Volume 10 Supplement 1

The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08)

Open Access

Word-based characterization of promoters involved in human DNA repair pathways

  • Jens Lichtenberg1Email author,
  • Edwin Jacox2,
  • Joshua D Welch1,
  • Kyle Kurz1,
  • Xiaoyu Liang1,
  • Mary Qu Yang2,
  • Frank Drews1,
  • Klaus Ecker1,
  • Stephen S Lee3,
  • Laura Elnitski2 and
  • Lonnie R Welch1, 4, 5
BMC Genomics200910(Suppl 1):S18

DOI: 10.1186/1471-2164-10-S1-S18

Published: 7 July 2009

Abstract

Background

DNA repair genes provide an important contribution towards the surveillance and repair of DNA damage. These genes produce a large network of interacting proteins whose mRNA expression is likely to be regulated by similar regulatory factors. Full characterization of promoters of DNA repair genes and the similarities among them will more fully elucidate the regulatory networks that activate or inhibit their expression. To address this goal, the authors introduce a technique to find regulatory genomic signatures, which represents a specific application of the genomic signature methodology to classify DNA sequences as putative functional elements within a single organism.

Results

The effectiveness of the regulatory genomic signatures is demonstrated via analysis of promoter sequences for genes in DNA repair pathways of humans. The promoters are divided into two classes, the bidirectional promoters and the unidirectional promoters, and distinct genomic signatures are calculated for each class. The genomic signatures include statistically overrepresented words, word clusters, and co-occurring words. The robustness of this method is confirmed by the ability to identify sequences that exist as motifs in TRANSFAC and JASPAR databases, and in overlap with verified binding sites in this set of promoter regions.

Conclusion

The word-based signatures are shown to be effective by finding occurrences of known regulatory sites. Moreover, the signatures of the bidirectional and unidirectional promoters of human DNA repair pathways are clearly distinct, exhibiting virtually no overlap. In addition to providing an effective characterization method for related DNA sequences, the signatures elucidate putative regulatory aspects of DNA repair pathways, which are notably under-characterized.

Background

Genomic signature techniques were originally developed for identifying organism-specific characterizations [1, 2]. Genomic signature methods carry the limitation that they were not designed for sub-categorization of sequences from within a single organism. To address this shortcoming, the authors present genomic signature techniques that can be used to identify regulatory signatures, i.e. to classify DNA sequences regarding related biological units within an organism, such as particular functions, pathways and tissues.

The term genomic signature was introduced by Karlin and Burge to refer to a function characterizing genomes based on compositional variation [2]. Karlin and others showed that a di-nucleotide odds-ratio was an effective genomic signature. In addition to the odds ratio, oligonucleotide frequencies (as n-mers) and machine learning methods have been employed to classify sequences based on their organism of origin [1, 320], and to identify unique features of genomic data sets. Such approaches were effectively employed in a more refined focus examining tissue-specific categorization of regulatory sequences in liver or muscle [2124].

Here, the authors employ a word-based genomic signature method. That is, given a group of related sequences, a set of characteristic subsequences is discovered. Each subsequence is called a genomic word. The set of characteristic subsequences and their attributes constitute a word-based genomic signature. It is hypothesized that each functionally related group of sequences has a detectable word-based signature, consisting of multiple genomic words. Furthermore, it is hypothesized that the genomic words that constitute a word-based genomic signature are functional genomic elements. Unlike most existing types of genomic signatures, a word-based genomic signature provides insights that are directly applicable to the problem of identifying functional DNA elements, because the words identify putative transcription factor binding sites.

The authors have identified two primary components of word-based genomic signatures that are useful for characterizing a set of related genomic sequences, RGS. The set of statistically overrepresented words that can be derived from RGS can be regarded as a word-based signature (SIG1) since it provides information about the complete set of potential control elements regulating the set of RGS. A second signature (SIG2) provides a set of words related to the elements of SIG1. The similarity between the sets can be measured based on evolutionary distance metrics, e.g. hamming and edit distance (also called Levenshtein distance, see Methods). In addition to SIG1 and SIG2 several post-processing steps built upon the two word-based signatures are undertaken to create the final regulatory genomic signature. These post-processing steps include sequence clustering, co-occurrence analysis, biological significance analysis, and a conservation analysis.

DNA repair genes represent a large network of genes that respond to DNA damage within a cell. Discrete pathways for DNA repair responses have been identified in the Reactome database [25]. A discernable feature among genes in these pathways is the promoter architecture. A large percentage of genes with DNA repair functions are regulated by bidirectional promoters [26, 27], whereas the rest are regulated by unidirectional promoters. Bidirectional promoters fall between the DNA repair gene and a partner gene that is transcribed in the opposite direction. The close proximity of the 5' ends of this pair of genes facilitates the initiation of transcription of both genes, creating two transcription forks that advance in opposite directions. DNA repair genes rarely share bidirectional promoters with other DNA repair genes. Rather, they are paired with genes of diverse functions [26].

The formal definition of a bidirectional promoter requires that the initiation sites of the genes are spaced no more than 1000 bp from one another. Using these criteria the authors have comprehensively annotated the human and mouse genomes for the presence of bidirectional promoters, using in silico approaches [26, 28]. Bidirectional promoters utilized repeatedly in the genome are known to regulate genes of a specific function [26] and serve as prototypes for complete promoter sequences for computational studies- i.e., one can deduce the full intergenic region because exons flank each side. These promoters represent a class of regulatory elements with a common architecture, suggesting a common regulatory mechanism could be employed among them. Recent molecular studies confirm that RNA PolII can dock at promoters while simultaneously facing both directions [29], rather than being restricted to a single direction.

DNA repair genes are likely to play a universal role in damage repair, therefore mutations that affect their regulation will become important diagnostic indicators in disease discovery. The authors have previously shown that bidirectional promoters regulate genes with characterized roles in both DNA repair and ovarian cancer [28]. A more detailed analysis of the regulatory motifs within this subset of promoters will address regulatory mechanisms controlling transcription of this important set of genes. This paper presents word-based genomic regulatory signatures based on statistically overrepresented oligonucleotides (6-8 mers) found in unidirectional and bidirectional promoters of genes in DNA repair pathways. The results demonstrate the effectiveness of using signatures for classifying biologically related DNA sequences. The oligonucleotides that comprise the signatures match known binding motifs from TRANSFAC [30] or JASPAR [31] databases. Furthermore, some examples overlap and agree with experimentally validated regulatory functions.

Results

The effectiveness of genomic regulatory signatures that are based on SIG1 and SIG2 was addressed by analyzing promoter sequences for genes in DNA repair pathways of humans. The promoters were divided into two classes, the bidirectional promoters and the unidirectional promoters, and distinct genomic signatures were calculated for each class. The human DNA repair pathways included 32 bidirectional promoters and 42 unidirectional promoters. Bidirectional promoters had a GC content ranging between 47.55% and 77.09% with an average of 59.87% while unidirectional promoters varied from 38.00% to 68.09%, averaging 50.84%.

Statistically overrepresented words

For each set of promoters, the statistically overrepresented words were identified. The top 25 overrepresented 8-mer words for each dataset are presented in Tables 1a and 1b, respectively (See Additional file 1 and Additional file 2 for the complete lists of words discovered in the bidirectional and unidirectional promoter set respectively). Each word is presented as an observed number or a statistical expectation, respectively, including the number of sequences the word is contained in (S or E S ), the number of overall occurrences of the word (0 or E S ), and a score measuring overrepresentation for the word https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq1_HTML.gif . Additional information such as reverse complement words, their relative positions in the list of top words, palindromic words, and p-values assessing the statistical relevance of the appearance of the word are also presented. A comparison of Tables 1a and 1b reveals that the characteristic words for the two sets are distinct, with no overlaps. The significance of the selected 25 words can be seen by comparing their scores and p-values to the scores and p-values for all words, which are plotted in Figures 1 and 2).
Table 1

Top 25 words. The top 25 words for the bidirectional promoter set (a) and the unidirectional promoter set (b) of DNA-repair pathways. The words are sorted in descending order according to their statistical overrepresentation.

