687a8e40-fc3a-46d4-b652-d0990466bb10 - samples
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ABSTRACT:
PROVIDER: EGAD00001004195 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Human single fibroblasts and T-lymphocytes were amplified using multiple annealing and looping-based amplification and multiple displacement amplification. Amplified products were next subjected to whole genome sequencing with the goal of performing a genetic variant analysis.
| EGAS00001003108 | EGA
Project description:This GEO submission includes RNAseq raw data (fastq) and processed data (using ASpli 1.6.0) from samples obtained in the wild type and the single prefoldin4 and lsm8 mutants in three different environmental conditions as well as in the triple prefoldin2 prefoldin4 prefoldin6 mutant growth in standard conditions.
2019-01-09 | GSE124812 | GEO
Project description:Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated through examination of the chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1482 distinct brain cell populations, adding over 446,000 new cCREs to the most recent such annotation in the mouse genome. Raw fastq files are demultiplexed. Out of 234 samples, fastq files for 6 samples are available in NCBI under the accession number GSE126724 (CEMBA180308_3B, CEMBA180312_3B, CEMBA180226_1A, CEMBA180227_1A, CEMBA180305_2B, CEMBA180306_2B). Processed data are SnapATAC2 (<= 2.4.0) h5ad files per sample, with raw fragments and 500-bp bmat. NOTE: due to the break changes in SnapATAC2 >= 2.5.0, the data can not be handled rightly in SnapATAC2 >= 2.5.0.
2023-11-02 | GSE246791 | GEO
Project description:The adult healthy human pancreas has been poorly studied given lack of indication to obtain tissue from the pancreas in the absence of disease and rapid postmortem degradation. We obtained pancreata from brain dead donors thus avoiding any warm ischemia time. We found that neoplastic lesions occur frequently in healthy organs, in donors as young as in their 3rd decade of life. Using the 10X Genomics Platform, we performed single cell RNA sequencing on 6 donor pancreata, 5 of which we separately sequenced tissue from the pancreas head and tail, for a total of 11 sequencing runs. This is a robust single-cell dataset of healthy, nonpathologic pancreas tissue and includes acinar, ductal, and non-epithelial cells (myeloid cells, fibroblasts, endothelial cells, lymphocytes). We compared this dataset of normal pancreata to single cell sequencing of tumor samples previously published in Steele, et al, Nature Cancer 2020, (raw fastq in dbGap phs002071) realigned to GRCh38 reference genome (CellRanger 6.0). This GEO series contains the raw and filtered feature matrices for the donor pancreata as well as the realigned tumor samples. Supplementary files contain the aggregate feature matrices and the metadata of the donor pancreata samples and the tumor samples.
2023-05-01 | GSE229413 | GEO
Project description:Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that can regulate molecular and cellular processes in brain development and diseasee. LncRNAs exhibit cell type- and tissue-specific expression, but little is known about the expression and function of lncRNAs in the developing human brain. Here, we deeply profiled lncRNAs from polyadenylated and total RNA obtained from human neocortex at different stages of development and integrated this resource to analyze the transcriptomes of single cells. While lncRNAs were generally detected at low levels in whole tissues, single cell transcriptomics revealed that many lncRNAs are abundantly expressed in individual cells and are cell type-specific. Furthermore, we used CRISRPi to show that LOC646329, a lncRNA enriched in radial glia but detected at low abundance in tissues, regulates cell proliferation. The discrete and abundant expression of lncRNAs among individual cells has important implications for both their biological function and utility for distinguishing neural cell types. 16 Bulk Tissue Samples from GW13-23; 226 Single Cells from GW19.5-23.5 ------------------ bulk_tpm.polya.txt: bulk RNA-seq expression; using polyA full reference scell_ncounts.genes.thresh.txt: single cell RNA-seq expression; using polyA stringent reference; includes 50 GW16, GW21, GW21p3 cells previously analyzed (Pollen et. al. 2014) polya_RNA_stringent_ref.gtf: bulk RNA-seq polyA stringent transcriptome reference polya_RNA_full_ref.gtf: bulk RNA-seq polyA full transcriptome reference total_RNA_stringent_ref.gtf: bulk RNA-seq total stringent transcriptome reference total_RNA_full_ref.gtf: bulk RNA-seq total full transcriptome reference GW13_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal
2016-03-28 | E-GEOD-71315 | biostudies-arrayexpress
Project description:This includes bulk RNA-seq samples for sorted LT-HSCs, ST-HSCs, and MPPs stimulated (or not) with LPS+PAM. Samples taken at various time points.
