BMC Genomics
Research article
The word landscape of the non-coding segments of the
Arabidopsis thaliana genome
Jens Lichtenberg*1, Alper Yilmaz2, Joshua D Welch1, Kyle Kurz1,
Xiaoyu Liang1, Frank Drews1, Klaus Ecker1, Stephen S Lee3, Matt Geisler4,
Erich Grotewold2 and Lonnie R Welch1,5,6
Address: 1Bioinformatics Laboratory, School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, USA, 2Department
of Plant Cellular and Molecular Biology, Plant Biotechnology Center, The Ohio State University, Columbus, Ohio, USA, 3Department of
Statistics, University of Idaho, Moscow, Idaho, USA, 4Department of Plant Biology, Southern Illinois University, Carbondale, Illinois, USA,
5Biomedical Engineering Program, Ohio University, Athens, Ohio, USA and 6Molecular and Cellular Biology Program, Ohio University, Athens,
Ohio, USA
E-mail: Jens Lichtenberg* - lichtenj@ohio.edu; Alper Yilmaz - yilmaz.11@osu.edu; Joshua D Welch - jw156605@ohio.edu;
Kyle Kurz - kk372703@ohio.edu; Xiaoyu Liang - xl187007@ohio.edu; Frank Drews - drews@ohio.edu; Klaus Ecker - ecker@ohio.edu;
Stephen S Lee - stevel@uidaho.edu; Matt Geisler - mgeisler@plant.siu.edu; Erich Grotewold - grotewold.1@osu.edu;
Lonnie R Welch - welch@ohio.edu
*Corresponding author
Published: 8 October 2009 Received: 23 January 2009
BMC Genomics 2009, 10:463 doi: 10.1186/1471-2164-10-463 Accepted: 8 October 2009
This article is available from: http://www.biomedcentral.com/1471-2164/10/463
© 2009 Lichtenberg et al; licensee 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.
Abstract
Background: Genome sequences can be conceptualized as arrangements of motifs or words.
The frequencies and positional distributions of these words within particular non-coding genomic
segments provide important insights into how the words function in processes such as mRNA
stability and regulation of gene expression.
Results: Using an enumerative word discovery approach, we investigated the frequencies and
positional distributions of all 65,536 different 8-letter words in the genome of Arabidopsis thaliana.
Focusing on promoter regions, introns, and 3’ and 5’ untranslated regions (3’UTRs and 5’UTRs), we
compared word frequencies in these segments to genome-wide frequencies. The statistically
interesting words in each segment were clustered with similar words to generate motif logos. We
investigated whether words were clustered at particular locations or were distributed randomly
within each genomic segment, and we classified the words using gene expression information from
public repositories. Finally, we investigated whether particular sets of words appeared together
more frequently than others.
Conclusion: Our studies provide a detailed view of the word composition of several segments of
the non-coding portion of the Arabidopsis genome. Each segment contains a unique word-based
signature. The respective signatures consist of the sets of enriched words, ‘unwords’, and word
pairs within a segment, as well as the preferential locations and functional classifications for the
signature words. Additionally, the positional distributions of enriched words within the segments
highlight possible functional elements, and the co-associations of words in promoter regions likely
represent the formation of higher order regulatory modules. This work is an important step
toward fully cataloguing the functional elements of the Arabidopsis genome.
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BioMed Central
Open Access

Background
All genomes are composed of nucleotides, which are
represented abstractly as letters (Adenine (A), Guanine
(G), Cytosine (C), and Thymine (T)). Strings of such
letters can be conceptualized as words, which provide
the blueprints for organisms. Each word is found a
specific number of times in a particular genome. Note
that the expected frequency of a word is inversely related
to the word’s length. Some nucleotides appear more
frequently than others (e.g. A/T in Arabidopsis), giving
each genome a distinct (G+C)% content and biasing
expected word frequencies. Higher order frequencies
(dinucleotide and trinucleotide) also show distinct
biases beyond those expected for single nucleotide
frequencies [1].
Distinct selective pressures shape words positioned in
different genomic regions. For example, a word in an open
reading frame (ORF) has a direct influence on the primary
amino acid sequence of a protein and hence is under
strong selective pressure. In contrast, words in introns are
likely to be under more relaxed selective constraints, unless
they are important for gene functions, for example by
providing docking sites for splicing factors [2] or for
enzymes involved in the post-transcriptional processing of
a transcript [3,4]. The gene sections corresponding to the
5’ and 3’ untranslated regions (5’UTRs and 3’UTRs,
respectively) are also likely to be under less selective
constraints than the ORFs, yet signatures of strong selection
in UTRs have been described (reviewed in [5]). The
constant formation of DNA microsatellites through slip-
page by the replication machinery, and the action of
viruses and transposons, also complicate the word land-
scape, especially in regions with lower selective constraints
(such as introns, UTRs and intergenic regions) [6,7].
This manuscript describes the results of a genome-wide
analysis to discover putative regulatory words. Within this
context, we define the cis-regulatory apparatus as all the
DNA segments that are located proximal to a gene, and
that also contribute to the gene’s expression. It is the
function of transcription factors, miRNAs, or other
molecules that interact with DNA, to interpret the words
(sequence code) hardwired in the cis-regulatory apparatus
and to ‘execute’ them, thereby generating signals to the
basal transcription machinery that result in changes to the
rate of RNA production by the corresponding DNA-
dependent RNA polymerases. When located upstream of
the transcription start site (TSS), the cis-regulatory appara-
tus is often referred to as the promoter of a gene.
Promoters are typically divided into three regions: core,
proximal and distal. The core promoter, a region at
location [+1;-100] relative to the TSS, performs a central
role in the formation of pre-initiation transcriptional
complexes. Immediately upstream of the core promoter
is the proximal promoter, which is located at position
[-101;-1000] relative to the TSS and serves as a docking
site for transcription factors. The distal promoter is located
at [-1001;-3000] relative to the TSS and contains the
regulatory elements that are commonly known as
enhancers and silencers. The participation of a particular
DNA segment in the regulation of gene expression can
only be demonstrated experimentally. Thus, understand-
ing the rules at play in deciphering the transcriptional
regulatory code remains one of the most significant
challenges in biology today.
Although most regulatory elements are present in the UTRs
and upstream regions, due to their proximity to the TSS,
studies have shown the presence of regulatory elements in
introns, and, to a much lesser extent, in coding regions
[2,8-16]. Building on this knowledge, a segment-based
analysis was performed that is focused on non-coding
regions within the open reading frames (i.e., introns) and
flanking non-coding regions (i.e., UTRs and upstream
regions). The coding regions were omitted from this
analysis because they are under other selection pressures
corresponding to the amino acid sequences of the proteins
they produce, and thus they are subjected to biases other
than regulation.
Arabidopsis thaliana provides an ideal reference organism to
investigate the word landscape of a plant genome, and to
relate said landscape to important biological features. The
Arabidopsis genome consists of 125 Mbp arranged into five
chromosomes [17,18]. The genome is well annotated and
regions corresponding to introns, 3’UTRs, 5’ UTRs, and
intergenic genomic spaces are all available from The
Arabidopsis Information Resource (TAIR, http://www.
arabidopsis.org) [19].
Many studies have characterized Arabidopsis DNA sequence
motifs that participate in the regulation of particular genes
(e.g., [20-23]), and public databases such as AthaMap [24]
and AGRIS [25] provide comprehensive collections of cis-
regulatory elements likely to participate in the regulation of
gene expression. However, a systematic analysis of all the
words present in the Arabidopsis genome is still lacking.
To analyze the different segments of the Arabidopsis
genome, an enumerative word discovery approach was
used to detect statistically overrepresented words. Similar
approaches have been successfully applied over the last
decade in the area of motif discovery [26-37]. In a 2005
study, Tompa et al. [38] showed that enumerative methods
outperformed heuristic methods in many cases. They are
particularly applicable in this research, because they allow
the study of the entire ‘word landscape’ of a genomic
data set.
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Our approach scans the sequences and produces a set of
words and word frequencies. This information is employed
by a Markov model to compute expected word frequencies.
Words with unexpectedly high frequencies are putative
functional elements, and thus they are further characterized
by comparing word frequencies and positions to gene
induction or suppression using the method of Geisler et al.
[39]. Additionally, clusters of similar words are formed and
used to create motifs for putative transcription factor
binding sites. Sequences that contain the same functional
elements are grouped together into putative ‘nodes’ of
regulatory networks. Words that co-occur often are
identified as putative transcription factor binding modules.
Results and Discussion
Distribution of 8-letter words in the Arabidopsis genome
To determine the word distributions in the segments of the
Arabidopsis thaliana genome that contribute to the cis-
regulatory apparatus, a comprehensive analysis of 8-letter
words in the entire genome was conducted and compared
with segments corresponding to non-coding regions. Words
of length 6-16were examined and the complete results have
been made available via AGRIS http://arabidopsis.med.
ohio-state.edu/ [25,40]. This article reports findings for
words of length eight because they correspond to the typical
DNA sequence length recognized by transcription factors
(usually 6-8 bp [38,41]). Furthermore, 8-mers are long
enough that there is enough diversity of word choices
(~64,000) to reduce false positive results, while retaining
sufficient word counts to be statistically informative.
The genome was sub-divided into segments comprising
the 3’ UTRs, 5’UTRs, promoters and introns (Table 1).
The promoter segment was further dissected into the
core promoter, corresponding to [-100; +1]; proximal
promoter [-1000; -101]; and distal promoter [-3000; -
1001]. The general properties of the six genome
segments are shown in Table 1. As in a similar study,
which was aimed at discovering regulatory elements
involved in human DNA-repair pathways [26], word-
based genomic signatures were created for each segment.
Specifically, the following were identified for each of the
genome segments: (1) the set of overrepresented words
(signature words), (2) words missing from the sequences
(unwords), (3) word-based clusters, (4) word co-occur-
rences and (5) functional categorizations of the signature
words. The results are detailed in the remainder of this
section.
Overrepresented Words
All 8-letter words present in the segments were identified
and scored using observed:expected frequency ratios (O/E).
Specifically, each word was scored and ranked by using
the function S*ln(S/ES), where S is the number of
sequences that contained the word, ‘ln’ is the natural
logarithm, and ES is the number of sequences in which
the word was expected to occur. Words discovered in the
whole genome were analyzed using the O*ln(O/EO)
score, with O referring to the overall occurrence of a
word across the entire genome and EO representing the
expected occurrence of that word. The 25 top-ranked
Table 1: Segment characteristics for Arabidopsis thaliana
Data Set # Sequences/
Chromosomes
Min. Seq.
Length
Max. Seq.
Length
Mean Seq.
Length
Std.
Deviation
Total
Nucleotides
Genome
Percentage
3’ UTRs 19,771 8 3,118 228.134 152.106 4,510,410 3.78
5’ UTRs 18,585 8 3,214 140.088 130.288 2,603,531 2.18
Introns 118,319 8 10,234 164.446 178.484 19,457,029 16.32
Core Promoters 27,023 100 100 100 0 2,702,300 2.27
Proximal Promoters 27,023 900 900 900 0 24,320,700 20.41
Distal Promoters 27,025 1,371 2,000 1,999.96 5.01105 54,048,839 45.35
Genome-wide 5 18,585,000 30,432,600 23,837,300 4,432,780 119,186,497 100.00
Overview of the characteristics properties for non-coding segments and the entire genome for Arabidopsis thaliana. The number of sequence refers to
the respective number of unique sequences in the specific segment. In case of the entire genome the sequences are the complete chromosomes. Min.
Seq. Length refers to the length of the shortest sequence in the set, while Max. Seq. Length refers to the length of the longest sequence in the set.
Mean Seq. Length provides the average length of the sequences in the set, while Std. Deviation describes the deviation from said mean. Finally Total
Nucleotides describes the total number of nucleotides contained within the sequences of the set and Genome Percentage elaborates on the
relationship between the nucleotide count of the set versus the entire genome.
Some sequences in the segments are shorter than 8 nucleotides. Since these sequences cannot harbour any putative regulatory elements in the
context of this study, the sequences are removed from the table. For the 3’UTRs this results in a total of 179 nt being omitted, for 5’UTRs 1207 nt
and for introns 26 nt. They are however included in the calculation of the background for the different segments since they contribute to the overall
nucleotide distribution.
