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Method: spRefine denoises and imputes spatial transcriptomics 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 gr.281001.125; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...spRefine denoises and imputes spatial1 transcriptomics with a reference-free framework2 powered by genomic language model3 Tianyu Liu1,2, Tinglin Huang3, Wengong Jin4,5, Tinyi Chu2, Rex4 Ying3, Hongyu Zhao1,2*5 1Interdepartmental Program in Computational Biology &6 Bioinformatics, Yale University...
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Perspective: Applications of single-cell genomics and computational strategies to study common disease and population-level variation
- Benjamin J. Auerbach,
- Jian Hu,
- Muredach P. Reilly,
- and Mingyao Li
Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
...–based methods have been developed for this purpose. For example, scVI (Lopez et al. 2018) removes batch effects by conditioning on batch information in a variational autoencoder, which learns a nonlinear embedding of cells; SAVERCAT (Huang et al. 2020) uses a conditional variational autoencoder to remove batch...
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Method: Unified integration of spatial transcriptomics across platforms with LLOKI
- Ellie Haber,
- Ajinkya Deshpande,
- Jian Ma,
- and Spencer Krieger
Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
...sets, confirming the importance of this step in our workflow.Batch integration with LLOKI-CAECell embeddings from LLOKI-FP effectively capture biological variation within each ST data set but still exhibit large batch effects between technologies. To address this, we develop a conditional autoencoder...
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Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
- Aayush Grover,
- Till Muser,
- Liine Kasak,
- Lin Zhang,
- Ekaterina Krymova,
- and Valentina Boeva
Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
.... 2012), making it sufficient to capture relevant regulatory variation. The 2048-bp input window also provides adequate genomic context for the models to learn both local and distal sequence features.View larger version: In this window In a new window Figure 1. Overview of the benchmarking setup. (A...
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Method: Quantifying pathological progression from single-cell transcriptomic data with scPSS
- Samin Rahman Khan,
- M Saifur Rahman,
- M. Sohel Rahman,
- and Md Abul Hassan Samee
Genome Res. February 2026 36: 375-386; Published in Advance January 14, 2026, doi:10.1101/gr.280411.125
...) of disease-relevant cellular states. Among them, scIDIST (Wehbe et al. 2025) integrates autoencoder-based dimensionality reduction with weak supervision, producing probabilistic disease labels. The labels are then used to train a neural network that assigns continuous disease progression scores to individual...
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Method: Deep structural clustering reveals hidden systematic biases in RNA sequencing data
- Qiang Su,
- Yi Long,
- Deming Gou,
- Junmin Quan,
- Xiaoming Zhou,
- and Qizhou Lian
Genome Res. November 2025 35: 2563-2577; Published in Advance September 19, 2025, doi:10.1101/gr.280713.125
...-dimensional RNA structures on local sequencing efficiency using an innovative unsupervised variational autoencoder-Gaussian mixture model (VAE-GMM). The VAE-GMM effectively captures intricate high-dimensional k-mer structural similarities by learning compact latent representations, which reduces dimensionality...
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Method: Graph-based deep reinforcement learning for haplotype assembly with Ralphi
- Enzo Battistella,
- Anant Maheshwari,
- Barış Ekim,
- Bonnie Berger,
- and Victoria Popic
Genome Res. December 2025 35: 2617-2625; Published in Advance November 14, 2025, doi:10.1101/gr.280569.125
..., it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.References ↵The 1000 Genomes Project Consortium. 2015. A global reference for human genetic variation. Nature 526: 68–74. doi:10.1038/nature15393 ↵Adey A...
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Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
- Fabien Wehbe,
- Levi Adams,
- Jordan Babadoudou,
- Samantha Yuen,
- Yoon-Seong Kim,
- and Yoshiaki Tanaka
Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
...the dimensionality reduction from a pretrained autoencoder.Dimensional reduction by variational autoencoder: variational_autoencoder.pyAs an alternative way for the dimensional reduction, we provide another Python script, variational_autoencoder.py. This script implements a variational autoencoder that employs...
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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
...of -wide variants. Nat Biotechnol doi:10.1038/s41587-024-02511-w ↵Brandes N, Linial N, Linial M. 2020. PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins. Genome Biol 21: 173. doi:10.1186/s13059-020-02089-x ↵Brandes N, Goldman G, Wang CH, Ye CJ, Ntranos V...
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Method: Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover
- An Wang,
- Stephanie Hicks,
- Donald Geman,
- and Laurent Younes
Genome Res. December 2025 35: 2744-2755; Published in Advance November 12, 2025, doi:10.1101/gr.280539.125
...clustering performance of the mouse brain Visium HD data set (10x Genomics 2024). Specifically, we evaluated how variations in key parameters—the gene neighborhood size threshold (m) and the sequence of incremental sizes ({kt}1:T)—affect the resolution of spatial structures. First, we fixed {kt}1:T = {80, 60...

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