(a) Bidirectional

Word

S

ES

O

EO

Sln(S/ES)

RevComp

Position

Palindrome

P-Value

TCGCGCCA

4

0.918299

4

0.9375

5.88611

TGGCGCGA

12538

No

0.015391

TCCCGGGA

8

3.97165

8

4.26667

5.60208

TCCCGGGA

2

Yes

0.068606

GGCCCGCC

10

5.85012

11

6.5

5.36123

GGCGGGCC

21073

No

0.066821

TCCCGGCT

6

2.54354

6

2.66667

5.14921

AGCCGGGA

NA

No

0.054084

CAGGGGCC

4

1.1085

4

1.13514

5.13315

GGCCCCTG

14546

No

0.028413

AGGGCCGT

5

1.80245

5

1.86667

5.10145

ACGGCCCT

613

No

0.04142

TCTGAGGA

5

1.84222

6

1.90909

4.99234

TCCTCAGA

5391

No

0.013499

CGTGGGGG

5

1.86693

5

1.93548

4.92572

CCCCCACG

20402

No

0.047015

TGCTGAGA

4

1.17067

4

1.2

4.91487

TCTCAGCA

NA

No

0.033766

CGCGGCCG

4

1.17067

4

1.2

4.91487

CGGCCGCG

20259

No

0.033766

TCTGGGAT

2

0.180188

2

0.181818

4.8138

ATCCCAGA

2854

No

0.014655

GGGGCCGG

5

1.92725

5

2

4.76672

CCGGCCCC

20866

No

0.052648

AGGGAGGG

6

2.73111

6

2.87234

4.7223

CCCTCCCT

9852

No

0.07159

AGAAAAGA

3

0.632564

3

0.642857

4.66976

TCTTTTCT

NA

No

0.027559

CGACTCCG

3

0.632564

3

0.642857

4.66976

CGGAGTCG

NA

No

0.027559

GGGCCAGG

7

3.61284

7

3.85714

4.6299

CCTGGCCC

19875

No

0.096315

ACTCCAGC

5

2.02051

5

2.1

4.53045

GCTGGAGT

NA

No

0.062121

CGGGCCGA

5

2.05153

5

2.13333

4.45426

TCGGCCCG

6128

No

0.065478

TGCGGAAT

2

0.220092

2

0.222222

4.41371

ATTCCGCA

NA

No

0.021321

GCCCCTCC

8

4.63031

9

5.03226

4.37454

GGAGGGGC

7041

No

0.070206

GCCGGCGA

3

0.707627

3

0.72

4.33335

TCGCCGGC

20143

No

0.036618

TGAAGCCA

4

1.38876

4

1.42857

4.23154

TGGCTTCA

NA

No

0.056996

GGCAGGGA

6

3.01111

6

3.18182

4.1367

TCCCTGCC

10531

No

0.103337

TGCCCGCG

5

2.19845

5

2.29167

4.10844

CGCGGGCA

NA

No

0.082773

CAGCAGCC

6

3.02748

6

3.2

4.10418

GGCTGCTG

19198

No

0.105399

(b) Unidirectional

Word

S

ES

O

EO

Sln(S/ES)

RevComp

Position

Palindrome

P-Value

ACCCGCCT

4

0.716577

4

0.727273

6.87826

AGGCGGGT

19440

No

0.006562

CTTCTTTC

5

1.7686

5

1.81818

5.19624

GAAAGAAG

13567

No

0.037733

AGGAAACA

4

1.16659

4

1.19048

4.92885

TGTTTCCT

21667

No

0.032947

GCAGGGCG

6

2.75716

6

2.86957

4.66535

CGCCCTGC

1311

No

0.071337

GGGGCTGC

5

2.036

5

2.1

4.49226

GCAGCCCC

16359

No

0.062122

TCTTCTTC

4

1.30438

4

1.33333

4.48225

GAAGAAGA

NA

No

0.046491

GGGGAGTA

3

0.682407

3

0.692308

4.44222

TACTCCCC

17991

No

0.033211

ATTAAAAT

4

1.36853

4

1.4

4.29023

ATTTTAAT

16078

No

0.053723

CGGAAACC

3

0.750393

3

0.761905

4.15731

GGTTTCCG

NA

No

0.042101

TGGGCGGA

4

1.44679

4

1.48148

4.06778

TCCGCCCA

NA

No

0.063337

CGGCGGCG

3

0.787559

3

0.8

4.01229

CGCCGCCG

22091

No

0.047421

TTTTTTGA

3

0.787559

3

0.8

4.01229

TCAAAAAA

NA

No

0.047421

TTTCTCCA

4

1.48541

4

1.52174

3.96242

TGGAGAAA

2378

No

0.068398

AGCCGGCT

3

0.805285

3

0.818182

3.94551

AGCCGGCT

14

Yes

0.050071

CCTCTTTA

2

0.282982

2

0.285714

3.91104

TAAAGAGG

NA

No

0.033814

CGCCCCTT

6

3.12976

6

3.27273

3.90482

AAGGGGCG

21917

No

0.113859

GCGCCGCG

5

2.33164

5

2.41379

3.81433

CGCGGCGC

15062

No

0.097601

ATTCCCAG

3

0.843245

3

0.857143

3.80733

CTGGGAAT

21297

No

0.055985

TCTCCCCT

4

1.56036

4

1.6

3.7655

AGGGGAGA

18183

No

0.07881

TCCGCCGG

3

0.855341

3

0.869565

3.7646

CCGGCGGA

NA

No

0.057938

CTCCCGCT

3

0.867789

3

0.882353

3.72126

AGCGGGAG

NA

No

0.059981

TGCGCCGA

2

0.316812

2

0.32

3.68519

TCGGCGCA

3202

No

0.041483

GGGCGCCC

4

1.59514

4

1.63636

3.67732

GGGCGCCC

23

Yes

0.083901

GTGCGTTT

3

0.884961

3

0.9

3.66247

AAACGCAC

NA

No

0.062855

TTGGTCTC

4

1.60537

4

1.64706

3.65176

GAGACCAA

NA

No

0.085429

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig1_HTML.jpg
Figure 1

Score-based scatterplots. Shown here are the scatterplots for the scores of all words contained in the bidirectional promoter dataset (a) and the unidirectional promoter dataset (b) of the DNA repair pathways.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig2_HTML.jpg
Figure 2

P-Value-based scatterplots. Scatterplots of the p-values for all words contained in the promoters of the DNA repair pathways exhibiting bi-directionality (a) and uni-directionality (b).

Missing words

The dataset of bidirectional promoters and unidirectional promoters contained 21,076 and 22,101 unique words of length 8, respectively, out of 65,536 unique possibilities. Thus, in each set, more than 43,000 possible words did not occur (See Additional file 3 and Additional file 4 for the complete lists of non-occurring words). The missing words in each set were enumerated, and ranked in descending order by their ES values. The top 25 missing words are shown in Tables 2a and 2b. The scatterplot of the ES values for all missing words is shown in Figure 3; note the outlier values, which correspond with the words in Tables 2a and 2b. The utility of using missing words as regulatory signatures, as reported in the literature [32, 33], was consistent with the observation of no overlapping words between bidirectional and unidirectional promoter sets.
Table 2

Top 25 words not part of promoter sets. The top 25 words that were not discovered as being part of the bidirectional (a) and unidirectional (b) promoter set of DNA-repair pathways. The words are sorted in descending order by the expected sequence occurrence (ES).

