Quantitative Biology

, Volume 7, Issue 3, pp 233–243 | Cite as

EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information

  • Shaoming Song
  • Hongfei Cui
  • Shengquan Chen
  • Qiao Liu
  • Rui JiangEmail author
Software Article



Transcription factor is one of the most important regulators in the transcriptional process. Nevertheless, the functional interpretation of transcription factors is still a main challenge due to the poor performance of methods relating to regulatory regions to genes. Epigenetic information, such as chromatin accessibility, contains genome-wide knowledge about transcription regulation and thus may shed light on the functional interpretation of transcription factors.


We propose EpiFIT (Epigenetic based Functional Interpretation of Transcription factors), a tool to infer functions of transcription factors from ChIP-seq data. Briefly, we adopt a variable distance rule to establish associations between regulatory regions and nearby genes. The associations are then filtered to ensure that the remaining regions and associated genes are co-open. Finally, GO enrichment is applied to all related genes and a ranking list of GO terms is provided as functional interpretation.


We first examined the chromatin openness correlation between regulatory regions and associated genes. The correlation can help EpiFIT purify regulatory region-gene associations. By evaluating EpiFIT on a set of real data, we demonstrated that EpiFIT outperforms other existing methods for precisely interpreting transcription factor functions. We further verify the efficiency of openness in interpretation and the ability of EpiFIT to build distal region-gene associations.


EpiFIT is a powerful tool for interpreting the transcription factor functions. We believe EpiFIT will facilitate the functional interpretation of other regulatory elements, and thus open a new door to understanding the regulatory mechanism.


The application is freely accessible at website:


transcription factor functional interpretation epigenetic information 



This work has been supported by the National Key Research and Development Program of China (No. 2018YFC0910404), the National Natural Science Foundation of China (Nos. 61873141, 61721003, 61573207, 71871019 and 71471016), and the Tsinghua-Fuzhou Institute for Data Technology.

Compliance with Ethics Guidelines

The authors Shaoming Song, Hongfei Cui, Shengquan Chen, Qiao Liu and Rui Jiang declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.


