EGAS00001002262-sc-20170810 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001003556 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:We will perform RNAseq to evaluate the effects of the loss of a list of TSGs on the transcriptome.
| EGAS00001002262 | EGA
Project description:The data contains Illumina 450k/EPIC array methylation files from 104 patients of the NOA-08 study comparing temozolomide alone vs. radiotherapy in elderly patients with glioblastoma
2019-10-31 | GSE122920 | GEO
Project description:To quantify tRNA expression, we perform hydro-tRNA sequencing (Gogakos et al. 2017) in HACAT (keratinoytes) and HepG2 (liver) human cell lines. This study contains 6 samples. Three replicates for each of the five cell lines. This data complements our previously published data (GEO: GSE137834), which contained five additional cell lines from different tissues: HEK293 (kidney), HCT116 (colon), HeLa (cervix), MDA-MB-231 (breast), and BJ fibroblasts. Therefore, with these extra two cell lines, this constitutes a tissue-wide dataset of tRNA sequencing covering a total of seven human cell lines.
2021-03-15 | E-MTAB-9905 | biostudies-arrayexpress
Project description:<p><b>The Genomics and Transcriptomics of Human Insulinoma</b><br/> The common forms of diabetes - Types 1 and 2 - ultimately result from a deficiency of insulin-producing pancreatic beta cells. The Genomics and Transcriptomics of Human Insulinoma study was performed in order to identify novel approaches to inducing human pancreatic beta cells to replicate and regenerate. As a corollary, developing drugs that are able to expand human beta cell mass in people with diabetes should reverse diabetes. Unfortunately, identifying druggable pathways that can enhance human beta cell replication has been a major challenge. In 2017, there is only one class of drugs - the harmine analogues - that can induce human beta cells to replicate, and in this case, higher replication rates are desirable. Thus, identifying additional drugs and druggable pathways is a priority in diabetes research.</p> <p>Insulinomas are rare, benign adenomas of the pancreatic beta cell that cause excess insulin production and hypoglycemia: exactly the opposite of Types 1 and 2 diabetes. Beta cell proliferation rates in insulinomas are abnormally high. Thus, the premise for The Genomics and Transcriptomics of Human Insulinoma study is that benign human insulinomas hold the genomic and transcriptomic "recipe", and the repertoire of druggable pathways, that can be exploited to induce regeneration or replication of human beta cells in diabetes. Because, insulinomas are so rare, are almost always benign (non-malignant), and are easily resected by laparoscopic surgery, little attention has been paid to understanding the genomics or transcriptomics of insulinoma. There are at present only three published studies employing next-gen sequencing in insulinoma (<a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=24326773">PMID:24326773</a>; <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=25787250">PMID:25787250</a>; and <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=25763608">PMID:25763608</a>). These studies contained 10, 7 and 8 insulinomas, respectively, and highlighted likely mutations in YY1 and MEN1. Our goal was to markedly expand the database and to add RNAseq to these earlier studies. </p> <p>The Genomics and Transcriptomics of Human Insulinoma study, in press in Nature Communications in 2017, reports next-gen sequencing on 38 insulinomas, by far the largest series of human insulinomas subjected to next-gen sequencing. This includes paired (genomic plus tumor) whole exome sequencing on 26 human insulinomas (22 sequenced at Mount Sinai, 4 downloaded from Cao <i>et al</i>, <a hre="https://www.ncbi.nlm.nih.gov/pubmed/?term=24326773">PMID:24326773</a>), and 25 sets of RNAseq from insulinomas, some of which also had paired whole exome seq, and some of which did not. The insulinoma RNAseq was compared to RNAseq from 22 sets of FACS-sorted normal human beta cells. Since insulinomas are so rare, the 38 insulinomas were collected by several investigators at several institutions over several decades, but most (22 whole exome sets, and all RNAseq) were sequenced at the Icahn School of Medicine at Mount Sinai in New York. </p> <p>The current dataset contains whole exome seq and RNAseq on the 11 insulinomas harvested at Mount Sinai. The four from Cao <i>et al</i> can be retrieved from Cao <i>et al</i> <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=24326773"> PMID:24326773</a>. Fastq files from the remaining 23 insulinomas will be added as the local IRBs and Institutional Certifications are acquired. Complete patient data are provided in our Nature Communications report. Going forward, our intention is to expand this series, with the goal of sequencing 100 human insulinomas. These will be added to dbGaP as they accrue.