Open Access

Short sequence motifs, overrepresented in mammalian conserved non-coding sequences

  • Simon Minovitsky1,
  • Philip Stegmaier2,
  • Alexander Kel2,
  • Alexey S Kondrashov3 and
  • Inna Dubchak1, 4Email author
BMC Genomics20078:378

DOI: 10.1186/1471-2164-8-378

Received: 21 February 2007

Accepted: 18 October 2007

Published: 18 October 2007

Abstract

Background

A substantial fraction of non-coding DNA sequences of multicellular eukaryotes is under selective constraint. In particular, ~5% of the human genome consists of conserved non-coding sequences (CNSs). CNSs differ from other genomic sequences in their nucleotide composition and must play important functional roles, which mostly remain obscure.

Results

We investigated relative abundances of short sequence motifs in all human CNSs present in the human/mouse whole-genome alignments vs. three background sets of sequences: (i) weakly conserved or unconserved non-coding sequences (non-CNSs); (ii) near-promoter sequences (located between nucleotides -500 and -1500, relative to a start of transcription); and (iii) random sequences with the same nucleotide composition as that of CNSs. When compared to non-CNSs and near-promoter sequences, CNSs possess an excess of AT-rich motifs, often containing runs of identical nucleotides. In contrast, when compared to random sequences, CNSs contain an excess of GC-rich motifs which, however, lack CpG dinucleotides. Thus, abundance of short sequence motifs in human CNSs, taken as a whole, is mostly determined by their overall compositional properties and not by overrepresentation of any specific short motifs. These properties are: (i) high AT-content of CNSs, (ii) a tendency, probably due to context-dependent mutation, of A's and T's to clump, (iii) presence of short GC-rich regions, and (iv) avoidance of CpG contexts, due to their hypermutability. Only a small number of short motifs, overrepresented in all human CNSs are similar to binding sites of transcription factors from the FOX family.

Conclusion

Human CNSs as a whole appear to be too broad a class of sequences to possess strong footprints of any short sequence-specific functions. Such footprints should be studied at the level of functional subclasses of CNSs, such as those which flank genes with a particular pattern of expression. Overall properties of CNSs are affected by patterns in mutation, suggesting that selection which causes their conservation is not always very strong.

Background

Genomes of multicellular eukaryotes mostly consist of DNA segments which do not encode proteins. Still, a sizeable fraction of such non-coding DNA is subject to selective constraint and, thus, is conserved between species. Typically, a long intergenic region consists of alternating segments with high and low rates of evolution [1]. A variety of terms have been used to refer to slowly-evolving segments [2, 3], here we will call them CNSs (conserved non-coding sequences).

A majority of mutations in segments which evolve at high rates are presumably selectively neutral or nearly-neutral. In contrast, a large fraction of mutations within CNSs must be deleterious enough to be removed by negative selection. Indeed, data on within-population genetic variability indicate that slow evolution of CNSs is due to negative selection, and not to locally reduced mutation rate [4]. In multicellular eukaryotes with compact genomes, such as Drosophila melanogaster, a majority of mutations affecting non-coding sequences may be removed by selection [5, 6]. For large-genome organisms, such as mammals, the fraction of selectively constrained non-coding sequences is probably between 3% [7] and ~10% [8].

Obviously, CNSs must perform important biological functions, but the whole range and nature of these functions remains unknown [9]. Still, many CNSs are certainly involved in regulation of transcription, and harbor binding sites of a variety of transcription factors [10]. Thus, we can expect some short sequence motifs to be overrepresented in at least some kinds of CNSs, as this is the case for proximal promoters [11]. Indeed, analyses of samples from human CNSs demonstrated overrepresentation of some short sequence motifs [12, 13].

New, powerful methods of detecting overrepresented motifs [e. g., [14, 15]], make it possible to undertake the analysis of small-scale composition of mammalian CNSs at the genomic level. Such analysis has a potential to reveal short sequence-specific function(s) common for all human CNSs. Here, we report the results of application of discriminating matrix enumerator (DME) [14] to all strong human CNSs.

