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. 2020 Sep 11;11:566569. doi: 10.3389/fgene.2020.566569

EAT-UpTF: Enrichment Analysis Tool for Upstream Transcription Factors of a Group of Plant Genes

Sangrea Shim 1,2,*, Pil Joon Seo 1,2,3,*
PMCID: PMC7516213  PMID: 33024441

Abstract

EAT-UpTF (Enrichment Analysis Tool for Upstream Transcription Factors of a group of plant genes) is an open-source Python script that analyzes the enrichment of upstream transcription factors (TFs) in a group of genes-of-interest (GOIs). EAT-UpTF utilizes genome-wide lists of TF-target genes generated by DNA affinity purification followed by sequencing (DAP-seq) or chromatin immunoprecipitation followed by sequencing (ChIP-seq). Unlike previous methods based on the two-step prediction of cis-motifs and DNA-element-binding TFs, our EAT-UpTF analysis enabled a one-step identification of enriched upstream TFs in a set of GOIs using lists of empirically determined TF-target genes. The tool is designed particularly for plant researches, due to the lack of analytic tools for upstream TF enrichment, and available at https://github.com/sangreashim/EAT-UpTF and http://chromatindynamics.snu.ac.kr:8080/EatupTF.

Keywords: transcription factor, cis-elements, plant, Arabidopsis, DAP-seq

Introduction

The rapid development of high-throughput technologies such as RNA sequencing (RNA-seq), DNA affinity purification followed by sequencing (DAP-seq), and chromatin immunoprecipitation followed by sequencing (ChIP-seq) has led to an explosion in the availability of sequence data. The high-throughput analyses produce lists of genes that are under a particular regulation. When such lists are generated, researchers usually try to understand the biological implications of groups of genes-of-interest (GOIs). To this end, routine follow-up studies typically include gene ontology (GO) enrichment analyses (Maere et al., 2005; Huang et al., 2009) and Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping (Kanehisa and Goto, 2000). In addition, transcription factor (TF) prediction analyses (Kreft et al., 2017; Kulkarni et al., 2018) can be performed to identify consensus upstream regulators of a subset of GOIs, giving a biological insight into the integrated role of the genes under specific conditions. Furthermore, comprehensive identification of TF binding sites and cognate TFs can be used to characterize regulatory networks containing GOIs.

Several bioinformatics tools have been developed to predict upstream TFs. The cis-element sequences that are commonly conserved in sets of input query genes can be identified using ab initio motif enrichment algorithms such as MEME (Bailey et al., 2009). The identified consensus sequences can be further analyzed to compare enrichment of TF candidates to the consensus binding motifs provided by databases of experimentally validated TF binding sites, such as JASPAR (Khan et al., 2018) and TRANSFAC (Matys et al., 2003). Recently, accumulating data have enabled that position weight matrix (PWM)-based enrichment methods solely cover a wide range of upstream TF prediction. This theoretical basis has been implemented in various upstream TF prediction tools, such as TFEA.ChIP, oPOSSUM, and PlantRegMap (Ho Sui et al., 2005; Puente-Santamaria et al., 2019; Tian et al., 2020). However, this approach occasionally produces a considerable number of false positives due to short and degenerate nature of TF-binding sites (Kreft et al., 2017). In addition, this method is complicated by the fact that TFs can sometimes bind to gene sequences that differ from their consensus binding sites, and that several TFs undergo protein–protein interactions that enable them to recognize additional DNA sequence motifs. Overall, it is clear that a simplified and realistic prediction of TFs controlling a group of GOIs is necessary to generate a confident conclusion.

In this regard, several bioinformatics tools implementing TF enrichment analysis have been developed using ChIP-seq datasets (Zambelli et al., 2012; Auerbach et al., 2013; Zheng et al., 2019). However, these tools are applicable mainly to animal systems, and no codes have been released to analyze enriched upstream TFs for other species. Based on explosive accumulation of plant DAP-seq and ChIP-seq data, there are growing needs to integrate the NGS data and use them to retrieve upstream TFs in plant researches. Notably, O’Malley and colleagues adapted the innovative DAP-seq method and have successfully produced a genome-wide collection of target genes for 349 TFs in Arabidopsis thaliana (O’Malley et al., 2016). In this study, we have developed the “Enrichment Analysis Tool for Upstream Transcription Factors of a group of plant genes” (EAT-UpTF) tool to provide upstream TF enrichment analysis (Shim and Seo, 2020). As a proof of concept, we combined it with the Arabidopsis DAP-seq database to analyze the enrichment of upstream TFs in a group of Arabidopsis GOIs. We found that EAT-UpTF was able to robustly evaluate the over-representation of experimentally validated upstream TFs binding to a group of GOIs without the prediction of cis-motifs.

