12312 results for "cancer rna-seq"

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• scATAC-seq and combined scRNA-seq of the human first trimester neurodevelopment

  Study EGAS00001007472

• Whole-Exome-Seq-Dataset

  Whole-Exome-Seq-Dataset

  Dataset EGAD00001000042

• whole genome seq

  20 whole genome seq

  Dataset EGAD00001004462

• scRNA-Seq & scATAC-Seq Feature-counting Results Derived from 10X Cellrange-arc-count pipeline

  The feature-counting outputs from Cellrange-arc-count pipeline contain information at the single cell level, jointly for both scRNA-Seq and scATAT-Seq on 103 DLBCL samples and 3 normal controls (Please note that sc-40 only has scRNA-Seq data without scATAC-Seq). These files can be used as the input files for further downstream analysis software (e.g. Seurat Package). Moreover, a matrix of single-cell barcodes and associated cell biological types (used in the study) is also provided for users’ reference. This dataset is large and split into two parts to facilitate downloads. This dataset is large and split into two parts to facilitate downloads. The Linux command line of "tar -xvf xxxx_PART1.gz" should be used to extract the individual files. For additional information regarding the genomic data associated with the study, please also see the paper companion website at https://llmpp.ccr.cancer.gov/local/DLBCL_singlecell/

  Dataset EGAD50000002490

• RNA sequencing of peripheral blood samples from 17 Greenlanders

  The data contain whole deep RNA sequencing of leukocytes from 17 Greenlanders. RNA was purified from peripheral blood with the PAXGene Blood miRNA Kit (Qiagen). The RNA sequencing library was prepared following the instructions of the TruSeq RNA Sample Prep Kit v2 (Illumina). For mRNA isolation and fragmentation 200 ng of total RNA was purified by oligo-dT beads. The qualified libraries were amplified on cBot to generate the cluster on the flowcell (TruSeq PE Cluster Kit V3–cBot–HS, Illumina). The amplified flow cell was sequenced paired-end on the HiSeq 4000 System (TruSeq SBS KIT-HS V3, Illumina).

  Dataset EGAD00001003814

• Medulloblastoma Cirlce-Seq

  Circle-Seq experiment.

