Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

C2990ad0-d9cf-4621-8681-f24959ab72e4 - samples

ABSTRACT: Targeted sequencing of non-small cell lung cancer samples. BAM files of paired end reads aligned to hg19 using BWA MEM v0.7.1573. This targeted panel covers 370 genes of clinical relevance in non-small cell lung cancer.

PROVIDER: EGAD00001007918 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

AIB1 and TEAD ChIP-seq

Project description:We report the global occupancy by ChIP-seq of AIB1 and TEAD4 in normal mammary epithelial cells (MCF10A). We performed ChIP and library preparation with the ActiveMotif Low Cell ChIP-seq kit. Data was aligned to Hg38 with BWA-MEM, data was sorted and de-duplicated with SAMTOOLS, and peaks were called with MACS2. Analysis was done with ChIP-seeker in R.

2019-11-01 | GSE137284 | GEO

1b5442ca-fc7c-45ce-8639-05a21557199e - samples

Project description:Tumors and control of nodal B-cell lymphoma of one patient. WES sequencing on Illumina HiSeq 4000 with Agilent SureSelect V5+UTRs. Bam files were aligned with bwa mem to hg19.

| EGAD00001006059 | EGA

Homo sapiens

Project description:Non Small Cell Lung Cancer

| PRJNA90997 | ENA

Next Generation Sequencing Facilitates Quantitative Analysis of C57BL/6J mice.

Project description:Purpose: Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method for mapping chromatin accessibility genome-wide. The goals of this study are to investigate chromatin accessibility of C57BL/6J mice through ATAC-seq. Results: ATAC-seq reads were aligned to the UCSC mm10 reference genome with BWA-MEM. ATAC-seq peaks were called through MACS2 . Peaks were annotated and known transcription factor binding motifs were further analyzed in the ATAC-seq peaks by HOMER.

2020-07-08 | GSE153993 | GEO

Integrated DIA proteomics and lipidomics analysis on non-small cell lung cancer patients with TCM syndromes

Project description:Lung cancer remains the leading cause of mortality from malignant tumors, non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases, and individualized diagnosis and treatment in traditional Chinese Medicine (TCM) is an effective trend. In this study, a proteomics research with DIA mode and a non-targeted lipidomics research were developed in NSCLC patients with Qi deficiency and Yin deficiency (QDYD) and Qi deficiency of lung-spleen (QDLS) syndromes.

| PXD024704 |

Project description:Case study of targeted therapy on human Non-small cell lung cancer

| PRJNA1174043 | ENA

Comprehensive multi-mark and multi-omic profiling reveals epigenetic and transcriptional reprogramming in lung tumors

Project description:We present comprehensive epigenomic and transcriptomic mapping of 18 tumor and 20 non-neoplastic tissues from non-small cell lung adenocarcinoma patients. Our profiling covers active marks (H3K4me3, H3K4me1, and H3K27ac), repressive marks (H3K27me3 and H3K9me3) and gene expression using only 20 mg of tissue. Genome-wide differentially modified peaks and differentially expressed genes associated with non-small cell lung cancer were identified. Key pathways related to cancer and cell proliferation were found to be enriched among these regions. 118 differential transcription factors (TF) were identified by Taiji, showing a global impact on TF in tumor. Regulation associated modules (RAM) analysis by findRAM uncovered consensus RAMs (cRAM) specific to non-plastic or tumor samples, most of which were marked with differential histone modification and gene expression. Integrative analysis by EpiSig identified 6 sections of genomic regions with distinct co-modification patterns. These clusters showed a clear distribution pattern over the specific cRAMs and were enriched in key cancer related pathways. Our results demonstrate the power of integrated analysis of multiple epigenomic and transcriptomic marks in patient samples.

2024-05-31 | GSE230932 | GEO

Whole Genome Sequencing of Tongue cancer samples and cell line

Project description:Whole genome sequencing (WGS) of tongue cancer samples and cell line was performed to identify the fusion gene translocation breakpoint. WGS raw data was aligned to human reference genome (GRCh38.p12) using BWA-MEM (v0.7.17). The BAM files generated were further analysed using SvABA (v1.1.3) tool to identify translocation breakpoints. The translocation breakpoints were annotated using custom scripts, using the reference GENCODE GTF (v30). The fusion breakpoints identified in the SvABA analysis were additionally confirmed using MANTA tool (v1.6.0).

2024-02-17 | E-MTAB-12534 | biostudies-arrayexpress

Differentially expressed genes among cells constituting an in vitro human lung carcinogenesis system

Project description:Genes differentially expressed among cells constituting an in vitro human lung carcinogenesis model consisting of normal, immortalized, transformed and tumorigenic bronchial epithelial cells were identified. The differentially expressed genes were then analyzed to determine their relevance to the gene expression patterns of clinical non-small cell lung cancer (NSCLC) samples as well as the clinical outcome of patients with this disease.

2009-07-16 | GSE17073 | GEO

ena-DATASET-INSTITUTE OF CANCER RESEARCH-13-08-2019-11:46:15:216-16 - samples

Project description:Targeted sequencing of infant high grade gliomas. BAM files of paired end reads aligned to GRCh37 with bwa. This targeted panel covers the exons of 435 genes commonly mutated in high grade gliomas in children.

| EGAD00001005248 | EGA

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