Methods

High-throughput sequencing analyses typically produce sets of GOIs that require further analyses to evaluate their biological implication and underlying regulatory mechanisms. EAT-UpTF is linked to a DAP-seq database (Plant Cistrome database1) that provides a list of TF-target genes (locus IDs). When a set of GOIs is input in the form of locus IDs, EAT-UpTF identifies the TF targets and compares their relative enrichment in the list of GOIs with that in the total genomic genes. As a result, target genes of certain TFs, which are enriched (over-represented) in the set of GOIs can be identified as a major upstream regulators of the gene group (Figure 1). To examine the statistical significance of over-representation, the SciPy module (Oliphant, 2007) is used to perform hypergeometric and binomial tests, which differ in that the binomial test considers replacement whereas the hypergeometric test does not. These two tests are used to compare the occurrence of x genes (a subset of TF-target genes) among n genes (GOIs) with that of X genes (total TF-target genes) among N genes (total reference genes). Comparisons with relatively large differences (x/n – X/N) can then be considered to identify upstream TFs that may play a particular role in regulating at least a subset of GOIs.

FIGURE 1.

FIGURE 1

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Workflow of EAT-UpTF. Manual database can be constructed based on binding profiles of multiple TFs generated by DAP-seq and ChIP-seq using manual database construction module (construct_manual_database.py). When a set of genes of interest (GOIs) is input along with database, EAT-UpTF performs enrichment analysis and shows the overrepresented upstream TFs for the GOIs. Network construction module (network.py) also visualizes regulatory networks of TFs and their target genes.

For the initial validation of EAT-UpTF, we used the DAP-seq Arabidopsis database, which lists the target genes of a vast majority of Arabidopsis TFs (∼349). Since EAT-UpTF performs enrichment analyses for hundreds of TFs simultaneously, a post hoc test should be applied to counteract the type I errors (false positives) originating from multiple testing. A number of post hoc analyses can be used to compensate for the increase in the false positive rate caused by multiple tests. The most widely used method is the family-wise error rate (FWER) correction, named after Carlo Emilio Bonferroni. The Bonferroni correction tests individual hypotheses at a significance level of a/m, where a is the desirable alpha level and m is the number of tests performed (Bonferroni et al., 1936; Dunn, 1961). This correction method is considered conservative when a large number of tests are conducted, but was likely appropriate in our analysis because the multiple hypothesis tests were limited to several hundreds of TFs. Another post hoc analysis option is the false discovery rate (FDR) correction described by Benjamini and Hochberg (1995). The Benjamini-Hochberg FDR correction tests hypotheses at a significance level of ka/m, where a is the desirable alpha level, m is the number of tests performed, and k is the rank of the p-value of the hypothesis. These two most popular post hoc analyses have been implemented in the current version of EAT-UpTF using the Statsmodels module of Python (Seabold and Perktold, 2010).

Results and Discussion

To validate the relevance of EAT-UpTF, we input a gene set bound by the LATE ELONGATED HYPOCOTYL (LHY) TF in Arabidopsis, which was identified via a ChIP-seq analysis (Adams et al., 2018). EAT-UpTF identified LHY as being an over-represented upstream TF in the test set. Specifically, 71.6% of the input genes were retrieved to be bound by LHY (Table 1) and LHY was identified as one of the top five enriched TFs in the test set (Table 1). The mismatch between the EAT-UpTF output and the ChIP-seq data might be related to the fact that DAP-seq is generally more stringent than ChIP-seq. Typically, DAP-seq produces a rigorous gene set and usually identifies a smaller number of TF-target genes than ChIP-seq. Indeed, all of the LHY-target genes identified by DAP-seq were included in the list of LHY-target genes identified by ChIP-seq analysis.

TABLE 1.

Summary statistics of the upstream transcription factor (TF) enrichment analysis for the Arabidopsis gene set bound by LHY (Adams et al., 2018).