  Dataset EGAD00001009483

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  Study Overview The Environmental Determinants of Diabetes in the Young (TEDDY) Study is a longitudinal study that investigates genetic and genetic-environmental interactions, including gestational events, childhood infections, dietary exposures, and other environmental factors after birth, in relation to the development of islet autoimmunity and type 1 diabetes (T1D). A consortium of six clinical centers assembled to participate in the development and implementation of the study to identify environmental triggers for the development of islet autoimmunity and T1D in genetically susceptible individuals. Beginning in 2004, the TEDDY study screened over 400,000 newborns for high-risk HLA-DR, DQ genotypes from both the general population and families already affected by T1D. The TEDDY study enrolled around 8,676 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) in the 15-year prospective follow-up. Participants are followed every three months for islet autoantibody (IA) measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive participants continue to be followed at three month intervals and autoantibody negative participants are followed at six-month intervals. In addition to the analysis of autoantibodies, additional data and sample collection are performed at each visit. Parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. Additional information on the TEDDY study is available in the following articles: Rewers et al., 2008, PMID: 19120261 and Hagopian et al., 2006, PMID: 17130573. Details of the TEDDY protocol can be found in Hagopian et al., 2011, PMID: 21564455. TEDDY data currently available in dbGaP include: gene expression, SNPs, exome, microbiome (gut, nasal, and plasma), RNA sequencing, and whole genome sequencing. For more information on TEDDY Study version history please refer to TEDDY Study dbGaP README File. ImmunoChip SNP DNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for SNP genotyping. Genotyping was performed by the Center for Public Health Genomics at the University of Virginia using the Illumina ImmunoChip SNP array, which contains around 196,000 SNPs from 186 regions associated with 12 autoimmune diseases (Hadley et al., 2015, PMID: 26010309). Data cleaning and validation included the removal of subjects with a low call rate (< 5% SNPs missing) and differences in reported sex and prior genotyping at the TEDDY HLA laboratory. Additionally, SNPs with a low call rate or Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements, were removed from the final dataset (Törn et al., 2015, PMID: 25422107).TEDDY-T1DExome ArrayDNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for genotyping. Genotyping was performed by the University of Virginia using the Illumina TEDDY-T1DExome array. The TEDDY-T1DExome array is a custom chip that contains 550,601 markers from the Infinium CoreExome-24 v1.1 BeadChip and an additional 90,214 tagSNPs specifically selected by the TEDDY investigators based on their associations with nutrients, vitamins, type 2 diabetes, autoimmune diseases, body-mass index, or other exposures and phenotypes measured by TEDDY study.The Illumina GenTrain2 algorithm was used for genotype calling. Sample quality control metrics included sample call rate, heterozygosity rate and concordance of gender between the information reported and genotyped. Gene Expression The TEDDY study collected peripheral blood for the extraction of total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA (200 ng) was further used for cRNA amplification and labeling with biotin using Target Amp cDNA synthesis kit (Epicenter catalog no. TAB1R6924). Labeled cRNA was hybridized to the Illumina HumanHT-12 Expression BeadChips based on the manufacturer's instructions. The HumanHT-12 Expression BeadChip provides coverage for more than 47,000 transcripts and known splice variants across the human transcriptome. Microbiome The TEDDY microbiome study aimed to characterize the longitudinal development of the microbiome, including bacteria, viruses and other microorganisms in the gut, plasma, and nasal cavity of prediabetic and diabetic subjects compared to autoantibody negative non-diabetic subjects. Stool samples used were collected monthly from 3 to 48 months, after which stool samples were collected every 3 months. Nasal swab samples were collected every 3 months starting at 9 months of age until 48 months, after which nasal swabs were collected every 6 months. Plasma samples were collected every 3 months starting at 3 months of age until 48 months, after which plasma samples were collected every 6 months. If the subject was autoantibody positive at 48 months then they remained on the 3 month collection interval for nasal swab and plasma samples. Samples underwent 16s rRNA gene sequencing, DNA and viral RNA metagenomics shotgun sequencing, and sequencing of the internal transcribed spacer (ITS) regions. Additional information on the TEDDY microbiome data is available in the following articles: Vatanen et al., 2018, PMID: 30356183, Stewart et al., 2018, PMID: 30356187, and Vehik et al., 2020, PMID: 31792456. RNA Sequencing The TEDDY study aimed to characterize the transcriptome in subjects with islet autoimmunity and type 1 diabetes compared to matched control subjects. Peripheral blood was collected to extract total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA was then sent to the Broad Institute for the generation of the TEDDY RNA sequencing (RNA-Seq) data. The RNA samples were prepped using Superscript III reverse transcriptase and Illumina's TruSeq Stranded mRNA Sample Prep Kit. The TruSeq libraries were run on the Illumina HiSeq2500 platform. Whole Genome Sequencing The TEDDY study aimed to conduct deep whole genome sequencing and examine the genomic variations in subjects with islet autoimmunity and type 1 diabetes compared to matched autoantibody negative and non-diabetic children. DNA from whole blood was obtained from TEDDY children for whole genome sequencing. The WGS data were generated on the Illumina HiSeq X Ten system.

  Study phs001442

• RNA sequencing data of in vitro differentiated megakaryocyte cells transduced with E527K and WT SRC

  For RNA sequencing (RNAseq), CD34+ hematopoietic stem cells (HSC) were isolated from healthy controls before transduction with WT-SRC and E527K-SRC lentiviral vectors in triplicate and differentiation to MK. RNA was extracted from the complete cell population as the aim was to detect differences in genes regulating MK maturation and differentiation. A total of six WT-SRC and six E527K-SRC RNA samples were used for RNAseq to identify differentially expressed genes.

