Using an ensemble of statistical metrics to quantify large sets of plant transcription factor binding sites

  • Parsa Hosseini1, 2, 3Email author,

    Affiliated with

    • Ivan Ovcharenko2 and

      Affiliated with

      • Benjamin F Matthews3

        Affiliated with

        Plant Methods20139:12

        DOI: 10.1186/1746-4811-9-12

        Received: 21 December 2012

        Accepted: 28 March 2013

        Published: 11 April 2013

        Abstract

        Background

        From initial seed germination through reproduction, plants continuously reprogram their transcriptional repertoire to facilitate growth and development. This dynamic is mediated by a diverse but inextricably-linked catalog of regulatory proteins called transcription factors (TFs). Statistically quantifying TF binding site (TFBS) abundance in promoters of differentially expressed genes can be used to identify binding site patterns in promoters that are closely related to stress-response. Output from today’s transcriptomic assays necessitates statistically-oriented software to handle large promoter-sequence sets in a computationally tractable fashion.

        Results

        We present Marina, an open-source software for identifying over-represented TFBSs from amongst large sets of promoter sequences, using an ensemble of 7 statistical metrics and binding-site profiles. Through software comparison, we show that Marina can identify considerably more over-represented plant TFBSs compared to a popular software alternative.

        Conclusions

        Marina was used to identify over-represented TFBSs in a two time-point RNA-Seq study exploring the transcriptomic interplay between soybean (Glycine max) and soybean rust (Phakopsora pachyrhizi). Marina identified numerous abundant TFBSs recognized by transcription factors that are associated with defense-response such as WRKY, HY5 and MYB2. Comparing results from Marina to that of a popular software alternative suggests that regardless of the number of promoter-sequences, Marina is able to identify significantly more over-represented TFBSs.

        Background

        Definitions and presumptions

        We define a list of transcription factor binding sites (TFBSs), t1,t2,…,t N , where t i is either a DNA motif, m i or position weight matrix (PWM), w i . The former is a variable-length character-string from the four-nucleotide DNA alphabet, while the latter is a two-dimensional matrix of preset weights.

        A group, G a , is a FASTA file populated with user-provided promoter sequences. Let G a ,Ga+1,…,G N represent a list of N groups such that N≥2. We define a contingency matrix, c i as a 2×2 matrix, used to model t i over-representation across G a and Ga+1. A set of statistical metrics, S, quantify degree of t i over-representation given c i .

        Transcription factors and binding site representation

        Plants are constantly surrounded by stimulus, be-they deletorious pathogens or positive stimuli such as light and nutrients. In order for the plant to respond to these signals, plants must utilize regulatory proteins known as transcription factors (TFs) to facilitate transcriptional reprogramming in a dynamic, tissue-dependent manner. These proteins bind to enhancer or promoter cis- elements and facilitate the recruitment of RNA polymerase II. This combinatorial binding of TFs facilitates downstream execution of adaptative signals in the face of drought, herbivory, and high salinity. By quantifying binding–sites for these regulatory proteins, inherent transcriptional dynamics and magnitude of over-representation can be inferred.

        TFs are classified into families by inherent DNA-binding signatures otherwise known as protein domains. In Arabidopsis thaliana, for instance, there are 64 known TF families[1], and it is not uncommon for different TF family members to exhibit relatively similar functionality. This redundancy is especially true when it comes to stress-response[24].

        DNA motifs and PWMs are two models frequently used to define a TFBS. The former is a short cis- element region presumed to be a TFBS, while the latter models nucleotide propensities of a TFBS in the form of a matrix[5, 6]. PWMs have been used across a broad spectrum of plant investigations such as identification of conserved exonic splice-site enhancers in Arabidopsis thaliana[7], prediction of potential seed-storage regulatory elements in mustards, grasses and legumes[8], and identification of novel regulatory elements in Arabidopsis thaliana[9]. With assays such as ChIP-ChIP and ChIP-Seq, novel regulatory elements can be identified and modeled as a PWM[10].

        Implementation

        Marina is an operating-system independent GUI software tool built using the Java programming language.

        This manuscript builds on the works of Chekmenev et. al[11], Loots et. al[12], and Kel et. al.[13], by implementing multiple statistical metrics to identify the maximum number of biologically-sound TFBSs, while accounting for cases when large promoter sets are provided.

        To begin analysis with Marina, at least two FASTA files populated with user-provided promoter sequences are required. Each FASTA file is known as a group. A group, for instance, could represent promoter sequences of interest for a particular condition or time point.

        The Marina workflow (Figure1) is partitioned into three distinct phases. The first phase performs abundance-estimation given a catalog of known TFBS models (Figure1a). Initial abundance derivation is performed via mapping of the TFBS onto user-provided promoter sequences. Following TFBS mapping, low-quality TFBSs are removed (Figure1b). Finally, a collection of statistical metrics quantify and rank TFBS over-representation (Figure1c).
        http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_Fig1_HTML.jpg
        Figure 1

        Marina workflow. a) A group is an umbrella-term to represent a set of promoter sequences. In order to run Marina, at least two groups must be provided. In doing so, TFBSs within each group can be contrasted and statistically quantified using TFBSs modeled as either DNA motifs or PWMs. Marina can also run if both these data-structures are provided, hence the name combined. b) Each group is modeled as a uni-directional graph, providing a means of trimming low-abundant promoter-sequences and TFBSs. c) A diverse collection of statistical metrics are used to model and quantify TFBS abundance. Magnitude of TFBS abundance is ranked and the hypergeometric distribution p-value computes significance of TFBS over-representation.

        Phase 1: Binding site mapping

        In order to effectively quantify TFBS abundance using this tool, TFBS models must be provided. These models are in the form of either DNA motifs or PWMs. Cumulatively, 1,240 TFBS models were mined and utilized throughout this study. Of these models, 1,160 were DNA motifs with the remaining 80 being PWMs; motif-to-PWM ratio of 13:1.

        Plant DNA motif and PWM models originated from AthaMap[14], AGRIS[15], PlantCARE[16], TRANSFAC[17], and JASPAR[18]. DNA motifs and PWMs were stored in either a tab-delimited or FASTA file format, respectively. Due to licensing restrictions, Marina does not come pre-packaged with a catalog of TFBS models, however several PWMs are provided, built from known PDB structures using the 3DTF web-server[19]. Be it PWMs or DNA motifs, a user-friendly schema is provided for importing custom TFBS profiles.

