The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Sequence and chromatin determinants of cell-type–specific transcription factor binding
Open accessAbstract
Gene regulatory programs in distinct cell types are maintained in large part through the cell-type–specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type–specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line–specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type–specific binding patterns, four display distinct cell-type–specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type–specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type–specific binding profiles.
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Cites
Heintzman, N. D., Stuart, R. K., Hon, G., Fu, Y., Ching, C. W., Hawkins, R. D., Barrera, L. O., Van Calcar, S., Qu, C., Ching, K. A., et al. (2007) · Nature Genetics · doi:10.1038/ng1966 · Cited by 3,398 · cited 1×
“In particular, chromatin marks (Heintzman et al. 2007; Whitington et al. 2009) and DNase-hypersensitive regions (Hesselberth et al. 2009) have been used as filters for PSSM motif hits to predict cell-type– specific gene expression.”
MethodMapping and analysis of chromatin state dynamics in nine human cell types
Ernst, J., Kheradpour, P., Mikkelsen, T. S., Shoresh, N., Ward, L. D., Epstein, C. B., Zhang, X., Wang, L., Issner, R., Coyne, M., et al. (2011) · Nature · doi:10.1038/nature09906 · Cited by 2,898
The accessible chromatin landscape of the human genome
Thurman, R. E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M. T., Haugen, E., Sheffield, N. C., Stergachis, A. B., Wang, H., Vernot, B., et al. (2012) · Nature · doi:10.1038/nature11232 · Cited by 2,679
Histone modifications at human enhancers reflect global cell-type-specific gene expression
Heintzman, N. D., Hon, G. C., Hawkins, R. D., Kheradpour, P., Stark, A., Harp, L. F., Ye, Z., Lee, L. K., Stuart, R. K., Ching, C. W., et al. (2009) · Nature · doi:10.1038/nature07829 · Cited by 2,533
DNA binding site sequence directs glucocorticoid receptor structure and activity
Meijsing, S. H., Pufall, M. A., So, A. Y., Bates, D. L., Chen, L., & Yamamoto, K. R. (2009) · Science · doi:10.1126/science.1164265 · Cited by 673
Global mapping of protein-DNA interactions in vivo by digital genomic footprinting
Hesselberth, J. R., Chen, X., Zhang, Z., Sabo, P. J., Sandstrom, R., Reynolds, A. P., Thurman, R. E., Neph, S., Kuehn, M. S., Noble, W. S., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1313 · Cited by 559
Self-citationPique-Regi, R., Degner, J. F., Pai, A. A., Gaffney, D. J., Gilad, Y., & Pritchard, J. K. (2011) · Genome Research · doi:10.1101/gr.112623.110 · Cited by 521
DNA methylation status predicts cell type‐specific enhancer activity
Wiench, M., John, S., Baek, S., Johnson, T. A., Sung, M., Escobar, T., Simmons, C. A., Pearce, K. H., Biddie, S. C., Sabo, P. J., et al. (2011) · The EMBO Journal · doi:10.1038/emboj.2011.210 · Cited by 245
Genome-wide prediction of transcription factor binding sites using an integrated model
Won, K.-J., Ren, B., & Wang, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-1-r7 · Cited by 116 · cited 2×
“combined PSSMs and chromatin data in probabilistic models (Ernst et al. 2010; Won et al. 2010; Pique-Regi et al. 2011) or”
Method
Cited by
Landscape of transcription in human cells
Djebali, S., Davis, C. A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., et al. (2012) · Nature · doi:10.1038/nature11233 · Cited by 5,187
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Absence of a simple code: how transcription factors read the genome
Slattery, M., Zhou, T., Yang, L., Dantas Machado, A. C., Gordân, R., & Rohs, R. (2014) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2014.07.002 · Cited by 537
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
Ghandi, M., Lee, D., Mohammad-Noori, M., & Beer, M. A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003711 · Cited by 489
Metadata
- Authors
- Arvey, A., Agius, P., Noble, W. S., & Leslie, C.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2012-09
- Volume
- 22
- Issue
- 9
- Pages
- 1723-1734
- ISSN
- 1088-9051 (ISSN-L)
- OpenAlex
- W1989338936
- Cited by
- 222
- References
- 9
- Type
- posted content
- Language
- en
- Open access
- bronze
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