L1 · Network-informed inverse design heuristics I T

ANANSE

Xu Q, Georgiou G, Frölich S, van der Sande M, Veenstra GJC, Zhou H, van Heeringen SJ

2021 · Nucleic acids research

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors.

Abstract

From the original paper, Nucleic acids research · PubMed

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

Summary

Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how ANANSE relates to the cross-modality inverse-design framework of the review.

Why this level

Level 1 because the method scores candidate interventions through static regulatory-network influence relative to a target GRN, without computing the post-intervention state $\hat P_{S,u}$ . Representation family is regulatory-network influence. Cited in §3.2 of the review. Editorial rationale pending review by the maintainer.