        DNA motif and PWM mapping

        To efficiently derive over-representation using DNA motifs, Marina scans promoter sequences for any occurrence of this motif using the Boyer-Moore-Horspool algorithm[20]. Due to the short length of many DNA motifs, elements such as ARF1 (TGTCTC)[21] may ubiquitously map throughout a promoter sequence with many mappings having little biological significance. Though this tool provides the option to filter short-length models be it PWMs or DNA motifs, resultant abundance estimations may seldom be biologically significant simply due to the likelihood of spurious mappings.

        Marina maps each PWM onto promoter sequences using a concurrent implementation of the P-MATCH algorithm[11]. P-MATCH calculates a likelihood that a particular candidate promoter region contains a TFBS. By default, Marina uses a probability-cutoff of 0.80; any sub-sequence with a score greater than this cutoff is rendered a potential TFBS.

        Alongside DNA motif and PWM extrapolations is a third pseudo-extrapolation known as combined mode. This mode simply performs the two former extrapolations back-to-back, merging their results into a singular data-structure. Combined mode capitalizes on the abundance of DNA motifs and probabilistic power of PWMs.

        Phase 2: Modeling TFBS over-representation

        TFBS abundances across all promoter sequences are modeled using a group-specific acyclic graph. Each graph is organized such that group name is the root-node and each TFBS is a child leaf node. Every TFBS node references a list of promoter sequences containing this TFBS.

        Per graph child node, two measures are used to model initial TFBS abundance: raw counts and support[22]. The former is simply defined as the number of promoter sequences which contain this particular TFBS. Raw counts are a useful, comparable metric if all groups have approximately the same number of promoter sequences. Unfortunately some groups may be larger than others, resulting in skewed and uncontrastable counts. To circumvent this possibility, the latter probabilistic measure, support, comes in helpful. Support, P(t i ,G a ), is a data-mining metric for representing abundance of a TFBS within a particular group[22]. A collection of statistical metrics continue where support leaves off, providing a means of deducing TFBS abundance.

        Both raw-counts and support serve as viable metrics to initially model TFBS abundance, however there may be cases were a rift between the two measures can appear. For example, suppose a single TFBS mapped only once to a group. Due to such minimal mapping, raw-count will be small but support would be large. Both low-support and low-count thresholds exist so as to filter corresponding graph nodes. Such graph trimming ensures that high-support and/or high-count TFBS nodes remain as they are more likely of having correlations to a particular group[23]. A caveat with threshold cutoffs is that low-abundance TFBSs will get discarded.

        Phase 3: Deriving over-represented TFBSs using numerous statistical metrics

        Given remaining TFBSs nodes, Marina aims to deduce magnitude of over-representation per TFBS, t i by contrasting its abundance across groups G a and Ga+1. To facilitate this objective, a collection of 7 knowledge discovery metrics, S, are implemented (Table1). Though a single metric can theoretically suffice, employing the entire set provides a means to appreciate unique features per measure and avoid individual bias. This table is by no means exhaustive as there are well over 20 frequently used metrics[24, 25]. The metrics in this table were selected so that there exists a sound mixture of both well-studied association and correlation measures.
        Table 1

        Statistical metrics

        Metric

        Equation

        Output range

        Reference

        Confidence (CF)

        m a x(P(G a |t i ),P(t i |G a ))

        0…1

        [26]

        Cosine (CO)

        P ( t i , G a ) P ( t i ) P ( G a ) http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq1_HTML.gif

        0 P ( t i , G a ) 1 http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq2_HTML.gif

        [27]

        Jaccard (JAC)

        P ( t i , G a ) P ( t i ) + P ( G a ) P ( t i , G a ) http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq3_HTML.gif

        0…1

        [28]

        Kappa coefficient (K)

        P ( t i , G a ) + P ( t i , G a ¯ ) P ( t i ) P ( G a ) P ( t i ¯ ) P ( G a ¯ ) ) 1 P ( t i ) P ( G a ) P ( t i ¯ ) P ( G a ¯ ) http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq4_HTML.gif

        −1…1

        [29]

        Laplace Correction (LP)

        max NP ( t i , G a ) + 1 NP ( t i ) + 2 , NP ( t i , G a ) + 1 NP ( G a ) + 2 http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq5_HTML.gif

        0…1

        [30]

        Lift (LI)

        P ( t i , G a ) P ( t i ) P ( G a ) http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq6_HTML.gif

        0…

        [31]

        Phi coefficient (PHI)

        P ( t i , G a ) P ( t i ) P ( G a ) P ( t i ) P ( G a ) ( 1 P ( t i ) ) ( 1 P ( G a ) ) http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq7_HTML.gif

        −1…1

        [32]

        Given a group, G a , and a TFBS, t i , magnitude of TFBS over-representation can be determined using a set of statistical metrics.

        In order to utilize such measures, TFBS abundances must be modeled in a suitable data-structure. A contingency matrix, c i , is an ideal data-structure candidate as it models TFBS distributions throughout multiple, independent groups (Table2). Each metric within S processes frequencies within a contingency matrix, c i , so as to quantitatively deduce over-representation of TFBS, t i . Certainly not all metrics deduce magnitude of TFBS over-representation the same, resulting in difficulties as to which TFBSs are unanimously most over-represented by all metrics. A solution to bringing uniform over-representation agreement across all metrics is to standardize contingency matrix counts using Iterative Proportional Fitting (IPF)[33].
        Table 2

        Contingency matrices model TFBS over-representation

         

        G a

        G a ¯ http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq8_HTML.gif

         

        t i

        c i (0,0)

        c i (1,0)

        n(t i )

        t i ¯ http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq9_HTML.gif

        c i (0,1)

        c i (1,1)

        n ( t i ¯ ) http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq10_HTML.gif

         

        n(G a )

        n ( G a ¯ ) http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq11_HTML.gif

        N

        TFBS abundance within specific groups can be modeled as a two-dimensional contingency matrix, c i .

        Iterative Proportional Fitting (IPF)

        IPF is an algorithm for standardizing counts in a two-dimensional contingency matrix such that matrix row and column marginals are equal to one another (Table3). Through such adjustment, inherent associations and correlations can be discovered[34]. By performing IPF-standardization, output for all 7 metrics become normalized so as to agree which TFBSs are the most over-represented.
        Table 3

        IPF-standardization yields equal marginals in a contingency matrix

         

        G a

        G a ¯ http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq12_HTML.gif

         

        t i

        x

        N/2−x

        N/2

        t i ¯ http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq13_HTML.gif

        N/2−x

        x

        N/2

         

        N/2

        N/2

        N

        Identical counts within diagonal cells leads to marginal rows and columns being equal to one another. Table adapted from[35].

