEO
Project description:E18 mouse brain single cell profiling using the 10x Genomics Chromium instrument workflow with either Illumina short read sequencing for the standard gene profiling and Nanopore PromethION long read sequencing for isoform profiling.
2020-07-10 | GSE130708 | GEO
Project description:We have used the genetic resources of Arabidopsis thaliana to generate mutant lines that have reactivated TE expression. We used these lines with long-read Oxford Nanopore sequencing technology to capture Transposable Element (TE) mRNAs for TE transcript annotation.
2020-07-17 | GSE145066 | GEO
Project description:Mouse brain 10x Genomics Visium Spatial Transcriptomics profiles sequence either with Illumina for standard profiling and Nanopore promethION long read sequencer for full-length profiling.
2020-08-26 | GSE153859 | GEO
Project description:Alternative splicing significantly contributes to transcriptome complexity and has critical implications for cellular functions. Recent advancements in single-cell isolation and capture techniques have enabled high-throughput quantification of gene expression at single-cell resolution. Long-read sequencing technologies can further be combined with single-cell technologies and enable an unambiguous identification of complete exon structures. Several computational methods have been developed to specifically address bioinformatics challenges associated with the processing of long read scRNA-seq data. Evaluating and comparing these computational methods becomes crucial. The goal of this study was to benchmark state-of-the-art computational tools for single-cell and spatial long-read transcriptomics. The scRNA-seq data were generated from two tumors developed by a mouse model, and designated as MPNST1 and MPNST2. Data were obtained by using the 10X Genomics technology, then generating sequencing libraries using either Illumina, Oxford Nanopore Technology (ONT) or scNaUmi-Seq protocols. Raw data were obtained after sequencing the libraries on Illumina, MinION or PromethION sequencing platforms. The two Illumina data were uploaded as part of the related submission E-MTAB-14222, with sample MPNST1 corresponding to 2020_23 and MPNST2 to 2022_26. This current submission contains the four long-read raw data et the data processed using the wf-single-cell pipeline. For the additional processed data, please refer to https://github.com/GenomiqueENS/scKenver.
2025-08-26 | E-MTAB-15190 | biostudies-arrayexpress