The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Unsupervised pattern discovery in human chromatin structure through genomic segmentation
Abstract
We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes
Siepel, A., Bejerano, G., Pedersen, J. S., Hinrichs, A. S., Hou, M., Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L. W., Richards, S., et al. (2005) · Genome Research · doi:10.1101/gr.3715005 · Cited by 3,971
Research works printed but not linked: 30
- ENCODE Project Consortium. PLoS Biol. 9, e1001046 (2011). · DOI · Paper page
- Day, N., Hemmaplardh, A., Thurman, R.E., Stamatoyannopoulos, J.A. & Noble, W.S. Bioinformatics 23, 1424–1426 (2007). · DOI
- Erdman, C. & Emerson, J.W. Bioinformatics 24, 2143–2148 (2008). · DOI
- Jaschek, R. & Tanay, A. in Research in Computational Molecular Biology, Lecture Notes in Computer Science Vol. 5541 (ed. Batzoglou, S.) 170–183 (Springer, Berlin, 2009). · DOI
- Ernst, J. & Kellis, M. Nat. Biotechnol. 28, 817–825 (2010). · DOI · Paper page
- Filion, G.J. et al. Cell 143, 212–224 (2010). · DOI · Paper page
- Kharchenko, P.V. et al. Nature 471, 480–485 (2011). · DOI · Paper page
- Bilmes, J. & Bartels, C. IEEE Signal Process. Mag. 22, 89–100 (2005). · DOI
- Reynolds, S.M., Käll, L., Riffle, M.E., Bilmes, J.A. & Noble, W.S. PLOS Comput. Biol. 4, e1000213 (2008). · DOI · Paper page
- Wang, Z., Schones, D.E. & Zhao, K. Curr. Opin. Genet. Dev. 19, 127–134 (2009). · DOI
- Hon, G., Ren, B. & Wang, W. PLOS Comput. Biol. 4, e1000201 (2008). · DOI
- Raney, B.J. et al. Nucleic Acids Res. 39, D871–D875 (2011). · DOI · Paper page
- Hoffman, M.M., Buske, O.J. & Noble, W.S. Bioinformatics 26, 1458–1459 (2010). · DOI
- Johnson, N.L. Biometrika 36, 149–176 (1949). · DOI · Paper page
- Bilmes, J. in UAI '00: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (eds. Boutilier, C. & Goldszmidt, M.) 38–45 (Morgan Kaufmann, San Francisco, 2000).
- Grundy, W.N., Bailey, T.L., Elkan, C.P. & Baker, M.E. Comput. Appl. Biosci. 13, 397–406 (1997).
- Bilmes, J. & Bartels, C. in UAI '03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence (eds. Meek, C. & Kjærulff, U.) 47–56 (Morgan Kaufmann Publishers, San Francisco, 2003).
- Viterbi, A.J. IEEE Trans. Inf. Theory 13, 260–269 (1967). · DOI · Paper page
- Fujita, P.A. et al. Nucleic Acids Res. 39, D876–D882 (2011). · DOI · Paper page
- Harrow, J. et al. Genome Biol. 7, S4.1–S4.9 (2006). · DOI · Paper page
- Takahashi, H., Kato, S., Murata, M. & Carninci, P. Methods Mol. Biol. 786, 181–200 (2012). · DOI
- Buske, O.J., Hoffman, M.M., Ponts, N., Roch, K.G.L. & Noble, W.S. BMC Bioinformatics 12, 415 (2011). · DOI
- Davis, J. & Goadrich, M. in Proceedings of the 23rd International Conference on Machine Learning 233–240 (ACM, New York, 2006). · DOI · Paper page
- Flicek, P. et al. Nucleic Acids Res. 39, D800–D806 (2011). · DOI · Paper page
- UniProt Consortium. Nucleic Acids Res. 39, D214–D219 (2011). · DOI · Paper page
- Berriz, G.F., Beaver, J.E., Cenik, C., Tasan, M. & Roth, F.P. Bioinformatics 25, 3043–3044 (2009). · DOI · Paper page
- Wingender, E. et al. Nucleic Acids Res. 28, 316–319 (2000). · DOI · Paper page
- Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W. & Lenhard, B. Nucleic Acids Res. 32, D91–D94 (2004). · DOI · Paper page
- Grant, C.E., Bailey, T.L. & Noble, W.S. Bioinformatics 27, 1017–1018 (2011). · DOI · Paper page
- Bickel, P.J., Boley, N., Brown, J.B., Huang, H. & Zhang, N.R. Ann. Appl. Stat. 4, 1660–1697 (2010). · DOI
Cited by
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183
An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550
Cunningham, F., Amode, M. R., Barrell, D., Beal, K., Billis, K., Brent, S., Carvalho-Silva, D., Clapham, P., Coates, G., Fitzgerald, S., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1010 · Cited by 1,115
