Perspective on recent developments and challenges in regulatory and systems genomics

doi:10.1093/bioadv/vbaf106

Review

. 2025 May 9;5(1):vbaf106.

doi: 10.1093/bioadv/vbaf106. eCollection 2025.

Perspective on recent developments and challenges in regulatory and systems genomics

Julia Zeitlinger^{1

2}, Sushmita Roy^{3

4}, Ferhat Ay^{5

6

7}, Anthony Mathelier^{8

9

10}, Alejandra Medina-Rivera¹¹, Shaun Mahony¹², Saurabh Sinha^{13

14}, Jason Ernst^{15

16

17

18

19

20}

Affiliations

¹ Stowers Institute for Medical Research, Kansas City, MO 64112, United States.
² Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS 66160, United States.
³ Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States.
⁴ Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, United States.
⁵ Centers for Autoimmunity, Inflammation and Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA 92037, United States.
⁶ Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, United States.
⁷ Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, United States.
⁸ Norwegian Centre for Molecular Biosciences and Medicine (NCMBM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway.
⁹ Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo 0450, Norway.
¹⁰ Bioinformatics in Life Science (BiLS) initiative, Department of Pharmacy, University of Oslo, Oslo 0371, Norway.
¹¹ Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro 76230, Mexico.
¹² Center for Eukaryotic Gene Regulation, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, United States.
¹³ Walter H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.
¹⁴ H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.
¹⁵ Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁶ Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁷ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁸ Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁹ Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, United States.
²⁰ Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, United States.

PMID: 40626041
PMCID: PMC12233095
DOI: 10.1093/bioadv/vbaf106

Review

Perspective on recent developments and challenges in regulatory and systems genomics

Julia Zeitlinger et al. Bioinform Adv. 2025.

. 2025 May 9;5(1):vbaf106.

doi: 10.1093/bioadv/vbaf106. eCollection 2025.

Authors

Affiliations

¹ Stowers Institute for Medical Research, Kansas City, MO 64112, United States.
² Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS 66160, United States.
³ Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States.
⁴ Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, United States.
⁵ Centers for Autoimmunity, Inflammation and Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA 92037, United States.
⁶ Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, United States.
⁷ Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, United States.
⁸ Norwegian Centre for Molecular Biosciences and Medicine (NCMBM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway.
⁹ Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo 0450, Norway.
¹⁰ Bioinformatics in Life Science (BiLS) initiative, Department of Pharmacy, University of Oslo, Oslo 0371, Norway.
¹¹ Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro 76230, Mexico.
¹² Center for Eukaryotic Gene Regulation, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, United States.
¹³ Walter H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.
¹⁴ H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.
¹⁵ Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁶ Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁷ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁸ Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at University of California, Los Angeles, Los Angeles, CA 90095, United States.
¹⁹ Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, United States.
²⁰ Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, United States.

PMID: 40626041
PMCID: PMC12233095
DOI: 10.1093/bioadv/vbaf106

Abstract

Summary: 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 toward solving this fundamental problem. We describe how cis-regulatory elements are mapped with various genomics assays and how studies of the 3D chromatin organization could help identifying long-range regulatory effects. We discuss how the cis-regulatory 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 describe current methods for mapping gene regulatory networks to 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.

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Conflict of interest statement

J.Z. owns a patent on ChIP-nexus (no. 10287628). The other authors have no conflicts of interest to declare.

Figures

**Figure 1.**
Regulatory genomics studies the intricate relationships between transcription factor activities and target gene expression, mediated by cis-regulatory elements. Researchers seek to identify general principles of gene regulation, such as how enhancer activities, motif syntax rules, 3D genome organization, and chromatin states relate to each other and ultimately to gene expression. A major goal is to build accurate and generalizable sequence-function models that can not only reveal underlying mechanisms but make predictions of variant effects. Another major theme is the reconstruction of gene regulatory networks to describe biological processes of interest. Research into computational methods and models in regulatory genomics strives to make best use of diverse and rapidly advancing experimental technologies.

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References

1. Agarwal R, Melnick L, Frosst N et al. Neural additive models: interpretable machine learning with neural nets. In: Advances in Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates, Inc., 2021, 4699–4711.
1. Aibar S, González-Blas CB, Moerman T et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017;14:1083–6. - PMC - PubMed
1. Alexandari AM, Horton CA, Shrikumar A et al. De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein–DNA binding. bioRxiv Prepr. Serv. Biol., 10.1101/2023.05.11.540401, 2023, preprint: not peer reviewed. - DOI
1. Alipanahi B, Delong A, Weirauch MT et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015;33:831–8. - PubMed
1. AlQuraishi M, Sorger PK. Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nat. Methods 2021;18:1169–80. - PMC - PubMed

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[1] Agarwal R, Melnick L, Frosst N et al. Neural additive models: interpretable machine learning with neural nets. In: Advances in Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates, Inc., 2021, 4699–4711.

[2] Agarwal R, Melnick L, Frosst N et al. Neural additive models: interpretable machine learning with neural nets. In: Advances in Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates, Inc., 2021, 4699–4711.

[3] Aibar S, González-Blas CB, Moerman T et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017;14:1083–6. - PMC - PubMed

[4] Aibar S, González-Blas CB, Moerman T et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017;14:1083–6. - PMC - PubMed

[5] Alexandari AM, Horton CA, Shrikumar A et al. De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein–DNA binding. bioRxiv Prepr. Serv. Biol., 10.1101/2023.05.11.540401, 2023, preprint: not peer reviewed. - DOI

[6] Alexandari AM, Horton CA, Shrikumar A et al. De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein–DNA binding. bioRxiv Prepr. Serv. Biol., 10.1101/2023.05.11.540401, 2023, preprint: not peer reviewed. - DOI

[7] Alipanahi B, Delong A, Weirauch MT et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015;33:831–8. - PubMed

[8] Alipanahi B, Delong A, Weirauch MT et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015;33:831–8. - PubMed

[9] AlQuraishi M, Sorger PK. Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nat. Methods 2021;18:1169–80. - PMC - PubMed

[10] AlQuraishi M, Sorger PK. Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nat. Methods 2021;18:1169–80. - PMC - PubMed

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Perspective on recent developments and challenges in regulatory and systems genomics

Affiliations

Perspective on recent developments and challenges in regulatory and systems genomics

Authors

Affiliations

Abstract

Conflict of interest statement

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