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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 Lê Cao
Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
..., Canberra 2601, Australia; 3Bioinformatics and Cellular Genomics, St Vincent’s Institute, Fitzroy, Victoria 3065, Australia Gene networks provide a fundamental framework for understanding the molecular mechanisms that govern gene expression. Advances in single-cell RNA sequencing (scRNA-seq) have enabled...
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Method: scSHEFT enables multiomics label transfer from scRNA-seq to scATAC-seq through dual alignment
- Zhitao Huang,
- Ruiqing Zheng,
- Pengzhen Jia,
- Xuhua Yan,
- Jinmiao Chen,
- and Min Li
Genome Res. February 2026 36: 387-396; Published in Advance January 20, 2026, doi:10.1101/gr.280410.125
...can be divided into two types. Their major difference lies in the way to process the raw omics features. The first type of methods directly uses gene expression count data from scRNA-seq and peak count data from scATAC-seq as input to the model, and uses omics-specific neural networks to embed them...
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Method: Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet
- Chuanyuan Wang and
- Zhi-Ping Liu
Genome Res. February 2025 35: 340-354; Published in Advance December 18, 2024, doi:10.1101/gr.279551.124
...the global architecture information within a GRN and generates a corresponding network structure from scRNA-seq data. As a highly adaptable and flexible tool, DigNet necessitates merely single-cell gene expression profiles to iteratively generate a GRN from a random starting point. The extracted structure...
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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
...-transformed along with gene-specific and sample-specific filters. Based on the data source, normalized gene expression was processed to merge replicates and exclude miRNA and scRNA-seq samples. (B) Number of samples which were annotated to be noncancerous and cancerous based on available metadata across GTEx, SRA...
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Method: Bayesian inference of sample-specific coexpression networks
- Enakshi Saha,
- Viola Fanfani,
- Panagiotis Mandros,
- Marouen Ben Guebila,
- Jonas Fischer,
- Katherine H. Shutta,
- Dawn L. DeMeo,
- Camila M. Lopes-Ramos,
- and John Quackenbush
Genome Res. September 2024 34: 1397-1410; Published in Advance August 12, 2024, doi:10.1101/gr.279117.124
...equally to this work. Corresponding author: johnq@hsph.harvard.eduAbstractGene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression...
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Method: Minimizing reference bias with an imputed personalized reference
- Kavya Vaddadi,
- Mao-Jan Lin,
- Sina Majidian,
- Taher Mun,
- and Ben Langmead
Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.280989.125
...)). The Giraffe workflows use graph-based alignment against either a linear reference, a population-scale pan, or a sample-specific diploid graph, whereas LevioSAM2 performs coordinate lifting and alignment on impute-first personalized diploid references. For the Giraffe(diploid) workflow, the sample...
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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
...) for supervised link prediction in GRN inference, leveraging scRNA-seq data and existing regulatory information to predict latent TF–gene interactions. Similarly, Grace (Wang et al. 2024a) integrates structural causal models with graph neural networks to infer both GRNs and gene causality from scRNA-seq data...
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Method: Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches
- Muyoung Lee,
- Qingqing Guo,
- Mijeong Kim,
- Joonhyuk Choi,
- Alia Segura,
- Alper Genceroglu,
- Lucy LeBlanc,
- Nereida Ramirez,
- Yu Jin Jang,
- Yeejin Jang,
- Bum-Kyu Lee,
- Edward M. Marcotte,
- and Jonghwan Kim
Genome Res. March 2024 34: 484-497; Published in Advance April 5, 2024, doi:10.1101/gr.277926.123
...clusters (Supplemental Table S3). The high efficiency of cell fate changes by inducing TFs was also observed in a few validation cases in the recent scRNA-seq-based single TF overexpression screen (>80% efficiency confirmed through testing reporter gene expression) (Ng et al. 2021). We found many...
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Research: Mapping multitissue regulatory variants reveals a liver-centric coexpression network associated with duck egg-laying performance
- Yang Xi,
- Jingjing Qi,
- Zhao Yang,
- Yutian Zeng,
- Huicong Zhang,
- Qiuyu Tao,
- Mengru Xu,
- Anqi Huang,
- Shenqiang Hu,
- Chunchun Han,
- Lili Bai,
- Jiwei Hu,
- Jiwen Wang,
- Liang Li,
- Lingzhao Fang,
- and Hehe Liu
Genome Res. October 2025 35: 2211-2225; Published in Advance September 10, 2025, doi:10.1101/gr.280345.124
..., we performed weighted gene coexpression network analysis (WGCNA) across four tissues (Supplemental Fig. S8A). The analysis revealed that LOC101800576 and LOC101790890 were assigned to the shell gland ME3 module, GLP2R to the spleen ME7 module, and LOC119713219 to the ovary ME6 module (Supplemental...
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Method: Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries
- Matthew Wiens,
- Hossein Farahani,
- R. Wilder Scott,
- T. Michael Underhill,
- and Ali Bashashati
Genome Res. August 2024 34: 1196-1210; Published in Advance August 15, 2024, doi:10.1101/gr.277066.122
..., this is not because of differences in expression levels, as the gene on which the edit is located (Htra1) is expressed in all involved clusters.Overall, this constitutes evidence that RNA-editing events may be seen in Chromium scRNA-seq libraries and used to infer heterogeneous presentation of RNA editing. Although...

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