The adapted Activity-By-Contact model for enhancer–gene assignment and its application to single-cell data

Author:

Hecker Dennis123ORCID,Behjati Ardakani Fatemeh123,Karollus Alexander4,Gagneur Julien4567ORCID,Schulz Marcel H123ORCID

Affiliation:

1. Institute of Cardiovascular Regeneration, Goethe University Hospital

2. Cardio-Pulmonary Institute, Goethe University

3. German Centre for Cardiovascular Research, Partner site Rhine-Main , Frankfurt am Main 60590

4. School of Computation, Information and Technology, Technical University of Munich , Garching 85748

5. Institute of Human Genetics, Technical University of Munich , Munich 81675

6. Computational Health Center, Helmholtz Center Munich , Neuherberg 85764

7. Munich Data Science Institute, Technical University of Munich , Garching 85748, Germany

Abstract

AbstractMotivationIdentifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model which scores enhancer–gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a gene’s perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is neither implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data.ResultsWe demonstrate that the ABC score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalized ABC model with TF binding information and illustrated an analysis of a single-cell ATAC-seq dataset of the human heart, where we were able to characterize cell type-specific regulatory interactions and predict gene expression based on TF affinities. All executed processing steps are incorporated into our new computational pipeline STARE.Availability and implementationThe software is available at https://github.com/schulzlab/STAREContactmarcel.schulz@em.uni-frankfurt.deSupplementary informationSupplementary data are available at Bioinformatics online.

Funder

German Centre for Cardiovascular Research

Cardio-Pulmonary Institute

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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5. Prediction of single-cell gene expression for transcription factor analysis;Behjati Ardakani;GigaScience,2020

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