Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA324504 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:ATAC-seq samples from 2 species and 2 cell types were generated to study cis-regulatory element evolution. Briefly, previously generated urinary stem cell derived iPS-cells (Homo sapiens) of 2 human individuals and fibroblast derived cynomolgus macaque iPSCs (Macaca fascicularis) of 2 individuals (Geuder et al. 2021) were differentiated to neural progenitor cells via dual-SMAD inhibition as three-dimensional aggregation culture (Chambers et al. 2009; Ohnuki et al. 2014). The NPC lines were cultured in NPC proliferation medium and passaged 2 - 4 times until they were dissociated and subjected to ATAC-seq together with the respective iPSC clones. ATAC-seq libraries were generated using the Omni-ATAC protocol (Corces et al. 2017) with minor modifications.
2023-10-25 | E-MTAB-13373 | biostudies-arrayexpress
Project description:RNA extracted at 240min. Samples are rRNA depleted. Expression measurement of Homo sapiens genes.
2019-07-31 | GSE114179 | GEO
Project description:RNA sequencing data for 30 bladder cancer cell lines
| PRJNA382834 | ENA
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