Results

We studied representation of short sequence motifs in all human CNSs against three backgrounds: unconserved or only weakly conserved segments of intergenic regions (non-CNSs), near-promoter non-coding sequences, and randomized sequences with the same nucleotide composition as that of CNSs. CNSs are relatively AT-rich [9]: frequencies of nucleotides A, T, G, and C are 30.7%, 30.7%, 19.3%, and 19.3% in CNSs, 26.3%, 26.4%, 23.6%, and 23.7% in non-CNSs, and 23.7%, 23.7%, 26.3%, and 26.3% in near-promoter sequences. Dinucleotide compositions of sequences of different classes were also substantially different (Fig. 1).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-378/MediaObjects/12864_2007_Article_1091_Fig1_HTML.jpg
Figure 1

Percentages of dinucleotide frequencies, in CNSs (red), non-CNSs (green), near-promoters (lue), and random sequences (black).

CNSs from human chromosomes with odd and even numbers were analyzed separately, to check the results for consistency. The overall lengths of CNSs were 27,112,333 on odd chromosomes and 24,962,379 on even chromosomes. Tables 1, 2, and 3 list top 30 motifs, overrepresented within CNSs over these three backgrounds. Overrepresentation was calculated as the ratio of the number of occurrences of a motif within CNSs, normalized to their overall length, over normalized number of occurrences of the motif within the background sequences.
Table 1

