Perspective on recent developments and challenges in regulatory and systems genomics

TL;DR

This paper reviews recent computational advances and ongoing challenges in decoding the cis-regulatory code, crucial for understanding gene regulation and genetic variation effects in human disease.

Contribution

It provides a comprehensive overview of current methods, highlights gaps and limitations, and discusses emerging technologies for modeling gene regulation across diverse biological contexts.

Findings

Neural networks effectively learn sequence-to-function mappings.

3D chromatin organization insights can reveal long-range regulatory effects.

Emerging technologies like spatial transcriptomics expand understanding of gene regulation.

Abstract

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals.