Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1242827 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR32883748_1.fastq.gz | Fastqsanger.gz | |||
| SRR32883748_2.fastq.gz | Fastqsanger.gz | |||
| SRR32883749_1.fastq.gz | Fastqsanger.gz | |||
| SRR32883749_2.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 11 |
Similar Datasets
Project description:The goal of this study is to compare the transcriptome stability of DN1wt versus DN1gl/gl and DN1CD2-Ostm1gl/glTR Methods: DN1 thymocytes mRNA profiles of 19-day-old wild-type (WT), osteopetrotic grey-lethal (gl/gl) and transgenic CD2-Ostm1 gl/gl mice were generated by deep sequencing,DN1 cells from 2-3 mice per genotype were pooled, no technical replicates, using Illumina HiSeq 2000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: TopHat and Cufflinks. qRT–PCR validation was performed using SYBR Green assays Results: DN1 gl/gl showed a distinctive transcriptome signature in comparison to DN1 wt and DN1 gl/glTR with respectivelly 146 and 205 differentially expressed genes and 205. Migration genes were significantly affected in DN1 gl/gl samples and RAC1 and S1PR1 gene expressions were confirmed using RT-qPCR. Conclusions: RNA seq allowed us to identify the enhanced expression of migration genes (RAC1 and S1PR1) in DN1gl/gl, that were both normalized in the DN1 cells from transgenic gl/glTR mice, which suggests defective T cell migration associated to the osteopetrotic thymus phenotype.
2016-09-01 | GSE72184 | GEO
Project description:Bordel2018 - GSMM for Human Metabolic
Reactions (HMR database)
This model is described in the article:
Constraint based modeling of
metabolism allows finding metabolic cancer hallmarks and
identifying personalized therapeutic windows
Sergio Bordel
Oncotarget. 2018; 9:19716-19729
Abstract:
In order to choose optimal personalized anticancer
treatments, transcriptomic data should be analyzed within the
frame of biological networks. The best known human biological
network (in terms of the interactions between its different
components) is metabolism. Cancer cells have been known to have
specific metabolic features for a long time and currently there
is a growing interest in characterizing new cancer specific
metabolic hallmarks. In this article it is presented a method
to find personalized therapeutic windows using RNA-seq data and
Genome Scale Metabolic Models. This method is implemented in
the python library, pyTARG. Our predictions showed that the
most anticancer selective (affecting 27 out of 34 considered
cancer cell lines and only 1 out of 6 healthy mesenchymal stem
cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
all the considered cell lines can be selectively affected by
targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
of drugs or siRNAs combined in patient specific manners could
be at the core of metabolism based personalized treatments.
This model is hosted on
BioModels Database
and identified by:
MODEL1707250000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1707250000 | BioModels
Project description:The goal of this study is to compare the transcriptome stability of DN1wt versus DN1gl/gl and DN1CD2-Ostm1gl/glTR Methods: DN1 thymocytes mRNA profiles of 19-day-old wild-type (WT), osteopetrotic grey-lethal (gl/gl) and transgenic CD2-Ostm1 gl/gl mice were generated by deep sequencing,DN1 cells from 2-3 mice per genotype were pooled, no technical replicates, using Illumina HiSeq 2000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: TopHat and Cufflinks. qRTâ??PCR validation was performed using SYBR Green assays Results: DN1 gl/gl showed a distinctive transcriptome signature in comparison to DN1 wt and DN1 gl/glTR with respectivelly 146 and 205 differentially expressed genes and 205. Migration genes were significantly affected in DN1 gl/gl samples and RAC1 and S1PR1 gene expressions were confirmed using RT-qPCR. Conclusions: RNA seq allowed us to identify the enhanced expression of migration genes (RAC1 and S1PR1) in DN1gl/gl, that were both normalized in the DN1 cells from transgenic gl/glTR mice, which suggests defective T cell migration associated to the osteopetrotic thymus phenotype. DN1 thymocytes mRNA profiles of 19-day-old wild-type (WT), osteopetrotic grey-lethal (gl/gl) and transgenic CD2-Ostm1 gl/gl mice were compared by deep sequencing,DN1 cells from 2-3 mice per genotype were pooled, no technical replicates, using Illumina HiSeq 2000.
2016-09-01 | E-GEOD-72184 | biostudies-arrayexpress
Project description:DZ (B220+ IgDlo GL-7+ CD95+CXCR4+CD86lo) or LZ (B220+ IgDlo GL-7+ CD95+CXCR4loCD86+) GC B cells were sorted from male Sle1.yaa lupus mice at 2,4,6 months of age. RNA-seq was employed to assess transcriptional changes in the different GC B cell subsets throughout course of disease.
2024-11-08 | GSE250607 | GEO
Project description:The aim of this data set is to perform a differential expression analysis between wild type eye-antennal imaginal disc and discs that are homozygous glass mutant gl[60j]. This data set is used to validate Glass target gene predictions identified by i-cisTarget on a set of conserved eye-specific genes. RNA-seq was performed in eye-antennal imaginal discs of two D.melanogaster wild-type strains (Canton S and strain RAL-208 (Jordan et al. 2007, Ayroles et al. 2009)), representing two biological replicates; and in glass mutant (gl[60j]) discs for two technical replicates.
2012-09-21 | E-GEOD-39781 | biostudies-arrayexpress
Project description:RNA-seq data from human lymphoma cell lines
| PRJNA470706 | ENA
Project description:RNA-Seq data of neuroblastoma classical cell lines
| PRJEB55331 | ENA
Project description:Input control for ChIP-seq on transgenic flies expressing gl-eGFP fusion proteins. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODE_Data_Use_Policy_for_External_Users_03-07-14.pdf
2024-02-27 | GSE258833 | GEO
Project description:ChIP-seq on transgenic flies expressing gl-eGFP fusion proteins. The IP was performed using an anti-GFP antibody. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODE_Data_Use_Policy_for_External_Users_03-07-14.pdf
2024-02-27 | GSE258749 | GEO