  1. 1.
    Johnson, D. S., Mortazavi, A., Myers, R. M. and Wold, B. (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science, 316, 1497–1502CrossRefGoogle Scholar
  2. 2.
    Mardis, E. R. (2007) ChIP-seq: welcome to the new frontier. Nat. Methods, 4, 613–614CrossRefGoogle Scholar
  3. 3.
    Tu, S. and Shao, Z. (2017) An introduction to computational tools for differential binding analysis with ChIP-seq data. Quant. Biol., 5, 226–235CrossRefGoogle Scholar
  4. 4.
    Hoffman, M. M., Ernst, J., Wilder, S. P., Kundaje, A., Harris, R. S., Libbrecht, M., Giardine, B., Ellenbogen, P. M., Bilmes, J. A., Birney, E., et al. (2013) Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res., 41, 827–841CrossRefGoogle Scholar
  5. 5.
    Blahnik, K. R., Dou, L., O’Geen, H., McPhillips, T., Xu, X., Cao, A. R., Iyengar, S., Nicolet, C. M., Ludäscher, B., Korf, I., et al. (2010) Sole-Search: an integrated analysis program for peak detection and functional annotation using ChIP-seq data. Nucleic Acids Res., 38, e13CrossRefGoogle Scholar
  6. 6.
    Huang, W., Sherman, B. T. and Lempicki, R. A. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44–57CrossRefGoogle Scholar
  7. 7.
    Huang, W., Sherman, B. T. and Lempicki, R. A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1–13CrossRefGoogle Scholar
  8. 8.
    McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M. and Bejerano, G. (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol., 28, 495–501CrossRefGoogle Scholar
  9. 9.
    Natarajan, A., Yardimci, G. G., Sheffield, N. C., Crawford, G. E. and Ohler, U. (2012) Predicting cell-type-specific gene expression from regions of open chromatin. Genome Res., 22, 1711–1722CrossRefGoogle Scholar
  10. 10.
    Valouev, A., Johnson, D. S., Sundquist, A., Medina, C., Anton, E., Batzoglou, S., Myers, R. M. and Sidow, A. (2008) Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat. Methods, 5, 829–834CrossRefGoogle Scholar
  11. 11.
    Cao, S., Zhou, Y., Wu, Y., Song, T., Alsaihati, B. and Xu, Y. (2017) Transcription regulation by DNA methylation under stressful conditions in human cancer. Quant. Biol., 5, 328–337CrossRefGoogle Scholar
  12. 12.
    Liu, Q., Xia, F., Yin, Q. and Jiang, R. (2018) Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics, 34, 732–738CrossRefGoogle Scholar
  13. 13.
    Sherwood, R. I., Hashimoto, T., O’Donnell, C. W., Lewis, S., Barkal, A. A., van Hoff, J. P., Karun, V., Jaakkola, T. and Gifford, D. K. (2014) Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat. Biotechnol., 32, 171–178CrossRefGoogle Scholar
  14. 14.
    Wang, Y., Jiang, R. and Wong, W. H. (2016) Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data. Natl. Sci. Rev., 3, 240–251CrossRefGoogle Scholar
  15. 15.
    Chen, S., Wang, Y. and Jiang, R. (2019) OPENANNO: annotating genomic regions with chromatin accessibility. BioRxivGoogle Scholar
  16. 16.
    Davis, C. A., Hitz, B. C., Sloan, C. A., Chan, E. T., Davidson, J. M., Gabdank, I., Hilton, J. A., Jain, K., Baymuradov, U. K., Narayanan, A. K., et al. (2018) The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res., 46, D794–D801CrossRefGoogle Scholar
  17. 17.
    ENCODE Project Consortium. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57–74CrossRefGoogle Scholar
  18. 18.
    Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) Gene ontology: tool for the unification of biology. Nat. Genet., 25, 25–29CrossRefGoogle Scholar
  19. 19.
    The Gene Ontology Consortium. (2019) The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res., 47, D330–D338CrossRefGoogle Scholar
  20. 20.
    Min, X., Zeng, W., Chen, N., Chen, T. and Jiang, R. (2017) Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding. Bioinformatics, 33, i92–i101CrossRefGoogle Scholar
  21. 21.
    Duren, Z., Chen, X., Jiang, R., Wang, Y. and Wong, W. H. (2017) Modeling gene regulation from paired expression and chromatin accessibility data. Proc. Natl. Acad. Sci. USA., 114, E4914–E4923CrossRefGoogle Scholar
  22. 22.
    Huntley, R. P., Sawford, T., Mutowo-Meullenet, P., Shypitsyna, A., Bonilla, C., Martin, M. J. and O’Donovan, C. (2015) The GOA database: gene Ontology annotation updates for 2015. Nucleic Acids Res., 43, D1057–D1063CrossRefGoogle Scholar
  23. 23.
    Croft, D., Mundo, A. F., Haw, R., Milacic, M., Weiser, J., Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., et al. (2014) The Reactome pathway knowledgebase. Nucleic Acids Res., 42, D472–D477CrossRefGoogle Scholar
  24. 24.
    Boyer, L. A., Lee, T. I., Cole, M. F., Johnstone, S. E., Levine, S. S., Zucker, J. P., Guenther, M. G., Kumar, R. M., Murray, H. L., Jenner, R. G., et al. (2005). Core transcriptional regulatory circuitry in human embryonic stem cells. cell, 122, 947–956CrossRefGoogle Scholar
