SeesawPred
Hartmann A, Okawa S, Zaffaroni G, Del Sol A
2018 · Scientific reports
Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell.
Abstract
From the original paper, Scientific reports · PubMed
Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell. Despite the increasing research effort, identification of cell-fate determinants (transcription factors (TFs) determining cell fates during differentiation) still remains a challenge, especially when closely related cell types from a common progenitor are considered. Here, we develop SeesawPred, a web application that, based on a gene regulatory network (GRN) model of cell differentiation, can computationally predict cell-fate determinants from transcriptomics data. Unlike previous approaches, it allows the user to upload gene expression data and does not rely on pre-compiled reference data sets, enabling its application to novel differentiation systems. SeesawPred correctly predicted known cell-fate determinants on various cell differentiation examples in both mouse and human, and also performed better compared to state-of-the-art methods. The application is freely available for academic, non-profit use at http://seesaw.lcsb.uni.lu.
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 SeesawPred 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 . Representation family is regulatory-network influence. Cited in §3.2 of the review. Editorial rationale pending review by the maintainer.