Searching journal content for articles similar to Saranga et al. 11 (12): 1988.

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  1. 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
    ...(1) a closely related species from the same genus could serve as biological pseudoreplicates for the considered environmental transition, and/or (2) arid and corresponding mesic species were all dissected and sequenced by us at the same time, ensuring minimal confounding effects.View larger version...
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  2. Method: High-resolution genotype-free mapping of genetic variation with CRI-SPA-Map
    • Sheila Lutz,
    • Megan Lawler,
    • Samuel Amidon,
    • and Frank W Albert
    Genome Res. March 23, 2026 gr.281514.125; Published in Advance March 23, 2026, doi:10.1101/gr.281514.125
    ..., and gene-by-gene-by 354 environment interactions of the MKT1 and SAL1 causal alleles 355 To explore the extent of epistasis between MKT1-30G and SAL1-nfs (i.e., GxG) and the 356 environmental dependence of the effects of these alleles (i.e., GxE), we grew our strains 357 carrying all combinations...
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  3. Research: Adaptation of centromeres to breakage through local genomic and epigenomic remodeling in wheat
    • Jingwei Zhou,
    • Yuhong Huang,
    • Huan Ma,
    • Yiqian Chen,
    • Chuanye Chen,
    • Fangpu Han,
    • and Handong Su
    Genome Res. November 2025 35: 2461-2471; Published in Advance September 30, 2025, doi:10.1101/gr.280913.125
    ...-quality reads and adapters were removed using fastp (v0.20.1) (Chen et al. 2018). The cleaned reads were aligned to the near gap-free reference of CS (Liu et al. 2025; Wang et al. 2025) using BWA-MEM (v0.7.17) with default parameters (Li and Durbin 2009). Genomic data visualization was facilitated using...
  4. Method: Automated interpretable artificial intelligence genomic prediction with AlGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ...DNA variants to phenotypes, spans the entire central dogma of molecular biology; incorporates various nongenetic factors, such as environmental influences (Yao et al. 2025); and is one of the most challenging tasks in biological prediction. Genomic selection (GS) is the most advanced methodology...
  5. 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
    ...genomic databases and offer mechanisms for sharing synthetic data possessing equivalent statistical properties, all while safeguarding the privacy of individuals. Several recent studies have explored the effectiveness of generative deep neural networks in generating simulated genotypes. Montserrat et al...
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  6. 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
    ...heritable phenotype. Additionally, SPCs improve rare variant association analyses, reducing genomic inflation (e.g., from 7.6 to 1.2 in one analysis), and provide more accurate heritability estimates. Spatial autocorrelation analysis further confirms the ability of SPCs to account for environmental effects...
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  7. Method: Secure phasing of private genomes in a trusted execution environment with TX-Phase
    • Natnatee Dokmai,
    • Kaiyuan Zhu,
    • S. Cenk Sahinalp,
    • and Hyunghoon Cho
    Genome Res. December 2025 35: 2626-2636; Published in Advance November 12, 2025, doi:10.1101/gr.280558.125
    ...poses a significant challenge in maintaining practical performance under privacy constraints. Here, we introduce TX-Phase, a secure haplotype phasing method based on the framework of trusted execution environments (TEEs). TX-Phase allows users’ private genomic data to be phased while ensuring data...
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  8. 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
    ...environments throughout evolution (Taylor and Larson 2019). Species must strike a delicate balance between preserving genetic stability and generating sufficient variability to both maintain species identity and respond to environmental changes. This equilibrium in evolution underpins the unique genetic...
  9. 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
    ...family member (Sierant et al. 2025). This indicates that although most defects can be explained in large part by patient genetics, our current knowledge is insufficient to pinpoint the full genetic origin of most individual cases. In addition to environmental factors, unexplained cases presumably involve...
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  10. 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. February 3, 2026 ; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...acceleration rate based on spatial location. The area with a strong aging association was highlighted in a red circle. (F) Heatmap of cell proximity effects by measuring the interactions of different cell types. spRefine imputes and denoises ST data Genome Research 9 www..org In this article, we have proposed...
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