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words, corresponding to ~0.04% of all possible words,
which also corresponds to ~0.04% of the discovered
words, were taken as an exemplary subset of the results
and further examined (see Tables 2, 3, 4, 5, 6, 7, &8 and
Additional files 1, 2, 3, 4, 5, 6, &7).
A detailed analysis of the words identified aminimal overlap
between the sets of overrepresented words for the different
segments. Specifically, considering the list of top 25 words
discovered in any of the six segments (and in the genome
wide analysis), 175 words were unique to one specific set, 15
Table 2: The top 25 words in 3’UTRs
Unmasked Masked Unmasked
Word S ES O EO SlnSES S ES O EO SlnSES RevComp RC_Pos Pal PValues
TTTTTGTT 2264 2066.82 2488 2306.04 206.297 2279 2066.89 2501 2331.04 222.643 AACAAAAA 40 No 9.38E-05
TTTTTCTT 2171 1981.63 2404 2203.7 198.149 2183 1978.5 2427 2222.83 214.723 AAGAAAAA 49 No 1.34E-05
TTTTTTGG 998 824.458 1046 877.255 190.646 1003 831.208 1053 888.417 188.434 CCAAAAAA 651 No 1.71E-08
ATTTTGTA 732 583.938 752 615.741 165.421 738 599.956 759 634.768 152.831 TACAAAAT 37 No 6.00E-08
TAATTTTT 787 642.133 810 678.585 160.101 797 646.36 821 685.263 166.97 AAAAATTA 164 No 5.24E-07
ATGTTTTA 589 469.818 601 493.292 133.161 610 486.404 624 512.055 138.116 TAAAACAT 284 No 1.48E-06
TTTGTTTT 2517 2402.46 2847 2715.8 117.227 2555 2406.15 2897 2753.88 153.362 AAAACAAA 1963 No 0.006347
GTTTTTGA 491 390.189 504 408.466 112.838 512 407.532 527 427.529 116.841 TCAAAAAC 5031 No 2.76E-06
AAATTTTG 588 491.471 603 516.445 105.443 604 504.212 621 531.22 109.069 CAAAATTT 376 No 0.00011
ATTTTTTA 482 387.674 498 405.795 104.97 492 406.16 510 426.064 94.3317 TAAAAAAT 100 No 5.33E-06
ATTTTTCA 446 354.812 450 370.941 102.014 453 365.873 457 383.118 96.7633 TGAAAAAT 170 No 3.83E-05
TGTTTTGT 1227 1133.19 1326 1219.91 97.5897 1255 1162.02 1359 1260.07 96.6082 ACAAAACA 659 No 0.001413
ATAAAAAT 564 474.529 580 498.326 97.4203 566 480.088 581 505.265 93.1776 ATTTTTAT 27 No 0.000192
TTTTTTCT 1721 1628.11 1839 1786.09 95.4882 1722 1625.78 1847 1798.84 99.0176 AGAAAAAA 106 No 0.107802
AAAAATTG 397 312.488 400 326.178 95.0296 414 323.794 419 338.423 101.744 CAATTTTT 66 No 4.26E-05
TATAATAT 505 419.081 519 439.185 94.1802 514 429.108 530 450.594 92.7844 ATATTATA 275 No 0.000114
CTCTGTTT 763 674.497 814 713.654 94.0706 796 706.86 852 751.4 94.5386 AAACAGAG 227 No 0.000125
TTTTTAAT 897 808.297 929 859.536 93.4009 905 811.646 942 866.766 98.5274 ATTAAAAA 95 No 0.009964
TTCTTTTT 1884 1795.18 2075 1982.05 90.9811 1879 1764.9 2059 1964.59 117.709 AAAAAGAA 130 No 0.019465
TTTTTGGT 989 902.56 1029 963.191 90.453 1006 920.175 1052 987.344 89.7087 ACCAAAAA 9144 No 0.018455
ATTTTCTG 324 245.197 330 255.296 90.2932 340 264.756 346 275.991 85.047 CAGAAAAT 241 No 4.24E-06
AATATATT 462 382.795 474 400.615 86.8857 477 412.829 490 433.187 68.9186 AATATATT 21 Yes 0.000195
TTTGTGTG 688 607.303 705 640.94 85.8355 705 625.577 726 662.623 84.2635 CACACAAA 8153 No 0.006617
TGTTTTTT 1716 1632.37 1839 1791.05 85.7404 1730 1636.78 1864 1811.88 95.8269 AAAAAACA 1065 No 0.131261
Top 25 overrepresented words for the 3’Untranslated Regions in Arabidopsis thaliana. The Word attribute describes the short nucleotide sequence
associated with a putative word. S and ES describe the number of sequences a word occurs in and the number of sequences the word was expected
to occur in respectively, while O and EO describe the total number of occurrences and the expected total number of occurrences. The score SlnSES
describes a statistical coverage of the sequences analyzed in the set and is based on a Markov Chain Background Model. Each set of attributes
was computed for the masked as well as the unmasked version of the corresponding segment with the emphasis placed on the unmasked version
(i.e. sorting of the table based on the unmasked SlnSES score).
Further information for the word is provided through its reverse complement (RevComp) and the position of the reverse complement in the set of
results (RC_Pos) as well as a notion describing if the word is a genomic palindrome (Pal).
Finally, PValues describes a p-value that is assigned in order to provide statistical insight allowing the determination if a word is relevant or was
discovered as interesting by random chance.
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words occurred uniquely in two sets, 7 in three sets, 4 in four
sets and none in five sets. Only two words (ATTTTTTA, and
AATATATT)were shared in six out of seven sets (neither word
was present in the 5’UTR set). Note that the word AATATATT
has a significant similarity to the sequence of the TATA-box, a
regulatory element that is (1) often found in core promoters
and (2) known to contribute to the correct positioning of the
core transcriptional machinery [42]. It is conceivable that the
absence of AATATATT in the 5’UTR set prevents the initiation
of transcription at incorrect sites.
Table 3: The top 25 words in 5’UTRs
Unmasked Masked Unmasked
Word S ES O EO SlnSES S ES O EO SlnSES RevComp RC_Pos Pal PValues
CTCTTCTC 871 614.433 992 668.648 303.928 883 669.295 972 729.203 244.68 GAGAAGAG 4 No -2.22E-16
CTTTCTCT 1154 1003.84 1293 1115.45 160.868 1204 1040.02 1327 1164.52 176.278 AGAGAAAG 15 No 1.14E-07
AACAAAAA 1051 920.535 1134 1018.31 139.302 1082 933.212 1157 1036.72 160.064 TTTTTGTT 16 No 0.000192
TTTCTTCA 611 492.734 631 532.75 131.443 808 714.439 849 780.981 99.4364 TGAAGAAA 227 No 1.88E-05
GAGAAGAG 316 211.511 360 225.309 126.863 305 219.262 327 231.047 100.664 CTCTTCTC 0 No 0
TTCTCTCC 455 346.314 464 371.543 124.193 504 412.082 517 440.518 101.482 GGAGAGAA 130 No 2.11E-06
CTTTCTTC 883 771.778 929 846.965 118.876 960 807.394 1006 888.66 166.197 GAAGAAAG 87 No 0.00285
CTCTCTTT 1229 1116.97 1351 1248.77 117.468 1284 1161.65 1410 1312.47 128.577 AAAGAGAG 9 No 0.002211
TTTCTCTC 1421 1308.64 1554 1478.35 117.051 1494 1385.35 1636 1591.45 112.808 GAGAGAAA 74 No 0.025997
AAAGAGAG 666 561.408 709 609.221 113.781 625 511.53 649 550.867 125.216 CTCTCTTT 7 No 4.30E-05
AGAAAAAA 1078 972.588 1154 1078.91 110.928 1097 983.999 1179 1097.24 119.255 TTTTTTCT 93 No 0.012195
AAAGAAAA 978 875.456 1093 966.097 108.328 1000 886.23 1111 981.116 120.779 TTTTCTTT 35 No 3.32E-05
ATCTCTCA 332 243.705 342 260.045 102.647 380 308.328 392 327.073 79.4223 TGAGAGAT 448 No 6.93E-07
AAAAAACA 759 663.266 803 723.672 102.333 774 675.404 814 736.19 105.466 TGTTTTTT 298 No 0.001952
TTTTTCTT 1020 923.944 1116 1022.27 100.884 1501 1398.57 1742 1608.22 106.097 AAGAAAAA 20 No 0.001995
AGAGAAAG 589 496.468 634 536.894 100.664 548 457.974 578 491.244 98.3457 CTTTCTCT 1 No 2.45E-05
TTTTTGTT 811 719.391 885 787.265 97.2085 1506 1441.03 1818 1662.31 66.4099 AACAAAAA 2 No 0.000332
ACAAAAAA 845 754.352 901 827.069 95.888 865 767.534 916 842.311 103.408 TTTTTTGT 37 No 0.005817
TAAAAAAG 231 152.899 238 162.371 95.3195 272 196.748 284 206.973 88.0952 CTTTTTTA 149 No 1.66E-08
CAAAAACC 357 273.395 362 292.183 95.2547 386 290.194 393 307.419 110.121 GGTTTTTG 59 No 4.45E-05
AAGAAAAA 1104 1013.1 1209 1126.3 94.8599 1134 1021.85 1230 1142.64 118.087 TTTTTCTT 14 No 0.007636
CCTCTCTT 351 268.225 358 286.579 94.4052 372 313.865 375 333.083 63.2147 AAGAGAGG 550 No 2.65E-05
TCTTCTCC 907 817.38 946 899.203 94.3624 899 804.147 934 884.875 100.239 GGAGAAGA 676 No 0.062179
TTCTCTCA 473 387.786 484 416.951 93.9572 538 481.457 555 517.331 59.7404 TGAGAGAA 126 No 0.000721
Top 25 overrepresented words for the 5’Untranslated Regions in Arabidopsis thaliana. The Word attribute describes the short nucleotide sequence
associated with a putative word. S and ES describe the number of sequences a word occurs in and the number of sequences the word was expected
to occur in respectively, while O and EO describe the total number of occurrences and the expected total number of occurrences. The score SlnSES
describes a statistical coverage of the sequences analyzed in the set and is based on a Markov Chain Background Model. Each set of attributes was
computed for the masked as well as the unmasked version of the corresponding segment with the emphasis placed on the unmasked version
(i.e. sorting of the table based on the unmasked SlnSES score).
Further information for the word is provided through its reverse complement (RevComp) and the position of the reverse complement in the set of
results (RC_Pos) as well as a notion describing if the word is a genomic palindrome (Pal).
Finally, PValues describes a p-value that is assigned in order to provide statistical insight allowing the determination if a word is relevant or was
discovered as interesting by random chance.
