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Method: Tree-based differential testing using inferential uncertainty for RNA-seq
- Noor Pratap Singh,
- Euphy Y. Wu,
- Jason Fan,
- Michael I. Love,
- and Rob Patro
Genome Res. October 2025 35: 2326-2338; Published in Advance August 21, 2025, doi:10.1101/gr.279981.124
...also be explored in the future.Finally, it would be intriguing to explore the development of a TreeTerminus- and mehenDi-like approach in the context of single-cell RNA-seq analysis, where there are quite distinct but related opportunities for data-driven aggregation to increase the power...
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Resource: Multitissue single-nucleus RNA-seq reveals cell type–specific regulatory patterns of alternative polyadenylation in pigs
- Qiuhan Wen,
- Zhen Wang,
- Qi Bao,
- Tianli Ding,
- Haihan Zhang,
- Jianbo Li,
- Zhuang Liu,
- Jieping Huang,
- and Guoqiang Yi
Genome Res. September 2025 35: 2116-2129; Published in Advance June 16, 2025, doi:10.1101/gr.280095.124
...polyadenylation (APA) plays a crucial role in gene regulation and phenotypic diversity. Whereas extensive studies have explored the global APA landscape using bulk RNA-seq data, in-depth analyses of APA events at the single-cell level remain limited—particularly in farm animals. In this study, we construct...
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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 Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh; 4Department of Integrative Physiology, Baylor College of Medicine, Houston, Texas 77030, USA Corresponding authors: msrahman@cse.buet.ac.bd, samee@bcm.eduAbstractThe surge in single-cell data sets...
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Method: A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data
- Mengyuan Zhao,
- Jiawei Li,
- Xiaoyi Liu,
- Ke Ma,
- Jijun Tang,
- and Fei Guo
Genome Res. July 2024 34: 1036-1051; Published in Advance August 12, 2024, doi:10.1101/gr.278439.123
...1. Overview of the scHGR. (A) The input of scHGR includes reference data sets equipped with single-cell resolution gene expression profiles as well as type information for each cell, and query data sets equipped with scRNA-seq data. (B) The underlying gene regulatory relationships to train sc...
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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
...for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: awang87@jhu.eduAbstractThe selection of marker gene panels is critical for capturing the cellular and spatial heterogeneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial...
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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
...and target genes (Gao et al. 2023).Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at the individual cell level, revealing cellular heterogeneity with single-cell resolution and significantly enhancing the understanding of cell type–specific gene regulation (Chen and Liu 2022; Kartha...
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Method: A unified analysis of atlas single-cell data
- Hao Chen,
- Nam D. Nguyen,
- Matthew Ruffalo,
- and Ziv Bar-Joseph
Genome Res. May 2025 35: 1219-1233; Published in Advance February 18, 2025, doi:10.1101/gr.279631.124
...intestine, spleen, and thymus, and three different data modalities, which include single-cell RNA-seq (scRNA-seq), single-cell ATAC-seq (scATAC-seq), and spatial transcriptomics (Slide-seq). Figure 1, A through C, presents an overview of GIANT. We first construct gene graphs for cell clusters from each...
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Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
- Quanyou Cai,
- Lihui Lin,
- Xin Liu,
- and Jiekai Chen
Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
...is robust against spatial data noiseTo validate the noise tolerance of Polyomino, we simulated ground-truth ST data by substituting each spatial cell of seqFISH+ data by the single cell from snRNA-seq data of the same tissue (Fig. 2E; Tasic et al. 2018). Stringent comparison was performed between Polyomino...
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Method: Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq
- Alejandro Paniagua,
- Cristina Agustín-García,
- Francisco J. Pardo-Palacios,
- Thomas Brown,
- Maite De Maria,
- Nancy D. Denslow,
- Camila J. Mazzoni,
- and Ana Conesa
Genome Res. April 2025 35: 1053-1064; Published in Advance December 23, 2024, doi:10.1101/gr.279864.124
...inform complete gene structures. However, no extensive studies have been carried out to evaluate the best strategy for using lrRNA-seq in the evidence-driven approach, leading to a disconnect between the latest sequencing technologies and the annotation pipelines (Cook et al. 2019).In this work, we...
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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
...and AI Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: ajbattle@jhu.eduAbstractGene coexpression networks (GCNs) describe relationships among genes that maintain cellular identity and homeostasis. However, typical RNA-seq experiments often lack sufficient sample...
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