  Study EGAS00001005808

• Lipomatous tumors with 12q amplification

  The project concerns whole genome sequencing (short-read and long-read) and RNA-sequencing of lipomatous tumors with 12q-amplification. A subset of lipomatous tumors is driven by amplification of genes mapping to chromosome arm 12q, including the MDM2 gene. The goals of the study were to compare expression levels of genes mapping to 12q in tumors with amplification in rod-shaped or circularized chromosomes as well as to assess and compare the structural variants in those tumors. In total, 20 samples were analyzed, and the data were correlated with genomic data on bulk and single cell DNA from the same tumors. The fastq files from the tumors were uploaded to EGA.

  Study EGAS50000000062

• Whole exome sequencing, RNA sequencing and single-cell RNA sequencing of 4 melanoma patients

  We perfomed whole exome sequencing and RNA sequencing of 4 melanoma patients to assess the detailed biological mechanisms of resistance to immune checkpoint inhibitors.

  Study JGAS000285

• Elucidation of male-specific genetic regulation through multi-layered omics analysis

  We performed single-cell RNA-sequencing of peripheral blood mononuclear cells from asthma patients and single-cell multiome (RNA+ATAC) profiling from COVID-19 patients.

  Study JGAS000862

• ChIP-seq of primary AML patients with t(3;3)/inv(3)

  ChIP-seq was conducted in blasts from patients with t(3;3) AML to assess differences of the GATA2 super-enhancer between the translocated allele and the non-translocated allele. The dataset includes 2x H3K27ac ChIP-seq and 1x MYB ChIP-seq. ChIP samples were processed according to the Illumina TruSeq ChIP Sample Preparation Protocol (Illumina) or Diagenode Library V3 preparation protocol (Diagenode) and either sequenced single-end (1x 50 bp) on the HiSeq 2500 platform (Illumina) or paired-end (2x100 bp) on the Novaseq 6000 platform (Illumina). Briefly, reads were aligned to the human reference genome build hg19 with bowtie for single-end runs and bowtie2 for paired-end runs.

  Dataset EGAD00001006821

• ATAC-seq in KMS11 vs TKO cells

  ATAC-seq to determine chromatin accessibility in t(4;14) Myeloma cell line KMS11 and KMS11 cells with knock-out of the translocated NSD2 allele.

  Study EGAS50000000076

• ChIP Seq data of multiple myeloma and plasma cell leukaemia cell lines

  ChIP-seq data (H3K4Me1, H3K4Me3, H3K27Ac histone modifications) on multiple myeloma cell line KMS11 and plasma cell leukaemia cell lines L363 and JJN3

  Study EGAS00001002414

• SF11949 snATAC Seq IDH1 mutant oligodendroglioma Male

  IDH1 mutant oligodendroglioma male, 40. Single Nuclei ATAC seq data from low grade human glioma sample. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005398

• SF12017 snATAC Seq IDH1 Mutant GBM 55, Male

  IDH1 mutant GBM 55, Male. Single Nuclei ATAC seq data from high grade human glioma samples. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005405

• SF11215 snATAC Seq GBM

  High grade glioma sample, Gender Male Age 46. Single Nuclei ATAC seq data from high grade human glioma samples. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005407

• SF11331 snATAC Seq Primary GBM Male

  SF11331 Primary GBM Male,55 Single Nuclei ATAC seq data from high grade human glioma sample. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005408

• SF11612 snATAC Seq Recurrent oligodendroglioma

  SF11612 Recurrent oligodendroglioma. Gender Male Age 67. Single Nuclei ATAC seq data from low grade human glioma samples. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005413

• CITE-Seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) of CLL_24

  To explore intratumor-heterogeneity of CLL_24 using single-cell multi-omics approach, we generated single-cell CITE-seq data for CLL_24, coupling scRNA-seq and protein surface marker measurements with oligo-tagged antibodies.