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Table 4: The top 25 words in Introns
Unmasked Masked Unmasked
Word S ES O EO SlnSES S ES O EO SlnSES RevComp RC_Pos Pal PValues
TTTTTGTT 10048 9365.74 11094 10679.8 706.524 9819 9103.26 10783 10355.3 743.17 TTTTTGTT 10048 9365.74 3.44E-05
TTTTTCTT 9144 8495.68 10021 9609.91 672.454 8939 8293.57 9751 9363.74 669.915 TTTTTCTT 9144 8495.68 1.58E-05
CTTTTTTC 2764 2170.42 2821 2314.32 668.224 2713 2187.97 2767 2333.43 583.515 CTTTTTTC 2764 2170.42 8.88E-16
GTTTTTGA 2673 2105.13 2742 2243.33 638.372 2631 2056.65 2696 2190.66 647.973 GTTTTTGA 2673 2105.13 -2.22E-16
TTTTGCAG 3505 2959.4 3523 3179.19 593.06 3452 2920.63 3470 3136.4 577.016 TTTTGCAG 3505 2959.4 1.07E-09
TTTTTTGT 7618 7067.97 8198 7889.79 570.901 7400 6823.86 7922 7600.06 599.8 TTTTTTGT 7618 7067.97 0.000286
TTTTTTGG 3765 3238.3 3942 3487.94 567.378 3635 3124.76 3795 3362.05 549.804 TTTTTTGG 3765 3238.3 2.62E-14
TTTTCTTT 9256 8733.23 10299 9900.39 538.109 9041 8500.1 9994 9615.3 557.761 TTTTCTTT 9256 8733.23 3.48E-05
TGTTTTTT 7487 6984.58 8028 7790.67 520.072 7254 6759.65 7750 7524.05 512 TGTTTTTT 7487 6984.58 0.003768
CTCTCTTT 3193 2716.79 3289 2911.9 515.697 3086 2625.01 3165 2811.09 499.291 CTCTCTTT 3193 2716.79 3.97E-12
ATTTTTTA 2508 2044.78 2645 2177.76 512.128 2383 2003.78 2486 2133.28 413.027 ATTTTTTA 2508 2044.78 3.33E-16
TTTTTTCC 3166 2702.47 3253 2896.16 501.186 3086 2616.31 3161 2801.55 509.528 TTTTTTCC 3166 2702.47 4.13E-11
TGTTTCAG 2215 1790.21 2239 1902.05 471.614 2153 1745.3 2177 1853.55 451.987 TGTTTCAG 2215 1790.21 3.01E-14
GGTTTTTG 2029 1611.17 2092 1708.92 467.851 1997 1584.97 2058 1680.71 461.47 GGTTTTTG 2029 1611.17 1.11E-16
TTTTGTTT 12142 11689.3 13879 13619.2 461.327 11843 11368.1 13438 13205.7 484.659 TTTTGTTT 12142 11689.3 0.013306
TTTGTTTT 11017 10569.9 12527 12188.1 456.39 10729 10259.7 12106 11796.5 479.827 TTTGTTTT 11017 10569.9 0.00113
CTTTTTTA 2234 1828.76 2282 1943.72 447.149 2178 1816.31 2220 1930.26 395.524 CTTTTTTA 2234 1828.76 4.17E-14
AATATATT 2022 1642.55 2143 1742.72 420.253 1925 1679.14 2019 1782.16 263.038 AATATATT 2022 1642.55 4.44E-16
ATTTTTCA 2411 2030.35 2467 2162.1 414.291 2349 1971.89 2398 2098.68 411.073 ATTTTTCA 2411 2030.35 7.51E-11
ATTTTTTC 2810 2425.9 2881 2592.99 413.021 2736 2412.96 2800 2578.85 343.758 ATTTTTTC 2810 2425.9 1.43E-08
CAATTTTT 2402 2023.84 2481 2155.04 411.472 2320 1952.98 2388 2078.19 399.534 CAATTTTT 2402 2023.84 3.73E-12
TTTTTTCT 7674 7280.17 8254 8142.69 404.295 7476 7074.7 8001 7897.8 412.475 TTTTTTCT 7674 7280.17 0.109849
TGTTGCAG 1922 1563.72 1933 1657.84 396.507 1891 1543.21 1902 1635.78 384.332 TGTTGCAG 1922 1563.72 2.42E-11
TTTCATTT 4636 4258.39 4840 4630.74 393.879 4538 4169.05 4731 4529.8 384.813 TTTCATTT 4636 4258.39 0.001152
TTTTTATT 5647 5276.08 6142 5792.21 383.658 5417 5037.47 5842 5517.96 393.481 TTTTTATT 5647 5276.08 2.72E-06
Top 25 overrepresented words for the Introns in Arabidopsis thaliana. The Word attribute describes the short nucleotide sequence associated with a
putative word. S and ES describe the number of sequences a word occurs in and the number of sequences the word was expected to occur in
respectively, while O and EO describe the total number of occurrences and the expected total number of occurrences. The score SlnSES describes a
statistical coverage of the sequences analyzed in the set and is based on a Markov Chain Background Model. Each set of attributes was computed for
the masked as well as the unmasked version of the corresponding segment with the emphasis placed on the unmasked version (i.e. sorting of the table
based on the unmasked SlnSES score).
Further information for the word is provided through its reverse complement (RevComp) and the position of the reverse complement in the set of
results (RC_Pos) as well as a notion describing if the word is a genomic palindrome (Pal).
Finally, PValues describes a p-value that is assigned in order to provide statistical insight allowing the determination if a word is relevant or was
discovered as interesting by random chance.
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Table 5: The top 25 words in Core Promoters
Unmasked Masked Unmasked
Word S ES O EO SlnSES S ES O EO SlnSES RevComp RC_Pos Pal PValues
TATAAATA 1355 1071.69 1369 1175.57 317.831 1300 1029.92 1311 1128.85 302.753 TATTTATA 69 No 2.02E-08
CTATAAAT 712 474.27 716 514.446 289.286 704 464.711 708 503.987 292.416 ATTTATAG 2504 No 7.77E-16
CTATATAA 636 410.261 638 444.486 278.826 626 450.579 628 488.533 205.839 TTATATAG 18530 No 1.11E-16
ATATAAAC 560 350.797 560 379.643 261.928 554 347.685 554 376.253 258.091 GTTTATAT 26957 No 4.44E-16
TAAAAAAT 473 295.342 480 319.301 222.765 453 298.58 460 322.82 188.835 ATTTTTTA 12 No -2.22E-16
ATATATAC 544 394.869 559 427.688 174.295 507 330.093 515 357.099 217.573 GTATATAT 5651 No 7.41E-10
AATATATT 300 181.346 300 195.646 151.012 287 195.452 287 210.918 110.256 AATATATT 6 Yes 2.74E-12
TTATATAA 524 397.031 529 430.047 145.398 514 430.79 518 466.905 90.7739 TTATATAA 7 Yes 2.22E-06
AAGAAAAA 1261 1129.24 1318 1240.05 139.165 1189 1063 1238 1165.84 133.189 TTTTTCTT 25 No 0.014544
ATATAAAG 378 262.861 380 284.014 137.316 375 261.181 377 282.19 135.643 CTTTATAT 377 No 3.41E-08
TATATAAA 1260 1131.11 1276 1242.15 135.966 1234 1102.41 1250 1209.97 139.143 TTTATATA 1458 No 0.171817
AGAAAAAA 1127 1000.04 1170 1095.49 134.693 1063 936.863 1099 1025.06 134.271 TTTTTTCT 31 No 0.01331
ATTTTTTA 312 204.097 315 220.282 132.415 299 207.163 302 223.604 109.715 TAAAAAAT 4 No 1.17E-09
TTTTAAAA 688 568.245 696 617.46 131.571 658 543.865 665 590.7 125.351 TTTTAAAA 13 Yes 0.001019
CTCTTCTC 402 294.202 429 318.061 125.499 371 277.661 390 300.087 107.516 GAGAAGAG 444 No 1.97E-09
ACAAAAAA 958 840.585 988 918.052 125.259 917 799.552 939 872.564 125.681 TTTTTTGT 45 No 0.011607
ATAAATAC 578 466.039 582 505.44 124.446 574 459.992 578 498.825 127.095 GTATTTAT 14072 No 0.000465
TTATAAAA 507 397.553 508 430.617 123.294 490 386.47 491 418.525 116.302 TTTTATAA 945 No 0.000153
AAATTAAA 718 609.913 745 663.251 117.144 682 578.03 705 628.206 112.806 TTTAATTT 96 No 0.000967
GCCCATTA 374 273.89 396 295.991 116.512 372 272.658 394 294.653 115.571 TAATGGGC 190 No 1.82E-08
AAAAAACA 893 787.368 924 859.073 112.42 849 736.927 874 803.277 120.193 TGTTTTTT 33 No 0.014723
TTAAAAAA 805 701.565 828 764.227 110.71 768 667.112 788 726.227 108.159 TTTTTTAA 27 No 0.01177
ATTAAAAA 708 609.58 719 662.885 105.969 671 581.412 681 631.921 96.1611 TTTTTAAT 316 No 0.016276
GCCCAATA 322 231.782 340 250.291 105.859 321 228.286 337 246.5 109.41 TATTGGGC 130 No 4.26E-08
Top 25 overrepresented words for the core promoter regions in Arabidopsis thaliana. The Word attribute describes the short nucleotide
sequence associated with a putative word. S and ES describe the number of sequences a word occurs in and the number of sequences the
word was expected to occur in respectively, while O and EO describe the total number of occurrences and the expected total number of
occurrences. The score SlnSES describes a statistical coverage of the sequences analyzed in the set and is based on a Markov Chain Background
Model. Each set of attributes was computed for the masked as well as the unmasked version of the corresponding segment with the emphasis
placed on the unmasked version (i.e. sorting of the table based on the unmasked SlnSES score).
Further information for the word is provided through its reverse complement (RevComp) and the position of the reverse complement in the
set of results (RC_Pos) as well as a notion describing if the word is a genomic palindrome (Pal).
Finally, PValues describes a p-value that is assigned in order to provide statistical insight allowing the determination if a word is relevant or was
discovered as interesting by random chance.
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Table 6: The top 25 words in Proximal Promoters
Unmasked Masked Unmasked
Word S ES O EO SlnSES S ES O EO SlnSES RevComp RC_Pos Pal PValues
TAAAAAAT 4249 3411.11 4837 3674.74 933.272 3681 3028.65 4071 3237.18 718.039 ATTTTTTA 1 No 0
ATTTTTTA 3876 3135.31 4372 3358.5 822.011 3313 2758.58 3636 2932.38 606.738 TAAAAAAT 0 No 2.22E-16
TTATATAA 3094 2505.92 3390 2650.31 652.239 2712 2508.38 2934 2653.02 211.674 TTATATAA 2 Yes 7.77E-16
AATATATT 3636 3104.08 4093 3322.92 575.097 3178 3009.54 3503 3215.49 173.09 AATATATT 3 Yes 1.67E-15
GAAAAAAG 2066 1652.5 2182 1718.49 461.395 1956 1621.19 2053 1684.9 367.226 CTTTTTTC 5 No 1.11E-16
CTTTTTTC 1960 1578.31 2072 1638.97 424.512 1869 1559.58 1969 1618.92 338.269 GAAAAAAG 4 No 1.11E-16
AAAAATTG 2975 2595.17 3208 2749.61 406.363 2737 2368.41 2938 2497.98 395.888 CAATTTTT 9 No -6.66E-16
TAAAATTT 4339 3951.48 5058 4305.15 405.93 3764 3348.9 4214 3603.07 439.821 AAATTTTA 10 No -6.66E-16
TAATTTTT 4656 4272.02 5336 4686.12 400.739 4125 3726.41 4609 4040.78 419.188 AAAAATTA 19 No 0
CAATTTTT 2872 2499.79 3110 2643.5 398.638 2633 2269.83 2829 2389.32 390.785 AAAAATTG 6 No 6.66E-16
AAATTTTA 4239 3880.57 4921 4221.59 374.5 3651 3305.77 4102 3553.5 362.665 TAAAATTT 7 No 8.88E-16
TACAAAAT 2589 2241.1 2821 2357.73 373.61 2344 2040.96 2514 2138.69 324.496 ATTTTGTA 26 No 6.66E-16
ATTTTCTA 2206 1886.09 2346 1970.39 345.622 2022 1748.93 2142 1822.19 293.357 TAGAAAAT 17 No 8.88E-16
TGAAAAAT 2374 2075.6 2517 2176.47 318.891 2230 1927.32 2354 2015.09 325.288 ATTTTTCA 21 No 5.64E-13
AAAAAATC 3874 3607.85 4265 3902.57 275.738 3494 3280.06 3823 3524 220.77 GATTTTTT 68 No 5.63E-09
CATTTTTC 1675 1426.93 1760 1477.44 268.478 1558 1356.8 1624 1402.92 215.428 GAAAAATG 29 No 5.16E-13
TAAGAAAT 1895 1645.36 1990 1710.83 267.683 1773 1553.49 1856 1612.42 234.336 ATTTCTTA 23 No 2.52E-11
TAGAAAAT 2154 1904.65 2281 1990.5 265.005 1971 1754.61 2083 1828.31 229.215 ATTTTCTA 12 No 1.04E-10
GGAAAAAA 2679 2426.86 2853 2562.63 264.801 2506 2238.07 2643 2354.4 283.363 TTTTTTCC 98 No 9.20E-09
AAAAATTA 4735 4477.84 5547 4933.58 264.404 4109 3862.67 4667 4200.51 254.025 TAATTTTT 8 No 1.33E-15
CAAAATTT 3347 3092.9 3655 3310.2 264.267 3054 2796.42 3304 2974.88 269.093 AAATTTTG 60 No 1.95E-09
ATTTTTCA 2338 2088.5 2489 2190.56 263.846 2169 1928.62 2295 2016.5 254.769 TGAAAAAT 13 No 2.29E-10
TTTTTTGG 3369 3120.79 3724 3341.96 257.829 3050 2802.67 3330 2981.91 257.935 CCAAAAAA 28 No 4.49E-11
ATTTCTTA 1947 1705.79 2052 1775.75 257.518 1800 1598.57 1900 1660.66 213.623 TAAGAAAT 16 No 8.37E-11
Top 25 overrepresented words for the proximal promoters in Arabidopsis thaliana. The Word attribute describes the short nucleotide sequence
associated with a putative word. S and ES describe the number of sequences a word occurs in and the number of sequences the word was
expected to occur in respectively, while O and EO describe the total number of occurrences and the expected total number of occurrences.