Motifs overrepresented in CNSs over non-CNSs

Odd Chromosomes

Even Chromosomes

Motif

Number of occurrences

Overrepre-sentation

Motif

Number of occurrences

Overrepresen-tation

SYTAATTA

10620

3.45

TTAATTAV

12637

3.72

CTRATTAS

6152

3.14

TAATTRCW

12019

3.43

WGYAATTA

12596

3.09

GYAATTAS

6142

3.39

TTAATTAV

13141

3.08

TTTAATBA

15060

3.14

STAATTGV

8267

2.89

ATTAATBA

10910

3.07

VWGCTAAT

10503

2.84

TAATTWGM

10885

3.04

TTTAATBA

15800

2.77

GMWTAATT

9941

2.97

GMWTAATT

10290

2.72

CWTAATKA

10028

2.94

TAATTATV

10100

2.72

ATTAAWTT

11570

2.85

STTAATKG

5905

2.71

TTAATBAT

10115

2.79

ATTVAATT

12177

2.68

CWKTAATT

13079

2.75

ATTAATBA

11006

2.61

VWGCTAAT

9823

2.71

CWKTAATT

13577

2.59

CMATWAAT

10129

2.65

ATAATTAV

10536

2.58

ATTTVATT

15715

2.64

SMAATTAA

12754

2.57

CAATTRCH

8188

2.61

SBTAATGA

8828

2.56

MCWAATTA

9605

2.61

VATTWGCA

14265

2.53

ATTWWGCA

9959

2.61

TWAATCAR

10639

2.52

GKTAATTW

9019

2.59

AATTAVTT

12668

2.51

AATTAMCW

10053

2.58

GTAATTMM

7484

2.49

MATTDGCA

13694

2.58

Table 2

Motifs overrepresented in CNSs over near-promoter sequences

Odd Chromosomes

Even Chromosomes

Motif

Number of occurrences

Overrepre- sentation

Motif

Number of occurrences

Overrepresen-tation

STAATTAS

7576

4.55

SYTAATTA

9852

4.26

TTAATKAR

17516

4.33

TTAATTAD

14561

4.07

GBTAATKA

12299

3.96

CTRATTAS

5744

3.90

VTAATTGM

10174

3.91

ATTAATGN

9762

3.74

TTTMATKA

19449

3.86

TAATTATD

11760

3.73

MTTMATTA

13688

3.82

TTTAATDA

16633

3.66

AATKYAAT

15204

3.73

ATAATTAB

9233

3.62

TTAATKGV

12925

3.72

TAATKSAA

10418

3.59

RTAATKAA

13613

3.68

STAATTGV

7823

3.55

MMTAATTA

12518

3.68

GYAATWAA

10608

3.55

TSTAATTW

14964

3.49

TGYAATTW

13322

3.51

AATKMATT

18824

3.48

AATGMWTT

15412

3.49

TGATWAAW

12898

3.46

AGYAATTW

12585

3.41

KATAATKA

10739

3.46

AATTDATT

14693

3.39

CATTAAKV

10838

3.42

AATTATAD

10379

3.36

CATWAWTT

14599

3.39

TWAATTGR

8896

3.35

CATTWAAW

19325

3.37

AWTARCAT

9601

3.35

CAATTAKV

9515

3.33

TAATTHAT

12789

3.34

ATRATTYA

13356

3.30

CWTTAATR

9114

3.32

ATTTYMAT

20983

3.29

ATTSMATT

11547

3.27

Table 3

Motifs overrepresented in CNSs over randomized sequences

Odd Chromosomes

Even Chromosomes

Motif

Number of occurrences

Overrepre-sentation

Motif

Number of occurrences

Overrepre-sentation

CWGSCWGS

32472

7.50

CWGSCWGV

38927

5.78

SCCHGSCH

42207

5.68

SCCWGGSN

33122

5.63

GGSWGGSN

39555

5.55

CYCWSCCH

33976

5.50

CWGSCCWS

24103

5.52

RGCWGSCH

30738

4.95

RGTCCTBY

22100

5.45

GGSDGRGV

34873

4.93

GRGSWGRG

25293

5.36

CWGSCYCH

29902

4.78

CCYYYCCH

40727

5.22

CWSCWGGV

31840

4.73

SCCWGGRV

33839

5.20

SCWGCWGV

30968

4.71

CWGSCYCH

36409

5.04

CWGGGRRV

31866

4.64

SCWGGGSN

36038

5.03

CWGRGSCH

28886

4.61

SCHGSCCH

36013

4.91

CCWGGRRV

31578

4.61

CWGRGSCH

35318

4.77

SCHGGSCH

28689

4.50

SCYCWGCH

34141

4.56

GGRARGRR

29240

4.47

NCAGCTGN

32928

4.52

RRGGCWGV

30772

4.44

CAGCTGNN

32867

4.51

RGGGRARR

29828

4.41

TWACWGAA

14781

4.48

GVWGGGRR

31019

4.37

RGGGRRAR

32929

4.42

CYCYVSCC

19097

4.37

CWGSAGSY

24140

4.37

KCCWSCCH

26417

4.33

SCWGGRAR

32065

4.37

CAGCYSNG

16617

4.28

GGARRGRR

33390

4.37

KKGGCWGV

28051

4.13

In order to study a possible similarity of the overrepresented CNS motifs with known binding sites for transcription factors (TF), we applied our recently developed method m2transfac [16], and compared all the motifs found at the previous step with the TRANSFAC library of positional weight matrices (PWMs). Relatively few matches between the motifs and the TF matrices were found. Out of 12000 motifs reported at the previous step as being overrepresented in CNS versus the three different backgrounds, we have identified just 20 motifs that match TF matrices with E-values lower than 0.001 and satisfy factor class-specific cut-offs (Table 4). The majority of these matches involved matrices for the factors of "Forkhead DNA-binding domain", especially of the FOX family, which were repeatedly found over two rather different backgrounds: of non-CNSs and randomized sequences. Among the motifs found over the background of near-promoter sequences, there was only one that matched a PWM.
Table 4

Motifs found matching transcription factor PWMs from TRANSFAC

Accession

Consensus/ID

Factor class

Taxon

Binding factors

acns even

    

DME280

ATAAACAN

Forkhead DNA-binding domain

Vertebrate

FOXI1a,FOXF1,FOXL1,FOXO4

DME424

WGTAAAYA

Forkhead DNA-binding domain

Vertebrate

FOXC1,FOXA4a,HNF-3beta

DME768

WTGTCATV

Basic region + leucine zipper (bZIP)

Nematode

Skn-1

DME1427

WGTCATSM

Basic region + leucine zipper (bZIP)

Nematode

Skn-1

acns odd

    

DME27

VATTWGCA

POU

Vertebrate

POU2F1

DME349

ATAAACAN

Forkhead DNA-binding domain

Vertebrate

FOXI1a,FOXF1,FOXL1,FOXO4

DME1014

GTMAACAD

Forkhead DNA-binding domain

Vertebrate

FOXD1,HNF-3beta,FOXO1a

DME1700

CCAATMAB

DNA-binding domain with Histone fold

Fungal

HAP2,HAP3,HAP4

promoters even

   

promoters odd

   

