期刊论文详细信息
BMC Genomics
Differential motif enrichment analysis of paired ChIP-seq experiments
Timothy L Bailey1  Philip Machanick2  James Johnson1  Tom Lesluyes1 
[1] Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, 4072 Brisbane, Australia;Department of Computer Science, Rhodes University, Drosty Road, 6140 Grahamstown, Eastern Cape Province, South Africa
关键词: Gene expression;    Regulation of transcription;    ChIP-seq;    MCF10A-ER-Src cells;    Constrained differential motif enrichment analysis;    Comparative ChIP-seq analysis;   
Others  :  1140926
DOI  :  10.1186/1471-2164-15-752
 received in 2014-02-17, accepted in 2014-08-22,  发布年份 2014
PDF
【 摘 要 】

Background

Motif enrichment analysis of transcription factor ChIP-seq data can help identify transcription factors that cooperate or compete. Previously, little attention has been given to comparative motif enrichment analysis of pairs of ChIP-seq experiments, where the binding of the same transcription factor is assayed under different conditions. Such comparative analysis could potentially identify the distinct regulatory partners/competitors of the assayed transcription factor under different conditions or at different stages of development.

Results

We describe a new methodology for identifying sequence motifs that are differentially enriched in one set of DNA or RNA sequences relative to another set, and apply it to paired ChIP-seq experiments. We show that, using paired ChIP-seq data for a single transcription factor, differential motif enrichment analysis identifies all the known key transcription factors involved in the transformation of non-cancerous immortalized breast cells (MCF10A-ER-Src cells) into cancer stem cells whereas non-differential motif enrichment analysis does not. We also show that differential motif enrichment analysis identifies regulatory motifs that are significantly enriched at constrained locations within the bound promoters, and that these motifs are not identified by non-differential motif enrichment analysis. Our methodology differs from other approaches in that it leverages both comparative enrichment and positional enrichment of motifs in ChIP-seq peak regions or in the promoters of genes bound by the transcription factor.

Conclusions

We show that differential motif enrichment analysis of paired ChIP-seq experiments offers biological insights not available from non-differential analysis. In contrast to previous approaches, our method detects motifs that are enriched in a constrained region in one set of sequences, but not enriched in the same region in the comparative set. We have enhanced the web-based CentriMo algorithm to allow it to perform the constrained differential motif enrichment analysis described in this paper, and CentriMo’s on-line interface (http://meme.ebi.edu.au webcite) provides dozens of databases of DNA- and RNA-binding motifs from a full range of organisms. All data and output files presented here are available at http://research.imb.uq.edu.au/t.bailey/supplementary\_data/Lesluyes2014 webcite.

