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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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Research: Degrees of convergent evolution in rodent adaptations to arid environments
- Domitille Chalopin,
- Carine Rey,
- Jeremy Ganofsky,
- Juliana Blin,
- Pascale Chevret,
- Marion Mouginot,
- Laurent Guéguen,
- Bastien Boussau,
- Sophie Pantalacci,
- and Marie Sémon
Genome Res. March 2026 36: 472-486; Published in Advance January 21, 2026, doi:10.1101/gr.280089.124
...and the amount of genomic convergence. To our knowledge, we so far lacked study designs that could quantify genomic convergence accounting for both species relatedness and age of transition.Rodentia is the most diversified order of mammals with living representatives spanning 70 million years (MY) of evolution...
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Method: Gene duplication is associated with gene diversification and potential neofunctionalization in lung cancer evolution
- Paul Ashford,
- Alexander M. Frankell,
- Zofia Piszka,
- Camilla S.M. Pang,
- Mahnaz Abbasian,
- Maise Al Bakir,
- Mariam Jamal-Hanjani,
- Nicholas McGranahan,
- Charles Swanton,
- and Christine A. Orengo
Genome Res. March 2026 36: 561-577; Published in Advance February 19, 2026, doi:10.1101/gr.278663.123
...6BT, United Kingdom; 2Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, United Kingdom; 3Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, United Kingdom; 4University College London Cancer...
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Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
- Wenhao Zhang,
- Lan Cao,
- Xiaoxuan Gu,
- Yongyu Long,
- and Ying Wang
Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
.... To improve accuracy and efficiency, machine learning–based methods have been introduced. Genie3 (Huynh-Thu et al. 2010) and GRNBoost2 (Moerman et al. 2019) use tree-based ensemble methods and support vector machines, respectively, to infer regulatory networks, capturing nonlinear interactions and producing...
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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 Le Cao
Genome Res. March 5, 2026 gr.281171.125; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
...1Scalable cell-specific coexpression networks for granular regulatory 2 pattern discovery with NeighbourNet 3 Yidi Deng1,2, Jiadong Mao1,† & Jarny Choi3,† & Kim-Anh Lê Cao1,*,† 4 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, 5 3010, Australia 6...
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Mini-Review: A Hitchhiker's Guide to long-read genomic analysis
- Medhat Mahmoud,
- Daniel P. Agustinho,
- and Fritz J. Sedlazeck
Genome Res. April 2025 35: 545-558; doi:10.1101/gr.279975.124
..., scalability, and accuracy advancements have driven this evolution. Concurrently, novel analytical methods have emerged to harness the full potential of long reads. These advancements have enabled milestones such as the first fully completed human , enhanced identification and understanding of complex genomic...
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Resource: A map of enhancer regions in primary human neural progenitor cells using capture STARR-seq
- Sophia C. Gaynor-Gillett,
- Lijun Cheng,
- Manman Shi,
- Jason Liu,
- Gaoyuan Wang,
- Megan Spector,
- Qiuyu Guo,
- Le Qi,
- Mary Flaherty,
- Martha Wall,
- Ahyeon Hwang,
- Mengting Gu,
- Zhanlin Chen,
- Yuhang Chen,
- PsychENCODE Consortium,
- Jennifer R. Moran,
- Jing Zhang,
- Donghoon Lee,
- Mark Gerstein,
- Daniel Geschwind,
- and Kevin P. White
Genome Res. August 2025 35: 1887-1901; Published in Advance July 11, 2025, doi:10.1101/gr.279584.124
...in a final panel size of 22,314 regions for Panel 1 and 48,507 regions for Panel 2. The software then designed KAPA Target Enrichment Probes covering the inputted regions. These probes are 120 bp in length and, following hybridization with genomic DNA, can be captured through a bead-based capture method...
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Research: Transposable elements contribute to the evolution of host shift–related genes in cactophilic Drosophila species
- Daniel Siqueira de Oliveira,
- Anaïs Larue,
- William Vilas Boas Nunes,
- Francois Sabot,
- Alejandra Bodelón,
- María Pilar García Guerreiro,
- Cristina Vieira,
- and Claudia Marcia Aparecida Carareto
Genome Res. March 2026 36: 487-505; Published in Advance February 10, 2026, doi:10.1101/gr.280463.125
...different preferences. Transposable elements are a source of genetic novelty between populations and species, driving rapid adaptive evolution. However, the extent of TEs’ contribution to host shift remains unexplored. Here, we perform genomic and transcriptomic analyses in six s of cactophilic species...
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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. March 5, 2026 gr.281006.125; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
...remains a major genetic challenge. 21 Traditional statistical methods (such as GBLUP and BayesR) have limitations, including 22 reliance on artificial prior assumptions, and hard to capture epistatic effects. Machine learning 23 (ML) has emerged as a powerful alternative for genomic prediction; however...
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Method: PoreMeth2 for decoding the evolution of methylome alterations with nanopore sequencing
- Gianluca Mattei,
- Marta Baragli,
- Barbara Gega,
- Alessandra Mingrino,
- Martina Chieca,
- Tommaso Ducci,
- Gianmaria Frigè,
- Luca Mazzarella,
- Romina D'Aurizio,
- Francesco De Logu,
- Romina Nassini,
- Pier Giuseppe Pelicci,
- and Alberto Magi
Genome Res. November 2025 35: 2501-2512; Published in Advance October 20, 2025, doi:10.1101/gr.280259.124
...not differentially expressed. Such results suggest that a large fraction of the DMRs observed in our samples may be merely passenger events that accompany cancer evolution with weak or no effect on gene expression (Kalari and Pfeifer 2010).The methylation state of a genomic region (a group of adjacent CpG sites...

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