DME1268

STGASTYA

Basic region + leucine zipper (bZIP)

Vertebrate

NF-E2,AP-1

random even

    

DME90

VCAGATGN

Basic region + helix-loop-helix motif

Vertebrate

ITF-2,Tal-1beta

DME94

CATCTGBN

Basic region + helix-loop-helix motif

Vertebrate

ITF-2,Tal-1beta,E47

DME765

RTGWSTCA

Basic region + leucine zipper (bZIP)

Vertebrate

NF-E2,AP-1,Fos,Jun,Fra

DME1106

TGTTBACW

Forkhead DNA-binding domain

Vertebrate

HNF-3beta

DME1111

ATAAACAH

Forkhead DNA-binding domain

Vertebrate

FOXI1a,FOXF1,FOXL1,FOXO4

DME1920

CCACGTGG

Basic region + helix-loop-helix motif

Plant, Vertebrate

PIF3,c-Myc:Max

random odd

    

DME11

CAGCTGNN

Basic region + helix-loop-helix motif

Vertebrate

AP-4

DME456

MAYAAACA

Forkhead DNA-binding domain

Vertebrate

FOXF1

DME790

TATGVAAA

POU

Vertebrate

POU2F1

DME930

ATAAAYAT

Forkhead DNA-binding domain

Vertebrate, Insect

FOXI1a,Croc

DME1145

TGTTBACW

Forkhead DNA-binding domain

Vertebrate

HNF-3beta

Discussion

We treated all human CNSs as a single class of sequences. Comparison of this class against three different backgrounds demonstrates that many short sequence motifs are substantially overrepresented within CNSs (Tables 1, 2, 3). CNSs from odd- and from even-numbered human chromosomes show very similar patterns, which is consistent with the lack of any large-scale heterogeneity within CNSs. At a first glance, these results may seem to suggest that CNSs as a whole possess some complex sequence pattern(s), with possible implications for their functioning. However, this is probably not the case. Instead, the results can be explained by simple, generic properties of CNSs.

Indeed, when CNSs are analyzed against a background of non-CNSs (Table 1) or of near-promoter sequences (Table 2), almost all overrepresented motifs possess two common features: (i) they are AT-rich (consist of 75% or more of A and/or T) and (ii) they contain runs of A's and/or T's. Feature (i) simply reflects a well-known, although poorly understood, fact that CNSs are more AT-rich than the genome as a whole [9, 17] or that these two classes of background sequences. Feature (ii) appears to be due to general excess of AA and TT dinucleotides in CNSs, relatively to corresponding random sequences. This tendency of A's and T'e to clump is probably due to patterns in mutation, and not to any functional constraint. Indeed, context-dependence of spontaneous mutation in mammals tends to produce runs of A's and T's, because at a site preceded and followed by A's (T's) T>A (A>T) transversions are ~2 times more common than A>T transversions [18, 19]; Table 2.

Obviously, it is neccessary to consider CNSs against a background of the same nucleotide composition, as otherwise the impact of different compositions is the leading factor causing overrepresentation of some motifs. When CNSs are analyzed against a background of random sequences of the same, AT-rich, nucleotide composition, the results are very different (Table 3), and overrepresented motifs can be naturally subdivided into two classes. The first, larger class contains a variety of GC-rich motifs which, however, are devoid of CpG dinucleotides and are correspondingly enriched with CpA and CpT dinucleotides and with CWG short motif. The second, smaller class contains several motifs which are either purine- or pyrimidine-rich. Overrepresentation of motifs from the first class appear to be due to two simple factors: i) the presence, within CNSs, of short GC-rich segments and ii) hypermutability of CpG dinucleotides [18]. Indeed, CNSs are depleted of CpG's more than the other two classes of genomic sequences (Fig. 1), which might reflect strong methilation of CNSs. Overrepresentation of motifs of the second class simply reflects a well-known [20], although poorly understood, abundance of short segments with strong purine/pyrimidine imbalance between the two DNA stands within the human genome.