  Dataset EGAD00001009174

• CTCF ChIP-seq of human acute leukemias

  CTCF ChIP-seq of 39 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP).

  Dataset EGAD00001011059

• Microbiomic and immunogenic biomarkers of adjuvant chemotherapy efficacy in stage III colorectal cancer

  The standard treatment for stage III colorectal cancer (CRC) is surgery, followed by adjuvant chemotherapy (AC). However, the efficacy of AC is limited, and approximately 30% of patients experience recurrence. Therefore, easily assessable molecular biomarkers that can predict response to AC are needed. Here, we aimed to identify predictive biomarkers of response to AC using tumor transcriptomic data. Tumor specimens from 253 patients who underwent surgery for stage III CRC between 1997 and 2019 were analyzed using RNA sequencing. Transcriptional subtyping and analyses of gut microbiome, immune cell fractions, and T-cell receptor (TCR) were performed to identify factors associated with recurrence-free survival (RFS) in patients treated with or without AC. Of 253 patients, 118 (46.6%) received AC. Twenty phyla and 799 species of the microbiomes were identified, among which Fusobacteria were identified in 247 patients (97.6%). Multivariable Cox proportional hazards regression analysis of patients who received AC showed that intratumoral Fusobacterium and M1 macrophage abundance and diversity of TCR alpha and delta (TRAD) were independently associated with RFS. Fusobacterium and M1 macrophage abundance, and TRAD diversity were associated with the response to AC, suggesting that these factors could serve as biomarkers for personalized treatment in stage III CRC.

  Study JGAS000875

• RNA sequencing data from glioblastoma primary cell lines treated with indisulam data access committee

  Four glioblastoma primary cell lines (BT592/pt1, BT1007/pt2, BT1009/pt3; BT1012/pt4) were derived from glioblastoma Cavitron Ultrasonic Aspirator (CUSA) material and cultured as neurospheres (GB-NS) in DMEM/F12+Glutamax medium containing B27 supplement (Thermofisher, Waltham, MA, USA) and the mitogenic factors epidermal growth factor (EGF) and fibroblast growth factor b (bFGF) (Peprotech, Rocky Hill, NJ, USA). GB-NS were confirmed mycoplasma-free by PCR Test. Cells were seeded in triplicate in 6-well plates, treated with 5 μM indisulam (Sigma Aldrich, Merk), or vehicle control, and collected after 6 and 16 hours for RNA extraction. Total RNA was extracted using TRIzol Reagent (Life Technologies, Thermo Fisher, cat n. 15596026), RNeasy Mini Kit, and RNase-Free DNase Set (Qiagen, cat n. 74104 and 79254). Sample QC, RNA library preparations, and sequencing reactions were conducted at GENEWIZ (Germany GmbH). The concentration of RNA was quantified using a Qubit Fluorometer (Life Technologies, Carlsbad, CA) and RNA integrity was assayed using a TapeStation. Sequencing libraries were prepared with RNA with PolyA selection and ERCC spike-in with standard ERCC kit (ERCC RNA Spike-In Mix,Thermo Fisher 4456740). Final libraries (Illumina, RNA with PolyA selection) were sequenced on a NovaSeq platform, producing 2×150 base paired-end reads.

  Dac EGAC50000000407

• Single cell RNA sequencing of human umbilical cord blood lymphoid progenitors

  We performed single cell RNA sequencing of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage deeply, our target cells contain CD34+CD38+CD45RA+CD19-CD7-CD10+ cells (CD10SP). All these populations are evaluated by single cell RNA sequencing.

  Study JGAS000551

• Study of tumor RNA expression differences between treated and untreated PitNET patients

  The goal of the study is to evaluate response of PitNET to drug therapy before surgery. Patients with and without drug therapy are compared in RNA level. The RNA is extracted from surgery material, sequenced and deferentially expressed genes (DEG) determined.

  Study EGAS00001004736