(a) Bidirectional

(b) Unidirectional

Word

ES

Word

ES

GCGGCCCG

3.34859

CGCCCCTG

4.12035

GGAGGCGC

2.94738

GGCGGAGG

3.91749

GCCTCTCC

2.84694

AAAGGGGC

3.15484

GCTGAGGA

2.59894

CTGGTCTC

3.14943

GCCGGGGC

2.56699

GCCTGGGC

2.75165

GCGCCTCC

2.56699

GTTTGAAA

2.47933

GCGAGGCG

2.54354

GCGCGAGG

2.25604

AGTGGGGG

2.46473

TTCTTTTC

2.23192

CTGGAGGC

2.45191

ATTCTGGA

2.21123

CGGGGGTG

2.41485

CAGGCAGG

2.17759

GAGGGGAG

2.41485

ATTTTGTT

2.15141

TGCCCGCC

2.39066

CAAAAAAA

2.13045

GCACCCCC

2.23699

AAACCTCA

2.11329

GCCTCTGG

2.23699

TCCCGCCT

2.11329

TGCCTGCG

2.23699

CCCCGCCG

2.05605

GGGCTCGC

2.21328

GAGGAGGC

2.05268

GGCAGGGC

2.18091

AGCACTGG

2.02023

CAGCAAGG

2.1341

TTATCTGC

2.02023

CGAGGCCT

2.12325

CCGCCCCA

1.99873

GAGGGAAG

2.12325

CCCGCCCT

1.94132

GGAGCTGA

2.11348

CTCTTTCT

1.94132

CCTGTCCT

2.10187

GAGAGAGC

1.94132

TCCAGGAC

2.0706

GGCCCAAC

1.94132

CCAGGCCG

2.06039

GTCTGGGC

1.94132

CGCCTGTC

2.06039

TAGGGGGC

1.94132

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig3_HTML.jpg
Figure 3

Scatterplot of words not detected in the promoters. Scatterplots for the expected number of sequence occurrences for every word not detected in the bidirectional (a) or unidirectional (b) promoters.

Word-based clusters

For the top 2 overrepresented words, clusters were created using two different distance metrics, hamming distance and edit distance (Tables 3, 4, 5, 6, See Additional File 5, 6, 7, 8 for the complete lists of hamming distance and edit distance based clusters for bidirectional and unidirectional promoters). Each table contains the set of words that clustered around a given 'seed' word. A comparison of the sequence logos for the hamming-distance-based clusters, presented in (Figures 4, 5), shows no overlap between the two promoter sets. Similarly, no overlap existed for clusters based on edit-distance (Figures 6, 7).
Table 3

Top 2 clusters for the bidirectional promoter. The word-based clusters for the two most overrepresented words for the bidirectional promoters. Rank 1 refers to word TCGCGCCA and Rank 2 to TCCCGGGA.

(a) Rank 1

Word

S

ES

O

EO

Sln(S/ES)

RevComp.

Position

Palindrome

TCGCGCCA

4

0.918299

4

0.9375

5.88611

TGGCGCGA

12538

No

TCGCCCCA

3

0.805161

3

0.820513

3.94598

TGGGGCGA

2834

No

TAGCGCCA

1

0.263929

1

0.266667

1.33207

TGGCGCTA

4918

No

TCGAGCCA

1

0.469775

1

0.47619

0.755501

TGGCTCGA

NA

No

TCGCGACA

1

0.655751

1

0.666667

0.421975

TGTCGCGA

NA

No

TCGGGCCA

1

0.683955

1

0.695652

0.379863

TGGCCCGA

NA

No

TTGCGCCA

1

0.693903

2

0.705882

0.365423

TGGCGCAA

NA

No

TCGCGGCA

1

0.826074

1

0.842105

0.191071

TGCCGCGA

NA

No

TCGCGTCA

1

0.84063

1

0.857143

0.173604

TGACGCGA

4051

No

TCGCGCCC

1

1.51582

1

1.5625

-0.41596

GGGCGCGA

13089

No

CCGCGCCA

2

2.5054

2

2.625

-0.4506

TGGCGCGG

NA

No

(b) Rank 2

Word

S

ES

O

EO

Sln(S/ES)

RevComp.

Position

Palindrome

TCCCGGGA

8

3.97165

8

4.26667

5.60208

TCCCGGGA

2

Yes

TCCAGGGA

2

0.941495

2

0.961538

1.50687

TCCCTGGA

NA

No

TCCCGAGA

2

1.05556

2

1.08

1.27816

TCTCGGGA

13248

No

TGCCGGGA

1

0.514348

1

0.521739

0.664856

TCCCGGCA

NA

No

TCCCGTGA

1

0.702073

1

0.714286

0.353718

TCACGGGA

NA

No

TCCCAGGA

4

3.71413

5

3.97222

0.296597

TCCTGGGA

19059

No

TCTCGGGA

2

1.73986

2

1.8

0.278683

TCCCGAGA

3074

No

ACCCGGGA

1

0.785281

1

0.8

0.241714

TCCCGGGT

20941

No

TCCCCGGA

1

0.852649

1

0.869565

0.159407

TCCGGGGA

NA

No

TCCCGCGA

1

1.01424

1

1.03704

-0.01414

TCGCGGGA

NA

No

TCCCGGAA

3

3.29619

3

3.5

-0.28247

TTCCGGGA

NA

No

TCCTGGGA

1

1.32696

1

1.36364

-0.28289

TCCCAGGA

13129

No

TCCCGGGG

3

3.34568

3

3.55556

-0.32717

CCCCGGGA

21071

No

TCCCGGGT

1

2.38044

1

2.48889

-0.86729

ACCCGGGA

13746

No

CCCCGGGA

1

2.78651

1

2.93333

-1.02479

TCCCGGGG

19211

No

GCCCGGGA

1

3.73853

2

4

-1.31869

TCCCGGGC

21163

No

TCCCGGGC

3

5.1829

4

5.68889

-1.64025

GCCCGGGA

21138

No

Table 4

Top 2 clusters for the unidirectional promoter. The word-based clusters for the two most overrepresented words for the bidirectional promoters. Rank 1 refers to word ACCCGCCT and Rank 2 to CTTCTTTC.

(a) Rank 1

Word

S

ES

O

EO

Sln(S/ES)

RevComp.

Position

Palindrome

ACCCGCCT

4

0.716577

4

0.727273

6.87826

AGGCGGGT

19440

No

ATCCGCCT

1

0.132296

1

0.133333

2.02271

AGGCGGAT

NA

No

ACCAGCCT

2

0.738772

2

0.75

1.99183

AGGCTGGT

1303

No

AGCCGCCT

1

0.657331

1

0.666667

0.419567

AGGCGGCT

1056

No

ACCCACCT

1

0.738772

1

0.75

0.302766

AGGTGGGT

NA

No

ACGCGCCT

1

1.16147

1

1.18519

-0.14969

AGGCGCGT

NA

No

CCCCGCCT

1

2.45503

2

2.54545

-0.89814

AGGCGGGG

21912

No

(b) Rank 2

Word

S

ES

O

EO

Sln(S/ES)

RevComp.

Position

Palindrome

CTTCTTTC

5

1.7686

5

1.81818

5.19624

GAAAGAAG

13567

No

CTACTTTC

1

0.180301

1

0.181818

1.71313

GAAAGTAG

NA

No

CTTCTTCC

1

0.304671

1

0.307692

1.18852

GGAAGAAG

5306

No

CTGCTTTC

2

1.15305

2

1.17647

1.10147

GAAAGCAG

9703

No

CGTCTTTC

1

0.371023

1

0.375

0.991491

GAAAGACG

20167

No

CTCCTTTC

3

2.36561

3

2.45

0.712729

GAAAGGAG

11346

No

CTTCTATC

1

0.607134

1

0.615385

0.499005

GATAGAAG

NA

No

CTTCCTTC

1

0.921427

1

0.9375

0.0818318

GAAGGAAG

10908

No

GTTCTTTC

1

1.07027

1

1.09091

-0.067912

GAAAGAAC

17502

No

CTTTTTTC

1

1.2055

1

1.23077

-0.186894

GAAAAAAG

NA

No

TTTCTTTC

2

3.4628

2

3.63636

-1.09786

GAAAGAAA

NA

No

Table 5

Edit cluster for bidirectional promoters. The word-based clusters for the two most overrepresented words for the bidirectional promoters according to the edit distance metric. Rank 1 refers to word TCGCGCCA and Rank 2 to TCCCGGGA.

