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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: A spectral component approach leveraging identity-by-descent graphs to address recent population structure in genomic analysis
- Ruhollah Shemirani,
- Gillian M. Belbin,
- Sinead Cullina,
- Christa Caggiano,
- Christopher R. Gignoux,
- Noah Zaitlen,
- and Eimear E. Kenny
Genome Res. March 2026 36: 534-546; Published in Advance December 23, 2025, doi:10.1101/gr.280659.125
...structure and the complex genetic architecture they reveal.To address these limitations, estimates of pairwise haplotypes shared identical-by-descent, or identity-by-descent (IBD), offer a more precise approach for detecting recent fine-scale population structure in large genomic data sets (Shemirani et al...
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Method: Pangenome-based genome inference using integer programming
- Ghanshyam Chandra,
- Md Helal Hossen,
- Stephan Scholz,
- Alexander T. Dilthey,
- Daniel Gibney,
- and Chirag Jain
Genome Res. December 2025 35: 2661-2670; Published in Advance August 21, 2025, doi:10.1101/gr.280567.125
...in the human MHC. Int J Biochem Cell Biol 131: 105882. doi:10.1016/j.biocel.2020.105882 ↵Dilthey A, Cox C, Iqbal Z, Nelson MR, McVean G. 2015. Improved inference in the MHC using a population reference graph. Nat Genet 47: 682–688. doi:10.1038/ng.3257 ↵Ebert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D...
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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
...to support genomic research. 41 Introduction 42 Using genomic mutations to predict phenotypes and explain phenotypic variation is one of the 43 primary objectives of precision medicine, as well as contemporary animal and plant breeding 44 (Moon et al. 2023; Wiggans et al. 2011). The process of genomic...
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Method: Multicondition and multimodal temporal profile inference during mouse embryonic development
- Ran Zhang,
- Chengxiang Qiu,
- Galina N. Filippova,
- Gang Li,
- Jay Shendure,
- Jean-Philippe Vert,
- Xinxian Deng,
- William Stafford Noble,
- and Christine M. Disteche
Genome Res. October 2025 35: 2339-2351; Published in Advance August 14, 2025, doi:10.1101/gr.279997.124
...Ran Zhang1,2, Chengxiang Qiu1, Galina N. Filippova3, Gang Li1,2, Jay Shendure1,4,5,6,7, Jean-Philippe Vert8, Xinxian Deng3, William Stafford Noble1,9 and Christine M. Disteche3,10 1Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; 2eScience Institute...
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Research: Landscape of microRNA and target expression variation and covariation in single mouse embryonic stem cells
- Marcel Tarbier,
- Sebastian D. Mackowiak,
- Vaishnovi Sekar,
- Franziska Bonath,
- Etka Yapar,
- Bastian Fromm,
- Omid R. Faridani,
- Inna Biryukova,
- and Marc R. Friedländer
Genome Res. February 2026 36: 291-302; Published in Advance January 12, 2026, doi:10.1101/gr.279914.124
...target expression variation and covariation, but these observations have been limited to a few microRNAs. Here we systematically study microRNA alternative functions in mouse embryonic stem cells (mESCs) by genetically deleting Drosha, leading to global loss of microRNAs. We apply complementary single...
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Resource: The SynMall resource for characterizing the functional impact of synonymous variation
- Chen Ye,
- Xiaoyan Li,
- Na Cheng,
- Yansen Su,
- and Junfeng Xia
Genome Res. February 2026 36: 421-431; Published in Advance December 17, 2025, doi:10.1101/gr.281257.125
...annotation databases offer limited functional insights for sSNVs. Here, we present SynMall, a comprehensive resource designed to decipher the functional impact of synonymous variation. SynMall catalogs 25 million potential human sSNVs and integrates evolutionary and population information of sSNVs from 45...
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Method: ScisTree2 enables large-scale inference of cell lineage trees and genotype calling using efficient local search
- Haotian Zhang,
- Yiming Zhang,
- Teng Gao,
- and Yufeng Wu
Genome Res. December 2025 35: 2781-2791; Published in Advance September 3, 2025, doi:10.1101/gr.280542.125
...variants, which constitute the primary data used for inferring cell lineage trees. The primary class of genomic variants studied in this paper is the single nucleotide variant (SNV). We do not consider more complex variants such as copy number variations (CNVs) for cell lineage tree inference (see...
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Research: Pangenome analysis reveals families of ubiquitin-ligase adaptors as key genomic divergence drivers that lead to hybrid incompatibility
- Dongying Xie,
- Pohao Ye,
- Yiming Ma,
- and Zhongying Zhao
Genome Res. March 2026 36: 506-521; Published in Advance February 17, 2026, doi:10.1101/gr.280885.125
...species (Fig. 1E,F; Supplemental Table S2). Taken together, these results demonstrate consistently larger sizes and higher gene numbers in C. nigoni populations compared with those of C. briggsae, with greater variations observed in C. nigoni.Genome size variation between C. nigoni and C. briggsae...
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Method: Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks
- Prashanthi Ravichandran,
- Princy Parsana,
- Rebecca Keener,
- Kasper D. Hansen,
- and Alexis Battle
Genome Res. September 2025 35: 2087-2103; Published in Advance July 17, 2025, doi:10.1101/gr.280808.125
...-specific networks, with some variation in the strength of the trend across tissues likely driven by sample size differences and differential power in inferring these networks (Fig. 3D; Supplemental Fig. S22). We then examined the enrichment of eQTL-deficient, ClinVar, OMIM, and FDA drug-targeted genes across...
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