TF ID (AGI ID) xa nb Observed (%) Xc Nd Expected (%) p-Value Corrected p-valuee Gene symbols Gene names
AT5G02840 287 722 39.8 4,110 27,206 15.1 5.84 × 10–60 2.04 × 10–57 LCL1 LHY/CCA1-LIKE 1
AT3G09600 426 722 59.0 8,276 27,206 30.4 2.59 × 10–58 4.52 × 10–56 RVE8, LCL5 LHY-CCA1-LIKE5, REVEILLE 8
AT3G56850 275 722 38.1 3,936 27,206 14.5 6.43 × 10–57 7.48 × 10–55 AREB3, DPBF3 ABA-RESPONSIVE ELEMENT BINDING PROTEIN 3
AT2G46270 318 722 44.0 5,255 27,206 19.3 2.09 × 10–53 1.82 × 10–51 GBF3 G-BOX BINDING FACTOR 3
AT1G01060 517 722 71.6 11,896 27,206 43.7 3.01 × 10–53 2.10 × 10–51 LHY LATE ELONGATED HYPOCOTYL
AT2G36270 274 722 38.0 4,188 27,206 15.4 8.13 × 10–51 4.73 × 10–49 ABI5, GIA1 GROWTH-INSENSITIVITY TO ABA 1, ABA INSENSITIVE 5
AT3G62420 327 722 45.3 5,764 27,206 21.2 7.54 × 10–49 3.76 × 10–47 BZIP53 BASIC REGION/LEUCINE ZIPPER MOTIF 53
AT1G18330 619 722 85.7 16,878 27,206 62.0 3.63 × 10–46 1.58 × 10–44 EPR1, RVE7 EARLY-PHYTOCHROME-RESPONSIVE 1, REVEILLE 7
AT5G17300 585 722 81.0 15,403 27,206 56.6 6.78 × 10–45 2.63 × 10–43 RVE1 REVEILLE 1
AT1G32150 357 722 49.4 6,979 27,206 25.7 6.05 × 10–44 2.11 × 10–42 bZIP68 BASIC REGION/LEUCINE ZIPPER TRANSCRIPTION FACTOR 68
AT4G34590 381 722 52.8 7,781 27,206 28.6 1.94 × 10–43 6.15 × 10–42 GBF6, BZIP11, ATB2 ARABIDOPSIS THALIANA BASIC LEUCINE-ZIPPER 11, G-BOX BINDING FACTOR 6
AT5G52660 224 722 31.0 3,280 27,206 12.1 6.20 × 10–43 1.80 × 10–41
AT5G15830 336 722 46.5 6,440 27,206 23.7 2.94 × 10–42 7.88 × 10–41 bZIP3 BASIC LEUCINE-ZIPPER 3
AT2G18160 178 722 24.7 2,268 27,206 8.3 4.60 × 10–41 1.15 × 10–39 GBF5, bZIP2, ATBZIP2, FTM3 BASIC LEUCINE-ZIPPER 2, FLORAL TRANSITION AT THE MERISTEM3, G-BOX BINDING FACTOR 5
AT4G01280 339 722 47.0 6,654 27,206 24.5 1.88 × 10–40 4.38 × 10–39
AT3G10113 579 722 80.2 15,664 27,206 57.6 6.91 × 10–39 1.51 × 10–37
AT1G45249 165 722 22.9 2,112 27,206 7.8 1.45 × 10–37 2.98 × 10–36 ABF2, AREB1 ABSCISIC ACID RESPONSIVE ELEMENTS-BINDING PROTEIN 1, ABSCISIC ACID RESPONSIVE ELEMENTS-BINDING FACTOR 2
AT3G10800 132 722 18.3 1,469 27,206 5.4 7.86 × 10–36 1.52 × 10–34 BZIP28
AT4G36780 269 722 37.3 4,944 27,206 18.2 1.02 × 10–34 1.88 × 10–33 BEH2 BES1/BZR1 HOMOLOG 2
AT2G35530 137 722 19.0 1,630 27,206 6.0 3.89 × 10–34 6.79 × 10–33 bZIP16 BASIC REGION/LEUCINE ZIPPER TRANSCRIPTION FACTOR 16
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aThe number of genes bound by the specific TF in the test set. bThe number of genes in the test set. cThe number of genes bound by the specific TF in the reference set. dThe number of genes in the reference set. eThe p-value after Bonferroni or Benjamini-Hochberg correction.