(a) Rank 1

Word

S

ES

O

EO

Sln(S/ES)

RevComp.

Position

Palindrome

TCGCGCCA

4

0.918299

4

0.9375

5.88611

TGGCGCGA

12538

No

TCGCCCCA

3

0.805161

3

0.820513

3.94598

TGGGGCGA

2834

No

TAGCTCCA

2

0.352982

2

0.357143

3.46897

TGGAGCTA

NA

No

TCTCGCGA

2

0.438673

2

0.444444

3.0343

TCGCGAGA

4937

No

TCGCCACA

2

0.455424

2

0.461538

2.95935

TGTGGCGA

4669

No

...

        

(b) Rank 2

Word

S

ES

O

EO

Sln(S/ES)

RevComp.

Position

Palindrome

TCCCGGGA

8

3.97165

8

4.26667

5.60208

TCCCGGGA

2

Yes

TCCCGGCT

6

2.54354

6

2.66667

5.14921

AGCCGGGA

NA

No

ATCCGGGA

2

0.395077

2

0.4

3.24364

TCCCGGAT

NA

No

TCTCGCGA

2

0.438673

2

0.444444

3.0343

TCGCGAGA

4937

No

TTCCTGGA

2

0.493082

2

0.5

2.80045

TCCAGGAA

9505

No

...

        
Table 6

Edit cluster for unidirectional promoters. The word-based clusters for the two most overrepresented words for the unidirectional promoters according to the edit distance metric. Rank 1 refers to word ACCCGCCT and Rank 2 to CTTCTTTC.

(a) Rank 1

Word

S

ES

O

EO

Sln(S/ES)

Rev.Comp.

Position

Palindrome

ACCCGCCT

4

0.716577

4

0.727273

6.87826

AGGCGGGT

19440

No

AGCCGGCT

3

0.805285

3

0.818182

3.94551

AGCCGGCT

14

Yes

AGGCGCCT

3

1.11427

3

1.13636

2.97124

AGGCGCCT

92

Yes

AAGCGCCT

4

2.15617

4

2.22727

2.47184

AGGCGCTT

5872

No

ACCTGCAT

2

0.592063

2

0.6

2.43458

ATGCAGGT

NA

No

...

        

(b) Rank 2

Word

S

ES

O

EO

Sln(S/ES)

Rev.Comp.

Position

Palindrome

CTTCTTTC

5

1.7686

5

1.81818

5.19624

GAAAGAAG

13567

No

TCTTCTTC

4

1.30438

4

1.33333

4.48225

GAAGAAGA

NA

No

CCTCTTTA

2

0.282982

2

0.285714

3.91104

TAAAGAGG

NA

No

CTTTTTCA

3

0.917377

3

0.933333

3.55455

TGAAAAAG

NA

No

GTTCATTC

2

0.359828

2

0.363636

3.43055

GAATGAAC

NA

No

...

        
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig4_HTML.jpg
Figure 4

Sequence logos for bidirectional promoters. Sequence logos corresponding to the word-based clusters of the top 2 overrepresented words of the bidirectional promoters. Rank 1 (a) is corresponding to the word TCGCGCCA, while Rank 2 (b) refers to TCCCGGGA.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig5_HTML.jpg
Figure 5

Sequence logo for unidirectional promoters. Sequence logos corresponding to the word-based clusters of the top 2 overrepresented words of the unidirectional promoters. Rank 1 (a) is corresponding to the word ACCCGCCT, while Rank 2 (b) refers to CTTCTTTC.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig6_HTML.jpg
Figure 6

Edit distance cluster for bidirectional promoters. Sequence alignments corresponding to the word-based clusters of the top 2 overrepresented words of the bidirectional promoters. For each cluster, five words were chosen based on their overall overrepresentation in the promoter set. Rank 1 (a) is corresponding to the word TCGCGCCA, while Rank 2 (b) refers to TCCCGGGA.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig7_HTML.jpg
Figure 7

Edit distance cluster for unidirectional promoters. Sequence logos corresponding to the word-based clusters of the top 2 overrepresented words of the unidirectional promoters. Rank 1 (a) is corresponding to the word ACCCGCCT, while Rank 2 (b) refers to CTTCTTTC.

Sequence-based clusters

Sequences can be clustered and categorized into different families (and subfamilies). The sequence-based clusters presented here are restricted to two promoters per cluster. Sequence clustering is a measure of the co-existence of statistically overrepresented words shared between pairs of promoters as shown in Tables 7a,b. Each cluster contains IDs for the sequences that make up the cluster and the number of overrepresented words not shared within the cluster (distance). Sequences in each set were grouped into clusters based on the set of statistically overrepresented words. The shared words for the top-scoring sequence cluster of each data set were illustrated using the GBrowse environment [34] (Figures 8, 9). The visualization shows a strong positional correlation between the sequences of the top sequence cluster for the bidirectional promoters (Word: GCCCAGCC) and minor correlation between the sequences for the unidirectional promoters (Words: AGCAGGGC, GCAGGGCG).
Table 7

Sequence clusters (pairs of sequences). Sequence clusters containing pairs of sequences for the bidirectional (a) and unidirectional (b) promoter sets. Each sequence occurs in only one cluster. The sequences are clustered based on the number of words (within the top 60 overrepresented words) that are shared between them with the distance denoting the number of words not shared between them.

(a) Bidirectional

(b) Unidirectional

Sequence 1

Sequence 2

Distance

Sequence 1

Sequence 2

Distance

chr3:185561446–185562546

chr11:832429–833529

54

chr10:50416978–50418078

chr3:188006884–188007984

57

chr19:53365272–53366372

chr19:7600339–7601439

55

chr12:52868924–52870024

chr7:73306574–73307674

57

chr11:18299718–18300818

chr15:41589928–41591028

56

chr5:68890824–68891924

chr19:55578407–55579507

58

chr4:57538069–57539168

chr19:48776246–48777346

56

chr6:30982955–30984055

chr9:99499360–99500460

58

chr11:107598052–107599152

chr12:131773918–131775018

56

chr10:131154509–131155609

chr19:50618917–50620017

58

chr13:107668425–107669525

chr1:11674165–11675265

57

chr5:86744492–86745592

chr17:30330654–30331754

58

chr6:43650922–43652022

chr16:2037768–2038868

57

chr11:118471287–118472387

chr8:55097461–55098561

58

chr22:36678663–36679763

chr11:61315725–61316825

58

chr16:13920523–13921623

chr8:101231014–101232114

58

chr5:60276548–60277648

chr22:40346240–40347340

58

chr5:131919528–131920628

chr19:1046236–1047336

58

chr11:93866588–93867688

chr3:130641442–130642542

58

chr12:108015528–108016628

chr16:56053079–56054179

59

chr17:7327421–7328521

chr17:1679094–1680194

58

chr1:28113723–28114823

chr2:216681376–216682476

59

chr20:5055168–5056268

chr15:38773660–38774760

58

chr8:91065972–91067072

chr4:39044247–39045347

59

chr14:19992129–19993229

chr11:66877493–66878593

59

chr14:60270222–60271322

chr11:47192088–47193188

59

chr17:38530557–38531657

chr13:31786616–31787716

59

chr7:7724663–7725763

chr11:62284590–62285690

59

chr12:122683333–122684433

  

chr13:33289233–33290333

chr12:116937892–116938992

59

chr5:82408167–82409267

  

chr9:109084364–109085464

chr7:101906286–101907386

59

chr2:127768122–127769222

  

chr8:42314186–42315286

chr19:50565569–50566669

59

chr12:102882746–102883846

  

chr3:9764704–9765804

chr14:49224583–49225683

59

   

chr13:102295174–102296274

chr6:30790834–30791934

59

   

chr12:912403–913503

  
   

chr2:128332074–128333174

  
   

chr7:44129555–44130655

  
   

chr11:73980276–73981376

  
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig8_HTML.jpg
Figure 8

GBrowse visualization for primary bidirectional sequence cluster. The GBrowse visualization of the two sequences for the top sequence-based cluster in the bidirectional promoter set. Shown are the words from the set of top 60 words that are detected in these two sequences.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig9_HTML.jpg
Figure 9

GBrowse visualization for primary unidirectional sequence cluster. The GBrowse visualization of the two sequences for the top sequence-based cluster in the unidirectional promoter set. Shown are the words from the set of top 60 words that are detected in these two sequences.