The score SlnSES describes a statistical coverage of the sequences analyzed in the set and is based on a Markov Chain Background Model. Each
set of attributes was computed for the masked as well as the unmasked version of the corresponding segment with the emphasis placed on the
unmasked version (i.e. sorting of the table based on the unmasked SlnSES score).
Further information for the word is provided through its reverse complement (RevComp) and the position of the reverse complement in the set of
results (RC_Pos) as well as a notion describing if the word is a genomic palindrome (Pal).
Finally, PValues describes a p-value that is assigned in order to provide statistical insight allowing the determination if a word is relevant or was
discovered as interesting by random chance.
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The large differences between the various sets of
words provide evidence for the existence of segment-
specific signatures. Of additional interest is the uniqueness
of the word-based genomic signatures in comparison to
the signature for the entire Arabidopsis genome. Clearly, the
segments’ signatures distinguish them from each other and
from the entire genome.
In addition to uniquely characterizing each segment, the
top words discovered in each data set also have a strong
Table 7: The top 25 words in Distal Promoters
Unmasked Masked Unmasked
Word S ES O EO SlnSES S ES O EO SlnSES RevComp RC_Pos Pal PValues
ATTTTTTA 5789 4874.02 7202 5393.37 995.937 4920 4189.9 5773 4568.53 790.309 TAAAAAAT 1 No 6.66E-16
TAAAAAAT 5865 4983.57 7314 5527.8 955.154 5003 4269.17 5877 4662.83 793.568 ATTTTTTA 0 No 6.66E-16
GAAAAAAG 3578 2825.77 3921 2995.09 844.484 3394 2744.34 3697 2903.99 721.112 CTTTTTTC 3 No 8.88E-16
CTTTTTTC 3546 2878.92 3904 3054.71 739.005 3345 2798.31 3662 2964.33 596.918 GAAAAAAG 2 No 0
TTATATAA 4781 4107.17 5656 4470.46 726.305 4138 3955.09 4717 4291.1 187.08 TTATATAA 4 Yes 0
AATATATT 5432 4895.21 6702 5419.31 565.205 4688 4574.65 5538 5029.33 114.742 AATATATT 5 Yes 0
CAAGAAAC 2910 2459.44 3187 2587.64 489.513 2818 2410.32 3089 2533.47 440.364 GTTTCTTG 7 No -4.44E-16
GTTTCTTG 2912 2482.93 3182 2613.58 464.176 2842 2430.36 3108 2555.55 444.685 CAAGAAAC 6 No 0
GAAAAATG 3158 2736.51 3416 2895.24 452.402 2871 2566.09 3080 2705.63 322.343 CATTTTTC 29 No 0
GTTTTTGA 3516 3093.27 3830 3296.52 450.382 3207 2816.69 3462 2984.91 416.186 TCAAAAAC 13 No 8.88E-16
GAAAAAAC 3013 2605.34 3240 2749.19 438.004 2744 2495.22 2935 2627.17 260.786 GTTTTTTC 26 No 5.55E-16
CAATTTTT 4457 4041.77 4991 4393.18 435.864 4009 3601.54 4440 3878.67 429.685 AAAAATTG 25 No 1.67E-15
ATTTTGTA 4098 3689.96 4626 3981.23 429.814 3735 3342.23 4123 3580.11 414.995 TACAAAAT 69 No 1.55E-15
TCAAAAAC 3414 3011.29 3688 3203.78 428.513 3129 2749.95 3358 2910.25 404.054 GTTTTTGA 9 No 7.77E-16
GAAGAAAG 3851 3448.5 4291 3702.07 425.126 3664 3290.44 4048 3520.87 394.006 CTTTCTTC 59 No 1.11E-16
GTTTTATG 2173 1793.07 2293 1861.81 417.607 2048 1720.91 2156 1784.36 356.372 CATAAAAC 57 No 1.11E-16
CTTTATTC 1618 1250.45 1676 1284.79 416.937 1500 1215.7 1548 1248.25 315.217 GAATAAAG 43 No 4.44E-16
GTTTTAAG 1957 1584.64 2054 1638.71 413.031 1791 1482.73 1871 1530.29 338.304 CTTAAAAC 28 No 1.33E-15
ATTTTTCA 4081 3695.36 4496 3987.5 405.1 3743 3364 4095 3605.05 399.585 TGAAAAAT 40 No 6.66E-16
TAAGAAGT 1465 1112.41 1517 1139.93 403.359 1388 1100.56 1435 1127.54 322.073 ACTTCTTA 62 No -8.88E-16
CTTGTTTC 2351 1980.52 2504 2064.03 403.153 2269 1929.76 2415 2009.12 367.453 GAAACAAG 35 No 0
CAAAAAAG 3391 3011.99 3696 3204.57 401.915 3126 2864.52 3392 3038.54 273.068 CTTTTTTG 88 No 0
TAGAAAAT 3556 3178.38 3887 3393.13 399.217 3219 2901.76 3488 3080.38 333.981 ATTTTCTA 41 No 0
ATTCTTCA 2716 2348.17 2896 2465.08 395.248 2529 2255.7 2691 2363.65 289.221 TGAAGAAT 31 No 1.11E-16
Top 25 overrepresented words for the distal promoters in Arabidopsis thaliana. The Word attribute describes the short nucleotide sequence
associated with a putative word. S and ES describe the number of sequences a word occurs in and the number of sequences the word was expected
to occur in respectively, while O and EO describe the total number of occurrences and the expected total number of occurrences. The score SlnSES
describes a statistical coverage of the sequences analyzed in the set and is based on a Markov Chain Background Model. Each set of attributes was
computed for the masked as well as the unmasked version of the corresponding segment with the emphasis placed on the unmasked version
(i.e. sorting of the table based on the unmasked SlnSES score).
Further information for the word is provided through its reverse complement (RevComp) and the position of the reverse complement in the set of
results (RC_Pos) as well as a notion describing if the word is a genomic palindrome (Pal).
Finally, PValues describes a p-value that is assigned in order to provide statistical insight allowing the determination if a word is relevant or was
discovered as interesting by random chance.
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Table 8: The top 25 words in the entire genome
Unmasked Masked Unmasked
Word S ES O EO OlnOEO S ES O EO OlnOEO RevComp RC_Pos Pal PValues
AAAAAAAA 5 5 128631 119310 9675.67 5 5 101229 95334 6073.66 TTTTTTTT 1 No 0
TTTTTTTT 5 5 126533 117302 9585.11 5 5 98883 93091.2 5968.36 AAAAAAAA 0 No 1.67E-15
TATATATA 5 5 58215 49385.7 9575.32 5 5 29264 27159.9 2183.54 TATATATA 2 Yes 3.89E-15
ATATATAT 5 5 59429 53453 6298.28 5 5 30192 29596.8 601.111 ATATATAT 3 Yes 3.00E-15
TAAAAAAT 5 5 14823 11276.3 4053.8 5 5 11492 9148.23 2621.21 ATTTTTTA 5 No 4.44E-16
ATTTTTTA 5 5 14743 11385.1 3810.52 5 5 11392 9219.87 2409.99 TAAAAAAT 4 No 3.33E-16
GAAGAAGA 5 5 30102 26908.7 3375.68 5 5 22784 20523.6 2380.53 TCTTCTTC 7 No 0
TCTTCTTC 5 5 30267 27090.3 3356.11 5 5 23044 20902.7 2247.42 GAAGAAGA 6 No 0
TTTTAAAA 5 5 29354 26314.9 3208.24 5 5 19409 17519.9 1987.46 TTTTAAAA 8 Yes 2.55E-15
AATATATT 5 5 14170 11353.5 3140.06 5 5 11168 10179.5 1035.06 AATATATT 9 Yes 1.11E-16
TTTTCTTT 5 5 31066 28174.8 3034.69 5 5 26876 24423.6 2571.58 AAAGAAAA 11 No 0
AAAGAAAA 5 5 31033 28187.3 2984.8 5 5 26861 24502.1 2469 TTTTCTTT 10 No 1.11E-16
AGAGAGAG 5 5 19376 16630.5 2960.63 5 5 12615 11397.8 1280.05 CTCTCTCT 16 No 1.11E-16
TCTCTCTC 5 5 19179 16519.7 2862.73 5 5 12912 11634.1 1345.64 GAGAGAGA 14 No 4.44E-16
GAGAGAGA 5 5 20064 17413.4 2842.81 5 5 13136 11970.7 1220.21 TCTCTCTC 13 No 1.89E-15
AAGAAGAA 5 5 32397 29731.9 2781.12 5 5 24352 23296.2 1079.35 TTCTTCTT 19 No 0
CTCTCTCT 5 5 18513 15956.1 2751.61 5 5 12312 11212.7 1151.45 AGAGAGAG 12 No 1.11E-16
AGAAGAAG 5 5 26477 24049.7 2545.91 5 5 19161 18013.6 1183.17 CTTCTTCT 20 No 8.88E-16
TTATATAA 5 5 11402 9138.11 2523.66 5 5 9262 8518.12 775.46 TTATATAA 18 Yes 1.11E-15
TTCTTCTT 5 5 32333 29910 2518.58 5 5 24550 23579.9 989.811 AAGAAGAA 15 No 0
CTTCTTCT 5 5 26463 24183.9 2383.23 5 5 19432 18332.3 1132.03 AGAAGAAG 17 No 0
TTTTTCTT 5 5 30561 28331 2315.57 5 5 26516 24717.1 1862.84 AAGAAAAA 22 No 0
AAGAAAAA 5 5 30461 28234.7 2311.9 5 5 26488 24756.8 1790.32 TTTTTCTT 21 No 4.44E-16
TTTGTTTT 5 5 32141 29931 2289.6 5 5 27813 26102.2 1765.71 AAAACAAA 36 No 8.88E-16
Top 25 overrepresented words for the entire genome of Arabidopsis thaliana. The Word attribute describes the short nucleotide sequence associated
with a putative word. S and ES describe the number of chromosomes a word occurs in and the number of chromosomes the word was expected to
occur in respectively, while O and EO describe the total number of occurrences and the expected total number of occurrences. The score OlnOEO
describes a statistical overrepresentation of the word in the genome and is based on a Markov Chain Background Model. Each set of attributes
was computed for the masked as well as the unmasked version of the corresponding segment with the emphasis placed on the unmasked version
(i.e. sorting of the table based on the unmasked OlnOEO score).
Further information for the word is provided through its reverse complement (RevComp) and the position of the reverse complement in the set of
results (RC_Pos) as well as a notion describing if the word is a genomic palindrome (Pal).
Finally, PValues describes a p-value that is assigned in order to provide statistical insight allowing the determination if a word is relevant or was
discovered as interesting by random chance.
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probability of being functional regulatory elements. This
argument was strengthened by a functional analysis,
which is described later in this section.
Missing Words
Another interesting component of our word-based
signature is the set of words NOT contained within the
different segments (see Tables 9, 10, 11, &12 and
Additional files 8, 9, 10, &11), referred to as unwords
[43] or nullomers [44,45]. The absence of words in
particular segments is an interesting phenomenon and
may represent negative selection pressure or increased
mutation rates specific to these words, or structural
constraints on DNA [44]. Thus, the missing word sets,
which show unwords and their associated scores, serve as
important ‘fingerprints’ for the segments.