We also compared EAT-UpTF analysis to a conventional motif enrichment analysis for a similar purpose. DREME, a motif enrichment algorithm of MEME suite (Bailey et al., 2009), identified 33 conserved sequence motifs that can be bound by 157 TFs (Supplementary Table 1). While the LHY transcription factor was predicted, which could bind to two motifs, AAATATCK and GATATTTW (Supplementary Table 1), a vast number of additional cis-elements, which are not related to LHY, were also suggested. These results indicate that a motif enrichment analysis possibly produces a considerable number of false positives, but EAT-UpTF enables to suggest realistic upstream TFs.

To ensure whether the EAT-UpTF analysis is relevant with less stringent data set, we input DEGs in ccal lhy double mutant relative to wild type identified by RNA-seq (Kamioka et al., 2016). Again, EAT-UpTF identified LHY as an over-represented upstream TF for the input gene set (Table 2). Since CCA1 and LHY are transcriptional repressors (Kamioka et al., 2016), a significant portion of up-regulated genes in cca1 lhy was supposed to be direct targets of CCA1 and LHY. Indeed, EAT-UpTF predicted LHY as a top ranked TF for up-regulated genes in cca1 lhy double mutant (Supplementary Table 2), whereas LHY was excluded but other bZIP TFs were identified to be bound to down-regulated genes in cca1 lhy (Supplementary Table 3).

TABLE 2.

Summary statistics of enriched upstream TFs for differentially expressed genes (DEGs) in cca1lhy double mutant (Kamioka et al., 2016).

TF ID (AGI ID) xa nb Observed (%) Xc Nd Expected (%) p-Value Corrected p-aluee Gene symbols Gene names
AT5G02840 267 824 32.4 4,110 27,206 15.1 9.65 × 10–37 3.37 × 10–34 LCL1 LHY/CCA1-LIKE 1
AT4G01280 329 824 39.9 6,654 27,206 24.5 1.75 × 10–23 3.05 × 10–21
AT5G52660 196 824 23.8 3,280 27,206 12.1 1.71 × 10–21 1.98 × 10–19
AT3G09600 374 824 45.4 8,276 27,206 30.4 3.27 × 10–20 2.85 × 10–18 LCL5, RVE8 LHY-CCA1-LIKE5, REVEILLE 8
AT1G01060 479 824 58.1 11,896 27,206 43.7 2.47 × 10–17 1.72 × 10–15 LHY1, LHY LATE ELONGATED HYPOCOTYL 1, LATE ELONGATED HYPOCOTYL
AT3G62420 275 824 33.4 5,764 27,206 21.2 1.20 × 10–16 6.99 × 10–15 BZIP53 BASIC REGION/LEUCINE ZIPPER MOTIF 53
AT4G34590 344 824 41.7 7,781 27,206 28.6 1.75 × 10–16 8.70 × 10–15 BZIP11, GBF6, ATB2 G-BOX BINDING FACTOR 6, NA, ARABIDOPSIS THALIANA BASIC LEUCINE-ZIPPER 11
AT2G46270 250 824 30.3 5,255 27,206 19.3 9.54 × 10–15 4.16 × 10–13 GBF3 G-BOX BINDING FACTOR 3
AT1G18330 610 824 74.0 16,878 27,206 62.0 9.71 × 10–14 3.76 × 10–12 RVE7, EPR1 REVEILLE 7, EARLY-PHYTOCHROME-RESPONSIVE1
AT3G56850 194 824 23.5 3,936 27,206 14.5 1.39 × 10–12 4.86 × 10–11 AREB3, DPBF3 ABA-RESPONSIVE ELEMENT BINDING PROTEIN 3
AT5G17300 560 824 68.0 15,403 27,206 56.6 8.36 × 10–12 2.65 × 10–10 RVE1 REVEILLE 1
AT1G32150 297 824 36.0 6,979 27,206 25.7 1.37 × 10–11 3.97 × 10–10 bZIP68, BASIC REGION/LEUCINE ZIPPER TRANSCRIPTION FACTOR 68
AT2G18160 126 824 15.3 2,268 27,206 8.3 1.78 × 10–11 4.77 × 10–10 GBF5, bZIP2, FTM3 BASIC LEUCINE-ZIPPER 2, G-BOX BINDING FACTOR 5, FLORAL TRANSITION AT THE MERISTEM 3
AT5G15830 278 824 33.7 6,440 27,206 23.7 2.02 × 10–11 5.03 × 10–10 bZIP3 BASIC LEUCINE-ZIPPER 3
AT1G45249 119 824 14.4 2,112 27,206 7.8 2.95 × 10–11 6.87 × 10–10 AREB1, ABF2 ABSCISIC ACID RESPONSIVE ELEMENTS-BINDING FACTOR 2, ABSCISIC ACID RESPONSIVE ELEMENTS-BINDING PROTEIN 1
AT2G36270 198 824 24.0 4,188 27,206 15.4 3.38 × 10–11 7.38 × 10–10 GIA1, ABI5 GROWTH-INSENSITIVITY TO ABA 1, ABA INSENSITIVE 5
AT2G35530 97 824 11.8 1,630 27,206 6.0 1.44 × 10–10 2.96 × 10–9 bZIP16, BASIC REGION/LEUCINE ZIPPER TRANSCRIPTION FACTOR 16
AT3G10113 559 824 67.8 15,664 27,206 57.6 5.25 × 10–10 1.02 × 10–8
AT1G75390 127 824 15.4 2,485 27,206 9.1 2.97 × 10–9 5.46 × 10–8 bZIP44 BASIC LEUCINE-ZIPPER 44
AT3G10800 82 824 10.0 1,469 27,206 5.4 7.24 × 10–8 1.26 × 10–6 BZIP28
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aThe number of genes bound by the specific TF in the test set. bThe number of genes in the test set. cThe number of genes bound by the specific TF in the reference set. dThe number of genes in the reference set. eThe p-value after Bonferroni or Benjamini-Hochberg correction.