Word co-occurrence

The promoter sets were characterized further by word co-occurrence analysis, in which word-pairs that appeared together more frequently than expected were identified. Interesting pairs of words were selected from the overrepresented words of Table 1 (Table 8a,b). Each word pair was characterized as the number of observed or expected occurrences for the word combination (S or E S ) and a statistical overrepresentation score https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq1_HTML.gif . No overlap was found between the bidirectional and the unidirectional set, nevertheless, the word pairs for the bidirectional promoter set achieved a higher number of sequence hits for the pairs.
Table 8

Word co-occurrence. The top 25 word pairs for the bidirectional (a) and unidirectional (b) promoter set. The word pairs are sorted in descending order by S*ln(S/ES) score.

(a) Bidirectional

(a) Bidirectional

(b) Unidirectional

Word 1

Word 2

S

ES

Sln(S/ES)

Word 1

Word 2

S

ES

Sln(S/ES)

TCTGAGGA

TCGCGCCA

3

0.0529

12.1158

GTTCATTC

TCCGCCGG

2

0.0073

11.2184

ACTCCAGC

TCGCGCCA

3

0.0580

11.8387

CTGTGTGC

TGCGCCGA

2

0.0074

11.1966

GCCCAGCC

TCCGCCGC

3

0.0722

11.1827

TGACGCGA

CTCCCGCT

2

0.0082

10.9997

GCCCAGCC

CGGAGCGC

2

0.0087

10.8711

AGCCGGCT

GGGGAGTA

2

0.0131

10.0590

TGCCCGCG

TCCCGGGA

4

0.2729

10.7404

ATTGCAGG

ATTCTCTC

2

0.0169

9.5459

GGCAGGGA

GGGCCAGG

4

0.3400

9.8609

GGGGAGTA

AGGAAACA

2

0.0190

9.3177

TCCCGGGA

TCGCGCCA

3

0.1140

9.8112

CTGGGAGC

GTTCATTC

2

0.0218

9.0337

AGCCTGTC

TCCCGGGA

3

0.1158

9.7646

CCTTCCGA

CTGGGAGC

2

0.0240

8.8439

GGAGGCTG

TCGCGCCA

3

0.1173

9.7250

TGGGCGGA

ACCCGCCT

2

0.0247

8.7895

TCCGCCGC

GCCCCTCC

4

0.3554

9.6830

TTTCTCCA

CGGAAACC

2

0.0265

8.6446

AGAAAAGA

TCGCGCCA

2

0.0182

9.4042

CCCCCGCG

ACCCGCCT

2

0.0280

8.5339

GCCCAGCC

GCCCCTCC

3

0.1360

9.2808

TCCGCCGG

GGGGCTGC

2

0.0415

7.7522

TGCCAAAA

GCCGGCGA

2

0.0195

9.2604

AGCTGGCT

CCAGGCTG

2

0.0422

7.7192

CAGCAGCC

TGCGGAAT

2

0.0208

9.1297

TTGGTCTC

AGGAAACA

2

0.0446

7.6068

AGGGCCGT

TCCCGGCT

3

0.1433

9.1249

CTGGGAGC

TCCGCCGG

2

0.0519

7.3020

CCTCCAGA

TTCCACCC

2

0.0216

9.0521

CTTTTCTC

GCGCCGCG

2

0.0545

7.2046

CGAGGAGA

TCGCGCCA

2

0.0220

9.0204

ATTGCAGG

ATTAAAAT

2

0.0585

7.0639

TCCGCCGC

CGGAGCGC

2

0.0228

8.9501

TGGAACCC

GCAGGGCG

2

0.0645

6.8693

ACCCTCGT

AGGGAGGG

2

0.0253

8.7380

GGGCAGGC

AGCTGGCT

2

0.0657

6.8326

GCCCAGCC

TCCACTGT

2

0.0254

8.7315

TTGGTCTC

CTTCTTTC

2

0.0676

6.7745

CAGCAGCC

AGGGCCGT

3

0.1705

8.6024

CTTTTTCA

CGCCCCTT

2

0.0684

6.7522

TGCCCGCG

TCCCGGCT

3

0.1747

8.5291

GCAGGGCG

AGGAAACA

2

0.0766

6.5251

CCCAGGAC

AGAGAGCT

2

0.0291

8.4590

GGGCAGGC

TTTCTCCA

2

0.0939

6.1181

TCTGGGAT

GGCCCGCC

2

0.0329

8.2123

CTGGGAGC

TCTCCCCT

2

0.0947

6.0996

AGCCGGGC

AGAAAAGA

2

0.0333

8.1930

AGCAGGGC

GGCTTTTA

2

0.0956

6.0805

Comparison of word-based properties

The distances between the scores for different word sets (Figure 10) provided a basis for discriminating among bidirectional promoters and unidirectional promoters, (Table 9 and Figure 11), whereas similarities were identified from correlated words (Table 10 and Figure 12). These tables and figures show that word-based genomic regulatory signatures can be used to describe promoter sets based on their uniqueness.
Table 9

Unique and interesting words for the promoter sets. The words for the unidirectional and bidirectional promoter set which exhibit a significant score-based distance to the other data set.