        Equations 1 and 2 present an implementation of the IPF algorithm originally outlined by Tan et al.[35]. The former equation adjusts counts, a, such that they are equal on the diagonal axis. The latter equation then subtracts the remainder of the counts from that of the entire matrix sum, N.
        c i 1 , 0 = c i 0 , 0 = a = N c i 1 , 1 c i 0 , 0 2 c i 1 , 1 c i 0 , 0 + c i 1 , 0 c i 0 , 1 http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_Equ1_HTML.gif
        (1)
        c i 0 , 1 = c i 1 , 0 = N 2 a http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_Equ2_HTML.gif
        (2)

        Results and discussion

        Case study: over-represented Glycine max TFBSs during a Phakopsora pachyrhizi time-course infection

        To evaluate the functionality of this software tool, we utilized a two time-course RNA-Seq study that investigates soybean (Glycine max) transcriptional dynamics upon pathogenesis with soybean rust (SR; Phakopsora pachyrhizi). As outlined in our previous study, susceptible Williams 82 soybean leaves were inoculated with SR and assayed using RNA-Seq 10 days after infection (dai)[36]. An accompanying uninoculated control was also assayed to serve as a baseline condition. In both the control and 10 dai samples, a total of 5,940,995 70bp reads and 5,574,892 40bp reads were respectively sequenced using the Illumina platform (GenomeAnalyzer IIx). Sequenced reads were deposited in NCBI SRA under accessions SRX100854, SRX129967 and SRX100853, SRX129959, respectively.

        Per run, quality assessment and control (QA/QC) entailed removal of low quality reads and trimming of low-quality 3’ ends should its quality score be less than 22. Reads were also discarded if they mapped at least once to either the human genome (Hg19) or the JCVI Microbial Resource[37]. Upon QA/QC completion, a total of 5,015,459 control reads and 5,420,745 10 dai reads passed filtering; quality-scores of 27 and 30, respectively. For each time point, reads were mapped with at-most 3 nucleotide mismatches onto the soybean transcriptome build (Glyma 1.0) using BWA[38]. Custom Python scripts inferred differential expression by deriving RPKM[39] and lo g 2 RPK M 10 dai RPK M 0 dai http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_IEq14_HTML.gif per transcript.

        Two gene-sets were then declared to contain the top 600 induced and 600 suppressed differentially expressed genes (DEGs), respectively. Per gene set, the promoter sequence 2.5kb upstream from each genes transcription start site (TSS) was retrieved and appended to a FASTA file. Both FASTA files in-conjunction with 80 plant PWMs and 1,160 plant-specific DNA motifs served as input into Marina.

        Marina identified 71 potentially over-represented TFBSs between the control and 10 dai groups (Table4). As shown in this table, there exists no consensus amongst the various metrics as to which TFBS is truly the most over-represented. There are however some TFBSs that are ranked by all metrics in a relatively uniform manner: AG, ATHB6, and ABFS. For all other TFBSs, it is difficult to deduce magnitude of over-representation. Such a scenario warrants IPF-standardization as it normalizes metric-ranks to agree in-concert which TFBSs are the most over-represented (Table5). By visually contrasting this table with that of Table4, it is clear that unstandardized ranks from Laplace Correction (LP), Confidence (CF) and Lift (LI) perfectly equal their IPF-standardized counterpart.
        Table 4

        Various metrics infer differing magnitudes of TFBS over-representation

         

        Metrics

        TFBS raw-abundance

        TF

        LP

        CO

        JAC

        LI

        CF

        K

        PHI

        p-value

        Suppressed

        Induced

        ABF1

        20

        39

        39

        20

        20

        3

        2

        8.211e-274

        130

        169

        ABFS

        9

        9

        10

        9

        9

        16

        12

        2.385e-31

        10

        20

        ABI3/FUS3

        67

        19

        17

        67

        67

        41

        58

        3.036e-47

        14

        7

        ABI4(2)

        64

        34

        33

        64

        64

        64

        67

        4.465e-172

        66

        43

        AG

        14

        20

        21

        14

        14

        13

        18

        4.611e-82

        30

        42

        AGP1

        48

        57

        56

        48

        48

        58

        49

        2.412e-720

        427

        398

        ALFIN1

        34

        58

        57

        34

        34

        34

        34

        1.580e-731

        440

        426

        ARF1

        65

        29

        24

        65

        65

        57

        62

        1.243e-113

        40

        25

        ARR10

        39

        65

        65

        39

        39

        43

        39

        1.836e-895

        579

        552

        ARR2

        69

        27

        22

        69

        69

        60

        69

        4.028e-99

        33

        15

        ATHB-5

        43

        68

        68

        43

        43

        49

        43

        1.542e-901

        584

        555

        ATHB1

        40

        67

        67

        40

        40

        45

        40

        3.162e-901

        584

        556

        ATHB5-1

        63

        21

        20

        63

        63

        44

        55

        3.202e-78

        26

        18

        ATHB5-2

        37

        60

        60

        37

        37

        37

        37

        9.771e-769

        470

        452

        ATHB6

        27

        23

        25

        27

        27

        29

        32

        3.067e-109

        41

        46

        ATHB9

        53

        38

        36

        53

        53

        55

        52

        2.105e-225

        95

        81

        AtLEC2

        55

        51

        51

        55

        55

        68

        61

        1.066e-611

        336

        284

        ATML1/PDF2

        71

        18

        11

        71

        71

        54

        71

        8.730e-38

        10

        1

        AtMYB2

        29

        33

        34

        29

        29

        23

        31

        1.606e-170

        70

        76

        AtMYB77

        60

        32

        31

        60

        60

        56

        57

        2.955e-141

        53

        40

        AtMYC2

        2

        2

        2

        2

        2

        30

        8

        0.0002735

        1

        7

        AtSPL3

        30

        45

        46

        30

        30

        8

        26

        7.997e-426

        220

        236

        BLR/RPL/PNY

        35

        61

        61

        35

        35

        35

        35

        1.444e-777

        478

        462

        bZIP910(2)

        10

        12

        16

        10

        10

        14

        11

        6.060e-42

        14

        26

        bZIP911

        12

        11

        13

        12

        12

        19

        14

        4.350e-37

        12

        21

        bZIP911(1)

