Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SNP data from 125 hepatocellular carcinomas and 112 paired normal samples

ABSTRACT: SNP array data from 125 hepatocellular carcinomas were used to detect recurrent copy number alterations. 99 hepatocellular carcinomas and 86 matched normal samples were analyzed with Illumina HumanCNV370-Duo v1.0 chips. 26 hepatocellular carcinomas and 26 matched normal samples were analyzed with Illumina HumanOmniExpress BeadChip.

SUBMITTER:

PROVIDER: S-DIXA-D-1103 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

SNP data from 125 hepatocellular carcinomas and 112 paired normal samples

Project description:SNP array data from 125 hepatocellular carcinomas were used to detect recurrent copy number alterations.

2012-02-27 | GSE32649 | GEO

SNP data from 125 hepatocellular carcinomas and 112 paired normal samples

Project description:SNP array data from 125 hepatocellular carcinomas were used to detect recurrent copy number alterations. 99 hepatocellular carcinomas and 86 matched normal samples were analyzed with Illumina HumanCNV370-Duo v1.0 chips. 26 hepatocellular carcinomas and 26 matched normal samples were analyzed with Illumina HumanOmniExpress BeadChip.

2012-02-27 | E-GEOD-32649 | biostudies-arrayexpress

SNP data from 126 hepatocellular adenomas and carcinomas

Project description: Not available

| S-DIXA-D-1146 | biostudies-other

SNP data from 126 hepatocellular adenomas and carcinomas

Project description:SNP array data from 127 hepatocellular adenomas and carcinomas were used to detect recurrent copy number alterations.

2013-12-18 | GSE43092 | GEO

COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients.

Project description:Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration.

| S-EPMC9526730 | biostudies-literature

SNP data from 126 hepatocellular adenomas and carcinomas

Project description:SNP array data from 127 hepatocellular adenomas and carcinomas were used to detect recurrent copy number alterations. 48 tumors were analyzed with Illumina HumanCNV370-Duo v1.0 chips. 79 tumors were analyzed with Illumina HumanOmniExpress BeadChip.

2013-12-18 | E-GEOD-43092 | biostudies-arrayexpress

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples.

Project description:PurposeFollowing automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability.MethodsThis manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer's confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing.ResultsAfter reading the SOP, average accuracy in somatic variant identification increased by 16.7% (p value = 0.0298) and average interreviewer agreement increased by 12.7% (p value < 0.001). Manual review conducted after reading the SOP did not significantly increase reviewer time.ConclusionThis SOP supports and enhances manual somatic variant detection by improving reviewer accuracy while reducing the interreviewer variability for variant calling and annotation.

| S-EPMC6450397 | biostudies-literature

Genome-wide identification of somatic aberrations from paired normal-tumor samples.

Project description:Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.

| S-EPMC3907544 | biostudies-literature

Affymetrix SNP array data for hepatocellular carcinoma tissue

Project description: Not available

| S-DIXA-D-1096 | biostudies-other

Transcriptional Profiles from Paired Normal Samples Offer Complementary Information on Cancer Patient Survival--Evidence from TCGA Pan-Cancer Data.

Project description:Although normal tissue samples adjacent to tumors are sometimes collected from patients in cancer studies, they are often used as normal controls to identify genes differentially expressed between tumor and normal samples. However, it is in general more difficult to obtain and clearly define paired normal samples, and whether these samples should be treated as "normal" due to their close proximity to tumors. In this article, by analyzing the accrued data in The Cancer Genome Atlas (TCGA), we show the surprising results that the paired normal samples are in general more informative on patient survival than tumors. Different lines of evidence suggest that this is likely due to tumor micro-environment instead of tumor cell contamination or field cancerization effect. Pathway analyses suggest that tumor micro-environment may play an important role in cancer patient survival either by boosting the adjacent metabolism or the in situ immunization. Our results suggest the potential benefit of collecting and profiling matched normal tissues to gain more insights on disease etiology and patient progression.

| S-EPMC4738355 | biostudies-literature

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