</p> <p>Paired-end whole exome seq (mean usable sequencing depth 79X and 105X for blood and insulinoma, respectively) was performed using an Illumina HiSeq 2500. Insulinoma and sorted normal beta cell RNAseq was performed on Ribozero and polyA paired end libraries using the Illumina HiSeq 2500. Complete sequencing and bioinformatic details are provided in our Nature Communications report.</p> <p>The principal findings from the study are that although each insulinoma has a different set of presumptive driver mutations, the majority converge on genes that are members of the Polycomb Complex, Trithorax Complex and other epigenetic modifying enzymes. In addition, 20% of insulinomas have copy number loss or loss of heterozygosity of all or most of chromosome 11, and the majority display abnormalities in CpG methylation and imprinting control on the imprinted Chr 11 p15.5-15.4 region that contains <a href="https://www.ncbi.nlm.nih.gov/gene/?term=INS">INS</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=IGF2">IGF2</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=CDKN1C">CDKN1C</a>, <a href="https://www.ncbi.nlm.nih.gov/gene/?term=KCNQ1">KCNQ1</a>, and other genes involved in beta cell specification and proliferation. </p>
| phs001422 | dbGaP
Project description:62 individual Brassica napus plants of the same accession grown in the same field were expression-profiled in autumn 2016 and phenotyped extensively until harvest in spring 2017. Machine learning models were used to link gene expression to the phenotypes of individual plants, with the purpose of assessing how much phenotype information in encoded in ‘noisy’ gene expression variation among individual plants of the same background grown under the same uncontrolled field conditions. Rosette leaf 8 blades of 62 individual Brassica napus plants of the same winter-type accession (BnASSYST-120, Darmor) grown in the same field (50°58'24.9\\"N 3°46'49.1\\"E, Merelbeke, Belgium) were RNA-seq profiled. No treatments or stresses were applied, all plants were profiled individually under uncontrolled field conditions. Sown at 2016-09-08, rosette leaf 8 sampled for RNA-seq at 2016-11-28, plants harvested at 2017-06-13.
2022-10-10 | E-MTAB-11904 | biostudies-arrayexpress
Project description:To associate the amount of tRNAs with codon usage, we perform hydro-tRNA sequencing (Gogakos et al. 2017) and quantify tRNA expression in HEK293 and HeLa cells.
2020-02-28 | E-MTAB-8144 | biostudies-arrayexpress
Project description:To perform a standard RNAseq experiment to evaluate the impact of the knockout of Zeus gene(CG9573) in D. melanogaster
| PRJNA793594 | ENA
Project description:The lateral septum contains a number of molecularly defined cell populations. We used the viral-TRAP protocol (Nectow et al.,2017) to profile lateral septum neurotensin neurons.
2020-08-30 | GSE154758 | GEO
Project description:The annotated MatLab script contains a simple algorithm for creating 1D bifurcation plots for saddle-node bifurcations.
The algorithm is intended as a DIY alternative for creating bifurcation diagrams with bistable models if the model does not run in more common software suites for bifurcation analysis.
Running the code creates a 1D bifurcation plot for a simple bistable model used for illustration.
For more details, please refer to:
https://insilicovitro.wordpress.com/2019/10/08/home-baked-bifurcation-diagrams/
2019-10-24 | MODEL1910220002 | BioModels
Project description:The fungus Puccinia striiformis f.sp. tritici (PST) is the causal pathogen of stripe rust in wheat. New highly virulent PST races appeared at the beginning of this century and spread rapidly causing significant yield losses in wheat production worldwide. Race PST-08/21 was isolated in the UK in 2008 Yr1, Yr2, Yr3, Yr4, Yr6, Yr9, Yr17, Yr27, Yr32, YrRob, YrSol. We applied the RNAseq approach to refine the gene prediction in de novo assembled PST 08/21 contigs and to determine which genes are expressed during wheat infections.
2013-08-19 | GSE42496 | GEO