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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
...and chromatin modifiers suggests that they interact (either directly or indirectly) in gene regulatory networks that are necessary for normal heart development. Genes not annotated with these functions, such as COL6A3 and MYO9A, could be target genes or have upstream or downstream regulatory functions...
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Research: Neuron-specific chromatin disruption at CpG islands and aging-related regions in Kabuki syndrome mice
- Leandros Boukas,
- Teresa Romeo Luperchio,
- Afrooz Razi,
- Kasper D. Hansen,
- and Hans T. Bjornsson
Genome Res. May 2024 34: 696-710; Published in Advance May 3, 2024, doi:10.1101/gr.278416.123
...–Fahrner syndrome; P = 0.007), and Chd1 (chromatin remodeler causing Pilarowski–Bjornsson syndrome whose targets have been implicated in neural crest stem cell transcriptional dysregulation upon KMT2D hapoinsufficiency; P = 0.005) (Supplemental Fig. S5C; Gabriele et al. 2021). In KS2, epigenetic regulators...
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Research: Spatial organization of chromosomes leads to heterogeneous chromatin motion and drives the liquid- or gel-like dynamical behavior of chromatin
- Hossein Salari,
- Marco Di Stefano,
- and Daniel Jost
Genome Res. January 2022 32: 28-43; Published in Advance December 28, 2021, doi:10.1101/gr.275827.121
...the “dynamic” nature of chromatin folding and its importance on key biological functions (Bystricky 2015; Tortora et al. 2020; Shaban et al. 2020a). Chromatin mobility has been proposed to impact the dynamics of promoter–enhancer interactions and thus regulates transcriptional bursting (Bartman et al. 2016...
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Method: Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells
- Emily R. Miraldi,
- Maria Pokrovskii,
- Aaron Watters,
- Dayanne M. Castro,
- Nicholas De Veaux,
- Jason A. Hall,
- June-Yong Lee,
- Maria Ciofani,
- Aviv Madar,
- Nick Carriero,
- Dan R. Littman,
- and Richard Bonneau
Genome Res. March 2019 29: 449-463; Published in Advance January 29, 2019, doi:10.1101/gr.238253.118
...of transcriptional regulation still describes differential gene expression as a sparse multivariate linear function of TF activities (TFAs), the methods to solve for the TF–gene interaction terms and estimate TFAs have advanced. For example, the current Inferelator (Arrieta-Ortiz et al. 2015) uses a Bayesian...
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Method: Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data
- Jiebiao Wang,
- Kathryn Roeder,
- and Bernie Devlin
Genome Res. October 2021 31: 1807-1818; Published in Advance April 9, 2021, doi:10.1101/gr.268722.120
...data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell type–specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses...
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Method: Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data
- Kaixuan Luo,
- Jianling Zhong,
- Alexias Safi,
- Linda K. Hong,
- Alok K. Tewari,
- Lingyun Song,
- Timothy E. Reddy,
- Li Ma,
- Gregory E. Crawford,
- and Alexander J. Hartemink
Genome Res. June 2022 32: 1183-1198; Published in Advance May 24, 2022, doi:10.1101/gr.272203.120
...observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TF occupancy profiler (TOP), a Bayesian hierarchical regression framework, to profile -wide quantitative occupancy of numerous TFs using data from a single chromatin accessibility experiment (DNase...
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Method: A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes
- Shang Gao,
- Yang Dai,
- and Jalees Rehman
Genome Res. July 2021 31: 1296-1311; Published in Advance June 30, 2021, doi:10.1101/gr.265595.120
...activities in scRNA-seq data, which in turn can be used for downstream analysis such as identifying cell clusters based on distinct inferred transcription factor activities as well as generating a weighted hierarchy of target genes.ResultsOverview of BITFAMOur Bayesian inference transcription factor activity...
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Research: Transcription factor activity rhythms and tissue-specific chromatin interactions explain circadian gene expression across organs
- Jake Yeung,
- Jérôme Mermet,
- Céline Jouffe,
- Julien Marquis,
- Aline Charpagne,
- Frédéric Gachon,
- and Felix Naef
Genome Res. February 2018 28: 182-191; Published in Advance December 18, 2017, doi:10.1101/gr.222430.117
...-seq analysis of circadian rhythms. Methods Enzymol 551: 349–367. ↵Liang F, Paulo R, Molina G, Clyde MA, Berger JO. 2008. Mixtures of g priors for Bayesian variable selection. J Am Stat Assoc 103: 410–423. ↵Matsuo T, Yamaguchi S, Mitsui S, Emi A, Shimoda F, Okamura H. 2003. Control mechanism of the circadian...
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Resource: Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes
- Timothy J. Durham,
- Riza M. Daza,
- Louis Gevirtzman,
- Darren A. Cusanovich,
- Olubusayo Bolonduro,
- William Stafford Noble,
- Jay Shendure,
- and Robert H. Waterston
Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
.... 2019), single-cell chromatin conformation (Kim et al. 2020), and single-cell gene expression data (Dey et al. 2017). LDA is a generative Bayesian modeling approach that was developed in the context of document classification. In the document classification task, the model is trained to identify...
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Method: Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
- Zeynep Kalender Atak,
- Ibrahim Ihsan Taskiran,
- Jonas Demeulemeester,
- Christopher Flerin,
- David Mauduit,
- Liesbeth Minnoye,
- Gert Hulselmans,
- Valerie Christiaens,
- Ghanem-Elias Ghanem,
- Jasper Wouters,
- and Stein Aerts
Genome Res. June 2021 31: 1082-1096; Published in Advance April 8, 2021, doi:10.1101/gr.260851.120
...somatic in origin between 206,724 and 304,754 of these per sample, based on their absence from the Genome Aggregation Database (gnomAD; v3.0). To dissect the contributions of distinct mutational processes, we estimated exposures to the COSMIC v3 single-base substitution signatures. We used the Bayesian...
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