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Method: Autoencoders for genomic variation analysis
- Margarita Geleta,
- Daniel Mas Montserrat,
- Xavier Giro-i-Nieto,
- and Alexander G. Ioannidis
Genome Res. February 2026 36: 348-360; Published in Advance January 20, 2026, doi:10.1101/gr.280086.124
...are needed in order to properly capture the genetic composition of populations. Here, we explore deep learning techniques, namely, variational autoencoders (VAEs), to process genomic data from a population perspective. We show the power of VAEs for a variety of tasks relating to the interpretation...
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Method: Automated interpretable artificial intelligence genomic prediction with AIGP
- Lei Wei,
- Ziqin Jiang,
- Baoliang Fan,
- Yidan Yan,
- Zhenqiang Xu,
- Xiaoxiang Hu,
- and Yuzhe Wang
Genome Res. April 2026 36: 814-826; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
...prediction; however, it often struggles with interpretability because of its black-box nature. Here, we evaluate 12 ML models alongside GBLUP and BayesR to identify key factors influencing genomic prediction performance across traits with different genetic architectures in multiple agricultural species...
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Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
- Yidi Deng,
- Jiadong Mao,
- Jarny Choi,
- and Kim-Anh Lê Cao
Genome Res. April 2026 36: 785-801; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
...and highlight that traditional expression-based TF activity inference may yield higher false discovery rates, warranting more cautious interpretation.In the second case study on early hematopoiesis, we introduced a novel perspective for analyzing cellular variation through coexpression analysis. We showed...
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Method: Secure discovery of genetic relatives across large-scale and distributed genomic data sets
- Matthew M. Hong,
- David Froelicher,
- Ricky Magner,
- Victoria Popic,
- Bonnie Berger,
- and Hyunghoon Cho
Genome Res. September 2024 34: 1312-1323; Published in Advance August 7, 2024, doi:10.1101/gr.279057.124
...to jointly analyze genomic data from multiple parties are essential for increasing the sample sizes to enhance the discovery of biomedical insights. However, when sharing individual-level genetic data is not feasible owing to privacy concerns (e.g., Erlich et al. 2018), the range of joint analyses that can...
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Method: BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations
- Antonio Nappi,
- Liubov Shilova,
- Theofanis Karaletsos,
- Na Cai,
- and Francesco Paolo Casale
Genome Res. December 2025 35: 2682-2690; Published in Advance October 24, 2025, doi:10.1101/gr.280689.125
...–disease links, including PRPH2 with retinal disease. Integrating BayesRVAT within omnibus frameworks further increases discoveries, demonstrating that flexible annotation modeling captures complementary signals beyond existing burden and variance-component tests.Understanding the role of rare genetic variants...
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Mini-Review: Large-scale genomic analysis of the domestic dog informs biological discovery
- Reuben M. Buckley and
- Elaine A. Ostrander
Genome Res. June 2024 34: 811-821; Published in Advance July 2, 2024, doi:10.1101/gr.278569.123
...drive rare variant discovery and assess the geographic distribution of canine diversity, which identifies Asia as a major source of missing variation. Finally, we review recent comparative genomic analyses that will facilitate annotation of the noncoding in dogs.The domestic dog has increasingly been...
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Method: Interactive visualization and interpretation of pangenome graphs by linear reference–based coordinate projection and annotation integration
- Zepu Miao and
- Jia-Xing Yue
Genome Res. February 2025 35: 296-310; Published in Advance January 13, 2025, doi:10.1101/gr.279461.124
...and structural variants (SVs) (Paten et al. 2017). Therefore, a species-representative pan graph is expected to shed novel insights into the interpretation of the genotype-to-phenotype association and the discovery of missing heritability.Although a number of tools have been developed to build pan graphs based...
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Method: Multisample motif discovery and visualization for tandem repeats
- Yaran Zhang,
- Marc Hulsman,
- Alex Salazar,
- Niccolò Tesi,
- Lydian Knoop,
- Sven van der Lee,
- Sanduni Wijesekera,
- Jana Krizova,
- Erik-Jan Kamsteeg,
- and Henne Holstege
Genome Res. April 2025 35: 850-862; Published in Advance November 13, 2024, doi:10.1101/gr.279278.124
...Yaran Zhang1, Marc Hulsman1,2, Alex Salazar1, Niccolò Tesi1,2, Lydian Knoop1, Sven van der Lee1,2,3, Sanduni Wijesekera1, Jana Krizova1, Erik-Jan Kamsteeg4 and Henne Holstege1,2,3,5 1Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit...
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Resource: Analyzing the large and complex SFARI autism cohort data using the Genotypes and Phenotypes in Families (GPF) platform
- Liubomir Chorbadjiev,
- Murat Cokol,
- Zohar Weinstein,
- Kevin Shi,
- Christopher Fleisch,
- Nikolay Dimitrov,
- Svetlin Mladenov,
- Ivo Todorov,
- Iordan Ivanov,
- Simon Xu,
- Steven Ford,
- Yoon-ha Lee,
- Boris Yamrom,
- Steven Marks,
- Adriana Munoz,
- Alex Lash,
- Natalia Volfovsky,
- and Ivan Iossifov
Genome Res. October 2025 35: 2352-2362; Published in Advance September 16, 2025, doi:10.1101/gr.280356.124
...made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale data sets requires specialized computational tools to organize and analyze the extensive data. Genotypes and Phenotypes in Families (GPF) is an open-source platform that manages...
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Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
- Tianyu Liu,
- Tinglin Huang,
- Wengong Jin,
- Tinyi Chu,
- Rex Ying,
- and Hongyu Zhao
Genome Res. April 2026 36: 754-768; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...the reliability of the interpretation of spatial transcriptomics.Efforts have been made to address the limitations of these two platforms. For imaging-based technologies, researchers utilize reference scRNA-seq data sets with a larger number of measured genes to impute the unmeasured genes in the spatial...
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