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Mylarshchikov D, Nikolskaya A, Bogomaz O, Zharikova A, Mironov A. BaRDIC: robust peak calling for RNA-DNA interaction data. NAR Genom Bioinform 2024; 6:lqae054. [PMID: 38774512 PMCID: PMC11106031 DOI: 10.1093/nargab/lqae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 05/24/2024] Open
Abstract
Chromatin-associated non-coding RNAs play important roles in various cellular processes by targeting genomic loci. Two types of genome-wide NGS experiments exist to detect such targets: 'one-to-al', which focuses on targets of a single RNA, and 'all-to-al', which captures targets of all RNAs in a sample. As with many NGS experiments, they are prone to biases and noise, so it becomes essential to detect 'peaks'-specific interactions of an RNA with genomic targets. Here, we present BaRDIC-Binomial RNA-DNA Interaction Caller-a tailored method to detect peaks in both types of RNA-DNA interaction data. BaRDIC is the first tool to simultaneously take into account the two most prominent biases in the data: chromatin heterogeneity and distance-dependent decay of interaction frequency. Since RNAs differ in their interaction preferences, BaRDIC adapts peak sizes according to the abundances and contact patterns of individual RNAs. These features enable BaRDIC to make more robust predictions than currently applied peak-calling algorithms and better handle the characteristic sparsity of all-to-all data. The BaRDIC package is freely available at https://github.com/dmitrymyl/BaRDIC.
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Affiliation(s)
- Dmitry E Mylarshchikov
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
| | - Arina I Nikolskaya
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
| | - Olesja D Bogomaz
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
| | - Anastasia A Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
- Kharkevich Institute for Information Transmission Problems RAS, Bolshoy Karetny per., Moscow 127051, Russia
| | - Andrey A Mironov
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
- Kharkevich Institute for Information Transmission Problems RAS, Bolshoy Karetny per., Moscow 127051, Russia
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Zhang Y, Cameron CJF, Blanchette M. Posterior inference of Hi-C contact frequency through sampling. FRONTIERS IN BIOINFORMATICS 2024; 3:1285828. [PMID: 38455089 PMCID: PMC10919286 DOI: 10.3389/fbinf.2023.1285828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/20/2023] [Indexed: 03/09/2024] Open
Abstract
Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.
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Affiliation(s)
- Yanlin Zhang
- School of Computer Science, McGill University, Montréal, QC, Canada
| | - Christopher J. F. Cameron
- School of Computer Science, McGill University, Montréal, QC, Canada
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montreal, QC, Canada
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Poinsignon T, Gallopin M, Grognet P, Malagnac F, Lelandais G, Poulain P. 3D models of fungal chromosomes to enhance visual integration of omics data. NAR Genom Bioinform 2023; 5:lqad104. [PMID: 38058589 PMCID: PMC10696920 DOI: 10.1093/nargab/lqad104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/11/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023] Open
Abstract
The functions of eukaryotic chromosomes and their spatial architecture in the nucleus are reciprocally dependent. Hi-C experiments are routinely used to study chromosome 3D organization by probing chromatin interactions. Standard representation of the data has relied on contact maps that show the frequency of interactions between parts of the genome. In parallel, it has become easier to build 3D models of the entire genome based on the same Hi-C data, and thus benefit from the methodology and visualization tools developed for structural biology. 3D modeling of entire genomes leverages the understanding of their spatial organization. However, this opportunity for original and insightful modeling is underexploited. In this paper, we show how seeing the spatial organization of chromosomes can bring new perspectives to omics data integration. We assembled state-of-the-art tools into a workflow that goes from Hi-C raw data to fully annotated 3D models and we re-analysed public omics datasets available for three fungal species. Besides the well-described properties of the spatial organization of their chromosomes (Rabl conformation, hypercoiling and chromosome territories), our results highlighted (i) in Saccharomyces cerevisiae, the backbones of the cohesin anchor regions, which were aligned all along the chromosomes, (ii) in Schizosaccharomyces pombe, the oscillations of the coiling of chromosome arms throughout the cell cycle and (iii) in Neurospora crassa, the massive relocalization of histone marks in mutants of heterochromatin regulators. 3D modeling of the chromosomes brings new opportunities for visual integration of omics data. This holistic perspective supports intuition and lays the foundation for building new concepts.
