The DOI matches its Crossref record; 39 of the 41 references it cites were found in a registry or an index.
Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2
Open accessAbstract
Fit-Hi-C is a programming application to compute statistical confidence estimates for Hi-C contact maps to identify significant chromatin contacts. By fitting a monotonically non-increasing spline, Fit-Hi-C captures the relationship between genomic distance and contact probability without any parametric assumption. The spline fit together with the correction of contact probabilities with respect to bin- or locus-specific biases accounts for previously characterized covariates impacting Hi-C contact counts. Fit-Hi-C is best applied for the study of mid-range (e.g., 20 kb-2 Mb for human genome) intra-chromosomal contacts; however, with the latest reimplementation, named FitHiC2, it is possible to perform genome-wide analysis for high-resolution Hi-C data, including all intra-chromosomal distances and inter-chromosomal contacts. FitHiC2 also offers a merging filter module, which eliminates indirect/bystander interactions, leading to significant reduction in the number of reported contacts without sacrificing recovery of key loops such as those between convergent CTCF binding sites. Here, we describe how to apply the FitHiC2 protocol to three use cases: (i) 5-kb resolution Hi-C data of chromosome 5 from GM12878 (a human lymphoblastoid cell line), (ii) 40-kb resolution whole-genome Hi-C data from IMR90 (human lung fibroblast), and (iii) budding yeast whole-genome Hi-C data at a single restriction cut site (EcoRI) resolution. The procedure takes ~12 h with preprocessing when all use cases are run sequentially (~4 h when run parallel). With the recent improvements in its implementation, FitHiC2 (8 processors and 16 GB memory) is also scalable to genome-wide analysis of the highest resolution (1 kb) Hi-C data available to date (~48 h with 32 GB peak memory). FitHiC2 is available through Bioconda, GitHub and the Python Package Index.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“many computational algorithms for the analysis of data from Hi-C and related techniques explicitly correct for distance decay, identifying “significant interactions” based on how unusual their contact frequency is at a given distance [277, 468–471].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [468]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212
Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Topological domains in mammalian genomes identified by analysis of chromatin interactions
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829
Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments
Durand, N. C., Shamim, M. S., Machol, I., Rao, S. S. P., Huntley, M. H., Lander, E. S., & Aiden, E. L. (2016) · Cell Systems · doi:10.1016/j.cels.2016.07.002 · Cited by 3,860
HiC-Pro: an optimized and flexible pipeline for Hi-C data processing.
Servant, N., Varoquaux, N., Lajoie, B. R., Viara, E., Chen, C.-J., Vert, J.-P., Heard, E., Dekker, J., & Barillot, E. (2015) · Genome Biology · doi:10.1186/s13059-015-0831-x · Cited by 2,565
Iterative correction of Hi-C data reveals hallmarks of chromosome organization
Imakaev, M., Fudenberg, G., McCord, R. P., Naumova, N., Goloborodko, A., Lajoie, B. R., Dekker, J., & Mirny, L. A. (2012) · Nature Methods · doi:10.1038/nmeth.2148 · Cited by 1,423
A high-resolution map of the three-dimensional chromatin interactome in human cells
Jin, F., Li, Y., Dixon, J. R., Selvaraj, S., Ye, Z., Lee, A. Y., Yen, C.-A., Schmitt, A. D., Espinoza, C. A., & Ren, B. (2013) · Nature · doi:10.1038/nature12644 · Cited by 1,208
Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations
Fulco, C. P., Nasser, J., Jones, T. R., Munson, G., Bergman, D. T., Subramanian, V., Grossman, S. R., Anyoha, R., Doughty, B. R., Patwardhan, T. A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0538-0 · Cited by 1,111
Exploring the three-dimensional organization of genomes: Interpreting chromatin interaction data
Dekker, J., Marti-Renom, M. A., & Mirny, L. A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3454 · Cited by 1,068
Cited by
Sorted by citations
Parallel analysis of transcription, integration, and sequence of single HIV-1 proviruses
Einkauf, K. B., Osborn, M. R., Gao, C., Sun, W., Sun, X., Lian, X., Parsons, E. M., Gladkov, G. T., Seiger, K. W., Blackmer, J. E., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.011 · Cited by 260
Roayaei Ardakany, A., Gezer, H. T., Lonardi, S., & Ay, F. (2020) · Genome Biology · doi:10.1186/s13059-020-02167-0 · Cited by 250
Self-citationSingle-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease
Sun, N., Akay, L. A., Murdock, M. H., Park, Y., Galiana-Melendez, F., Bubnys, A., Galani, K., Mathys, H., Jiang, X., Ng, A. P., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01334-3 · Cited by 186
Wang, X., Xu, J., Zhang, B., Hou, Y., Song, F., Lyu, H., & Yue, F. (2021) · Nature Methods · doi:10.1038/s41592-021-01164-w · Cited by 152
Computer vision for pattern detection in chromosome contact maps
Matthey-Doret, C., Baudry, L., Breuer, A., Montagne, R., Guiglielmoni, N., Scolari, V. F., Jean, É., Campeas, A., Chanut, P. H., Oriol, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19562-7 · Cited by 133
Metadata
- Authors
- Kaul, A., Bhattacharyya, S., & Ay, F.
- Affiliations
- Harvard University (US)
- La Jolla Institute for Immunology (US)
- Corresponding author
- Ay, Ferhat
- Venue
- Nature Protocols
- Published
- 2020-01-24
- Volume
- 15
- Issue
- 3
- Pages
- 991-1012
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- PMC
- PMC7451401
- OpenAlex
- W3002964216
- Cited by
- 226
- References
- 41
- Citations by year
- 2026: 24 · 2025: 40 · 2024: 49 · 2023: 36 · 2022: 29 · 2021: 32 · 2020: 15
- FWCI
- 9.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | Center for Information Technology (R35-GM128938)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Chromosomal and Genetic Variations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- Hi-C contact maps, chromatin interactions, spline fitting, loop detection, CTCF binding sites, genome-wide analysis, GM12878, Saccharomyces cerevisiae
- MeSH terms
- Animals, Binding Sites, Chromatin, Chromosomes, Humans, Software, Cluster Analysis, Genome, Genomics, Databases, Genetic, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
