Browse
Submit Data
Databases
API
Help

Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

6 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Human pituitary adenoma MiRnome

ABSTRACT: Small RNA (<100 bases in length) were purified from total RNA using miRNeasy kit (Qiagen), then sequenced. Libraries were prepared at the Genomics Platform of the Cochin Institute, following the TruSeq small RNA protocol (Illumina), starting from 1 µg of high quality total RNA. Single read (1 × 75 bp) sequencing was performed on a Nextseq 500 platform (Illumina). FASTQ sequences were aligned on miRBase v.2052, then counted with STAR (v.2.5.2a). Counts were normalized with DESeq v1.30.0

INSTRUMENT(S): NextSeq 500

ORGANISM(S): Homo sapiens

SUBMITTER: Guillaume ASSIE

PROVIDER: E-MTAB-7969 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Human pituitary adenoma expression profile

Project description:Total RNA was extracted using RNAble (Eurobio), then cleaned-up with RNeasy columns (Qiagen), then sequenced. The libraries were prepared following the TruSeq Stranded mRNA protocol (Illumina), starting from 1 μg of high quality total RNA. Paired end (2 × 75 bp) sequencing was performed on an Illumina Nextseq 500 platform(Illumina).

2020-01-13 | E-MTAB-7768 | biostudies-arrayexpress

Transcription profiling by array of breast cancer samples to define breast cancer subsets

Project description:A Cartes d'Identite des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net) | 537 samples on Affymetrix HG-U133 Plus 2,0 GeneChips arrays for 537 patients. 555 samples on CGH CIT v7 for 531 patients. Breast cancer molecular subtypes have been debated, pointing at their instability and dependence on the original set of samples or genes. Classification of breast cancer currently used in the clinic, albeit being simple, lacks precision. We aimed at defining more homogeneous breast cancer subsets at both the biological and clinical levels. | Submitter : Renaud Schiappa schiappar@ligue-cancer.net | Project leader : Charles Theillet charles.theillet@inserm.fr

2011-09-15 | E-MTAB-365 | biostudies-arrayexpress

Modeling protein destiny in developing tomato fruit

Project description:Proteomics and transcriptomics data of tomato fruits at 9 developmental stages were used to calculate with a mathematical model the rate constants of synthesis and degradation for over 1,000 proteins.

2019-05-06 | PXD012877 | Pride

Human pituitary adenomas DNA methylation profiling

Project description:86 samples processed with the Illumina Infinium®MethylationEPIC BeadChip, featuring 850k CpGs.

2020-01-02 | E-MTAB-7762 | biostudies-arrayexpress

Effects of selection on residual feed intake and diet on gene expression in pig tissues

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2016-07-01 | E-GEOD-70839 | biostudies-arrayexpress

Expression profiling by array of 44 adrenocortical carcinomas

Project description:Gene expression profiles of adrenocortical carcinomas were analyzed using Affymetrix Human Gene 2.0 ST Array to identify homogeneous molecular subgroups Gene expression profiles of 44 adrenocortical carcinomas were hybridized using Affymetrix Human Gene 2.0 ST Array

2014-04-29 | E-GEOD-49278 | biostudies-arrayexpress

ChIP-seq analyses of OCI-Ly10 cell lines transduced by doxycycline-inducible lentiviral vectors expressing SPI1 WT or mutant proteins

Project description:To characterize SPI1 mutant with respect to WT binding to DNA on a genome-wide scale

2019-05-01 | E-MTAB-7881 | biostudies-arrayexpress

Prognostic significance of copy-number alterations in multiple myeloma

Project description:Purpose Chromosomal aberrations are a hallmark of multiple myeloma but their global prognostic impact is largely unknown. Methods We performed a genome-wide analysis of malignant plasma cells from 192 newly myeloma patients using high-density, single-nucleotide polymorphism (SNP) arrays to identify genetic lesions associated with prognosis. Results Our analyses revealed deletions and amplifications in 98% of cases. Amplifications in 1q and deletions in 1p, 12p, 14q, 16q, and 22q were the most frequent lesions associated with adverse prognosis while recurrent amplifications of chromosomes 5, 9, 11, 15 and 19 conferred a favorable prognosis. Multivariate analysis retained three independent lesions: amp(1q23.3), amp(5q31.3) and del(12p13.31). When adjusted to the established prognostic variables ie t(4;14), and serum beta2-microglobulin (Sb2M), del(12p13.31) remained the most powerful independent marker (P <.0001; hazard ratio = 3.17) followed by Sb2M (P <.0001; hazard ratio = 2.78) and amp(5q31.3) (P =.0005; hazard ratio = 0.37). Cases with amp(5q31.3) alone and low Sb2M had an excellent prognosis (5-year overall survival = 87%) conversely cases with del(12p13.31) alone or amp(5q31.3) and del(12p13.31) and high Sb2M had a very poor outcome (5-year overall survival = 20%). Moreover, integration of SNP mapping and gene expression identified CD27 as potential critical gene responsible for poor prognosis of del(12p) myeloma patients. Conclusion These findings demonstrate the power and accessibility of molecular karyotyping to identify novel strong independent prognostic markers: amp(5q31.3) and del(12p13.31) and to provide insights into putative pathways deregulated in sub classes of cancer patients. Keywords: Human chromosome copy-number alterations study 192 myeloma patients at diagnosis examined with 500K Set Affymetrix chips

2009-08-19 | E-GEOD-12896 | biostudies-arrayexpress

ChIP-seq of EGR1 in human primary unstimulated monocytes and in unstimulated human monocytes from three CMML patients

Project description:Identification of EGR1 chromatin localization in human primary monocytes (2 donors), in human primary monocytes from 2 CMML patients with TET2 truncating mutations (high VAF) and in human primary monocytes from 1 CMML patients with TET2 truncating mutations with low VAF.

2021-11-15 | E-MTAB-11132 | biostudies-arrayexpress

Transcriptomic and nuclear architecture of immune cells after LPS-activation

Project description:Changes in the nuclear positioning of specific genes, depending on their expression status, have been observed in a large diversity of physiological processes. However, gene position is poorly documented in immune cells subjected to activation upon bacterial infection. Using the pig as a model organism, we focused our study on monocytes-derived macrophages and neutrophils, the first lines of defense against pathogens. We examine whether changes in gene expression due to LPS-activation imply genes repositioning in the nuclear space. A transcriptomic analysis was first performed to identify the genes differentially expressed and analyse the networks implicated during LPS-IFNγ activation in monocytes-derived macrophages. This allowed us to select 4 up-regulated (IL1β, IL8, CXCL10 and TNFα) and 4 down-regulated (VIM, LGALS3, TUBA3, and IGF2) genes to be further studied by 3D-FISH for their behavior in the nuclear space, particularly towards their chromosome territories (CT) during macrophages activation. Among the 4 up-regulated genes, 3 changed their position during the activation process while the 4 down-regulated ones did not. The analysis of gene behaviors towards their CT was extended to neutrophils for 3 up- and 2 down-regulated genes and similar results were obtained. Our data suggest that relocation in the nuclear space of genes differentially expressed in activated immune cells is gene specific and concern mostly up-regulated ones. Porcine monocyte-derived macrophages: Control vs. LPS-IFNγ activated vs. T-2 toxin/LPS-IFNγ activated. A common reference hybridization scheme was used to compare the three-condition experiment, CM vs. AM vs. TAM. The reference consisted of a pool of RNA from all samples. Biological replicates: 5 control, 5 LPS-IFNγ activated, 5 T-2 toxin/LPS-IFNγ activated independently. One replicate per array.

2011-11-02 | E-GEOD-25491 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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