In addition, we further examined the relevance of EAT-UpTF in upstream TF enrichment analysis using unoptimized datasets. Genes up-regulated and down-regulated in root tissues upon 1 μM IAA treatment for 6 h (Omelyanchuk et al., 2017) were used as input queries. As for the up-regulated genes, EAT-UpTF identified LATERAL ORGAN BOUNDARIES DOMAIN 19 (LBD19), LBD18 and LBD16 as upstream regulators, which are involved in auxin-dependent lateral root emergence (Feng et al., 2012) (Table 3). Meanwhile, BASIC REGION/LEUCINE ZIPPER MOTIF 53 (bZIP53) and bZIP11, which negatively regulate adventitious root formation and primary root growth in an auxin-dependent pathway (Weiste et al., 2017; Zhang et al., 2020), were retrieved as overrepresented upstream TFs for the IAA-repressed genes (Table 4). Overall, the EAT-UpTF analysis reliably identified upstream TFs for a group of GOIs. Although our study mainly focused on the enriched upstream TFs for input query genes, which provides essential interpretation of the GOIs in the context of biological pathways and networks, we cannot rule out that TFs regulating a subset of input genes are also sometimes important for estimating biological functions of GOIs, independently of statistical enrichment. Thus, EAT-UpTF can also be used for profiling all possible upstream TFs that potentially regulate GOIs.

TABLE 3.

Summary statistics of enriched upstream TFs for up-regulated genes in Arabidopsis roots upon 1 μM IAA treatment for 6 h (Omelyanchuk et al., 2017).