  25. 25.
    Zhao, M., Amiel, S. A., Christie, M. R., Muiesan, P., Srinivasan, P., Littlejohn, W., Rela, M., Arno, M., Heaton, N. and Huang, G. C. (2007) Evidence for the presence of stem cell-like progenitor cells in human adult pancreas. J. Endocrinol., 195, 407–114CrossRefGoogle Scholar
  26. 26.
    Lee, J., Kim, H. K., Han, Y. M. and Kim, J. (2008) Pyruvate kinase isozyme type M2 (PKM2) interacts and cooperates with Oct-4 in regulating transcription. Int. J. Biochem. Cell Biol., 40, 1043–1054CrossRefGoogle Scholar
  27. 27.
    Xu, H., Wang, W., Li, C., Yu, H., Yang, A., Wang, B. and Jin, Y. (2009) WWP2 promotes degradation of transcription factor OCT4 in human embryonic stem cells. Cell Res., 19, 561–573CrossRefGoogle Scholar
  28. 28.
    Yoon, S. J., Wills, A. E., Chuong, E., Gupta, R. and Baker, J. C. (2011) HEB and E2A function as SMAD/FOXH1 cofactors. Genes Dev., 25, 1654–1661CrossRefGoogle Scholar
  29. 29.
    Kristensen, D. M., Nielsen, J. E., Skakkebaek, N. E., Graem, N., Jacobsen, G. K., Rajpert-De Meyts, E. and Leffers, H. (2008) Presumed pluripotency markers UTF-1 and REX-1 are expressed in human adult testes and germ cell neoplasms. Hum. Reprod., 23, 775–782CrossRefGoogle Scholar
  30. 30.
    Trubiani, O., Zalzal, S. F., Paganelli, R., Marchisio, M., Giancola, R., Pizzicannella, J., Bühring, H. J., Piattelli, M., Caputi, S. and Nanci, A. (2010) Expression profile of the embryonic markers nanog, OCT-4, SSEA-1, SSEA-4, and frizzled-9 receptor in human periodontal ligament mesenchymal stem cells. J. Cell. Physiol., 225, 123–131CrossRefGoogle Scholar
  31. 31.
    Stefanovic, S., Abboud, N., Désilets, S., Nury, D., Cowan, C. and Pucéat, M. (2009) Interplay of Oct4 with Sox2 and Sox17: a molecular switch from stem cell pluripotency to specifying a cardiac fate. J. Cell Biol., 186, 665–673CrossRefGoogle Scholar
  32. 32.
    Lei, X. X., Xu, J., Ma, W., Qiao, C., Newman, M. A., Hammond, S. M. and Huang, Y. (2012) Determinants of mRNA recognition and translation regulation by Lin28. Nucleic Acids Res., 40, 3574–3584CrossRefGoogle Scholar
  33. 33.
    Bard, J. D., Gelebart, P., Amin, H. M., Young, L. C., Ma, Y. and Lai, R. (2009) Signal transducer and activator of transcription 3 is a transcriptional factor regulating the gene expression of SALL4. FASEB J., 23, 1405–1414CrossRefGoogle Scholar
  34. 34.
    Kunarso, G., Chia, N. Y., Jeyakani, J., Hwang, C., Lu, X., Chan, Y. S., Ng, H. H. and Bourque, G. (2010) Transposable elements have rewired the core regulatory network of human embryonic stem cells. Nat. Genet., 42, 631–634CrossRefGoogle Scholar
  35. 35.
    Li, J., & Wang, C. Y. (2008). TBL1–TBLR1 and β-catenin recruit each other to Wnt target-gene promoter for transcription activation and oncogenesis. Nat. cell Biol., 10, 160–169.CrossRefGoogle Scholar
  36. 36.
    Zhou, S., Fujimuro, M., Hsieh, J. J. D., Chen, L., Miyamoto, A., Weinmaster, G. and Hayward, S. D. (2000) SKIP, a CBF1-associated protein, interacts with the ankyrin repeat domain of NotchIC To facilitate NotchIC function. Mol. Cell. Biol., 20, 2400–2410CrossRefGoogle Scholar
  37. 37.
    Guenther, M. G., Barak, O. and Lazar, M. A. (2001) The SMRT and N-CoR corepressors are activating cofactors for histone deacetylase 3. Mol. Cell. Biol., 21, 6091–6101CrossRefGoogle Scholar
  38. 38.
    Yu, S. and Reddy, J. K. (2007) Transcription coactivators for peroxisome proliferator-activated receptors. BBA-MOL Cell Biol. L., 1771, 936–951.CrossRefGoogle Scholar
  39. 39.
    Feige, J. N., Gelman, L., Michalik, L., Desvergne, B. and Wahli, W. (2006) From molecular action to physiological outputs: peroxisome proliferator-activated receptors are nuclear receptors at the crossroads of key cellular functions. Prog. Lipid Res., 45, 120–159CrossRefGoogle Scholar
  40. 40.
    Ishii, S., Kurasawa, Y., Wong, J. and Yu-Lee, L. Y. (2008) Histone deacetylase 3 localizes to the mitotic spindle and is required for kinetochore-microtubule attachment. Proc. Natl. Acad. Sci. USA, 105, 4179–4184CrossRefGoogle Scholar
  41. 41.
    Ouyang, Z., Zhou, Q. and Wong, W. H. (2009) ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proc. Natl. Acad. Sci. USA, 106, 21521–21526CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Shaoming Song
    • 1
  • Hongfei Cui
    • 1
  • Shengquan Chen
    • 1
  • Qiao Liu
    • 1
  • Rui Jiang
    • 1
    Email author
  1. 1.MOE Key Laboratory of Bioinformatics; Beijing National Research Center for Information Science and Technology; Department of AutomationTsinghua UniversityBeijingChina

Personalised recommendations