        11

        10

        12

        11

        11

        20

        13

        2.529e-34

        11

        20

        bZIP911(2)

        18

        13

        14

        16

        16

        32

        29

        3.730e-38

        12

        16

        CBF

        43

        68

        68

        43

        43

        49

        43

        1.542e-901

        584

        555

        CDC5

        4

        4

        4

        4

        4

        18

        3

        1.343e-10

        3

        13

        DOF2

        42

        71

        71

        42

        42

        48

        42

        1.259e-902

        585

        556

        DPBF1/2

        51

        55

        55

        51

        51

        66

        54

        1.857e-712

        418

        379

        E2Fa

        70

        13

        9

        70

        70

        38

        64

        8.059e-24

        6

        1

        E2Fc/d

        1

        1

        1

        1

        1

        26

        5

        0.0003077

        1

        8

        EmBP-1

        25

        43

        43

        25

        25

        5

        17

        3.316e-397

        203

        228

        GAMYB

        47

        59

        59

        47

        47

        53

        47

        2.040e-743

        447

        422

        Gamyb

        58

        28

        26

        58

        58

        40

        50

        6.185e-120

        44

        36

        GATA-1

        17

        24

        28

        18

        18

        12

        16

        5.923e-120

        47

        62

        GATA-1/2/3/4

        16

        15

        18

        17

        17

        28

        27

        9.291e-54

        18

        24

        GT-3b

        13

        25

        29

        13

        13

        7

        7

        1.244e-128

        52

        76

        HAHB4

        46

        64

        64

        46

        46

        52

        46

        1.038e-891

        575

        546

        HAT5

        43

        68

        68

        43

        43

        49

        43

        1.542e-901

        584

        555

        HSE

        19

        26

        30

        19

        19

        11

        15

        1.641e-130

        52

        68

        HVH21

        41

        66

        66

        41

        41

        46

        41

        3.881e-900

        583

        555

        HY5

        6

        8

        8

        6

        6

        21

        10

        6.247e-20

        6

        15

        ID1

        28

        31

        32

        28

        28

        27

        33

        4.015e-146

        58

        63

        MYB.PH3(1)

        56

        41

        41

        56

        56

        61

        56

        4.159e-333

        154

        130

        MYB.PH3(2)

        52

        49

        49

        52

        52

        62

        53

        6.937e-564

        306

        276

        MYB98

        62

        36

        35

        62

        62

        65

        65

        2.648e-210

        85

        60

        O2

        33

        56

        58

        33

        33

        6

        28

        2.967e-731

        446

        457

        OsbHLH66

        26

        40

        40

        26

        26

        9

        20

        1.723e-308

        147

        165

        OsCBT

        3

        3

        3

        3

        3

        24

        6

        1.543e-7

        2

        10

        P

        57

        52

        52

        57

        57

        71

        66

        2.571e-629

        347

        286

        PCF2

        61

        47

        44

        61

        61

        70

        70

        3.566e-441

        215

        160

        PCF5

        59

        48

        48

        59

        59

        69

        68

        2.612e-498

        254

        201

        PEND

        31

        35

        37

        31

        31

        15

        30

        3.825e-230

        101

        108

        PIF3(2)

        21

        22

        23

        21

        21

        17

        25

        4.178e-99

        37

        46

        RAP2.2

        66

        30

        27

        66

        66

        59

        63

        1.614e-125

        45

        28

        RAV1(1)

        49

        54

        53

        49

        49

        63

        51

        1.957e-688

        400

        366

        RAV1(2)

        38

        62

        62

        38

        38

        39

        38

        1.073e-854

        543

        519

        STF1

        24

        37

        38

        24

        24

        10

        22

        1.243e-242

        109

        124

        TAC1

        68

        17

        15

        68

        68

        42

        59

        1.479e-44

        13

        6

        TaMYB80

        54

        50

        50

        54

        54

        67

        60

        2.700e-594

        324

        276

        TBP

        36

        63

        63

        36

        36

        36

        36

        1.545e-881

        568

        547

        TEIL

        50

        42

        42

        50

        50

        47

        48

        8.458e-340

        160

        146

        TGA1

        23

        46

        47

        23

        23

        2

        9

        3.416e-468

        253

        293

        TGA1a

        32

        53

        54

        32

        32

        4

        23

        3.325e-688

        413

        433

        WRKY11

        7

        7

        7

        8

        8

        31

        19

        2.346e-14

        4

        9

        WRKY18/40/62

        7

        6

        6

        7

        7

        33

        21

        2.879e-11

        3

        7

        WRKY26/38/43

        15

        16

        19

        15

        15

        25

        24

        4.164e-56

        19

        26

        WRKY6

        5

        5

        5

        5

        5

        22

        4

        1.091e-10

        3

        12

        ZAP1

        22

        44

        45

        22

        22

        1

        1

        2.468e-415

        219

        268

        Promoter sequences from the top 600 induced and top 600 suppressed genes 10 dai were identified and their TFBS abundances quantified using Marina. A catalog of pre-assembled DNA motifs (1,160 motifs) and PWMs (80 matrices) accompanied such groups.

        A total of 71 over-represented TFBSs were identified. Of these N TFBSs, magnitude of over-representation is ranked from 1 to N such that the most over-represented are close to 1 while the least over-represented are close to N. Since TFBS models can vary across source-organisms, certain over-represented TFBSs were found multiple times (i.e. GAMYB, bZIP911, and ATHB5). Furthermore, not all metrics rank the same. As a result, manually deducing degree of TFBS over-representation can be a challenging task. IPF-standardization is designed to circumvent such a scenario.