TF ID (AGI ID) xa nb Observed (%) Xc Nd Expected (%) p-Value Corrected p-valuee Gene symbols Gene names
AT1G72740 172 789 21.8 2,924 27,206 10.7 5.21 × 10–20 1.82 × 10–17
AT2G45410 303 789 38.4 6,835 27,206 25.1 4.78 × 10–17 1.67 × 10–14 LBD19 LOB DOMAIN-CONTAINING PROTEIN 19
AT2G45420 215 789 27.2 4,503 27,206 16.6 1.11 × 10–14 3.88 × 10–12 LBD18 LOB DOMAIN-CONTAINING PROTEIN 18
AT5G59430 49 789 6.2 563 27,206 2.1 9.88 × 10–12 3.45 × 10–9 TRP1, TELOMERIC REPEAT BINDING PROTEIN 1
AT3G46590 33 789 4.2 363 27,206 1.3 8.56 × 10–9 2.99 × 10–6 TRP2, TRFL1, ATTRP2 TRF-LIKE 1
AT5G67580 221 789 28.0 5,446 27,206 20.0 2.85 × 10–8 9.94 × 10–6 TRB2, TBP3 TELOMERE-BINDING PROTEIN 3, TELOMERE REPEAT BINDING FACTOR 2
AT1G34670 136 789 17.2 3,086 27,206 11.3 3.89 × 10–7 1.36 × 10–4 MYB93 MYB DOMAIN PROTEIN 93
AT4G32730 269 789 34.1 7,322 27,206 26.9 3.87 × 10–6 1.35 × 10–3 MYB3R1, PC-MYB1 MYB DOMAIN PROTEIN 3R1, C-MYB-LIKE TRANSCRIPTION FACTOR 3R-1
AT5G11510 83 789 10.5 1,732 27,206 6.4 4.80 × 10–6 1.68 × 10–3 AtMYB3R4 MYB DOMAIN PROTEIN 3R4
AT2G02820 249 789 31.6 6,794 27,206 25.0 1.36 × 10–5 4.75 × 10–3 MYB88 MYB DOMAIN PROTEIN 88
AT3G10030 42 789 5.3 751 27,206 2.8 4.44 × 10–5 1.55 × 10–2
AT1G06180 102 789 12.9 2,422 27,206 8.9 8.38 × 10–5 2.93 × 10–2 ATMYBLFGN, MYB13 MYB DOMAIN PROTEIN 13
AT3G15210 179 789 22.7 4,758 27,206 17.5 9.38 × 10–5 3.28 × 10–2 ERF4, RAP2.5 RELATED TO AP2 5, ETHYLENE RESPONSIVE ELEMENT BINDING FACTOR 4
AT3G04070 231 789 29.3 6,448 27,206 23.7 1.49 × 10–4 5.20 × 10–2 NAC047 NAC DOMAIN CONTAINING PROTEIN 47
AT5G02320 97 789 12.3 2,334 27,206 8.6 2.04 × 10–4 7.11 × 10–2 MYB3R5 MYB DOMAIN PROTEIN 3R-5
AT5G58850 181 789 22.9 4,895 27,206 18.0 2.13 × 10–4 7.44 × 10–2 MYB119 MYB DOMAIN PROTEIN 119
AT1G28370 205 789 26.0 5,657 27,206 20.8 2.21 × 10–4 7.72 × 10–2 ERF11 ERF DOMAIN PROTEIN 11
AT5G25190 161 789 20.4 4,281 27,206 15.7 2.38 × 10–4 8.32 × 10–2 ESE3 ETHYLENE AND SALT INDUCIBLE 3
AT5G65130 76 789 9.6 1,742 27,206 6.4 2.54 × 10–4 8.87 × 10–2
AT2G42430 31 789 3.9 540 27,206 2.0 2.75 × 10–4 9.60 × 10–2 ASL18, LBD16 LATERAL ORGAN BOUNDARIES-DOMAIN 16, ASYMMETRIC LEAVES2-LIKE 18
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aThe number of genes bound by the specific TF in the test set. bThe number of genes in the test set. cThe number of genes bound by the specific TF in the reference set. dThe number of genes in the reference set. eThe p-value after Bonferroni or Benjamini-Hochberg correction.

TABLE 4.

Summary statistics of enriched upstream TFs for down-regulated genes in Arabidopsis roots upon 1μM IAA treatment for 6 h (Omelyanchuk et al., 2017).