(a) Unidirectional

(b) Bidirectional

Word

Unidirectional

Bidirectional

Distance

Word

Unidirectional

Bidirectional

Distance

ACCCGCCT

6.87826

-0.0263597

4.882303411

TCCCGGGA

-0.0850495

5.60208

-4.021407835

GGGGCTGC

4.49226

-1.0872000

3.945274001

GGCCCGCC

0

5.36123

-3.790962089

CGGCGGCG

4.01229

-1.3139900

3.766248706

CGCGGCCG

-0.3641650

4.91487

-3.732841447

AGGAAACA

4.92885

0.1254760

3.396498328

TCCCGGCT

0

5.14921

-3.641041309

CTTCTTTC

5.19624

0.4219750

3.375915157

CAGGGGCC

0

5.13315

-3.629685174

TCCGCCGG

3.76460

-0.8986470

3.297413576

AGGGCCGT

0

5.10145

-3.607269889

TCTTCTTC

4.48225

0

3.169429370

TCTGAGGA

0

4.99234

-3.530117468

ATTAAAAT

4.29023

0

3.033650726

CGTGGGGG

0.0180292

4.92572

-3.470261445

GGGGAGTA

4.44222

0.3737000

2.876878081

TCTGGGAT

0

4.81380

-3.403870623

CGCCCCTT

3.90482

-0.1463740

2.864626749

AGGGAGGG

0

4.72230

-3.339170353

TTTTTTGA

4.01229

0

2.837117467

AGAAAAGA

0

4.66976

-3.302018963

TTTCTCCA

3.96242

0

2.801854052

GGGCCAGG

0

4.62990

-3.273833686

AGCCGGCT

3.94551

0

2.789896876

ACTCCAGC

0

4.53045

-3.203511917

TTGGTCTC

3.65176

-0.2608830

2.766656398

CCCCAGCT

-0.9904730

3.48143

-3.162112936

GCGCCGCG

3.81433

0

2.697138609

CGGGCCGA

0

4.45426

-3.149637451

ATTCCCAG

3.80733

0

2.692188861

TCCGCCGC

-0.8886350

3.55395

-3.141381979

GCAGGGCG

4.66535

0.8645290

2.687586303

TGCCCGCG

-0.3137370

4.10844

-3.126951344

GAGGGGCG

3.03108

-0.7557900

2.677721456

TGCGGAAT

0

4.41371

-3.120964271

CCCCCGCG

3.55664

-0.1908410

2.649869227

GCCGGCGA

0

4.33335

-3.064141170

AGGGGAGC

3.15866

-0.5635770

2.632019024

CAGCAGCC

-0.0679120

4.10418

-2.950114545

TGCGCCGA

3.68519

0

2.605822839

CGAGGAGA

0

4.09415

-2.895001228

CCGCGCCC

2.25420

-1.4189300

2.597295131

CGCAGGCG

-0.2779570

3.74626

-2.845551130

GTGCGTTT

3.66247

0

2.589757373

TTCCACCC

0

4.02098

-2.843262225

CTGGGAGC

3.36673

-0.2940760

2.588580747

TCGCCCCA

0

3.94598

-2.790229216

TGCCTCCC

3.34992

-0.2629130

2.554658714

GGGGCCGG

0.8548330

4.76672

-2.766121825

Table 10

Descriptive words for both the unidirectional and bidirectional promoter sets. The top 25 words that are correlated in the two promoter sets, according to their overrepresentation scores. The Words had to be overrepresented according to SlnSES with at least a score of 1.5. Shown are the words with a distance between -0.11 and 0.11.

Word

Unidirectional

Bidirectional

Distance

CTTTGGCC

2.08857

2.23024

-0.100175818

AGGCAGGA

1.51526

1.64780

-0.093719933

CTCAGGAT

1.58527

1.71375

-0.090849079

GGGGGGAC

1.61803

1.70814

-0.063717392

CTTGCGGA

1.65530

1.73350

-0.055295750

CTGAGCAG

1.99183

2.05890

-0.047425652

GCCTGAGG

1.99183

2.04796

-0.039689904

TGAAGTGG

1.61803

1.66175

-0.030914708

GCCATCCG

1.86393

1.89589

-0.022599133

AGGTTGCA

2.20477

2.23024

-0.018010010

TCTGTGCC

1.84096

1.85915

-0.012862272

TACCACTA

1.86393

1.88037

-0.011624835

CAAAGAAT

1.61803

1.61872

-0.000487904

ACCGCTCA

1.61803

1.61872

-0.000487904

TATCTTAG

1.61803

1.61872

-0.000487904

AGAGTTCC

1.62605

1.61872

0.005183093

GTCGGCTT

1.90512

1.88037

0.017500893

CGCGCGCA

1.94164

1.90263

0.027584236

CAGGCCAG

1.95383

1.86972

0.059474751

ACAGAAAG

2.79686

2.70295

0.066404398

GTCAGGAG

2.40520

2.25776

0.104255824

GGAAGTGA

1.96108

1.81095

0.106157941

TAGAGAGC

1.99183

1.84125

0.106476139

TGCCAGGG

1.75813

1.60511

0.108201480

GCACAAGC

1.95383

1.80053

0.108399470

TTCACTTA

2.15055

1.99725

0.108399470

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig10_HTML.jpg
Figure 10

Comparison analysis: plot for complete set of words. Comparison of the words detected for the two promoter sets based on their computed overrepresentation scores.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig11_HTML.jpg
Figure 11

Comparison analysis: plot for distinctive words. The words descriptive of the unidirectional promoter set (red) and the bidirectional promoter set (green). Words that are not sufficiently descriptive of either data set are eliminated from the plot.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Fig12_HTML.jpg
Figure 12

Comparison analysis: plot for general words. The words that are significantly correlated in both data sets.

Regulatory Database Lookup

We developed a method [35] to determine if these signatures matched any known motifs from TRANSFAC or JASPAR (Table 11). The words from bidirectional promoters matched known motifs in 8/10 cases, with the words from unidirectional promoters matching known motifs in 8/10 cases as well. Compared to the consensus sequences of the known motifs, the matches were off by no more than one letter. Some of the matches corresponded to nucleotide profiles determined from collections of phylogenetically conserved, cis-acting regulatory elements [36]. Imperfect matches resulted from bases that flanked the core motifs (Table 11a, b) (see also [37]). Such events decreased the detection score to slightly above the threshold of 85% similarity. Overall, the findings in Table 11 validate that the signatures have biological relevance and suggest that the remaining signatures, which do not match known motifs could represent novel binding sites.
Table 11

Lookup results for interesting words in the promoters. Information about the regulatory function of the top 10 overrepresented words for the bidirectional and unidirectional promoter set based on lookups in the TRANSFAC and JASPAR databases.

(a) Bidirectional

Sequence

Transcription Factor (Matrix Ida)

Sequence (bottom) aligned to matrix consensusb

Matchesc

Avg. Scored

Score Rangee

TCGCGCCA

PF0112f

KTGGCGGGAA

 TGGCGCGA 

4/6

89.0

86.5–96.8

TCCCGGGA

STAT5A

TTCYNRGAA

TCCCGGGA 

8/16

86.7

86.7–86.7

GGCCCGCC

SP1 (V$SP1_01)

DRGGCRKGSW

  GGCGGGCC

8/13

90.2

86.5–90.8

TCCCGGCT

ELK1 (MA0028)

NNNMCGGAAR

 AGCCGGGA 

3/6

86.9

86.5–87.7

CAGGGGCC

V$WT1_Q6

SVCHCCBVC

GGCCCCTG 

5/6

87.4

85.0–91.1

AGGGCCGT

MYB (V$MYB_Q3)

NNNBNCMGTTN

 AGGGCCGT  

2/7

91.2

89.8–92.6

TCTGAGGA

TFIIA (V$TFIIA_Q6)

TMTDHRAGGRVS

  TCTGAGGA  

2/8

88.1

85.8–90.5

CGTGGGGG

E2F (V$E2F1_Q3)

  BKTSSCGS

CGTGGGGG  

6/6

87.3

87.3–87.3

TGCTGAGA

No match.

    

CGCGGCCG

No match.

    

(b) Unidirectional

Sequence

Transcription Factor (Matrix Ida)

Sequence (bottom) aligned to matrix consensusb

Matchesc

Avg. Scored

Score Rangee

ACCCGCCT

SP1 (V$SP1_01)

DRGGCRKGSW

 AGGCGGGT 

4/7

86.2

85.9–87.3

CTTCTTTC

No match.

    

AGGAAACA

NFAT (V$NFAT_Q4_01)

NWGGAAANWB

 AGGAAACA 

5/5

87.3

85.8–88.1

GCAGGGCG

PF0096f

YGCANTGCR

 GCAGGGCG

10/10

86.8

86.5–87.1

GGGGCTGC

LRF (V$LRF_Q2)

 VDVRMCCCC

GCAGCCCC  

5/8

85.4

85.4–85.4

TCTTCTTC

No match.

    

GGGGAGTA

FOXC1 (MA0032)

NNNVNGTA

GGGGAGTA

4/4

95.5

95.5–95.5

ATTAAAAT

OCT1 ($OCT1_06)

MWNMWTKWSATRYN

   ATTTTAAT   

4/9

86.9

86.5–87.5

CGGAAACC

AREB6 (V$AREB6_04)

VBGTTTSNN

 GGTTTCCG

3/3

92.2

88.3–95.8

TGGGCGGA

GC (V$GC_01)

NNDGGGYGGRGYBD

  TGGGCGGA    

4/5

90.3

85.1–95.2

a. JASPAR id or TRANSFAC id.

b. The consensus is in IUPAC notation: R = G or A, Y = T or C, M = A or C, H = not G, K = G or T, W = A or T, B = not A, S = G or C, V = not T, N = anything.

c. Number of occurrences of the matrix that scored greater than 85% in the dataset.

d. Average score for the occurrences meeting the 85% threshold.

e. Range of scores for the occurrences meeting the 85% threshold.

f. A profile that was extracted from phylogenetically conserved gene upstream elements.