Parker, S. C. J., Stitzel, M. L., Taylor, D. L., Orozco, J. M., Erdos, M. R., Akiyama, J. A., van Bueren, K. L., Chines, P. S., Narisu, N., Program, N. C. S., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317023110 · Cited by 665
Determinants of enhancer and promoter activities of regulatory elements
Andersson, R., & Sandelin, A. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0173-8 · Cited by 713
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675
Self-citationZerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T., & Flicek, P. R. (2015) · Genome Biology · doi:10.1186/s13059-015-0621-5 · Cited by 429
Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin
Whalen, S., Truty, R. M., & Pollard, K. S. (2016) · Nature Genetics · doi:10.1038/ng.3539 · Cited by 456
Identification of Genetic Variants That Affect Histone Modifications in Human Cells
McVicker, G., van de Geijn, B., Degner, J. F., Cain, C. E., Banovich, N. E., Raj, A., Lewellen, N., Myrthil, M., Gilad, Y., & Pritchard, J. K. (2013) · Science · doi:10.1126/science.1242429 · Cited by 436
Towards a comprehensive catalogue of validated and target-linked human enhancers
Gasperini, M., Tome, J. M., & Shendure, J. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0209-0 · Cited by 396
Inoue, F., Kircher, M., Martin, B., Cooper, G. M., Witten, D. M., McManus, M. T., Ahituv, N., & Shendure, J. (2017) · Genome Research · doi:10.1101/gr.212092.116 · Cited by 331
Index and biological spectrum of human DNase I hypersensitive sites
Meuleman, W., Muratov, A., Rynes, E., Halow, J., Lee, K., Bates, D., Diegel, M., Dunn, D., Neri, F., Teodosiadis, A., et al. (2020) · Nature · doi:10.1038/s41586-020-2559-3 · Cited by 331
A sequence-based global map of regulatory activity for deciphering human genetics
Chen, K. M., Wong, A. K., Troyanskaya, O. G., & Zhou, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01102-2 · Cited by 269
Many human accelerated regions are developmental enhancers
Capra, J. A., Erwin, G. D., McKinsey, G., Rubenstein, J. L. R., & Pollard, K. S. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0025 · Cited by 235
Paternally inherited cis-regulatory structural variants are associated with autism
Brandler, W. M., Antaki, D., Gujral, M., Kleiber, M. L., Whitney, J., Maile, M. S., Hong, O., Chapman, T. R., Tan, S., Tandon, P., et al. (2018) · Science · doi:10.1126/science.aan2261 · Cited by 209
High-throughput functional testing of ENCODE segmentation predictions
Kwasnieski, J. C., Fiore, C., Chaudhari, H. G., & Cohen, B. A. (2014) · Genome Research · doi:10.1101/gr.173518.114 · Cited by 224
Chromatin accessibility profiling methods
Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206
Quang, D., & Xie, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.03.020 · Cited by 204
Nuclear stability and transcriptional directionality separate functionally distinct RNA species
Andersson, R., Refsing Andersen, P., Valen, E., Core, L. J., Bornholdt, J., Boyd, M., Heick Jensen, T., & Sandelin, A. (2014) · Nature Communications · doi:10.1038/ncomms6336 · Cited by 171
A supervised learning framework for chromatin loop detection in genome-wide contact maps
Salameh, T. J., Wang, X., Song, F., Zhang, B., Wright, S. M., Khunsriraksakul, C., Ruan, Y., & Yue, F. (2020) · Nature Communications · doi:10.1038/s41467-020-17239-9 · Cited by 165
Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions
Ernst, J., Melnikov, A., Zhang, X., Wang, L., Rogov, P., Mikkelsen, T. S., & Kellis, M. (2016) · Nature Biotechnology · doi:10.1038/nbt.3678 · Cited by 162