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Affiliation(s)
- Thibault Poinsignon
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Mélina Gallopin
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Pierre Grognet
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Fabienne Malagnac
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Gaëlle Lelandais
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Pierre Poulain
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
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Wang X, Gu WC, Li J, Ma BG. EVRC: reconstruction of chromosome 3D structure models using error-vector resultant algorithm with clustering coefficient. BIOINFORMATICS (OXFORD, ENGLAND) 2023; 39:btad638. [PMID: 37847746 DOI: 10.1093/bioinformatics/btad638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 09/28/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023]
Abstract
MOTIVATION Reconstruction of 3D structure models is of great importance for the study of chromosome function. Software tools for this task are highly needed. RESULTS We present a novel reconstruction algorithm, called EVRC, which utilizes co-clustering coefficients and error-vector resultant for chromosome 3D structure reconstruction. As an update of our previous EVR algorithm, EVRC now can deal with both single and multiple chromosomes in structure modeling. To evaluate the effectiveness and accuracy of the EVRC algorithm, we applied it to simulation datasets and real Hi-C datasets. The results show that the reconstructed structures have high similarity to the original/real structures, indicating the effectiveness and robustness of the EVRC algorithm. Furthermore, we applied the algorithm to the 3D conformation reconstruction of the wild-type and mutant Arabidopsis thaliana chromosomes and demonstrated the differences in structural characteristics between different chromosomes. We also accurately showed the conformational change in the centromere region of the mutant compared with the wild-type of Arabidopsis chromosome 1. Our EVRC algorithm is a valuable software tool for the field of chromatin structure reconstruction, and holds great promise for advancing our understanding on the chromosome functions. AVAILABILITY AND IMPLEMENTATION The software is available at https://github.com/mbglab/EVRC.
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Affiliation(s)
- Xiao Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wei-Cheng Gu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jie Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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Schuette G, Ding X, Zhang B. Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction. Biophys J 2023; 122:3425-3438. [PMID: 37496267 PMCID: PMC10502442 DOI: 10.1016/j.bpj.2023.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/10/2023] [Accepted: 07/24/2023] [Indexed: 07/28/2023] Open
Abstract
Genome-wide chromosome conformation capture (Hi-C) experiments have revealed many structural features of chromatin across multiple length scales. Further understanding genome organization requires relating these discoveries to the mechanisms that establish chromatin structures and reconstructing these structures in three dimensions, but both objectives are difficult to achieve with existing algorithms that are often computationally expensive. To alleviate this challenge, we present an algorithm that efficiently converts Hi-C data into contact energies, which measure the interaction strength between genomic loci brought into proximity. Contact energies are local quantities unaffected by the topological constraints that correlate Hi-C contact probabilities. Thus, extracting contact energies from Hi-C contact probabilities distills the biologically unique information contained in the data. We show that contact energies reveal the location of chromatin loop anchors, support a phase separation mechanism for genome compartmentalization, and parameterize polymer simulations that predict three-dimensional chromatin structures. Therefore, we anticipate that contact energy extraction will unleash the full potential of Hi-C data and that our inversion algorithm will facilitate the widespread adoption of contact energy analysis.
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Affiliation(s)
- Greg Schuette
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts.
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Schuette G, Ding X, Zhang B. Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.17.533194. [PMID: 36993500 PMCID: PMC10055272 DOI: 10.1101/2023.03.17.533194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Genome-wide chromosome conformation capture (Hi-C) experiments have revealed many structural features of chromatin across multiple length scales. Further understanding genome organization requires relating these discoveries to the mechanisms that establish chromatin structures and reconstructing these structures in three dimensions, but both objectives are difficult to achieve with existing algorithms that are often computationally expensive. To alleviate this challenge, we present an algorithm that efficiently converts Hi-C data into contact energies, which measure the interaction strength between genomic loci brought into proximity. Contact energies are local quantities unaffected by the topological constraints that correlate Hi-C contact probabilities. Thus, extracting contact energies from Hi-C contact probabilities distills the biologically unique information contained in the data. We show that contact energies reveal the location of chromatin loop anchors, support a phase separation mechanism for genome compartmentalization, and parameterize polymer simulations that predict three-dimensional chromatin structures. Therefore, we anticipate that contact energy extraction will unleash the full potential of Hi-C data and that our inversion algorithm will facilitate the widespread adoption of contact energy analysis.
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Affiliation(s)
- Greg Schuette
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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