CellNet statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

Protocols

CellNet specifications

Information


Unique identifier OMICS_23620
Name CellNet
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input data Some uncompressed raw expression files.
Computer skills Basic
Stability Stable
Maintained Yes

Maintainers


  • person_outline James Collins
  • person_outline George Daley

Information


Unique identifier OMICS_23620
Name CellNet
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License Artistic License version 2.0
Computer skills Advanced
Version 0.1
Stability Stable
Maintained Yes

Download


download.png

Versioning


No version available

Maintainers


  • person_outline James Collins
  • person_outline George Daley

Publication for CellNet

CellNet citations

 (33)
library_books

NOTCH signaling specifies arterial type definitive hemogenic endothelium from human pluripotent stem cells

2018
Nat Commun
PMCID: 5940870
PMID: 29739946
DOI: 10.1038/s41467-018-04134-7

[…] n T-cells; and RUNX1T1 and HOXB8, regulate expansion of blood progenitors, (Fig. ). Using the known transcription-target relationships obtained by combining largely complementary data from HTRIdb and CellNet, 163 regulatory interactions involving 110 transcription factors upstream of the nine differentially expressed transcription factor-encoding genes were pulled to construct a regulatory network […]

library_books

Tracing the origin of heterogeneity and symmetry breaking in the early mammalian embryo

2018
Nat Commun
PMCID: 5940674
PMID: 29739935
DOI: 10.1038/s41467-018-04155-2

[…] mere transcriptome data in different mammalian species, using the known properties of lineage specifiers as points of reference. This would be analogous to the use of computational frameworks such as CellNet and Mogrify (network biology-based computational algorithm) designed to predict transcription factors that can most efficiently change cellular state during cellular engineering/reprogramming, […]

call_split

A proteomics landscape of circadian clock in mouse liver

2018
Nat Commun
PMCID: 5908788
PMID: 29674717
DOI: 10.1038/s41467-018-03898-2
call_split See protocol

[…] their Pearson’s correlation coefficient, we linked each pathway-specific TF to three Mediators with highest Pearson’s correlation coefficient with it. The network among TFs and TGs was obtained from CellNet, Data visualization was done with Cytoscape v 3.3.0. […]

library_books

Metabolic characterization of directly reprogrammed renal tubular epithelial cells (iRECs)

2018
Sci Rep
PMCID: 5832874
PMID: 29497074
DOI: 10.1038/s41598-018-22073-7

[…] ferentiated tubular epithelial cells. In contrast to fibroblasts, iRECs express epithelial and tubular surface markers and tubule-specific transporters. Using transcriptional profiling techniques and CellNet- based characterization, we demonstrated that iRECs bear a substantial similarity to primary kidney tubule cells. On an ultra-structural level, they show tight junctions, a clear apico-basal p […]

library_books

Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection

2018
Nucleic Acids Res
PMCID: 5887474
PMID: 29325153
DOI: 10.1093/nar/gkx1314

[…] om Regulatory Circuit resource (). Meanwhile, for the construction of mouse pancreatic cell type-specific PGRN, we used mouse (Mus musculus) PIN from STRING () and mouse protein–DNA interactions from CellNet () (see details in Materials and Methods).In assessing the performance of ProTINA and the other methods, we compared the ranked list of protein target predictions for each compound with the re […]

library_books

Use of deep neural network ensembles to identify embryonic fetal transition markers: repression of COX7A1 in embryonic and cancer cells

2017
Oncotarget
PMCID: 5814259
PMID: 29487692
DOI: 10.18632/oncotarget.23748

[…] clearly see that iPSCs are often confused with ESCs (Figure ). This supports the fact that properly reprogrammed iPSCs are almost identical to ESCs on the transcriptome level [, ].In contrast to the CellNet approach [], our system does not distinguish between different types of differentiated cells (liver, muscle, kidney, etc.) but instead is aimed at recognizing different states of early embryon […]

Citations

Looking to check out a full list of citations?

CellNet institution(s)
Stem Cell Transplantation Program, Division of Pediatric Hematology and Oncology, Manton Center for Orphan Disease Research, Howard Hughes Medical Institute, Boston Children’s Hospital and Dana Farber Cancer Institute, Boston, MA, USA; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA; Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, USA; Howard Hughes Medical Institute, Department of Biomedical Engineering and Center of Synthetic Biology, Boston University, Boston, MA, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA; Graduate Program in Materials Science and Engineering, Federal University of Santa Catarina, Florianópolis, Brazil
CellNet funding source(s)
Supported by NIH grant R24DK092760, the HHMI, the NIH (Progenitor Cell Biology Consortium UO1-HL100001, R24DK092760, and P50HG005550), the Ellison Medical Foundation, Doris Duke Medical Foundation, the Harvard Stem Cell Institute, the Broad Institute, NIDDK (K01DK096013), NHLBI (T32HL066987 and T32HL007623), the Mayo Clinic Center for Individualized Medicine and Mayo Clinic Center for Regenerative Medicine, National Council for Scientific and Technological Development and the program Science Without Borders (CNPq, Brazil).

CellNet reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review CellNet