Table 9: Words not detected in the 3’UTRs
#WORD E_S E
CTAGCAGG 5.98269 6.17391
ACTGCCAG 4.99319 5.1526
CGCCTGAT 4.97776 5.13667
GCGTCCGA 4.52742 4.67187
GGGGTGGC 4.5248 4.66917
ACTCCGCC 4.38831 4.5283
CCCGTTCC 4.25101 4.3866
ACACGCCG 4.21714 4.35165
CCCGCTCA 4.193 4.32673
CTGGGCGT 4.06873 4.19847
GACCTGCG 3.71851 3.83704
GCGCAGTA 3.68699 3.80451
GCACCCGA 3.6084 3.7234
GCACCCTC 3.59671 3.71134
CGCACCCA 3.54333 3.65625
CCGCCGTC 3.53385 3.64646
GGGTCGGC 3.52406 3.63636
GCACGCCT 3.35465 3.46154
GCGCAGCC 3.31181 3.41732
CGTCCGCT 3.28252 3.3871
CTGGCGCC 3.2624 3.36634
GGCGACCT 3.25626 3.36
ATACGCCC 3.18816 3.28972
AGCGCTCC 2.98494 3.08
TAGCGCGG 2.98494 3.08
Top 25 words that were expected to occur in the 3’UTR but are not
part of the sequences. Each word is identified through is nucleotide
sequence and contains information about the expected number of
sequences it was computed to occur in (E_S) as well as the expected
number of total occurrences in the set of sequences (E). The words are
sorted by their expected sequence occurrence.
Table 10: Words not detected in the 5’UTRs
#WORD E_S E
GGAACTGC 5.1333 5.40909
GAGGACCC 5.02658 5.29661
GCCCTATA 5.015 5.2844
CCGTACCT 4.98236 5.25
GCGAGTAT 4.94491 5.21053
TATCGCAC 4.83088 5.09034
GGTTGCGG 4.69443 4.94652
GCGGAGTG 4.66421 4.91468
AGTACAGC 4.51745 4.76
GTGCCGAT 4.4368 4.675
GTCCTGGG 4.41572 4.65278
CGGCCGTG 4.3768 4.61176
GGTCGGGG 4.16843 4.39216
GTGCTGGG 4.13122 4.35294
TAGTGCAC 4.12843 4.35
TACCGGCC 4.08277 4.30189
GCCTACGC 4.03144 4.24779
CACCGCGG 3.94494 4.15663
GCGGCGTG 3.90217 4.11155
CGCCTTAG 3.77819 3.98089
CAGCCCAG 3.74709 3.94811
TGAACGGG 3.74703 3.94805
CGTACTGC 3.74638 3.94737
GTGCGCCG 3.68013 3.87755
AGTCCTGG 3.67692 3.87417
Top 25 words that were expected to occur in the 5’UTR but are not
part of the sequences. Each word is identified through is nucleotide
sequence and contains information about the expected number of
sequences it was computed to occur in (E_S) as well as the expected
number of total occurrences in the set of sequences (E). The words are
sorted by their expected sequence occurrence.
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Word-based Clusters
Any biologically required sequence experiences evolution-
ary pressure (in this case purifying selection) resulting in a
narrowing of the range of allowable sequence mutations.
Often, a word and various mutations of the word exhibit
the same functionality. To incorporate this into our
analysis, clusters were built around each of the top
overrepresented words, forming groups of words that are
similar to each ‘seed word.’ Word similarity was measured
through the Hamming distance metric, which models
single point mutations. A Hamming distance of 1 was used
to form the clusters. Each cluster is depicted via a sequence
logo, providing a visual motif of the characteristics of the
cluster.
Table 11: Words not detected in the Introns
#WORD E_S E
CGCGGACA 6.1805 6.4557
CCCGGGAG 4.57278 4.77632
CCGGCCCC 4.46781 4.66667
CGCCCCCC 4.45254 4.65072
GCCCACCG 4.16782 4.35331
GCCGCGGG 3.47686 3.63158
CCGAGGGG 3.34433 3.49315
AAGCGCCC 3.17737 3.31875
CGCCAGCG 2.99188 3.125
CGCTCGCG 2.91507 3.04478
GCGTCGCG 2.8245 2.95017
CCGGCACG 2.48216 2.59259
CCGGGGCG 2.25483 2.35514
CCCGCGCC 2.16189 2.25806
TCGGGCGC 2.11021 2.20408
GCGCACGG 2.02051 2.11039
CGCTCCGC 2.00514 2.09434
CGCGACGC 1.99945 2.0884
TGCGCCCG 1.9539 2.04082
GGTGCGCG 1.92911 2.01493
GCGGGCCC 1.90464 1.98936
CGCGGCGA 1.86163 1.94444
GCGCGACG 1.83299 1.91453
GGGCGGGC 1.79662 1.87654
CCGCCGGG 1.73887 1.81622
Top 25 words that were expected to occur in the introns but are not
part of the sequences. Each word is identified through is nucleotide
sequence and contains information about the expected number of
sequences it was computed to occur in (E_S) as well as the expected
number of total occurrences in the set of sequences (E). The words are
sorted by their expected sequence occurrence.
Table 12: Words not detected in the Core Promoters
#WORD E_S E
CGCACACC 5.86109 6.3029
GTCCGAAC 5.46787 5.88
GCCCTATG 5.23895 5.6338
GGACGTCG 4.98873 5.36471
GGCCCTAG 4.47129 4.80822
CGCGAGCG 4.35999 4.68852
GATCCCCC 3.92081 4.21622
GGCCGCAT 3.82028 4.10811
TACCCAGG 3.80429 4.09091
GGCCCCTG 3.67267 3.94937
CGCATCCG 3.66922 3.94565
CACGCCGA 3.56933 3.83824
CCGGCCGC 3.51312 3.77778
CGCGGTCA 3.51079 3.77528
AGGGCCCT 3.50922 3.77358
GGCGCTGT 3.49296 3.7561
ACGCCCTG 3.45587 3.71622
GCGGACAC 3.30648 3.55556
AGTGGCGC 3.29952 3.54808
GGGCGTTC 3.26995 3.51628
CGCGCAAG 3.25481 3.5
ACCCGCGT 3.22635 3.46939
TTACCCCG 3.22482 3.46774
CCGGTGCG 3.18249 3.42222
TAGGGCCG 3.18249 3.42222
Top 25 words that were expected to occur in the core promoters but
are not part of the sequences. Each word is identified through is
nucleotide sequence and contains information about the expected
number of sequences it was computed to occur in (E_S) as well as the
expected number of total occurrences in the set of sequences (E).
The words are sorted by their expected sequence occurrence.
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Selected clusters and the corresponding sequence logos are
shown in Additional file 12. Two representative motifs are
presented for each segment. Motifs for each segment were
chosen in order to provide a variety of examples of putative
binding sites for the non-coding segments.
The presented motifs correspond to well-known regulatory
elements and complex motifs, which represent sets of
putative regulatory elements. Of particular interest in
Additional file 12 are the word-based clusters for the core
promoters (in the left column) which correspond to the
TATA-box. Also known as the Goldberg-Hogness box [46],
the TATA-box is a well-characterized regulatory element
appearing 31 bp upstream of the transcription start site in
30% of the promoter sequences in Arabidopsis [23]. The
core promoters also contain another interesting motif,
(CGACGTCG), which is involved in stress response in
Arabidopsis thaliana [22]. An extensive functional character-
ization is described later in this section.
Word Location Distribution
The locations of a particular word within a segment can
provide insight into functional properties of the word.
For example, functional TATA motifs are located at a
specific distance upstream of the TSS [23,46]. We
identified the segment-specific locations of the seed
words of the clusters shown in Additional file 12. Being
selected for their high complexity, these words are
expected to exhibit a distribution bias, manifesting as
peaks in the scatterplots of the distribution across
sequences, as shown in Figures 1, 2, 3 and 4.
Figure 1
Word location distribution across introns. Word location distributions for interesting words within the introns.
The occurrences are shown on a log-scale in order to allow a comparison between the different segments as well as the
words visualized for the entire genome.
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The Figures contain histograms showing the numbers of
occurrences of specific words at each point along the
sequences. For uniformity, sequence lengths are normal-
ized to the range [1;100]. Strong peaks can indeed be
found for the words selected in the intron, core promoter,
and proximal promoter regions. The peaks detected for the
intron segment are at both the 5’ and 3’ ends of the introns,
which means that the words occur in close proximity to
flanking exons. The close proximity to the intron-exon
boundaries is expected for splicing regulatory sequences
[2,8-16]. The peaks exhibited in core and proximal
promoters are not surprising. The distributions of words
locations in these segments are expected to show clustering,
due to positional conservation of locations of cis-regulatory
elements [23]. Of particular interest is the location of
the peak for the first word chosen for the core promoter
distribution (TATAATA), the TATA-box. A location of
around 31 bp upstream from the TSS corresponds to the
findings in [23].
Interestingly, we also detect strong peaks for the example
words chosen for the genome wide word landscape,
possibly indicating an important chromosomal feature
that is not yet understood.
Word Co-occurrences
Genes are usually controlled by a combination of
multiple transcription factors, or by transcription factor
complexes binding to different sites embedded in the
genes’ regulatory non-coding regions. In order to detect
the interacting transcription factor binding sites of a
complex, we examined the positional relationships of
Figure 2
Word location distribution across core promoters. Word location distribution for interesting words within the core
promoters. The occurrences are shown on a log-scale in order to allow a comparison between the different segments as well
as the words visualized for the entire genome.
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words. The top 25 overrepresented words were paired,
and the overrepresentation of each pair was determined
using a Markovian background model of order 6. The
top 25 overrepresented word pairs for each segment are
displayed in Tables 13, 14, 15, 16, 17 and 18 (see also
Additional files 13, 14, 15, 16, 17, &18). The limited
overlap between the word pairs of different segments
indicates additional unique word-based signatures for
genomic segments.
AGRIS Lookup
The AGRIS database [25] contains a large collection of
known regulatory elements for Arabidopsis thaliana. The
words discovered in this study were compared to the
regulatory elements of equal or lesser length in AGRIS.
Table 19 provides the overview of the motifs and their
locations within the results.
Functional Categorizations of Words
In order to reveal biological meanings of overrepresented
words, we established associations between the over-
represented words and biological functions of the genes
that harbour a particular word in their corresponding
segment (Table 1). For a single word, all the genes that
contain that word in their selected segment were found
and the corresponding overrepresented Gene Ontology
(GO) terms were identified. Overrepresentation of a GO
term is determined by using the Arabidopsis gene GO
term distributions as a background model. The devel-
opmental and experimental parameters that perturb the
Figure 3
Word location distribution across proximal promoters. Word location distributions for interesting words within
proximal promoters. The occurrences are shown on a log-scale in order to allow a comparison between the different
segments as well as the words visualized for the entire genome.
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expression of genes harbouring a particular word was
determined by comparing the number of induced,
suppressed or neutral genes, to that expected by chance
in a collection of 1305 tissue and stress microarrays from
the public domain. Significant enrichment or depletion
of induced or suppressed genes has been shown to
correlate strongly with factors affecting regulation of a
cis-regulatory element [39].
As shown in Figures 5, 6, 7, 8, 9 and 10, we identified
overrepresented functional categories (y-axis) of genes
that carry a particular word (x-axis, top panel) in either
their 3’UTR (Figure 5), 5’UTR (Figure 6), intron (Figure
7), or promoter regions (Core, Proximal and Distal
Promoters, Figures 8, 9 and 10, respectively). The red
squares depict overrepresented categories with lowest p-
value, calculated for each segment separately, smaller
than 10E-16. For example, the word GTTTTTGA was
significantly enriched in the 3’UTRs of genes that
participate in the GO category “Protein Synthesis”
(including the sub-categories ribosome biogenesis,
ribosomal proteins, translation), and is correlated with
genes suppressed in flowers and early stage siliques (p-
value 4E-14). Based on microarray expression of micro-
dissected tissues (see methods), the word TGTTTTTT is
present in the 3’ UTR of genes induced in roots (p-value
1E-8), in the atrichoblast (hairless) cell files of the root
(p-value 7E-25), the root cortex (p-value 2E-23),
endodermis (p-value 2E-51), and lateral root cap (p-
value 4E-20). The word CTCTCTTT, enriched in introns,
was correlated with differential induction in cotyledons
(p-value 8E-20), suppressed in young flowers, especially
Figure 4
Word location distribution across the entire genome. Word location distributions for interesting words within the
genome. The occurrences are shown on a log-scale in order to allow a comparison between the different segments as well as
the words visualized for the entire genome.
