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About
Identification of cell-fate determinants for directing stem cell differentiation is one of the challenges in Sytems Biology. Little is known about how cell-fate determinants that regulate functionally important subnetworks in large gene-regulatory networks (i.e., GRN motifs). Here we provide a generalized computational framework for stem cell differentiation that can systematically predict cell-fate determinants.
The term ‘seesaw’ refers to the basic assumption that that the progenitor cell phenotype is maintained by the dynamic equilibrium of the opposing cell-fate determinants, and during binary cell-fate decisions, the equilibrium is shifted towards either of the two cell-fate determinants, until finally the gene expression stabilizes in the corresponding daughter cell type.
This software is made publicly available in the hope that it can guide differentiation experiments in stem cell research and regenerative medicine.
Authors
This software was developed in the [Computational Biology Group] (https://wwwfr.uni.lu/lcsb/research/computational_biology) by
Citation
Within any publication that uses any methods or results derived from or inspired by SeesawPred, please cite:
- Hartmann, A., Okawa, S., Zaffaroni, G., & del Sol, A., 2018. SeesawPred: A Web Application for Predicting Cell-fate Determinants in Cell Differentiation. Scientific Reports 8, 13355
- Okawa, S., Nicklas, S., Zickenrott, S., Schwamborn, J.C., del Sol, A., 2016. A Generalized Gene-Regulatory Network Model of Stem Cell Differentiation for Predicting Lineage Specifiers. Stem Cell Reports 7, 307–315.
Examples
The examples proviede along with the software are:
- Mouse Neural Stem Cell (mNSC) differentiation into neurons and astrocytes
- Mouse Hematopoietic Stem Cell (mHSC) differentiation to erythroids myeloids
Acknowledments
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Documentation
File formats
- The required input of SeesawPred is a tab separated value file where columns represent gene expression (microarray or RNA-Seq) replicas of the progenitor cell-type and the two daughter cell-types labeled as “Progenitor”, “Daughter1”, “Daughter2”, respectively, and the rows are labeled according to the gene symbols. See an example dataset here.
- The Prior Knowledge Network (PKN) is a network consisting of TF–TF regulatory interactions, that serve as potential links in the network. The file format PKN is also a tab separated values file containing the list of TF - TF interactions, where the first and the third column corresponds to source and target TF, and second colum describes the interaction type which can be “Activation”, “Inhibition” or “Unspecified”. See an example PKN here.
Application walkthrough
Upload transcriptomic files | |
| Custom input files containing transcriptomics data can be uploaded via the file chooser. The upload expects a tab separated value file containing the progenitor and daughter cell-type data. Valid extensions are .csv, .tsv and .txt . Naming convention for columns is "Parental.", "Daugher1.", and "Daughter2." for progenitor and the two daugther cell-types. Gene names should be in gene symbol format (HGNC for human and MGI for mouse). |
Upload prior knowledge network | |
| Prior knowledge networks can be uploaded via the PKN chooser. The upload expects a tab separated value file containing a directed edge list: The first column contains is the regulator gene, the second column contains sign information (Activation, Inhibition or Unspecified), and the third column is the regulated gene, (e.g.: MYBL1 Inhibition SP1) . Valid extensions are .csv, .tsv and .txt . If you do not have access to PKN, you can choose from two limited-size prior knowledge networks, currently available for mouse and human. |
Set the parameters | |
| The application has two tuning parameters. Complexity controls the number of Gene Regulatory Networks (GRNs) that are subject to SCC detection: Basic => 10, Complex => 100, Advanced => 1000 networks. Strategy controls the P-value for the significance test. |
Try an example | |
| The application contains various binary cell differentiation examples that can be loaded using the example chooser. The example chooser also sets the correct PKN and the tuning parameters. |
Run the analysis | |
| If you have followed the above steps, (e.g. you have uploaded your data, and set the PKN or loaded an example), this button turns active, and you can run the analysis by clicking on the button. Depending on the options you have set the analysis might take some time. |
Results | |
| The result browser is to visualize the results the output of the application. Detailed results are shown when you click on a line in the result browser. |
Use
The SeesawPred web-based tool (SOFTWARE for short) is provided free of charge for academic, non-profit use. For commercial use, please contact the authors for a license. Using the SOFTWARE means you accept the terms and conditions of the Disclaimer below.
Disclaimer
THE SOFTWARE IS NOT TO BE USED FOR TREATING OR DIAGNOSING HUMAN SUBJECTS.
THE SOFTWARE AND ALL CONTENT ON THIS SERVER ARE PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Source
The code is available under the terms of the Affero GPL license v3 at: the GitLab repository