        Table 5

        IPF-standardized abundances provides agreement amongst all metrics

         

        Metrics

        TF

        LP

        CO

        JAC

        LI

        CF

        K

        PHI

        ABF1

        20

        20

        20

        20

        20

        20

        20

        ABFS

        9

        9

        9

        9

        9

        9

        9

        ABI3/FUS3

        67

        67

        67

        67

        67

        67

        67

        ABI4(2)

        64

        64

        64

        64

        64

        64

        64

        AG

        14

        14

        14

        14

        14

        14

        14

        AGP1

        48

        48

        48

        48

        48

        48

        48

        ALFIN1

        34

        34

        34

        34

        34

        34

        34

        ARF1

        65

        65

        65

        65

        65

        65

        65

        ARR10

        39

        39

        39

        39

        39

        39

        39

        ARR2

        69

        69

        69

        69

        69

        69

        69

        ATHB-5

        43

        43

        43

        43

        43

        43

        43

        ATHB1

        40

        40

        40

        40

        40

        40

        40

        ATHB5-1

        63

        63

        63

        63

        63

        63

        63

        ATHB5-2

        37

        37

        37

        37

        37

        37

        37

        ATHB6

        27

        27

        27

        27

        27

        27

        27

        ATHB9

        53

        53

        53

        53

        53

        53

        53

        AtLEC2

        56

        56

        56

        56

        56

        56

        56

        ATML1/PDF2

        71

        71

        71

        71

        71

        71

        71

        AtMYB2

        29

        29

        29

        29

        29

        29

        29

        AtMYB77

        60

        60

        60

        60

        60

        60

        60

        AtMYC2

        2

        2

        2

        2

        2

        2

        2

        AtSPL3

        30

        30

        30

        30

        30

        30

        30

        BLR/RPL/PNY

        35

        35

        35

        35

        35

        35

        35

        bZIP910(2)

        10

        10

        10

        10

        10

        10

        10

        bZIP911

        12

        12

        12

        12

        12

        12

        12

        bZIP911(1)

        11

        11

        11

        11

        11

        11

        11

        bZIP911(2)

        17

        17

        17

        17

        17

        17

        17

        CBF

        43

        43

        43

        43

        43

        43

        43

        CDC5

        4

        4

        4

        4

        4

        4

        4

        DOF2

        42

        42

        42

        42

        42

        42

        42

        DPBF1/2

        51

        51

        51

        51

        51

        51

        51

        E2Fa

        70

        70

        70

        70

        70

        70

        70

        E2Fc/d

        1

        1

        1

        1

        1

        1

        1

        EmBP-1

        25

        25

        25

        25

        25

        25

        25

        GAMYB

        47

        47

        47

        47

        47

        47

        47

        Gamyb

        58

        58

        58

        58

        58

        58

        58

        GATA-1

        18

        18

        18

        18

        18

        18

        18

        GATA-1/2/3/4

        16

        16

        16

        16

        16

        16

        16

        GT-3b

        13

        13

        13

        13

        13

        13

        13

        HAHB4

        46

        46

        46

        46

        46

        46

        46

        HAT5

        43

        43

        43

        43

        43

        43

        43

        HSE

        19

        19

        19

        19

        19

        19

        19

        HVH21

        41

        41

        41

        41

        41

        41

        41

        HY5

        6

        6

        6

        6

        6

        6

        6

        ID1

        28

        28

        28

        28

        28

        28

        28

        MYB.PH3(1)

        55

        55

        55

        55

        55

        55

        55

        MYB.PH3(2)

        52

        52

        52

        52

        52

        52

        52

        MYB98

        62

        62

        62

        62

        62

        62

        62

        O2

        33

        33

        33

        33

        33

        33

        33

        OsbHLH66

        26

        26

        26

        26

        26

        26

        26

        OsCBT

        3

        3

        3

        3

        3

        3

        3

        P

        57

        57

        57

        57

        57

        57

        57

        PCF2

        61

        61

        61

        61

        61

        61

        61

        PCF5

        59

        59

        59

        59

        59

        59

        59

        PEND

        31

        31

        31

        31

        31

        31

        31

        PIF3(2)

        21

        21

        21

        21

        21

        21

        21

        RAP2.2

        66

        66

        66

        66

        66

        66

        66

        RAV1(1)

        49

        49

        49

        49

        49

        49

        49

        RAV1(2)

        38

        38

        38

        38

        38

        38

        38

        STF1

        24

        24

        24

        24

        24

        24

        24

        TAC1

        68

        68

        68

        68

        68

        68

        68

        TaMYB80

        54

        54

        54

        54

        54

        54

        54

        TBP

        36

        36

        36

        36

        36

        36

        36

        TEIL

        50

        50

        50

        50

        50

        50

        50

        TGA1

        23

        23

        23

        23

        23

        23

        23

        TGA1a

        32

        32

        32

        32

        32

        32

        32

        WRKY11

        8

        8

        8

        8

        8

        8

        8

        WRKY18/40/62

        7

        7

        7

        7

        7

        7

        7

        WRKY26/38/43

        15

        15

        15

        15

        15

        15

        15

        WRKY6

        5

        5

        5

        5

        5

        5

        5

        ZAP1

        22

        22

        22

        22

        22

        22

        22

        By having all metrics agree as to magnitude of over-representation per TFBS, the investigator will have an easier time identifying TFBSs of interest. Much like unstandardized ranks (Table4), standardized ranks also range from 1 to N such that ranks in the vicinity of 1 are most over-represented while ranks in the vicinity of N are least over-represented.

        Many over-represented TFBSs have defense or stress-response functions

        Given the list of IPF-standardized TFBSs (Table5), all 4 WRKY genes were over-represented at 10 dai. These abundances are supported by numerous studies which show that WRKY genes are perceived upon PAMP signals or abiotic stressors[4043]. WRKY genes drive defense-response by regulating NONEXPRESSOR OF PR1 (NPR1) expression by binding to W-box motifs in the NPR1 promoter. NPR1 protein binds with TGA TFs which regulate pathogenesis-response (PR) expression, hence providing a means of positively regulating SA-defense response[4446].

        Similar to WRKY, a bZIP family TFBS, HY5, was also over-represented 10 dai. Inextricably linked to photomorphogenesis, this TF is also known for its positive regulation of auxin signalling; a phytohormone which regulates defense response[47, 48]. Through interactions with HY1 and MYC2, HY5 is able to regulate photomorphogenesis, ABA and JA signaling[49, 50].

        Much like MYC2, AtMYB2 is not only over-represented at 10 dai but also plays a role in ABA-signaling. Microarray analyses on Arabidopsis plants with 35S:AtMYC2/AtMYB2 over-expression constructs revealed induced expression of several ABA-regulated genes[51].

        The GT (Trihelix) TF family member, GT-3b, was over-represented at 10 dai. Much is unknown about this TF family let alone GT-3b, however what is known is that many GT members, like HY5, regulate photomorphogenic signaling[52]. A recent study showed how GT-2a and GT-2b over-expression positively-regulates ABA-signaling[53]. Though an over-expressed GT-3b construct was not part of this recent study, translating findings from GT-2a and GT-2b over to GT-3b could reveal potentially novel insights into whether GT-3b plays a part in ABA and defense-signaling roles.