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carpals (p-value 1E-14) and heart stage embryos (p-value
3E-20). Surprisingly, the presence of these words in the
UTRs and introns were strongly correlated with tissue
specific profiles, but were only weakly enriched or
strongly depleted for responses by hormones, biotic
and abiotic stresses. There was no significant correlation
to any of the 1305 surveyed conditions if the words were
located in the 1000 bp upstream or downstream regions.
This is strikingly different to the well characterized
abscisic acid responsive element (ABRE) (CACGTGTC)
[22], which when found in the 1000 bp 5’upstream
region, was strongly correlated to induction by 10 μM
abscisic acid (ABA) (p-value 4E-49), cold, salt and
drought stresses (p-values < 1E-40), in flowers (p-value
Table 13: Co-occurrence in 3’UTRs
Word1 Word2 S ES S*ln(S/ES)
TTCTTTTT TTTTTCTT 322 238.5802 96.5504
TGTTTTTT TTTTTCTT 283 217.7183 74.2154
TTCTTTTT TTTTTTCT 260 197.5705 71.3925
TTTTTCTT TTTTTGTT 326 273.0848 57.7395
TCTTTTTT TTTTTCTT 270 218.9471 56.5898
TTTTCTTT TTTTTTCT 278 226.8886 56.479
TTTTTTGG TTTTTGTT 161 116.5969 51.9517
TTATTTTT TTTTTCTT 211 166.8299 49.5604
TTCTTTTT TTTTTGTT 290 248.3755 44.9324
TGTTTTTT TTCTTTTT 239 198.0677 44.8973
TTTTCTTT TCTTTTTT 270 228.7449 44.7699
TCTTTTTC TTTTTTCT 112 76.7939 42.2658
TGTTTTTT TTTTTTGG 129 93.1111 42.0564
TTTTTTGG TTTTTCTT 148 112.0287 41.2117
TTTTTTCT TTTTTTGG 128 92.8787 41.0542
TTTTCTTT TGTTTTTT 265 227.4605 40.4796
TTTGTTTT TTTTTTGG 170 134.4256 39.9138
TTCTTTTT TTTTTTGG 136 101.9687 39.1665
TCTTTTTT TTTTTTGG 127 93.6332 38.7099
TTTTCTTT TTCTTTTT 285 249.2674 38.1794
TTTTTATT TTATTTTT 137 103.7794 38.0467
TGTTTTTT TTTTTTCT 215 180.3272 37.8109
TCTTTTTT TTTTTTCT 216 181.3431 37.7758
TTTTTGGT TTTTTGTT 161 127.4072 37.6766
ATTTTTTA TTTTTCTT 82 53.2457 35.4078
Overrepresented non-overlapping word-pairs detected in the
3’Untranslated Regions of Arabidopsis thaliana. A word-pair is
characterized through the two nucleotide sequences associated with
it (Word1 and Word2), the number of sequences the pair occurs in (S)
as well as the expected number of sequences (ES) and a statistical
score symbolizing the overrepresentation of the word-pair in the
specific sequence set (S*ln(S/ES)).
Table 14: Co-occurrence in 5’UTRs
Word1 Word2 S ES S*ln(S/ES)
CTCTCTTT CTTTCTCT 209 108.1185 137.7533
TTTCTCTC CTCTCTTT 214 139.4419 91.6622
TTTCTCTC CTTTCTCT 198 125.808 89.7949
TTTTTTGT TTTTCTTT 97 41.7516 81.7683
CTTCTCTT CTCTTCTC 97 45.9973 72.3745
CTCTGTTT TTTTTCTT 105 54.0587 69.7085
TTTTTTGT TTTTTCTT 97 48.6186 66.9983
TTTTCTTT TTTTTCTT 122 71.3728 65.4048
TTTTTGTT TTTTTCTT 115 65.2326 65.2019
TTTCTCTC CTCTTCTC 128 78.07 63.2863
TTTTCTTT TTTTTGTT 103 56.0093 62.7487
CTCTGTTT TTTTTGTT 87 42.4337 62.4629
AAAGAAAA AGAAAAAA 130 82.9236 58.4498
CTCTCTGT CTTTCTCT 90 47.3124 57.8733
CTTTCTCT CTCTTCTC 105 60.5869 57.7376
TTTTCTCC CTCTTCTC 61 23.918 57.1107
ACAAAAAA AAAAAACA 92 49.5364 56.9554
CTTTCTTC CTCTTCTC 88 47.0073 55.179
AAGAAAAA AGAAAAAA 141 95.4769 54.9724
CTCTCTTT CTCTTCTC 109 67.1219 52.8472
GAAAGAGA AGAGAAAG 57 22.6518 52.6003
TTTCCTCT CTTTCTCT 79 40.6193 52.5511
TTTCCTCT TTTCTCTC 91 52.3194 50.3678
TTTTCTTT CTCTCTTT 127 85.6598 50.013
TTCTCTCC CTCTTCTC 53 21.4631 47.9097
Overrepresented non-overlapping word-pairs detected in the
5’Untranslated Regions of Arabidopsis thaliana. A word-pair is
characterized through the two nucleotide sequences associated with
it (Word1 and Word2), the number of sequences the pair occurs in (S)
as well as the expected number of sequences (ES) and a statistical
score symbolizing the overrepresentation of the word-pair in the
specific sequence set (S*ln(S/ES)).
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1E-31), and suppressed in roots (p-value 4E-7) but no
significant correlations were observed when ABRE was
present in the 3’UTRs, 5’UTRs or introns. We also
analyzed primary promoter regions where most of the
basal promoter elements are expected to be located. The
frequency of words is calculated as described above, and
genes that contain the high scoring word in their primary
promoter region were queried for enriched biological
function. For example, GCCCATTA is found in core
promoter regions of genes preferably involved in
ribosome biogenesis and translation. Genes with this
word in the upstream promoter are significantly
depleted for response to all hormones, biotic and abiotic
stresses (typically p-value 1E-8 or better). In other words,
Table 15: Co-occurrence in Introns
Word1 Word2 S ES S*ln(S/ES)
TTTTATTT ATTTTTTA 393 217.8144 231.9354
TTTTTATT ATTTTTTA 334 186.0726 195.3914
TAAAAAAT AATATATT 147 39.3119 193.8792
TTTTTAAT TTTTTATT 460 306.2869 187.084
TAAAAAAT TTTTATTT 273 140.3538 181.6284
TAATTTTT ATTTTTTA 238 113.2939 176.6639
CTCTGTTT CTGTTTTT 346 208.3136 175.5583
TTTTATTT AATATATT 308 175.8151 172.6854
TTTTATTT TTTTTAAT 505 358.7745 172.6415
TAAAAAAT ATTTTTTA 149 48.6332 166.8264
TAAAAAAT TTTTTAAT 189 79.759 163.0573
TAAAAAAT TAATTTTT 179 73.1119 160.2756
TTTTATTT TAATTTTT 461 328.5857 156.0948
TTTTTAAT ATTTTTTA 238 123.6151 155.9133
TAAAAAAT TTTTTCTT 305 185.7949 151.1788
TAAAAAAT TTTTTATT 230 119.9486 149.7338
TTTTTATT AATATATT 261 150.2261 144.1709
TAATTTTT TTTTTAAT 300 186.1617 143.1501
TTTTTAAT AATATATT 202 99.8493 142.3303
TTTTATTT TTTTTATT 670 542.1648 141.8441
TAAAAAAT TTTTTTGT 262 157.163 133.898
TAATTTTT AATATATT 187 91.5206 133.6198
ATTTTTTA TTTTTTGT 354 243.9756 131.769
TAAAAAAT TTTTGTTT 357 246.9371 131.5909
TTTTTAAT TTTTTGTT 638 519.9558 130.5312
Overrepresented non-overlapping word-pairs detected in the introns of
Arabidopsis thaliana. A word-pair is characterized through the two
nucleotide sequences associated with it (Word1 and Word2), the
number of sequences the pair occurs in (S) as well as the expected
number of sequences (ES) and a statistical score symbolizing the
overrepresentation of the word-pair in the specific sequence set
(S*ln(S/ES)).
Table 16: Co-occurrence in Core Promoters
Word1 Word2 S ES S*ln(S/ES)
GCCCAATA GCCCATTA 32 2.3492 83.5729
TTTTTTCT TTTTTCTT 68 22.9531 73.8516
AATAAAAA AAGAAAAA 84 41.5798 59.069
CTCTCTTT CTTTCTCT 40 9.1626 58.95
AATAAAAA ATTAAAAA 57 22.4453 53.1222
ACAAAAAA AAGAAAAA 71 35.1265 49.9645
ACAAAAAA AGAAAAAA 66 31.1075 49.6455
ATTTCTCA TATAAATA 30 6.1031 47.772
AATAAAAA TAAAAAAT 38 10.8748 47.5432
AAAAAACA ACAAAAAA 56 24.4921 46.3121
AAAAATAT AAAAAACA 44 15.5191 45.8533
AACAAAAA AAGAAAAA 77 42.5433 45.6828
AACAAAAA AGAAAAAA 69 37.6758 41.7512
TTTCTTTT TTTTTTGT 40 14.2927 41.1653
AAAAAACA ATATAAAG 30 7.659 40.9596
AAAAAACA CTATATAA 36 11.9538 39.689
AAAAATAT CTATATAA 30 8.0863 39.3309
TATATAAA TAAAAAAT 36 12.3623 38.4793
AATAAAAA TTAAAAAA 53 25.8324 38.0892
TTTTATTT TTTTTTAA 38 14.0039 37.9336
TTTTATTT TTTTTCTT 50 23.5743 37.5932
TTCTTTTT TTTTTCTT 46 20.3942 37.416
AAATTAAA ACAAAAAA 44 18.9721 37.0137
AATAAAAA AGAAAAAA 65 36.8225 36.938
TTTCTTTT TTTTTGTT 41 16.8429 36.4755
Overrepresented non-overlapping word-pairs detected in the core
promoters of Arabidopsis thaliana. A word-pair is characterized through
the two nucleotide sequences associated with it (Word1 and Word2),
the number of sequences the pair occurs in (S) as well as the expected
number of sequences (ES) and a statistical score symbolizing the
overrepresentation of the word-pair in the specific sequence set
(S*ln(S/ES)).
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genes harbouring this word in their upstream promoter
region tend to be less responsive to stresses than
randomly chosen genes. However, the word CTATAAAT
was found in core promoter regions of genes preferably
functioning as storage facilitating proteins (Figure 8).
Genes with this word in the upstream promoter are
rapidly induced by 10 nM brassinolide (p-value 1E-9)
and by salt stress in roots (p-value 4E-9). These genes are
also induced in roots, flowers, pollen, and during seed
development, and strongly suppressed in young leaves
and cotyledons.
