Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Oncology

ABSTRACT: Oncology Raw sequence reads

PROVIDER: PRJNA1049638 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR27129000_1.fastq.gz	Fastqsanger.gz
	SRR27129000_2.fastq.gz	Fastqsanger.gz
	SRR27129001_1.fastq.gz	Fastqsanger.gz
	SRR27129001_2.fastq.gz	Fastqsanger.gz
	SRR27129002_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 23

Similar Datasets

Morus alba Raw protein sequence reads

Project description:Morus alba L. Raw protein sequence reads and sequence

2024-02-08 | PXD049451 |

Patient-derived micro-organospheres (MOS) enable clinical precision oncology

Project description:Patient-derived xenografts (PDX) and organoids (PDO) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of Micro- Organospheres (MOS) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of newly diagnosed metastatic colorectal cancer (CRC) patients using a MOS-based precision oncology pipeline reliably predicted patient treatment outcome within 14 days, a timeline suitable for guiding treatment decisions in clinic. Furthermore, MOS capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors.

2022-09-01 | GSE184242 | GEO

Quantitative profiling of differentially expressed miRNAs in siCTL- and siOASL-transfected HUVECs

Project description:Purpose: The goal of this study is to compare endothelial small RNA transcriptome to identify the target of OASL under basal or stimulated conditions by utilizing miRNA-seq. Methods: Endothelial miRNA profilies of siCTL or siOASL transfected HUVECs were generated by illumina sequencing method, in duplicate. After sequencing, the raw sequence reads are filtered based on quality. The adapter sequences are also trimmed off the raw sequence reads. rRNA removed reads are sequentially aligned to reference genome (GRCh38) and miRNA prediction is performed by miRDeep2. Results: We identified known miRNA in species (miRDeep2) in the HUVECs transfected with siCTL or siOASL. The expression profile of mature miRNA is used to analyze differentially expressed miRNA(DE miRNA). Conclusions: Our study represents the first analysis of endothelial miRNA profiles affected by OASL knockdown with biologic replicates.

2022-09-06 | GSE186354 | GEO

Sequencing of siRNA from soybean containing silencing vectors and empty vectors

Project description:A cDNA library was constructed by Novogene (CA, USA) using a Small RNA Sample Pre Kit, and Illumina sequencing was conducted according to company workflow, using 20 million reads. Raw data were filtered for quality as determined by reads with a quality score > 5, reads containing N < 10%, no 5' primer contaminants, and reads with a 3' primer and insert tag. The 3' primer sequence was trimmed and reads with a poly A/T/G/C were removed

2021-07-21 | GSE180131 | GEO

Expression Project for Oncology (expO)

Project description:The mission of expO is to build on the technologies and outcomes of the Human Genome Project to accelerate improved clinical management of cancer patients. IGC's Expression Project for Oncology (expO) seeks to integrate longitudinal clinical annotation with gene expression data for a unique and powerful portrait of human malignancies, providing critical perspective on diagnostic markers, prognostic indicators, and therapeutic targets. The goal of expO and its consortium supporters is to procure tissue samples under standard conditions and perform gene expression analyses on a clinically annotated set of deidentified tumor samples. The tumor data is updated with clinical outcomes and is released into the public domain without intellectual property restriction. Series-matrices are available at ftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/SeriesMatrix/GSE2109/. For more information, see http://www.intgen.org/ Keywords: cancer portraits

2005-01-15 | E-GEOD-2109 | biostudies-arrayexpress

Assessment of Spatial Genomics for Oncology Discovery

Project description:Assessment of Spatial Genomics for Oncology Discovery

| PRJNA872761 | ENA

The OncoLoop Network-based Precision Oncology Framework

Project description:The OncoLoop Network-based Precision Oncology Framework

| PRJNA774549 | ENA

CPTAC: Microscaled Proteogenomic Methods for Precision Oncology

Project description:CPTAC: Microscaled Proteogenomic Methods for Precision Oncology

| PRJNA574688 | ENA

CPTAC: Microscaled Proteogenomic Methods for Precision Oncology

Project description:CPTAC: Microscaled Proteogenomic Methods for Precision Oncology

| PRJNA574689 | ENA

Assessment of Spatial Genomics for Oncology Discovery

Project description:Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for Oncology Discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis that provide a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

2022-11-01 | GSE211895 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data