Conservation analysis

To address selective constraint in the word sets, sequence conservation was examined for pairs of co-occurring words. The top ten word-pairs from the unidirectional and bidirectional datasets were examined in 28-way sequence alignments using the PhastCons [38] dataset in the UCSC Human Genome Browser [39]. The results are presented in Table 12. The bidirectional promoters revealed 9/10 word sets had a record of sequence conservation in one or both words (Table 12a). The analysis of the unidirectional promoters, presented in Table 12b, showed partial conservation in only one of the word-pairs.
Table 12

Conservation analysis. The results for conservation analysis of the top 10 word pairs in the bidirectional (a) and unidirectional (b) promoter set. For each word pair, the occurrence location of the pair is given, as well as an identifier for the conservation of the sites, and a PhastCons score for the quality of the conservation across 28 organisms. Conservation can be categorized as: none (no word was conserved), partial (one word was conserved) and complete (all words were conserved).

(a) Bidirectional

Word 1

Word 2

Location

Conservation

Hit

Score

TCTGAGGA

TCGCGCCA

chr19:53365272–53366372

None

  
  

chr19:48776246–48777346

None

  
  

chr19:7600339–7601439

Partial

TCGCGCCA

385

ACTCCAGC

TCGCGCCA

chr4:57538069–57539168

None

  
  

chr19:48776246–48777346

None

  
  

chr19:7600339–7601439

Partial

TCGCGCCA

385

GCCCAGCC

TCCGCCGC

chr3:185561446–185562546

Partial

TCCGCCGC

310

  

chr14:19992129–19993229

None

  
  

chr11:832429–833529

None

  

GCCCAGCC

CGGAGCGC

chr3:185561446–185562546

None

  
  

chr14:19992129–19993229

None

  

TGCCCGCG

TCCCGGGA

chr19:53365272–53366372

Partial

TCCCGGGA

390

  

chr13:107668425–107669525

None

  
  

chr20:5055168–5056268

None

  
  

chr11:832429–833529

None

  

GGCAGGGA

GGGCCAGG

chr19:53365272–53366372

Partial

GGGCCAGG

390

  

chr22:40346240–40347340

Complete

GGCAGGGA

325

    

GGGCCAGG

522

  

chr5:60276548–60277648

None

  
  

chr12:131773918–131775018

None

  

TCCCGGGA

TCGCGCCA

chr19:53365272–53366372

Partial

TCCCGGGA

390

  

chr4:57538069–57539168

None

  
  

chr19:7600339–7601439

Partial

TCGCGCCA

385

AGCCTGTC

TCCCGGGA

chr17:38530557–38531657

None

  
  

chr13:107668425–107669525

Partial

AGCCTGTC

244

  

chr4:57538069–57539168

None

  

GGAGGCTG

TCGCGCCA

chr4:57538069–57539168

None

  
  

chr19:48776246–48777346

None

  
  

chr19:7600339–7601439

Partial

TCGCGCCA

385

TCCGCCGC

GCCCCTCC

chr3:185561446–185562546

Partial

TCCGCCGC

310

  

chr14:19992129–19993229

None

  
  

chr1:11674165–11675265

Partial

GCCCCTCC

360

  

chr11:832429–833529

None

  

(b) Unidirectional

Word 1

Word 2

Location

Conservation

Hit

Score

GTTCATTC

TCCGCCGG

chr7:73306574–73307674

None

  
  

chr12:52868924–52870024

Partial

TCCGCCGG

325

CTGTGTGC

TGCGCCGA

chr10:131154509–131155609

None

  
  

chr19:1046236–1047336

None

  

TGACGCGA

CTCCCGCT

chr12:116937892–116938992

None

  
  

chr17:30330654–30331754

None

  

AGCCGGCT

GGGGAGTA

chr6:30982955–30984055

None

  
  

chr16:13920523–13921623

None

  

ATTGCAGG

ATTCTCTC

chr5:86744492–86745592

None

  
  

chr17:30330654–30331754

None

  

GGGGAGTA

AGGAAACA

chr16:13920523–13921623

None

  
  

chr8:101231014–101232114

None

  

CTGGGAGC

GTTCATTC

chr7:73306574–73307674

None

  
  

chr12:52868924–52870024

None

  

CCTTCCGA

CTGGGAGC

chr5:68890824–68891924

None

  
  

chr7:73306574–73307674

None

  

TGGGCGGA

ACCCGCCT

chr6:30982955–30984055

None

  
  

chr9:99499360–99500460

None

  

TTTCTCCA

CGGAAACC

chr8:55097461–55098561

None

  
  

chr11:118471287–118472387

None

  

Biological implications

The words in the list of bidirectional promoters were examined for known biological evidence. For instance, the gene POLH has a known binding motif, TCCCGGGA, annotated as a PAX-6 binding site in the cis-RED database http://​www.​cisred.​org/​. This is the same sequence as the second most common word in the bidirectional promoters. Along with sequences that cluster with this word, we found that 19/32 genes in the bidirectional promoter set had a match to this word cluster (cluster 2) within 1 kb of their TSS, while 15/32 bidirectional promoters had a match to the words of cluster 1. Furthermore, this word also represents a Stat5A recognition site (Table 11). The RAD51 gene, which is known to be regulated by STAT5A, showed two examples from this word cluster (TGCCGGGA and TCCCGGGC).

Limitations of the approach

The presented approach does not attempt to automate the process of finding a small set of regulatory elements for a limited set of related genomic signatures like MEME [40] or AlignACE [41]. The different approach presented here produces more detailed information outside of the limited list by showing a larger (complete) set of words that are ranked based on their statistical significance. Additionally, word- and sequence-based clusters, word co-occurrences and functional significance of the words have been computed as a means of adding more detail to the retrieval of putative elements allowing a more informed interpretation of the actual regulatory function of a word.

Conclusion

This paper presents a word-based genomic signature that characterizes a set of sequences with (1) statistically overrepresented words, (2) missing words, (3) word-based clusters, (4) sequence-based clusters and (5) co-occurring words. The word-based signatures of bidirectional and unidirectional promoters of human DNA repair pathways showed virtually no overlap, thereby demonstrating the signature's utility.

In addition to providing an effective characterization method for related DNA sequences, the signatures elucidate putative regulatory aspects of DNA repair pathways. Genes in DNA repair pathways contribute to diverse functions such as sensing DNA damage and transducing the signal, participating in DNA repair pathways, cell cycle signalling, and purine and pyrimidine metabolism. The synchronization of these functions implies co-regulatory relationships of the promoters of these genes to ensure the adequate production of all the necessary components in the pathway. We present a subtle, yet detectable signature for bidirectional promoters of DNA repair genes. The consensus patterns, detected as words and related clusters of words, provide a DNA pattern that is strongly represented in these promoters. Although the proteins that bind these sequences must be examined experimentally, the data show that a protein such as STAT5A could be involved in regulating many of these promoters. STAT5A has biological relevance in DNA repair pathways, playing a known role in the regulation of the RAD51 gene. We propose that this initial study of a network of DNA repair genes serve as a model for studies that examine regulatory networks. As the relationships among genes involved in DNA repair pathways are elucidated more thoroughly, the analyses of their regulatory relationships will gain more power to detect a larger number of DNA words that are shared in common among the network of genes. The results of this analysis are supported by evidence of sequence conservation and overlap between predicted sites and known functional elements.

Methods

Two fundamental elements of word-based genomic signatures are created with the approach presented in [42, 43]. SIG1 identifies the set of statistically overrepresented words, while SIG2 represents a set of words from SIG1 that is in itself similar to the elements of SIG1, based on a specific distance measure.

The set SIG1 is computed as described in [42, 43], which is summarized as follows:
  1. 1.

    Identify maximally repeated words of length [m, n].

     
  2. 2.

    Remove low complexity words, redundant words, and words that are contained in repeat elements.

     
  3. 3.

    For each word compute a 'score' that characterizes the statistical overrepresentation of the word.