A set of 10 frequently enriched cis-elements was
recently provided for the ATH95 gene coexpression
Table 17: Co-occurrence in Proximal Promoters
Word1 Word2 S ES S*ln(S/ES)
AAATTTTA TAAAAAAT 996 489.8445 706.8206
ATTTTTTA TAAAAAAT 869 395.77 683.4771
TAAATTTT TAAAAAAT 970 501.8706 639.1852
AAAAATTA TAAAAAAT 1040 565.2386 634.1171
TAAAATTT TAAAAAAT 963 498.7952 633.5171
TAAAATTT ATTTTTTA 892 458.4645 593.7003
AAATTTTA ATTTTTTA 868 450.2375 569.7695
AAAAATTA ATTTTTTA 947 519.5356 568.5445
AAAATTTA TAAAAAAT 919 496.1801 566.4231
TAATTTTT TAAAAAAT 965 539.2575 561.5671
AAAATTTA ATTTTTTA 865 456.0608 553.6894
TAATTTTT ATTTTTTA 907 495.6552 548.0656
AATATATT TAAAAAAT 776 391.8276 530.2646
AAAATTTA AAATTTTA 973 564.4665 529.8015
AAATTTTA TAAAATTT 976 567.4415 529.3092
AAAAATTA TAATTTTT 1125 707.8947 521.1483
AATATATT ATTTTTTA 730 360.1459 515.7708
TAAATTTT ATTTTTTA 845 461.2912 511.4845
AAAAATTA TAAAATTT 1052 654.7789 498.8066
AAAATTTA AAAAATTA 1044 651.346 492.5318
AAAATTTA TAAAATTT 958 574.7807 489.4031
AAATTTTA TAATTTTT 993 613.4724 478.2242
TAATTTTT TAAAATTT 995 624.6821 463.1724
AAAATTTA TAATTTTT 990 621.407 461.0615
TTATATAA TAAAAAAT 645 316.3233 459.5531
Overrepresented non-overlapping word-pairs detected in the proximal
promoters of Arabidopsis thaliana. A word-pair is characterized through
the two nucleotide sequences associated with it (Word1 and Word2),
the number of sequences the pair occurs in (S) as well as the expected
number of sequences (ES) and a statistical score symbolizing the
overrepresentation of the word-pair in the specific sequence
set (S*ln(S/ES)).
Table 18: Co-occurrence in Distal Promoters
Word1 Word2 S ES S*ln(S/ES)
TAAAAAAT ATTTTTTA 1855 898.8038 1344.087
AATATATT TAAAAAAT 1759 902.7094 1173.429
AATATATT ATTTTTTA 1692 882.8679 1100.631
TTATATAA ATTTTTTA 1478 740.7429 1020.99
TTATATAA TAAAAAAT 1464 757.3903 964.8477
AATATATT TTATATAA 1447 743.9616 962.6287
AAAAATTG TAAAAAAT 1301 747.7933 720.4442
CAATTTTT TAAAAAAT 1279 745.3293 690.6698
AAAAATTG ATTTTTTA 1237 731.3568 650.0966
ATTTTGTA ATTTTTTA 1156 665.4975 638.3272
CAATTTTT ATTTTTTA 1200 728.947 598.171
TAGAAAAT TAAAAAAT 1024 586.114 571.3484
ATTTTGTA TAAAAAAT 1108 680.4539 540.2074
CAATTTTT AATATATT 1162 732.1145 536.7987
ATTTTTCA ATTTTTTA 1078 666.4705 518.3745
AAAAATTG AATATATT 1148 734.5348 512.627
CAATTTTT TTATATAA 1003 614.2579 491.8069
TAGAAAAT AATATATT 956 575.7221 484.8189
ATTTTCTA ATTTTTTA 952 574.2477 481.2399
ATTTTCTA TAAAAAAT 964 587.1534 477.9562
TAGAAAAT ATTTTTTA 941 573.2313 466.4103
ATTTTTCA TAAAAAAT 1058 681.4487 465.4297
TGAAAAAT ATTTTTTA 1020 658.2655 446.7086
TGAAAAAT TAAAAAAT 1033 673.0593 442.5259
AAAAATTG TTATATAA 970 616.2886 439.9733
Overrepresented non-overlapping word-pairs detected in the distal
promoters of Arabidopsis thaliana. A word-pair is characterized through
the two nucleotide sequences associated with it (Word1 and Word2),
the number of sequences the pair occurs in (S) as well as the expected
number of sequences (ES) and a statistical score symbolizing the
overrepresentation of the word-pair in the specific sequence set
(S*ln(S/ES)).
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Table 19: AGRIS Lookup
3’UTRs 5’UTRs Intron Core Promoters Proximal Promoters Distal Promoters
Regulatory Element from
AGRIS database [25]
Rank Score Rank Score Rank Score Rank Score Rank Score Rank Score
Bellringer/replumless/
pennywise BS3 IN AG
- - - - 43503 0.0479784 - - 64618 0.955909 56341 -103.557
CBF1 BS in cor15a - - - - 48346 -1.48116 - - 4852 1.34988 11624 24.1708
Octamer promoter motif - - - - 41435 0.673899 - - 11935 1.28979 23858 4.69741
Bellringer/replumless/
pennywise BS1 IN AG
72 67.6311 352 35.2087 574 127.468 19 117.144 875 1.0759 58337 -186.12
ABRE-like binding site motif 5445 11.7462 1138 21.7556 15242 16.0488 304 41.9698 53 1.45099 109 255.929
G-box promoter motif 1852 21.1577 1138 21.7556 12023 20.8282 304 41.9698 53 1.45099 102 260.604
DPBF1&2 binding site motif 3720 14.7278 2963 13.7441 3460 54.8094 355 39.8827 137 1.36496 102 260.604
MYB1 binding site motif 4306 13.6223 446 32.0594 1407 86.7638 400 38.3647 1785 1.11027 2557 76.5745
RAV1-A binding site motif 568 34.0603 148 49.0095 2000 73.6726 451 36.3111 135 1.20169 289 186.355
W-box promoter motif 751 30.7769 675 27.0198 458 139.172 533 34.175 176 1.19182 756 131.24
CBF2 binding site motif and
GBF1/2/3 BS in ADH1
- - - - 34949 2.87187 540 34.0562 729 1.293 998 117.554
ARF and ARF1 binding site
motif
976 27.5809 216 42.5544 741 116.214 568 33.5619 2852 1.07934 2306 80.856
L1-box promoter motif 2697 17.6326 - - 5824 38.2912 585 33.083 2889 1.05367 2235 81.9035
GATA promoter motif 1186 25.6353 741 26.1103 1247 91.6715 802 29.355 355 1.08161 1033 115.612
ATB2/AtbZIP53/AtbZIP44/
GBF5 BS in ProDH
1757 21.6648 1225 20.9254 2890 60.5806 908 27.9139 1313 1.12688 3204 67.6808
SORLIP2 3658 14.8663 9024 6.91197 16361 14.6754 1006 26.5383 550 1.34186 780 129.375
MYB binding site promoter 4762 12.8183 2462 15.1743 1897 75.734 1032 26.1692 4931 1.06605 2010 86.739
CCA1 binding site motif 1230 25.1325 371 34.5029 5202 41.532 1225 24.4536 61990 0.99765 58013 -161.161
TGA1 binding site motif - - 13290 4.96662 10326 24.0526 1233 24.3919 1660 1.21323 1879 89.7072
SORLIP1 5297 11.9625 6172 9.0064 11076 22.5348 1286 23.8899 4965 1.15533 4097 58.1886
T-box promoter motif 639 32.6567 1532 19.0267 774 114.265 1325 23.5609 193 1.27522 205 212.153
Ibox promoter motif 2156 19.649 358 35.0463 3223 57.1901 1797 20.4507 1081 1.14622 628 140.679
Box II promoter motif 1403 23.9863 4993 10.3195 1437 85.6577 1804 20.4254 1986 1.30314 669 136.891
Hexamer promoter motif 7590 9.4166 1616 18.5991 10347 24.0156 2225 18.6568 3477 1.24419 1252 107.567
AtMYC2 BS in RD22 1193 25.5614 4026 11.6309 3460 54.8094 2823 16.6193 646 1.21499 2073 85.133
RAV1-B binding site motif 7054 9.94571 8250 7.4051 11589 21.6087 2996 16.0975 6084 1.12709 2017 86.5658
RY-repeat promoter motif 182 49.4382 - - 530 132.253 3097 15.8378 72 1.29305 61 302.629
MYB3 binding site motif 5128 12.2348 10575 6.06616 1407 86.7638 3292 15.3953 3288 1.08324 11546 24.3649
Bellringer/replumless/
pennywise BS2 IN AG
3126 16.2923 - - 64424 -30.4349 3694 14.5011 62777 0.97976 58184 -172.62
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Table 19: AGRIS Lookup (Continued)
AtMYB2 BS in RD22 6797 10.1949 9630 6.55608 4961 42.997 4480 13.0383 3570 1.07359 3218 67.5209
E2F binding site motif and
E2F/DP BS in AtCDC6
- - 4078 11.5443 46644 -0.929602 4953 12.223 60966 1.20703 55143 -85.466
ERF1 BS in AtCHI-B and
GCC-box promoter motif
- - 681 26.9446 20822 10.4265 6359 10.5016 4340 1.35349 1735 93.0802
Z-box promoter motif - - - - 36029 2.48082 10144 7.62515 39199 1.00107 26784 1.42726
LTRE promoter motif - - 6230 8.95512 16036 15.0374 11248 7.01938 11296 1.13624 7155 38.6247
SORLIP5 5170 12.1706 3175 13.3137 14017 17.6817 11614 6.82909 14984 1.04471 22267 6.5221
ABFs and ABRE binding site
motif
8540 8.6035 6266 8.92287 29109 5.33319 12250 6.52158 725 1.25598 1490 100.349
PI promoter motif 9436 7.96403 - - 60410 -9.96838 14596 5.56209 24540 1.01231 7902 35.621
Observations about the regulatory elements (length = 8) contained in the AGRIS database [25].
Figure 5
Cellular functions in 3’UTRs. Enriched functional categories within the set of genes associated with each word in the top
25 words of the 3’UTRs. The lookup was conducted against the MIPS Functional Catalogue Database (FunCatDB) [54].
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Figure 6
Cellular functions in 5’UTRs. Enriched functional categories within the set of genes associated with each word in the top
25 words of the 5’UTRs. The lookup was conducted against the MIPS Functional Catalogue Database (FunCatDB) [54].
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Figure 7
Cellular functions in introns. Enriched functional categories within the set of genes associated with each word in the top 25
words of the introns. The lookup was conducted against the MIPS Functional Catalogue Database (FunCatDB) [54].
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Figure 8
Cellular functions in core promoters. Enriched functional categories within the set of genes associated with each word in
the top 25 words of the core promoters. The lookup was conducted against the MIPS Functional Catalogue Database
(FunCatDB) [54].
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Figure 9
Cellular functions in proximal promoters. Enriched functional categories within the set of genes associated with each
word in the top 25 words of the proximal promoters. The lookup was conducted against the MIPS Functional Catalogue
Database (FunCatDB) [54].
Figure 10
Cellular functions in distal promoters. Enriched functional categories within the set of genes associated with each word in
the top 25 words of the distal promoters. The lookup was conducted against the MIPS Functional Catalogue Database
(FunCatDB) [54].
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neighbourhood (AAACCCTA, CTTATCCN, GGCCCANN,
GCCACGTN, GCGGGAAN, GACCGTTN, AANGTCAA,
CNGATCNA, NCGTGTCN, CATGCANN) [47]. Our
results show a direct overlap with two of those words
(AAACCCTA, NCGTGTCN), which are detected and
marked as ‘interesting’ in the 5’UTRs, and the proximal
promoters, respectively. Several words were hit partially
as members of the ‘interesting ’ word clusters
(CTTATCCN, GCCACGTN, AANGTCAA, CNGATCNA),
while others were not represented in the selected word
clusters and the top 25 words. While no overlap for
GACCGTTN could be found, it is possible to validate the
significance of GGCCCANN and GCGGGAAN through
the detection of these two words as unwords in the
introns, marking them interesting regulatory elements
associated with the expression, but not necessarily with
the regulation of the associated alternative splicing
process.
Conclusion
The analyses described here provide a first view of the
word landscape within the non-coding regions of the
Arabidopsis thaliana genome. An analysis centred on the
statistically interesting words furnishes important
insights into the unique elements of each segment. The
correlations of particular words with cellular functions or
expression patterns provide valuable hypotheses for
further experimentation. Correlation between word
position and expression also seems strong, with one
class of words only present in the 5’/3’UTRs and introns,
and another class of words putatively functioning only
in the region upstream of the TSS. Words in the first
class seem more directed at regulation of tissue and
cellular identity, while words which function upstream
appear more likely to be involved in environmental
responses.
Methods
Word-based genomic signatures are the union of results
generated by applying the software pipeline shown in
Figure 11. Statistically relevant words are extracted from
a set of genomic sequences, and are analyzed to
determine similarity, location distribution, groupings,
and predicted cellular function.
