The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Histone modification levels are predictive for gene expression
Open accessAbstract
Histones are frequently decorated with covalent modifications. These histone modifications are thought to be involved in various chromatin-dependent processes including transcription. To elucidate the relationship between histone modifications and transcription, we derived quantitative models to predict the expression level of genes from histone modification levels. We found that histone modification levels and gene expression are very well correlated. Moreover, we show that only a small number of histone modifications are necessary to accurately predict gene expression. We show that different sets of histone modifications are necessary to predict gene expression driven by high CpG content promoters (HCPs) or low CpG content promoters (LCPs). Quantitative models involving H3K4me3 and H3K79me1 are the most predictive of the expression levels in LCPs, whereas HCPs require H3K27ac and H4K20me1. Finally, we show that the connections between histone modifications and gene expression seem to be general, as we were able to predict gene expression levels of one cell type using a model trained on another one.
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.
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
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Chromatin modifications and their function
Kouzarides, T. (2007) · Cell · doi:10.1016/j.cell.2007.02.005 · Cited by 10,883
High-Resolution Profiling of Histone Methylations in the Human Genome
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845
Genome-wide maps of chromatin state in pluripotent and lineage-committed cells
Mikkelsen, T. S., Ku, M., Jaffe, D. B., Issac, B., Lieberman, E., Giannoukos, G., Alvarez, P., Brockman, W., Kim, T.-K., Koche, R. P., et al. (2007) · Nature · doi:10.1038/nature06008 · Cited by 4,129
Combinatorial patterns of histone acetylations and methylations in the human genome
Wang, Z., Zang, C., Rosenfeld, J. A., Schones, D. E., Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Peng, W., Zhang, M. Q., et al. (2008) · Nature Genetics · doi:10.1038/ng.154 · Cited by 2,331
Bernstein, B. E., Meissner, A., & Lander, E. S. (2007) · Cell · doi:10.1016/j.cell.2007.01.033 · Cited by 2,086
Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes
Wang, Z., Zang, C., Cui, K., Schones, D. E., Barski, A., Peng, W., & Zhao, K. (2009) · Cell · doi:10.1016/j.cell.2009.06.049 · Cited by 1,344
Cited by
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473
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
Bonn, S., Zinzen, R. P., Girardot, C., Gustafson, E. H., Perez-Gonzalez, A., Delhomme, N., Ghavi-Helm, Y., Wilczyński, B., Riddell, A., & Furlong, E. E. M. (2012) · Nature Genetics · doi:10.1038/ng.1064 · Cited by 502
Regulation of Alternative Splicing Through Coupling with Transcription and Chromatin Structure
Naftelberg, S., Schor, I. E., Ast, G., & Kornblihtt, A. R. (2015) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-060614-034242 · Cited by 448
Dorighi, K. M., Swigut, T., Henriques, T., Bhanu, N. V., Scruggs, B. S., Nady, N., Still, C. D., Garcia, B. A., Adelman, K., & Wysocka, J. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.04.018 · Cited by 403
Metadata
- Authors
- Karlić, R., Chung, H.-R., Lasserre, J., Vlahoviček, K., & Vingron, M.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-02
- Volume
- 107
- Issue
- 7
- Pages
- 2926-2931
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2127081697
- Cited by
- 792
- References
- 8
- FWCI
- 18.90
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Histone modification levels are predictive for gene expression. https://app.cochat.ai/papers/10.1073/pnas.0909344107
- The paper
- Karlić, R., Chung, H.-R., Lasserre, J., Vlahoviček, K., & Vingron, M. (2010). Histone modification levels are predictive for gene expression. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0909344107
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.