     
  4. 4.

    Select the words with the highest scores.

     
The set SIG2 is found by taking each of the elements of SIG1 and performing 'word clustering'. For each word w SIG1, this involves a two-step process:
  1. 1.

    Construct a set (cluster) of words from RGS that have a 'distance' of no more than h from word w. Hamming distance and edit distance are used for this step.

     
  2. 2.

    Construct a motif that characterizes the set of words found in step 1.

     

Word-based signature (SIG1)

As the foundation of the signature generation it is necessary to compute the set of distinct words W wc in a set of input sequences S. In order to determine the statistical significance of w W wc it is necessary to count the total number of occurrences of a given word w j , o j , as well as the number of sequences containing the word, s j . The occurrence information is modelled as a set of tuples https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq2_HTML.gif . Assuming a binomial model for the distribution of words across the input sequences, it is possible to model the total occurrence of a word w by introducing the random variable https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq3_HTML.gif , where l is the complete sequence length, v the length of w, and Y i a binary random variable indicating if a word occurs at position i, or not, leading to the series of yes/no Bernoulli experiments. An expected value for the specific number of occurrences for a word w can then be computed as https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq4_HTML.gif where p w is the probability of word w. Following a similar modelling approach, the expected number of sequences a word occurs in is given by https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq5_HTML.gif . The actual probabilities are determined by a homogenous Markov chain model of a specific order m. Based on the expected values we compute multiple scores for each word:

  • https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq6_HTML.gif : This scoring function, called SlnSES, enables the inclusion of sequence coverage into the score. A highly scored word occurs in a large percentage of sequences in the data set. It does not necessarily have to be highly significant if the overall number of occurrences is taken into account, but it is of particular use for the discovery of shared regulatory elements across multiple sequences.

  • p-Value: The p-value is defined as the probability of obtaining at least as many words as the actual observed number of words: https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq7_HTML.gif , where |S| represents the number of sequences in S and l j is the length of sequence j.

Word-based clusters (SIG2)

Two methods are employed for the detection of similarities between the words that make up SIG1: hamming distance and Levenshtein distance (also called edit distance). While hamming distance is defined as the number of positions for which the corresponding characters of two words of the same length differ, edit distance allows the comparison of different length words and accounts for three edit operations (insert, delete and substitute), rather than the plain mismatch (corresponds to substitute) employed by the hamming distance.

The biological reasoning for employing distance metrics in order to group similar words together can be found in the evolution of sequences. A biological structure is constantly exposed to mutation pressure. These mutations can occur as insertions, deletions or substitutions, however insertions and deletions are deleterious in most cases, leading to the issue that edit distance provides a very detailed model of the mutations but hamming distance is a reasonable abstraction and will work well for this case. The motif logos for the hamming distance clusters were constructed using the TFBS Perl module by Lenhard and Wasserman [44]. ClustalW2 [45] was used to align the words of the edit distance clusters.

Sequence clustering

The sequence clustering conducted in this research is focussed on the words shared between element of a set of sequences. A set of words is taken as the input for the clustering. A binary vector s i = (si,1, si,2,..., si, k) for each sequence s i is created, marking an element si, kwhere k is the number of words used to distinguish the sequences with k ≤ |W wc |. The element si, kof the vector is populated with a '1' if the word k is found in sequence i, and '0' if it is not. The similarity between sequences is determined by the dot product between the binary sequence vectors, and is deducted from the complete number of words in the vector space. In order to determine the distance between k sequences (with k ≥ 2), the dot product is extended to accommodate multiple sequences.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Equa_HTML.gif

The cluster with the smallest distance is visualized using GMOD's GBrowse framework [34]. For each of the sequences contained in the cluster, the words pertaining to SIG1 are displayed.

Biological significance (lookup)

Once genomic signatures are identified, the next step is to discern their biological role. One important aspect of this role, crucial to understanding gene regulation [46], is the location of the preferred binding sites for certain proteins (transcription factor binding sites or TFBSs). To locate these sites, the signatures are compared to a set of known binding sites, which are usually represented as weighted matrices [47]. However, a simple scoring scheme can misclassify results when applied to the typically short sequences produced by signature finders. In this simple approach, short signatures are aligned to each matrix by ignoring the parts of the matrices that are longer than the signature. This results in erroneous scores since a signature could match just the very end of large matrix, which is often of little significance (the core of the matrix generally represents the sites of strongest binding).

To give a more significant measure of similarity, we developed a tool that uses a window around the original sequences (those which the signature is based upon) to improve the comparison. The naive implementation of this approach is to use a window of base pairs around each signature and find the optimal alignment to each TFBS matrix by scoring every possible sub-sequence containing the signature. For instance, if a signature is located 10 times within the set of sequences, each matrix is aligned to each of the 10 loci containing the signatures. Our tool uses a faster approach; it finds all occurrences of TFBSs meeting the desired threshold in every sequence, and subsequently uses this information to quickly score the signatures. As a benefit, the list of TFBS can be reused to quickly score new signatures or to redo the analysis with interesting subsets of sequences, such as all sequences which in liver cells are highly expressed.

Co-occurrence analysis

The co-occurrence analysis aims to determine the expected number of sequences containing a given pair of not necessarily distinct words at least once. If n denotes the word length, m the number of sequences, https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq8_HTML.gif the probability for a word i to occur anywhere in the sequence, and l k the length of sequence k, the expected number of sequences containing a given pair of words can be calculated as:
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_Equb_HTML.gif

The https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq9_HTML.gif score is used as the main scoring function in the co-occurrence analysis.

Conservation analysis

Sequence conservation was mapped using PhastCons conservation scores [38] calculated on 28 species, which are based on a two-state (conserved state vs. Non-conserved region) phylo-HMM. PhastCons scores were obtained from the UCSC Human Genome Browser [39]. The scores reported by the UCSC Human Genome Browser contain transformed log-odds scores, ranging from 0–1000. Conserved regions were required to cover the majority of the word length.

Comparison

Words can have significantly different scores for each of the data sets in which they occur. In order to analyze the words based on their impact on the data sets it is useful to assign a distance metric that determines which data set is described best by a given word. Based on a graphical analysis, three points of interest can be determined: the point where the perpendicular of a given point on the x-axis crosses the main diagonal, the point where the perpendicular of a given point on the main diagonal crosses the main diagonal and finally the point where the perpendicular from a given point on the y-axis crosses the main diagonal. Based on the conventional techniques of fold-change detection in microarray analysis, we consider the perpendicular on the main diagonal. The resulting distance formula is: https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-S1-S18/MediaObjects/12864_2009_Article_2546_IEq10_HTML.gif , with y0 being the score for the word within the unidirectional data set, and x0 being the score of the word in the bidirectional data set.

Declarations

Acknowledgements

The Ohio University team acknowledges the support of the Stocker Endowment, Ohio University's Graduate Research and Education Board (GERB), the Ohio Plant Biotechnology Consortium, the Ohio Supercomputer Center, and the Choose Ohio First Initiative of the University System of Ohio.

The Ohio University team further acknowledges Sarah Wyatt for providing the initial motivation and guidance for the work in regulatory genomics as well as Mo Alam, Jasmine Bascom, Kaiyu Shen, Nathaniel George, Dazhang Gu, Eric Petri and Haiquan Zhang for their support during the development of the approach.

LE is supported by the Intramural Program of the National Human Genome Research Institute.

The authors would like to thank to anonymous reviewers for their insights and comments.

This article has been published as part of BMC Genomics Volume 10 Supplement 1, 2009: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08). The full contents of the supplement are available online at http://​www.​biomedcentral.​com/​1471-2164/​10?​issue=​S1.

Authors’ Affiliations

(1)
Bioinformatics Laboratory, School of Electrical Engineering and Computer Science, Ohio University
(2)
Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health
(3)
Department of Statistics, University of Idaho
(4)
Biomedical Engineering Program, Ohio University
(5)
Molecular and Cellular Biology Program, Ohio University

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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.

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