Sequence Data
This manuscript reports the results of analyzing DNA
sequences of Arabidopsis thaliana. The non-coding geno-
mic segments (specifically, the 3’UTRs, 5’UTRs, promo-
ters and introns) and the entire genomic sequence (as
complete chromosomes) were obtained from TAIR
(release 8) [19]. Both masked and unmasked versions
of the genome were analyzed. Ambiguous nucleotides,
depicted in the sequences by the letters [R, Y, W, S, K, M],
were removed because they represent sequencing
anomalies; this resulted in the removal of 0.15% (or
188,820) of the nucleotides.
In this study, only protein-coding genes were considered as
genes, and transposable-like, or pseudo-genes, were
omitted. Thus, the total number of genes in this study is
~27,000. Due to different lengths and locations of the
promoter elements it is possible that, while core promoters
can occur for a specific gene, no distal promoter for that
gene exists due to the fact that its location would fall into
another gene or even outside of a chromosome. The
difference in number of genes in 3’UTRs and 5’UTRs sets
compared to other sets is due to genes that lack annotated
UTR (it is yet to be discovered).
Whenever multiple spliced transcripts were available
for a gene, a major transcript was chosen (Atngnnnnn.1)
to prevent bias towards genes that contain multiple
transcripts. Likewise, only introns of major transcripts
were selected.
Word Enumeration and Scoring
The first pipeline stage employs a radix trie data structure
[48] to enumerate all subsequences (words) of a
specified length in the given DNA input sequences. For
each word w, with o total occurrences in s sequences, a
word score is computed as s*ln(s/Es(w)). The expected
number of sequences containing word w, Es(w), is
computed as the product of (1) the probability for
each observed word to occur anywhere in the input
sequences and (2) the total length of the sequences. This
model implicitly assumes a binomial model for the word
distribution, i.e., that the word probabilities are inde-
pendent of the positions of the words within the
sequences [49,50]. The probability is computed by
using a maximum-order homogeneous Markov chain
model [49] where the transition probabilities are
determined using the Maximum Likelihood Method
[50]. (Note that under this model, the (G+C)% biasing is
achieved for any order of Markov model greater than or
equal to zero, since the frequencies of individual
nucleotides are taken into consideration for all orders.)
The order of the Markov model was chosen by using a
standard chi-square test to assess the appropriateness of
Markov chains of orders 0 to 6. To provide the highest
precision for computation of expected values, the highest
order model that passed the chi-square test was selected.
Thus, an order 6 model was selected.
A p-value for each word (representing the probability of
obtaining a score at least as high as the one observed [51]) is
calculated by using a binomial word distribution to
determine the probability of obtaining at least o repeats
in the s input sequences that contain w.
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Word Clustering
The Word Clustering stage computes a cluster for each of
the top scoring words (seed words) identified in the
Word Scoring phase. A cluster is computed from a seed
word by determining the set of words whose Hamming
distance is within a user-specified threshold. A Position/
Weight Matrix (PWM) is constructed for each cluster
[52], and a sequence logo is created from each PWM
using the TFBS module by Lenhart and Wasserman [53].
For example, the PWM for the seed word ATTTTGTA in
the 3’UTRs is as follows:
0 78 0 05 0 03 0 03 0 01 0 05 0 05 0 81
0 05 0 03 0 02 0 01 0 01 0 03 0
. . . . . . . .
. . . . . . .02 0 03
0 06 0 03 0 04 0 03 0 02 0 89 0 03 0 04
0 11 0 89 0 91 0 93 0 9
.
. . . . . . . .
. . . . . 6 0 04 0 90 0 12. . .
⎛
⎝
⎜⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟
Figure 11
Process Flowchart. Methodology flow applied for the discovery of word-based genomic signatures in non-coding Arabidopsis
thaliana.
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The columns of the PWM correspond to nucleotide
positions and the rows correspond to the nucleotides A,
C, G, and T, respectively.
Word Location Distribution
For selected words from the different segments it was
determined if they were clustered at specific locations
along the corresponding sequences in which they occur.
In order to detect a location bias, representative of such
clusters, histograms were created to show the num-
bers of occurrences of a specific word at each point
corresponding to a positional offset from the transcrip-
tion start site (TSS). For uniformity, sequence lengths
were normalized to the range [1;100], to represent the
number of nucleotides between the position and the
TSS.
Co-Occurrence Analysis
The Co-Occurrence Analysis considers all non-overlapping
pairs of the top ranked words and computes the expected
number of sequences that contain both words. Subse-
quently, the observed number of sequences that contain
both words is determined, and an observed-to-expected
ratio is computed (using a binomial word distribution) for
each word pair.
AGRIS Lookup
Previously published and curated binding site motifs
which are equal to or shorter than eight base pairs were
extracted from the AGRIS AtcisDB database [25], and
were compared with the word lists generated for the
different segments. For each motif the corresponding
entries in word list were determined and the highest
scoring word was identified.
Determine Cellular Function
The MIPS Functional Catalogue Database (FunCatDB)
[54], was used for determining over-represented cellular
functions in each gene list containing a particular
word. The workflow of the cellular function analysis,
labelled as “Cellular Function” in the larger process flow
(Figure 11) is as follows. For each word in the ‘top 25’
lists (Tables 2, 3, 4, 5, 6, 7, &8) we determined the list of
genes that contained the word being analyzed in the
corresponding region. Then we determined the func-
tional category of each gene by using the functional
category scheme (version 2.1) retrieved from FuncatDB.
The p-values for enrichment of categories were calcu-
lated by statistical tests with the hypergeometric dis-
tribution. After filtering out p-values greater than 1E-5,
results were visualized by the matrix2png software
package [55].
Analysis of the correlation between word location and
gene expression was done as described in [39] with the
following exceptions. A larger database was constructed
from 1305 available raw microarray datasets (Addi-
tional file 19) present in NASC affyarrays http://www.
arabidopsis.info and the gene expression omnibus
http://www.ncbi.nlm.nih.gov/geo/. The p-value was
calculated using a chi-squared test comparing genes 2-
fold induced, 2-fold suppressed, or neutral between
observed (all genes harbouring the word) and expected
values (based on genomic average). The Bonferroni
correction was used to adjust for multiple hypothesis
testing. Microarray sources included a large tissue
macro-dissection [56], and the follow-up studies on
stress, hormones, and pathogens [57]. We included the
laser capture microdissected tissue microarray datasets
[58], the gene expression profile of the Arabidopsis
root [59], analysis of brassinosteroids [60], and the
numerous other experiments found in the collected
dataset in the above mentioned repositories. Data
were normalized using global scaling of the middle
96% data points, and then noise filtered using a t-test
of signal vs. background, and a t-test of signal vs.
control.
Authors’ contributions
JL contributed in the development of algorithms and
models, the implementation of algorithms, generation
of the results and drafting of the document. JDW
contributed in the development of the models and
algorithms and the implementation of the approaches.
KK contributed in the development, implementation
and testing of models and algorithms. XL contributed in
the development of the models and algorithms for co-
occurrence analysis and generated the respective data. FD
contributed in the development of models and algo-
rithms, and in the implementation of the methods. MG
conducted correlation analysis between word presence/
location and gene expression pattern. KE contributed the
idea of Hamming-distance-based clustering. SSL con-
tributed to the statistical foundations of the scoring
model. AY’s contributions include extraction of data sets,
functional analysis of words, and writing the manuscript.
EG contributed to writing the manuscript and integrating
the identified words with existing knowledge on control
of gene expression. In addition to architecting the
software pipeline employed in this research, LRW
contributed to the design, implementation and valida-
tion of models and algorithms (especially in the areas of
word searching and word scoring) and to the writing of
this manuscript.
All authors read and approved the final manuscript.
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Page 28 of 31
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Additional material
Additional file 1
Words discovered in 3'UTRs. Entire set of words discovered in the
3'UTRs with occurrences, expected occurrences, scores, reverse
complement information and p-value.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S1.CSV]
Additional file 2
Words discovered in 5'UTRs. Entire set of words discovered in the
5'UTRs with occurrences, expected occurrences, scores, reverse
complement information and p-value.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S2.CSV]
Additional file 3
Words discovered in introns. Entire set of words discovered in the
introns with occurrences, expected occurrences, scores, reverse
complement information and p-value.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S3.CSV]
Additional file 4
Words discovered in core promoters. Entire set of words discovered in
the core promoters [-100;+1] with occurrences, expected occurrences,
scores, reverse complement information and p-value.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S4.CSV]
Additional file 5
Words discovered in proximal promoters. Entire set of words
discovered in the proximal promoters [-1,000;-101] with occurrences,
expected occurrences, scores, reverse complement information and p-
value.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S5.CSV]
Additional file 6
Words discovered in distal promoters. Entire set of words discovered in
the distal promoters [-3,000;-1,001] with occurrences, expected
occurrences, scores, reverse complement information and p-value.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S6.CSV]
Additional file 7
Words discovered in entire genome. Entire set of words discovered in
the complete genome with occurrences, expected occurrences, scores,
reverse complement information and p-value.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S7.CSV]
Additional file 8
Words missed in 3'UTRs. Entire set of words expected to occur but not
discovered in the 3'UTRs with expected occurrences.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S8.CSV]
Additional file 9
Words missed in 5'UTRs. Entire set of words expected to occur but not
discovered in the 5'UTRs with expected occurrences.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S9.CSV]
Additional file 10
Words missed in introns. Entire set of words expected to occur but not
discovered in the introns with expected occurrences.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S10.CSV]
Additional file 11
Words missed in core promoters. Entire set of words expected to occur
but not discovered in the core promoters with expected occurrences.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S11.CSV]
Additional file 12
Word based clusters. Word-based clusters built around 2
overrepresented words of each non-coding segment of Arabidopsis
thaliana represented by the word cluster and the sequence logo
associated with said cluster. A word in a word cluster is presented
through the nucleotide sequence associated with the word, the sequence
count, the overall count and the SlnSES score.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S12.DOC]
Additional file 13
Word co-occurrences in 3'UTRs. Entire set of co-occurring words
(taken from the top 25 words) discovered in the 3'UTRs with
occurrence, expected occurrences and scores.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S13.CSV]
Additional file 14
Word co-occurrences in 5'UTRs. Entire set of co-occurring words
(taken from the top 25 words) discovered in the 5'UTRs with
occurrence, expected occurrences and scores.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S14.CSV]
BMC Genomics 2009, 10:463 http://www.biomedcentral.com/1471-2164/10/463
Page 29 of 31
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Additional file 15
Word co-occurrences in introns. Entire set of co-occurring words (taken
from the top 25 words) discovered in the introns with occurrence,
expected occurrences and scores.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S15.CSV]
Additional file 16
Word co-occurrences in core promoters. Entire set of co-occurring
words (taken from the top 25 words) discovered in the core promoters
with occurrence, expected occurrences and scores.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S16.CSV]
Additional file 17
Word co-occurrences in proximal promoters. Entire set of co-occurring
words (taken from the top 25 words) discovered in the proximal
promoters with occurrence, expected occurrences and scores.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S17.CSV]
Additional file 18
Word co-occurrences in distal promoters. Entire set of co-occurring
words (taken from the top 25 words) discovered in the distal promoters
with occurrence, expected occurrences and scores.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S18.CSV]
Additional file 19
NASC Microarrays. Entire set of microarray experiments available in
NASC that were used for the cellular functional analysis.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-463-S19.XLS]
Acknowledgements
The Ohio University team acknowledges the support of the Stocker
Endowment, Ohio University’s Graduate Research and Education Board
(GERB), the Ohio Supercomputer Center, the Choose Ohio First
Initiative of the University System of Ohio. We also wish to thank
Sarah Wyatt for proofreading the manuscript. The Ohio University team
further acknowledges that salaries and research support are provided by
state funds appropriated to the Ohio Plant Biotechnology Consortium
through The Ohio State University, Ohio Agricultural Research and
Development Center. MG acknowledges the support of the plant biology
department at Southern Illinois University Carbondale, and would like to
thank Patrick Brown and Elisabeth Fitzek for help in assembling and
analysis of the microarray database and tools. Funding for EG was
provided by National Science Foundation grants MCB-0418891, MCB-
0705415 and by state funds appropriated to the Ohio Plant Biotechnology
Consortium through The Ohio State University, Ohio Agricultural
Research and Development Center. Finally we would like to acknowledge
the anonymous reviewers who helped in enhancing the quality of the
manuscript presented here.
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