【 授权许可】

   
2014 Lesluyes et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150325153322120.pdf 870KB PDF download
Figure 5. 61KB Image download
Figure 4. 35KB Image download
Figure 3. 30KB Image download
Figure 2. 45KB Image download
Figure 1. 121KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】
  • [1]ENCODE Consortium: An integrated encyclopedia of DNA elements in the human genome. Nature 2012, 489(7414):57-74. doi:10.1038/nature11247
  • [2]Newburger DE, Bulyk ML: UniPROBE: an online database of protein binding microarray data on protein-DNA interactions. Nucleic Acids Res 2009, 37(Sp. Iss. SI):77-82. doi:10.1093/nar/gkn66
  • [3]Jolma A, Yan J, Whitington T, Toivonen J, Nitta KR, Rastas P, Morgunova E, Enge M, Taipale M, Wei G, Palin K, Vaquerizas JM, Vincentelli R, Luscombe NM, Hughes TR, Lemaire P, Ukkonen E, Kivioja T, Taipale J: DNA-binding specificities of human transcription factors. Cell 2013, 152(1–2):327-339. doi:10.1016/j.cell.2012.12.009
  • [4]Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, Buchman S, Chen C-Y, Chou A, Ienasescu H, Lim J, Shyr C, Tan G, Zhou M, Lenhard B, Sandelin A, Wasserman WW: Jaspar 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res 2014, 42(1):142-147. doi:10.1093/nar/gkt997
  • [5]Ray D, Kazan H, Cook KB, Weirauch MT, Najafabadi HS, Li X, Gueroussov S, Albu M, Zheng H, Yang A, Na H, Irimia M, Matzat LH, Dale RK, Smith SA, Yarosh CA, Kelly SM, Nabet B, Mecenas D, Li W, Laishram RS, Qiao M, Lipshitz HD, Piano F, Corbett AH, Carstens RP, Frey BJ, Anderson RA, Lynch KW, Penalva LOF, et al.: A compendium of RNA-binding motifs for decoding gene regulation. Nature 2013, 499(7457):172-177. doi:10.1038/nature12311
  • [6]McLeay RC, Bailey TL: Motif enrichment analysis: a unified framework and an evaluation on ChIP data. BMC Bioinformatics 2010, 11:165. doi:10.1186/1471-2105-11-165 BioMed Central Full Text
  • [7]Wilbanks EG, Facciotti MT: Evaluation of algorithm performance in ChIP-seq peak detection. PLoS One 2010, 5(7):11471. doi:10.1371/journal.pone.0011471
  • [8]Bailey TL, Machanick P: Inferring direct DNA binding from ChIP-seq. Nucleic Acids Res 2012, 40(17):128. doi:10.1093/nar/gks433
  • [9]Carninci P, Sandelin A, Lenhard B, Katayama S, Shimokawa K, Ponjavic J, Semple CAM, Taylor MS, Engström PG, Frith MC, Forrest ARR, Alkema WB, Tan SL, Plessy C, Kodzius R, Ravasi T, Kasukawa T, Fukuda S, Kanamori-Katayama M, Kitazume Y, Kawaji H, Kai C, Nakamura M, Konno H, Nakano K, Mottagui-Tabar S, Arner P, Chesi A, Gustincich S, Persichetti F, et al.: Genome-wide analysis of mammalian promoter architecture and evolution. Nat Genet 2006, 38(6):626-635. doi:10.1038/ng1789
  • [10]Whitington T, Frith MC, Johnson J, Bailey TL: Inferring transcription factor complexes from ChIP-seq data. Nucleic Acids Res 2011, 39(15):98. doi:10.1093/nar/gkr341
  • [11]Iliopoulos D, Hirsch HA, Struhl K: An epigenetic switch involving NF-kappaB, Lin28, Let-7 MicroRNA, and IL6 links inflammation to cell transformation. Cell 2009, 139(4):693-706. doi:10.1016/j.cell.2009.10.014
  • [12]Odrowaz Z, Sharrocks AD: The ETS transcription factors ELK1 and GABPA regulate different gene networks to control MCF10A breast epithelial cell migration. PLoS One 2012, 7(12):49892. doi:10.1371/journal.pone.0049892
  • [13]Hai T, Curran T: Cross-family dimerization of transcription factors Fos/Jun and ATF/CREB alters DNA binding specificity. Proc Natl Acad Sci U S A 1991, 88(9):3720-3724.
  • [14]Lopez-Bergami P, Lau E, Ronai Z: Emerging roles of ATF2 and the dynamic AP1 network in cancer. Nat Rev Cancer 2010, 10(1):65-76. doi:10.1038/nrc2681
  • [15]Fleming JD, Pavesi G, Benatti P, Imbriano C, Mantovani R, Struhl K: Nf-y coassociates with fos at promoters, enhancers, repetitive elements, and inactive chromatin regions, and is stereo-positioned with growth-controlling transcription factors. Genome Res 2013, 23(8):1195-1209. doi:10.1101/gr.148080.112
  • [16]Pandey PR, Xing F, Sharma S, Watabe M, Pai SK, Iiizumi-Gairani M, Fukuda K, Hirota S, Mo Y-Y, Watabe K: Elevated lipogenesis in epithelial stem-like cell confers survival advantage in ductal carcinoma in situ of breast cancer. Oncogene 2013, 32(42):5111-5122. doi:10.1038/onc.2012.519
  • [17]Pizer ES, Chrest FJ, DiGiuseppe JA, Han WF: Pharmacological inhibitors of mammalian fatty acid synthase suppress DNA replication and induce apoptosis in tumor cell lines. Cancer Res 1998, 58(20):4611-4615.
  • [18]Qin C, Wilson C, Blancher C, Taylor M, Safe S, Harris AL: Association of ARNT splice variants with estrogen receptor-negative breast cancer, poor induction of vascular endothelial growth factor under hypoxia, and poor prognosis. Clin Cancer Res 2001, 7(4):818-823.
  • [19]Maia A-T, Antoniou AC, O’Reilly M, Samarajiwa S, Dunning M, Kartsonaki C, Chin S-F, Curtis CN, McGuffog L, Domchek SM, Easton DF, Peock S, Frost D, Evans DG, Eeles R, Izatt L, Adlard J, Eccles D, Sinilnikova OM, Mazoyer S, Stoppa-Lyonnet D, Gauthier-Villars M, Faivre L, Venat-Bouvet L, Delnatte C, Nevanlinna H, Couch FJ, Godwin AK, EMBRACE, et al.: Effects of BRCA2 cis-regulation in normal breast and cancer risk amongst BRCA2 mutation carriers. Breast Cancer Res 2012, 14(2):63. doi:10.1186/bcr3169 BioMed Central Full Text
  • [20]Stormo GD: DNA binding sites: representation and discovery. Bioinformatics 2000, 16(1):16-23.
  • [21]Fisher RA: On the interpretation ofχ2 from contingency tables, and the calculation of p. J R Stat Soc 1922, 85(1):87-94.
  • [22]Ma W, Noble WS, Bailey TL: Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nat Protoc 2014, 9(6):1428-1450. doi:10.1038/nprot.2014.083
  • [23]Portales-Casamar E, Thongjuea S, Kwon AT, Arenillas D, Zhao X, Valen E, Yusuf D, Lenhard B, Wasserman WW, Sandelin A: Jaspar 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res 2010, 38(Database issue):105-110. doi:10.1093/nar/gkp950
  文献评价指标  
  下载次数:34次 浏览次数:6次