ANANSE: ANalysis Algorithm for Networks Specified by Enhancers
What is ANANSE?
ANANSE is a computational approach to infer enhancer-based gene regulatory networks (GRNs) and to identify key transcription factors between two GRNs. You can use it to study transcription regulation during development and differentiation, or to generate a shortlist of transcription factors for trans-differentiation experiments.
ANANSE is written in Python and comes with a command-line interface that includes the following commands:
|ananse binding||predict transcription factor binding profiles|
|ananse network||infer a gene regulatory network|
|ananse influence||infer key transcription factors between two networks|
|ananse plot||plot influence results in a dotplot and optionally a GRN of the Top TFs|
|ananse view||inspect the output of
All functionality is also available through a Python API.
ANANSE is free and open source research software. If you find it useful please cite it:
Quan Xu, Georgios Georgiou, Siebren Frölich, Maarten van der Sande, Gert Jan C Veenstra, Huiqing Zhou, Simon J van Heeringen, ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination, Nucleic Acids Research, Volume 49, Issue 14, 20 August 2021, Pages 7966–7985, https://doi.org/10.1093/nar/gkab598
ANANSE can also be useful when using CAGE-seq data. If you used this tool with CAGE-seq data, please cite:
Heuts BMH, Arza-Apalategi S, Frölich S, Bergevoet SM, van den Oever SN, van Heeringen SJ, et al. Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework. Scientific Reports 2022 12:1 [Internet]. 2022 Nov 4 [cited 2022 Dec 6];12(1):1–12. Available from: https://www.nature.com/articles/s41598-022-21148-w
- Install ANANSE on Linux or Mac, see the Installation page for details.
- Have a look at these simple examples to get a taste of what is possible.
- Check out the command-line reference to get going with your own data.
- First, check the FAQ for common issues.
- The preferred way to get support is through the Github issues page.
- Finally, you can reach us by email to Simon J. van Heeringen or Quan Xu.
- Model description
- Input data
- Command-line reference
- API documentation