TF ID (AGI ID) xa nb Observed (%) Xc Nd Expected (%) p-Value Corrected p-valuee Gene symbols Gene names
AT3G62420 238 659 36.1 5,764 27,206 21.2 3.78 × 10–19 1.32 × 10–16 BZIP53 BASIC REGION/LEUCINE ZIPPER MOTIF 53
AT4G34590 289 659 43.9 7,781 27,206 28.6 2.33 × 10–17 8.12 × 10–15 BZIP11, GBF6, bZIP11, ATB2 G-BOX BINDING FACTOR 6, BASIC LEUCINE-ZIPPER 11
AT5G65310 451 659 68.4 14,295 27,206 52.5 3.52 × 10–17 1.23 × 10–14 ATHB5, HOMEOBOX PROTEIN 5
AT4G36740 460 659 69.8 14,742 27,206 54.2 8.29 × 10–17 2.89 × 10–14 HB-5, ATHB40 HOMEOBOX PROTEIN 40
AT5G66700 283 659 42.9 7,658 27,206 28.1 1.44 × 10–16 5.03 × 10–14 HB-8, ATHB53 HOMEOBOX-8, HOMEOBOX 53
AT5G03790 381 659 57.8 11,605 27,206 42.7 1.77 × 10–15 6.17 × 10–13 ATHB51, LMI1 HOMEOBOX 51, LATE MERISTEM IDENTITY1
AT5G15830 244 659 37.0 6,440 27,206 23.7 5.41 × 10–15 1.89 × 10–12 bZIP3 BASIC LEUCINE-ZIPPER 3
AT1G14687 393 659 59.6 12,176 27,206 44.8 6.05 × 10–15 2.11 × 10–12 HB32, ZHD14 HOMEOBOX PROTEIN 32, ZINC FINGER HOMEODOMAIN 14
AT3G56850 169 659 25.6 3,936 27,206 14.5 1.84 × 10–14 6.42 × 10–12 AREB3, DPBF3 ABA-RESPONSIVE ELEMENT BINDING PROTEIN 3
AT1G69780 422 659 64.0 13,486 27,206 49.6 2.64 × 10–14 9.22 × 10–12 ATHB13
AT1G12630 228 659 34.6 5,960 27,206 21.9 2.79 × 10–14 9.72 × 10–12
AT1G32150 254 659 38.5 6,979 27,206 25.7 1.30 × 10–13 4.53 × 10–11 bZIP68 BASIC REGION/LEUCINE ZIPPER TRANSCRIPTION FACTOR 68
AT2G18550 229 659 34.7 6,124 27,206 22.5 2.91 × 10–13 1.01 × 10–10 ATHB21, HB-2 HOMEOBOX-2, HOMEOBOX PROTEIN 21
AT3G50260 400 659 60.7 12,825 27,206 47.1 1.07 × 10–12 3.73 × 10–10 DEAR1, ATERF#011, CEJ1 COOPERATIVELY REGULATED BY ETHYLENE AND JASMONATE 1, DREB AND EAR MOTIF PROTEIN 1
AT2G18160 110 659 16.7 2,268 27,206 8.3 1.48 × 10–12 5.17 × 10–10 bZIP2, FTM3, ATBZIP2, GBF5 G-BOX BINDING FACTOR 5, BASIC LEUCINE-ZIPPER 2, FLORAL TRANSITION AT THE MERISTEM3
AT2G46270 201 659 30.5 5,255 27,206 19.3 2.40 × 10–12 8.38 × 10–10 GBF3 G-BOX BINDING FACTOR 3
AT5G52020 224 659 34.0 6,069 27,206 22.3 2.49 × 10–12 8.69 × 10–10
AT4G16750 353 659 53.6 10,971 27,206 40.3 2.63 × 10–12 9.18 × 10–10
AT1G75390 115 659 17.5 2,485 27,206 9.1 8.58 × 10–12 3.00 × 10–9 bZIP44 BASIC LEUCINE-ZIPPER 44
AT1G69010 328 659 49.8 10,132 27,206 37.2 2.20 × 10–11 7.69 × 10–9 BIM2 BES1-INTERACTING MYC-LIKE PROTEIN 2
AT2G36270 165 659 25.0 4,188 27,206 15.4 5.50 × 10–11 1.92 × 10–8 ABI5, GIA1 ABA INSENSITIVE 5, GROWTH-INSENSITIVITY TO ABA 1
AT5G51990 279 659 42.3 8,325 27,206 30.6 7.80 × 10–11 2.72 × 10–8 DREB1D, CBF4 C-REPEAT-BINDING FACTOR 4, DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN 1D
AT5G25810 82 659 12.4 1,602 27,206 5.9 1.22 × 10–10 4.26 × 10–8 TNY TINY
AT1G71450 310 659 47.0 9,574 27,206 35.2 1.60 × 10–10 5.60 × 10–8
AT3G10800 77 659 11.7 1,469 27,206 5.4 1.62 × 10–10 5.64 × 10–8 BZIP28
AT1G77200 175 659 26.6 4,646 27,206 17.1 4.32 × 10–10 1.51 × 10–7
AT4G25480 158 659 24.0 4,064 27,206 14.9 4.46 × 10–10 1.56 × 10–7 DREB1A, CBF3 C-REPEAT BINDING FACTOR 3, DEHYDRATION RESPONSE ELEMENT B1A
AT2G31220 55 659 8.3 913 27,206 3.4 6.64 × 10–10 2.32 × 10–7
AT3G28920 203 659 30.8 5,711 27,206 21.0 1.42 × 10–9 4.97 × 10–7 ZHD9, AtHB34 ZINC FINGER HOMEODOMAIN 9, HOMEOBOX PROTEIN 34
AT4G32730 246 659 37.3 7,322 27,206 26.9 2.20 × 10–9 7.67 × 10–7 MYB3R1, PC-MYB1 MYB DOMAIN PROTEIN 3R1, C-MYB-LIKE TRANSCRIPTION FACTOR 3R-1
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aThe number of genes bound by the specific TF in the test set. bThe number of genes in the test set. cThe number of genes bound by the specific TF in the reference set. dThe number of genes in the reference set. eThe p-value after Bonferroni or Benjamini-Hochberg correction.