        Strong relationship between degree of TFBS over-representation and IPF-rank

        Due to the multi-dimensional nature of unstandardized TFBS ranks (Table4), dimensionality reduction was performed to facilitate rank visualization on a 2D coordinate plane. To carry-out such analysis, Principle Component Analysis (PCA) followed by bi-variate clustering was executed using the R library clusplot[54]. All 71 TFBSs were partitioned into 6 discrete clusters and labeled based on their respective IPF-standardized rank (Figure2). Interestingly, there appears to be a strong relationship between the magnitude of TFBS over-representation and IPF-standardized rank. This suggests that IPF-standardization is suitable for deducing magnitude of over-represented TFBSs.
        http://static-content.springer.com/image/art%3A10.1186%2F1746-4811-9-12/MediaObjects/13007_2012_Article_234_Fig2_HTML.jpg
        Figure 2

        Clustering of over-represented TFBS. Performing dimensionality reduction on unstandardized TFBS ranks (Table4) reveals distinct clusters of over-representative TFBSs. Each point in this 2-D coordinate plane references a unique TFBS, labeled based on its IPF-rank. From these 6 clusters, there appears to be a strong relationship between magnitude of TFBS over-representation and TFBS IPF-rank. The first two clusters, for instance, encapsulate all WRKY genes, GT-3b and HY5: genes perceived during defense response. This suggests that IPF-standardized ranks can elucidate magnitude of TFBS over-representation.

        Comparative software analysis

        Several actively-used software tools and web-interfaces are available to quantify TFBS over-representation[14, 15, 18, 5557]. We classified such tools into two classes: software that deduce TFBS over-representation given either 1) one promoter-sequence set or 2) at least two promoter-sequence sets. Marina falls into this latter class and as does a popular software tool, F-MATCH[13]. Both tools require two FASTA files as input such that one file serves as a query sequence-set while the other a baseline control. Degree of over-representation is therefore deduced by statistically contrasting TFBS over-representation across these two groups.

        Both software tools were compared using three independent sets of promoter-sequences of varying sizes. Each of these three analyses encompassed promoter-sequences of DEGs 10 dai from our prior soybean – soybean rust RNA-Seq study[36]. F-MATCH and Marina identify relatively the same number of over-represented TFBSs when promoter-sequence sets are 600 sequences in size (Table6). As these promoter sets increase in size, Marina maintains steady consistency as to identification of over-representated TFBSs, while F-MATCH failed to detect any over-represented TFBSs. We believe the reasoning behind why F-MATCH yields 0 over-represented TFBSs while Marina identified almost 50 TFBSs to be attributed towards usage of the binomial distribution by F-MATCH, which is known to be sensitive to large test sets. As far as output consistency between the two tools, our only comparison pertains to results obtained with 600 sequences sets. Given the 44 F-MATCH and 47 Marina over-represented TFBSs, 21 TFBSs were shared between the two result-sets. Unlike F-MATCH, we did not include TRANSFAC Professional PWMs in our analysis. We believe by introducing such PWMs, the number of shared TFBSs would certainly increase.
        Table 6

        Comparing Marina and F-MATCH given catalogs of PWMs and DNA motifs

        Group size

        PWMs (x 80)

        DNA motifs (x 1,160)

        (# sequences)

        F-MATCH

        Marina

        F-MATCH

        Marina

        600

        44

        47

        N/A

        24

        1500

        0

        50

        N/A

        41

        2500

        0

        53

        N/A

        44

        A collection of 80 plant-specific PWMs were supplied to Marina. When group-sizes are relatively small and PWMs are used, both Marina and F-MATCH identify approximately the same number of over-represented TFBSs. However as group-sizes increased, Marina consistently identifies over-represented TFBSs. Marina also accepts DNA motifs if PWMs are not available; F-MATCH does not accept such models.

        Conclusions

        Marina is a operating-system independent software tool to identify over-represented TFBSs across different groups of promoter sequences. We illustrate its usage using an RNA-Seq plant-pathogen study, however promoter sequences from any organism can be analyzed using Marina as long as compatible TFBS models are provided. We also show its capability to identify over-represented TFBSs regardless of input size. Given large sets of DEGs, our results show that by contrasting their promoter sequences, TFBSs perceived during defense and stress response were significantly over-represented. Other lesser-known TFBSs joined these ranks, raising questions as to potential candidate TFs affiliated with defense-response.

        The underlying algorithms within this tool are guided by a catalog of user-provided TFBS models be-it DNA motifs or PWMs. Thankfully, many regulatory element resources and databases exist. By contrasting this software tool to a popular alternative, we show that Marina is built for large promoter-sequence sets while being able to identify biologically sound over-representative TFBSs.

        Availability and requirements

        Project name: Marina.

        Project home page: http://​mason.​gmu.​edu/​~phossein/​marina/​

        Operating system(s): Operating-system independent.

        Programming language: Java version 7+.

        Other requirements: None.

        License: BSD.

        Abbreviations

        ABA: 

        Abscisic acid

        CO: 

        Cosine metric

        CF: 

        Confidence metric

        DEG: 

        Differentially expressed gene

        IPF: 

        Iterative proportional fitting

        JA: 

        Jasmonic acid

        JAC: 

        Jaccard

        K: 

        Cohen’s kappa

        LP: 

        Laplace correction

        LI: 

        Lift

        NPR1: 

        Non-expressor of PR1

        PHI: 

        Phi-coefficient

        PWM: 

        Position weight matrix

        SR: 

        Soybean rust

        TF: 

        Transcription factor.

        Declarations

        Acknowledgements

        We wish to thank the United States Department of Agriculture – Soybean Genomics and Improvement Laboratory (USDA – SGIL) for research funding and support. We also wish to thank Margaret MacDonald, Eric Brewer, Sara Kabir and Leila Taher for their comments on Marina functionality. Our appreciations also go out to Bret Cooper for advice on RNA-Seq quality analysis and correction. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Library of Medicine.

