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Method: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
- Cameron Park,
- Shouvik Mani,
- Nicolas Beltran-Velez,
- Katie Maurer,
- Teddy Huang,
- Shuqiang Li,
- Satyen Gohil,
- Kenneth J. Livak,
- David A. Knowles,
- Catherine J. Wu,
- and Elham Azizi
Genome Res. September 2024 34: 1384-1396; Published in Advance September 5, 2024, doi:10.1101/gr.279126.124
...interactions and rare understudied cell types. Finally, existing tools only predict static interactions. They are not capable of inferring dynamic time-varying interactions from the integration of time-series data sets and do not obtain the strength or sign of interactions, for example, inhibitory...
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Method: The oligogenic inheritance test GCOD detects risk genes and their interactions in congenital heart defects
- Maureen Pittman,
- Kihyun Lee,
- Franco Felix,
- Yu Huang,
- Adrienne Lam,
- Mauro W. Costa,
- Deepak Srivastava,
- and Katherine S. Pollard
Genome Res. February 2026 36: 330-347; Published in Advance December 12, 2025, doi:10.1101/gr.281141.125
...genetic interactions. As sequencing is applied to more families and other disorders, GCOD will enable detection of increasingly large, novel gene combinations, shedding light on combinatorial causes of genetic diseases.Many diseases are genetically heterogeneous such that damaging variants at different...
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Method: ERC2.0 evolutionary rate covariation update improves inference of functional interactions across large phylogenies
- Jordan H. Little,
- Guillermo Hoffmann Meyer,
- Aakash Grover,
- Alex Michael Francette,
- Raghavendran Partha,
- Karen M. Arndt,
- Martin Smith,
- Nathan Clark,
- and Maria Chikina
Genome Res. September 2025 35: 2041-2051; Published in Advance August 7, 2025, doi:10.1101/gr.280586.125
...predictions.The omics era has greatly increased the number of sequenced s (Hotaling et al. 2021). With this influx of information, network and system biologists have developed approaches to study gene and protein interactions from a broader perspective and at a much larger scale. Networks of such interactions...
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Method: Modeling gene interactions in polygenic prediction via geometric deep learning
- Han Li,
- Jianyang Zeng,
- Michael P. Snyder,
- and Sai Zhang
Genome Res. January 2025 35: 178-187; Published in Advance November 19, 2024, doi:10.1101/gr.279694.124
...-Net, an interpretable geometric deep learning–based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the -wide PRS at the single-gene resolution and then explicitly encapsulates gene–gene interactions...
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Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
- Vishvak Raghavan,
- Yumin Zheng,
- Yue Li,
- and Jun Ding
Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
...). By studying these interactions, we can uncover mechanisms behind biological processes, informing research on disorders and aiding in the discovery of diagnostic and therapeutic strategies (Giladi et al. 2020).Inferring CCIs in biological processes is challenging owing to cellular heterogeneity in most...
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Method: Inferring the mode and strength of ongoing selection
- Gustavo V. Barroso and
- Kirk E. Lohmueller
Genome Res. April 2023 33: 632-643; Published in Advance April 13, 2023, doi:10.1101/gr.276386.121
...in protein structure (Moutinho et al. 2019) and interaction networks (Park et al. 2019; Ratnakumar et al. 2020), shedding light into both evolutionary and medical genetics.Existing methods to infer the strength of selection use the distribution of allele frequencies of a population known as the site...
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Method: Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe
- Arnaud Belcour,
- Jeanne Got,
- Méziane Aite,
- Ludovic Delage,
- Jonas Collén,
- Clémence Frioux,
- Catherine Leblanc,
- Simon M. Dittami,
- Samuel Blanquart,
- Gabriel V. Markov,
- and Anne Siegel
Genome Res. June 2023 33: 972-987; Published in Advance July 19, 2023, doi:10.1101/gr.277056.122
...Inferring and comparing metabolism across heterogeneous sets of annotated s using AuCoMe Arnaud Belcour1,4, Jeanne Got1,4, Méziane Aite1, Ludovic Delage2, Jonas Collén2, Clémence Frioux3, Catherine Leblanc2, Simon M. Dittami2, Samuel Blanquart1, Gabriel V. Markov2,5 and Anne Siegel1,5 1Univ Rennes...
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Method: Quantitative prediction of enhancer–promoter interactions
- Polina S. Belokopytova,
- Miroslav A. Nuriddinov,
- Evgeniy A. Mozheiko,
- Daniil Fishman,
- and Veniamin Fishman
Genome Res. January 2020 30: 72-84; Published in Advance December 2, 2019, doi:10.1101/gr.249367.119
...-f@ya.ruAbstractRecent experimental and computational efforts have provided large data sets describing three-dimensional organization of mouse and human s and showed the interconnection between the expression profile, epigenetic state, and spatial interactions of loci. These interconnections were utilized to infer the spatial...
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Research: Sir3 mediates long-range chromosome interactions in budding yeast
- Myriam Ruault,
- Vittore F. Scolari,
- Luciana Lazar-Stefanita,
- Antoine Hocher,
- Isabelle Loïodice,
- Romain Koszul,
- and Angela Taddei
Genome Res. March 2021 31: 411-425; Published in Advance February 12, 2021, doi:10.1101/gr.267872.120
...these experiments were performed in strains expressing a rap1 allele truncated for the Rap1–Sir3 interaction domain, we can infer that the Sir3-mediated pairing is also Rap1-independent. Therefore, this synthetic approach demonstrated that Sir3 can bond loci belonging to different chromosomes together...
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Resource: Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach
- Pablo Meyer,
- Geoffrey Siwo,
- Danny Zeevi,
- Eilon Sharon,
- Raquel Norel,
- DREAM6 Promoter Prediction Consortium,
- Eran Segal,
- and Gustavo Stolovitzky
Genome Res. November 2013 23: 1928-1937; Published in Advance August 15, 2013, doi:10.1101/gr.157420.113
...is available for this article.] One of the main objectives of the Dialogue for Reverse Engineering Assessments and Methods (DREAM) (Stolovitzky et al. 2007) is to catalyze the interaction between experiment and theory in systems biology, particularly for quantitativemodel building. For this purpose...

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