The EAT-UpTF analysis requires the input of an experimentally validated genome-wide list of TF-target genes in the form of locus ID. As mentioned above, we used the DAP-seq Arabidopsis database for the initial validation of EAT-UpTF. However, the EAT-UpTF analysis is not limited to the use of DAP-seq data and could also employ ChIP-seq data or any database that provides a list of TF-target genes. If only ‘bed’ files for DAP-seq and ChIP-seq are available, they can be converted to the EAT-upTF input format (Figure 1; see EAT-upTF homepage). In this regard, the EAT-UpTF analysis could be expanded to any plant species for which DAP-seq, ChIP-seq, or other appropriate sequencing data are available. In the future, a large-scale database integrating DAP-seq and ChIP-seq results would aid the identification of bona fide upstream TFs for groups of GOIs. EAT-UpTF is an open platform that can be improved by integrating updated TF databases. In addition, to ensure convenience for users, TF regulatory networks of GOIs identified by EAT-UpTF can also be visualized in Cytoscape (Figure 2). Compared to the previous webtools, such as TF2Network (Kulkarni et al., 2018) and AthaMap (Steffens et al., 2004), which conduct cis-element-based construction of TF regulatory networks, EAT-UpTF involves simple and rapid processing of data without cis-element identification, and thereby promptly visualizes gene regulatory networks showing TF-target gene interactions. While processing our study, a remarkable webtool ‘Plant Regulomics’2 has been released (Ran et al., 2020), which might implement a similar logic and code of EAT-UpTF, supporting the relevance of this analysis.

FIGURE 2.

FIGURE 2

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An example of a transcription factor regulatory network constructed by EAT-UpTF. A set of target genes of the LHY transcription factor (Adams et al., 2018) was used as a test input. The area of a node represents the edge count and the color intensity indicates the strength of the neighborhood connectivity. Black dots represent single nodes.

Conclusion

In summary, EAT-UpTF is a tool for analyzing the over-representation of upstream TFs based on the relative enrichment of TF-target genes in a group of GOIs in plants. EAT-UpTF can be used to identify upstream TFs for a group of genes without limitations on species and conservation of cis-motifs. With a regular update or manual construction of databases of TF-target genes in plant species, EAT-UpTF will become a powerful tool for TF regulatory network studies in plants. For user convenience, EAT-UpTF web service is also available at http://chromatindynamics.snu.ac.kr:8080/EatupTF.

Data Availability Statement

EAT-UpTF is available at https://github.com/sangreashim/EAT-UpTF; operating system(s): Linux, programming languages: Python3; other requirements: Python3 packages (SciPy, Statsmodels, Argparse). The EAT-UpTF home page provides detailed user manuals. EAT-UpTF is freely available. There are no restrictions on non-academics use.

Author Contributions

SS and PS: conceptualization and funding acquisition. SS: data curation and implementation and writing – original draft. PS: writing – review and editing. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This manuscript has been released as a pre-print at bioRXiv (doi: https://doi.org/10.1101/2020.03.22.001537) (SS and PS).

Funding. This work was supported by the National Research Foundation of Korea (NRF-2019R1I1A1A01061376 to SS, NRF-2019R1A2C2006915 to PS) and the Rural Development Administration (PJ01319304 to PS).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2020.566569/full#supplementary-material

Click here for additional data file. (38.1KB, XLSX)
Click here for additional data file. (49.1KB, XLSX)
Click here for additional data file. (47.9KB, XLSX)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (38.1KB, XLSX)
Click here for additional data file. (49.1KB, XLSX)
Click here for additional data file. (47.9KB, XLSX)

Data Availability Statement

EAT-UpTF is available at https://github.com/sangreashim/EAT-UpTF; operating system(s): Linux, programming languages: Python3; other requirements: Python3 packages (SciPy, Statsmodels, Argparse). The EAT-UpTF home page provides detailed user manuals. EAT-UpTF is freely available. There are no restrictions on non-academics use.

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