        Authors’ Affiliations

        (1)
        Department of Bioinformatics and Computational Biology, George Mason University
        (2)
        Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health
        (3)
        Soybean Genomics and Improvement Laboratory, United States Department of Agriculture

        References

        1. Riaño-Pachón DM, Ruzicic S, Dreyer I, Mueller-Roeber B: PlnTFDB: an integrative plant transcription factor database. BMC Bioinformatics. 2007, 8:
        2. Singh K, Foley RC, Oñate-Sánchez L: Transcription factors in plant defense and stress responses. Curr Opin Plant Biol. 2002, 5 (5): 430-436. 10.1016/S1369-5266(02)00289-3.View ArticlePubMed
        3. Dubos C, Stracke R, Grotewold E, Weisshaar B, Martin C, Lepiniec L: MYB transcription factors in Arabidopsis. Trends Plant Sci. 2010, 15 (10): 573-581. 10.1016/j.tplants.2010.06.005.View ArticlePubMed
        4. Carrasco JL, Ancillo G, Mayda E, Vera P: A novel transcription factor involved in plant defense endowed with protein phosphatase activity. EMBO. 2003, 22: 3376-3384. 10.1093/emboj/cdg323.View Article
        5. Bulyk ML: Computational prediction of transcription-factor binding site locations. Genome Biol. 2003, 5:
        6. Cartharius K, Frech K, Grote K, Klocke B, Haltmeier M, Klingenhoff A, Frisch M, Bayerlein M, Werner T: MatInspector and beyond: promoter analysis based on transcription factor binding sites. Bioinformatics. 2005, 21 (13): 2933-2942. 10.1093/bioinformatics/bti473.View ArticlePubMed
        7. Pertea M, Mount SM, Salzberg SL: A computational survey of candidate exonic splicing enhancer motifs in the model plant Arabidopsis thaliana. BMC Bioinformatics. 2007, 8: 159+-10.1186/1471-2105-8-159.PubMed CentralView ArticlePubMed
        8. Fauteux F, Stromvik M: Seed storage protein gene promoters contain conserved DNA motifs in Brassicaceae, Fabaceae and Poaceae. BMC Plant Biol. 2009, 9: 126+-10.1186/1471-2229-9-126.PubMed CentralView ArticlePubMed
        9. Vandepoele K, Casneuf T, Van de Peer Y: Identification of novel regulatory modules in dicotyledonous plants using expression data and comparative genomics. Genome Biol. 2007, 7 (11):
        10. Leelavati N, Ovcharenko I: Identifying regulatory elements in eukaryotic genomes. Brief Funct Genomic Proteomic. 2009, 8 (4): 215-230. 10.1093/bfgp/elp014.View Article
        11. Chekmenev DS, Haid C, Kel AE: P-Match: transcription factor binding site search by combining patterns and weight matrices. Nucleic Acids Res. 2005, 33: W432-W437. 10.1093/nar/gki441.PubMed CentralView ArticlePubMed
        12. Loots GG, Ovcharenko I, Pachter L, Dubchak I, Rubin EM: rVista for comparative sequence-based discovery of functional transcription factor binding sites. Genome Res. 2002, 12 (5): 832-839.PubMed CentralView ArticlePubMed
        13. Kel AE, Voss N, Jauregui R, Kel-Margoulis OV, Wingender E: Beyond microarrays: Finding key transcription factors controlling signal transduction pathways. BMC Bioinformatics. 2006, 7 (S-2):
        14. Bülow L, Engelmann S, Schindler M, Hehl R: AthaMap, integrating transcriptional and post-transcriptional data. Nucleic Acids Res. 2009, 37 (Database-Issue): D983-D986.PubMed CentralView ArticlePubMed
        15. Palaniswamy SK, James S, Sun H, Lamb RS, Davuluri RV, Grotewold E: AGRIS and AtRegNet: A platform to link cis-regulatory elements and transcription factors into regulatory networks. Plant Physiol. 2006, 140 (3): 818-829. 10.1104/pp.105.072280.PubMed CentralView ArticlePubMed
        16. Rombauts S, Florquin K, Lescot M, Rouzé P, van de Peer, c: Computational approaches to identify promoters and cis-regulatory elements in plant genomes. Plant Physiol. 2003, 132 (3): 1162-1176. 10.1104/pp.102.017715.PubMed CentralView ArticlePubMed
        17. Wingender E, Chen X, Fricke E, Geffers R, Hehl R, Liebich I, Krull M, Matys V, Michael H, Ohnhauser R, Pruss M, Schacherer F, Thiele S, Urbach S: The TRANSFAC system on gene expression regulation. Nucleic Acids Res. 2001, 29: 281-283. 10.1093/nar/29.1.281.PubMed CentralView ArticlePubMed
        18. Sandelin A, Alkema W, Engstrom P, Wasserman WW, Lenhard B: JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 2007, 32: D91-D94.View Article
        19. Gabdoulline RR, Eckweiler D, Kel AE, Stegmaier P: 3DTF: a web server for predicting transcription factor PWMs using 3D structure-based energy calculations. Nucleic Acids Res. 2012, 40 (Web-Server-Issue): 180-185.View Article
        20. Horspool NR: Practical fast searching in strings. Softw Pract Exp. 1980, 10 (6): 501-506. 10.1002/spe.4380100608.View Article
        21. Ulmasov T, Hagen G, Guilfoyle TJ: ARF1, a transcription factor that binds to auxin response elements. Science. 1997, 276: 1865-1868. 10.1126/science.276.5320.1865.View ArticlePubMed
        22. Ramakrishnan S, Rakesh A: Mining sequential patterns: generalizations and performance improvements. Proceedings 5th International Conference Extending Database Technology, EDBT. Avignon, France. 1996, London: Springer-Verlag, 3-17.
        23. Tan PN, Kumar V, Srivastava J: Selecting the right interestingness measure for association patterns. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’02. 2002, Edmonton, AB, Canada, 32-41.View Article
        24. Geng L, Hamilton HJ: Interestingness measures for data mining: a survey. ACM Comput Surv. 2006, 38 (3):
        25. Steinbach M, Tan PN, Xiong H, Kumar V: Objective measures for association pattern analysis. Contemp Math. 2007, 443: 205-226.View Article
        26. Agrawal R, Imielinski T, Swami A: Mining association rules between sets of items in large databases. ACM SIGMOD Intl. Conf. Management of Data. Washington, D.C. 1993, New York: ACM, 207-216.
        27. Merceron A, Yacef K: Interestingness measures for association rules in educational data. Proceedings of Educational Data Mining 2008: 1st International Conference on Educational Data Mining. 2008, Montreal, Quebec, 57-66.