2017-07-19 | GSE100425 | GEO
Project description:The investigation includes findings from our clinical trial, monitoring individualized response to pneumococcal vaccination, where we have carried out integrative profiling assessment of saliva pre and post vaccination in a single individual. This is to our knowledge the most extensive saliva-centered omics dataset on an individual, covering 100 timepoints over the course of one year. The time span covers a healthy period as well as comprehensive monitoring of innate and adaptive immune responses following pneumococcal vaccination. Protein and RNA from saliva were produced at each timepoint (100 timepoints), and mass spectrometry proteomics and RNA-sequencing were carried out for all samples in non-targeted comprehensive profiling. Specifically, a single individual (male, 38) was profiled over multiple timepoints during healthy periods, as well as post treatment with pneumococcal vaccine (PPSV23). Initially pre-immunization samples, including a 24 hour period with hourly sampling (samples P1052515H07-P1052615H08), were collected to provide a comparative baseline. A subsequent 24-hour time course was performed, with again hourly samples taken pre and post vaccination (P1060715H07-P1060815H06). The PPSV23 pneumococcal vaccine was admistered inbetween timepoints at approximately 10.30am, prior to datapoint P1060715H11. Following the vaccination, and after the 24 hour monitoring, daily samples were taken for about a month (up to sample P1070715H08), to capture innate and adaptive responses in saliva. Two more weekly samples followed, with then monthly samples till the end of the investigation. Omics sample analysis includes: RNA-sequencing of total RNA, small RNA sequencing in saliva extracellular vesicles and saliva mass spectrometry proteomics. Note on sample naming: The sample identifier/name P1MMDDYYHhh corresponds to: patient index:P1, date MMDDYY and hour hh preceded by H using 24 hour enumeration.
2020-01-31 | GSE108664 | GEO
Project description:Single-cell human genome analysis using whole-genome amplified product is hampered by allele bias during amplification. Using an oligonucleotide SNP array, we examined the nature of the allele bias and its effect on the chromosomal copy number analysis. Experiment Overall Design: TB106, lymphoblastoid cell line; CMK11-5 and CMK86, cell line; WGA, whole-genome amplified from bulk DNA; SC, whole-genome amplified product from single cell. Genotype and copy number status were compared between non-amplified products and their single cell products.
2008-06-16 | E-GEOD-8567 | biostudies-arrayexpress
Project description:Introduction: single-cell RNA sequencing identified multiple subpopulations in childhood posterior fossa ependymoma. The contribution of individual neoplastic subpopulations to bulk tumor transcriptome-based molecular classification and patient outcome was estimated by deconvolution in a cohort of clinically-annotated primary and recurrent EPN samples. The abundance of EPN subpopulations was estimated in primary EPN samples and showed that the ratio of subpopulation fractions dictated assignment to the two main transcriptomic classification subgroups in childhood posterior fossa ependymoma. Longitudinal analysis revealed that subpopulation fractions changed between presentation and recurrence. Outcome analyses demonstrated that a different proportions of subpopulations were associated with differential survival.
2020-07-31 | GSE125861 | GEO
Project description:The investigation includes findings from our clinical trial, monitoring individualized response to pneumococcal vaccination, where we have carried out integrative profiling assessment of saliva pre and post vaccination in a single individual. This is to our knowledge the most extensive saliva-centered omics dataset on an individual, covering 100 timepoints over the course of one year. The time span covers a healthy period as well as comprehensive monitoring of innate and adaptive immune responses following pneumococcal vaccination. Protein and RNA from saliva were produced at each timepoint (100 timepoints), and mass spectrometry proteomics and RNA-sequencing were carried out for all samples in non-targeted comprehensive profiling. Specifically, a single individual (male, 38) was profiled over multiple timepoints during healthy periods, as well as post treatment with pneumococcal vaccine (PPSV23). Initially pre-immunization samples, including a 24 hour period with hourly sampling (samples P1052515H07-P1052615H08), were collected to provide a comparative baseline. A subsequent 24-hour time course was performed, with again hourly samples taken pre and post vaccination (P1060715H07-P1060815H06). The PPSV23 pneumococcal vaccine was admistered inbetween timepoints at approximately 10.30am, prior to datapoint P1060715H11. Following the vaccination, and after the 24 hour monitoring, daily samples were taken for about a month (up to sample P1070715H08), to capture innate and adaptive responses in saliva. Two more weekly samples followed, with then monthly sample till the end of the investigation. Omics sample analysis includes: RNA-sequencing of total RNA, small RNA sequencing in saliva extracellular vesicles and saliva mass spectrometry proteomics. Note on sample naming: The sample identifier/name P1MMDDYYHhh corresponds to: patient index:P1, date MMDDYY and hour hh preceded by H using 24 hour enumeration.
2020-01-31 | GSE108666 | GEO