Gasperini, M., Findlay, G. M., McKenna, A., Milbank, J. H., Lee, C., Zhang, M. D., Cusanovich, D. A., & Shendure, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.010 · Cited by 143
Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis
Georgakopoulos-Soares, I., Morganella, S., Jain, N., Hemberg, M., & Nik-Zainal, S. (2018) · Genome Research · doi:10.1101/gr.231688.117 · Cited by 146
Interpretation of deep learning in genomics and epigenomics
Talukder, A., Barham, C., Li, X., & Hu, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa177 · Cited by 142
High-Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development
Wilderman, A., VanOudenhove, J., Kron, J., Noonan, J. P., & Cotney, J. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.129 · Cited by 139
DEEP: a general computational framework for predicting enhancers
Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1058 · Cited by 125
Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
Sethi, A., Gu, M., Gumusgoz, E., Chan, L., Yan, K.-K., Rozowsky, J., Barozzi, I., Afzal, V., Akiyama, J. A., Plajzer-Frick, I., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0907-8 · Cited by 109
Moore, J. E., Pratt, H. E., Purcaro, M. J., & Weng, Z. (2020) · Genome Biology · doi:10.1186/s13059-019-1924-8 · Cited by 106
Self-citationZacher, B., Michel, M., Schwalb, B., Cramer, P., Tresch, A., & Gagneur, J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169249 · Cited by 91
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104
PEDLA: predicting enhancers with a deep learning-based algorithmic framework
Liu, F., Li, H., Ren, C., Bo, X., & Shu, W. (2016) · Scientific Reports · doi:10.1038/srep28517 · Cited by 101
Prediction of enhancer-promoter interactions via natural language processing
Zeng, W., Wu, M., & Jiang, R. (2018) · BMC Genomics · doi:10.1186/s12864-018-4459-6 · Cited by 96
“Promoter and enhancer regions were identified using ENCODE Segway [26] and ChromHMM [27] annotations for K562, GM12878, HeLa-S3, and HUVEC cell lines, and using Roadmap [28] Epige-nomics ChromHMM annotations for NHEK and IMR90 cell lines.”
MethodLibbrecht, M. W., Ay, F., Hoffman, M. M., Gilbert, D. M., Bilmes, J. A., & Noble, W. S. (2015) · Genome Research · doi:10.1101/gr.184341.114 · Cited by 84
“We used a uniform signal-processing pipeline to generate a genome-wide vector for each data set, as described by Hoffman et al. (2013).”
InfluentialSelf-citationTranscriptional enhancers and their communication with gene promoters
Ray-Jones, H., & Spivakov, M. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03903-w · Cited by 81
Yen, A., & Kellis, M. (2015) · Nature Communications · doi:10.1038/ncomms8973 · Cited by 67
Gusev, A., Shi, H., Kichaev, G., Pomerantz, M., Li, F., Long, H. W., Ingles, S. A., Kittles, R. A., Strom, S. S., Rybicki, B. A., et al. (2016) · Nature Communications · doi:10.1038/ncomms10979 · Cited by 51
Universal chromatin state annotation of the mouse genome
Vu, H., & Ernst, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02994-x · Cited by 37
Gong, W., Koyano-Nakagawa, N., Li, T., & Garry, D. J. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0460-0 · Cited by 26
Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform
Madrigal, P., & Krajewski, P. (2015) · BioData Mining · doi:10.1186/s13040-015-0051-7 · Cited by 15
Metadata
- Authors
- Hoffman, M. M., Buske, O. J., Wang, J., Weng, Z., Bilmes, J. A., & Noble, W. S.
- Venue
- Nature Methods
- Published
- 2012-03-18
- Volume
- 9
- Issue
- 5
- Pages
- 473-476
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W4230875896
- Cited by
- 565
- References
- 2
- FWCI
- 15.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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
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.