        28. Jaccard P: Ètude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Société Vaudoise des Sci Naturelles. 1901, 37: 547-579.
        29. Cohen J: A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960, 20: 37-46. 10.1177/001316446002000104.View Article
        30. Good IJ: booktitle=The Estimation of Probabilities: An Essay on Modern Bayesian Methods, Volume 30 of Research Monograph,. 1965, Cambridge: M.I.T. Press
        31. Brin S, Motwani R, Ullman JD, Tsur S: Dynamic itemset counting and implication rules for market basket data. SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data. Tucson, Arizona. 1997, New York: ACM, 255-264.View Article
        32. Cramér H: Mathematical Methods of Statistics. Princeton mathematical series. 1946, Princeton University Press
        33. Deming WE, Stephan FF: On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Ann Math Stat. 1940, 11 (4): 427-444. 10.1214/aoms/1177731829.View Article
        34. Mosteller F: Association and estimation in contingency tables. J Am Stat Assoc. 1968, 63 (321): 1-28. 10.2307/2283825.
        35. Tan PN, Kumar V, Srivastava J: Selecting the right objective measure for association analysis. Inf Syst. 2004, 29: 293-313. 10.1016/S0306-4379(03)00072-3.View Article
        36. Tremblay A, Hosseini P, Alkharouf NW, Li S, Matthews BF: Gene expression in leaves of susceptible Glycine max during infection with Phakopsora pachyrhizi using next generation sequencing. Sequencing. 2011, 14-
        37. Peterson JD, Umayam LA, Dickinson TM, Hickey EK, White O: The comprehensive microbial resource. Nucleic Acids Res. 2001, 29: 123-125. 10.1093/nar/29.1.123.PubMed CentralView ArticlePubMed
        38. Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25 (14): 1754-1760. 10.1093/bioinformatics/btp324.PubMed CentralView ArticlePubMed
        39. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5 (7): 621-628. 10.1038/nmeth.1226.View ArticlePubMed
        40. Rushton PJ, Somssich IE, Ringler P, Shen QJ: WRKY transcription factors. Trends Plant Sci. 2010, 15 (5): 247-258. 10.1016/j.tplants.2010.02.006.View ArticlePubMed
        41. Eulgem T: Dissecting the WRKY web of plant defense regulators. PLoS Pathogy. 2006, 2 (11):
        42. Eulgem T, Rushton PJ, Robatzek S, Somssich IE: The WRKY superfamily of plant transcription factors. Journal. 2000, 5 (5): 199-206.
        43. Eulgem T, Somssich IE: Networks of WRKY transcription factors in defense signaling. Curr Opin Plant Biol. 2007, 10 (4): 366-371. 10.1016/j.pbi.2007.04.020.View ArticlePubMed
        44. Pandey SP, Somssich IE: The role of WRKY transcription factors in plant immunity. Plant Physiol. 2009, 150 (4): 1648-1655. 10.1104/pp.109.138990.PubMed CentralView ArticlePubMed
        45. Yu D, Chen C, Chen Z: Evidence for an important role of WRKY DNA binding proteins in the regulation of NPR1 gene expression. Plant Cell. 2001, 13 (7): 1527-1540.PubMed CentralView ArticlePubMed
        46. Dong X: NPR1, all things considered. Curr Opin Plant Biol. 2004, 7 (5): 547-552. 10.1016/j.pbi.2004.07.005.View ArticlePubMed
        47. Sibout R, Sukumar P, Hettiarachchi C, Holm M, Muday GK, Hardtke CS: Opposite root growth phenotypes of hy5 versus hy5 hyh mutants correlate with increased constitutive auxin signaling. PLoS Genet. 2004, 2 (11):
        48. Cluis CP, Mouchel CF, Hardtke CS: The Arabidopsis transcription factor HY5 integrates light and hormone signaling pathways. Plant. 2004, 38 (2): 332-347. 10.1111/j.1365-313X.2004.02052.x.View Article
        49. Prasad BRVP, Kumar SV, Nandi A, Chattopadhyay S: Functional interconnections of HY1 with MYC2 and HY5 in Arabidopsis seedling development. BMC Plant Biol. 2012, 12 (37):
        50. Boter M, Ruíz-Rivero O, Abdeen A, Prat S: Conserved MYC transcription factors play a key role in jasmonate signaling both in tomato and Arabidopsis. Journal. 2004, 18 (13): 1577-1591.
        51. Abe H, Urao T, Itom T, Seki M, Shinozaki K, Yamaguchi-Shinozaki K: Arabidopsis AtMYC2 (bHLH) and AtMYB2 (MYB) function as transcriptional activators in abscisic acid signaling. Plant Cell. 2003, 15: 63-78. 10.1105/tpc.006130.PubMed CentralView ArticlePubMed
        52. Kaplan-Levy RN, Brewer PB, Quon T, Smyth DR: The trihelix family of transcription factors - light, stress and development. Trends Plant Sci. 2012, 17 (3): 163-171. 10.1016/j.tplants.2011.12.002.View ArticlePubMed
        53. Xie ZM, Zou HF, Lei G, Wei W, Zhou QY, Niu CF, Liao Y, Tian AG, Ma B, Zhang WK, Zhang JS, Chen SY: Soybean trihelix transcription factors GmGT-2A and GmGT-2B improve plant tolerance to abiotic stresses in transgenic Arabidopsis. PLoS ONE. 2009, 4 (9): e6898-10.1371/journal.pone.0006898.PubMed CentralView ArticlePubMed
        54. Pison G, Struyf A, Rousseeuw PJ: Displaying a clustering with CLUSPLOT. Comput Stat Data Anal. 1999, 30 (4): 381-392. 10.1016/S0167-9473(98)00102-9.View Article
        55. Keilwagen J, Grau J, Paponov IA, Posch S, Strickert M, Grosse I: De-novo discovery of differentially abundant transcription factor binding sites including their positional preference. PLoS Comput Biology. 2011, 7 (2): e1001070+-View Article
        56. Yamamoto YY, Obokata J: ppdb: a plant promoter database. Nucleic Acids Res. 2008, 36 (Database issue): D977-D981.PubMed CentralPubMed
        57. Chang WCC, Lee TYY, Huang HDD, Huang HYY, Pan RLL: PlantPAN: Plant promoter analysis navigator, for identifying combinatorial cis-regulatory elements with distance constraint in plant gene groups. BMC Genomics. 2008, 9: 561+-10.1186/1471-2164-9-561.PubMed CentralView ArticlePubMed

        Copyright

        © Hosseini et al.; licensee BioMed Central Ltd. 2013

        This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.