The analysis of all human CNSs does not reveal clear "global" patterns consistent with overrepresentation of specific, functional motifs. A small number of the observed overrepresented motifs are similar to Position Weight Matrices (PWMs) from TRANSFAC database [21] (Table 4). Among them, the strongest similarity was to the PWMs of FOX and POU families of factors which are characterized by a specific AT-rich pattern. In order to test if the identification of FOX-domain matrices is merely an effect of the general AT-richness of the CNS regions we check carefully results of alignments of all other "AT-rich" matrices in TRANSFAC. There are approximately 64% of matrices in TRANSFAC with overall AT composition higher then 50%. 16 of them are characterised by the same and even higher AT-composition then any of the FOX and POU-domain matrices (e.g. matrices for such factors as TBP, Lhx3, Evi-1, Nkx3-1 and others). Nevertheless, non of them gave statistically significant results of the alignments with the motifs under study. This confirms the similarity of some motifs from the list specifically to the FOX- and POU-domain matrices. The FOX factors are involved in many cellular processes and often control very first steps of organism development as well as cell cycle and differentiation; e. g. FOXF1 is highly expressed in mouse embryonic extraembryonic and lateral mesoderm [22] and control murine gut development [23]; FOXD1 is predominantly expressed in embryonic forebrain neuroepithelium, head mesenchyme and adrenal cortex [24] and controls normal brain and kidney morphogenesis and cellularity in the renal capsule [25]; FOXO1 governs cell growth in the heart [26]. Factors of other families, such as POU and bZIP are often involved in regulation of basic cell cycle machinery; e.g. POU2F1 is an ubiquitous factor involved in stimulation of replication [27] and also participates in early mouse embryogenesis [28]. In summary, it might be tempting to speculate that at least some motifs overrepresented in all CNSs may play crucial role in organizing the process of development of the vertebrate organisms. However, the number of such motifs is not high., More specific classes of CNSs, such as those adjacent to genes with a particular pattern in expression [11, 12] should be considered in order to find a larger number of functional motifs.

In contrast, small-scale composition of human CNSs, considered as a whole, is strongly affected by patterns in mutation – hypermutability of CpG's and the tendency for A's and T's to form runs. This is unexpected because CNSs must be under negative selection which can overcome any impact of mutation [4]. Apparently, selective constraint on the evolution of individual nucleoitide site can be quite weak even within strongly conserved CNSs.

Conclusion

Abundance of short sequence motifs in all human CNSs is mostly dictated by their general features: overall AT-richness of CNSs, runs of A's and T's, GC-rich regions, avoidance of CpG's, and local purine/pyrimidine imbalance of the DNA strands. Apparently, CNSs as a whole are too broad a class to display strong overrepresentation of specific motifs. Instead, such motifs must be sought within subclasses of CNSs. In particular, tissue-specificity of expression of the genes adjacent to a CNS must be taken into account.

Methods

We used the VISTA pipeline infrastructure [29] with Shuffle-LAGAN glocal chaining algorithm [30] applied to local alignments produced by translated BLAT [31] for the construction of genome-wide pairwise human/mouse alignment. The level of conservation in the alignment was evaluated with the computational algorithm Gumby [32] that makes minimal assumptions about the statistical features of conserved noncoding regions and treating the sequence alignment as its own training set. Gumby [32] proceeds through five steps:

1. Noncoding regions in the input alignment are used to estimate the neutral mismatch frequency p N between each pair of aligned sequences. This is done simply by counting the number of mismatches in nonexonic positions and dividing by the number of aligned nonexonic positions.

2. A log-odds scoring scheme for constrained versus neutral evolution is then independently initialized for the pair of sequences, based on the assumption that the mismatch frequency p C in constrained regions equals p N/R, where the ratio R is an arbitrary parameter. For example, if R = 3/2 (default value), constrained regions are expected to evolve at 2/3 times the neutral rate, until sequence divergence begins to saturate.

The log-odds mismatch score for the sequence pair is then given by S0 = log((p N/R)/p N) = -log(R), and the match score is S1 = log((1 - p N/R)/(1 - p N)). The default R-ratio (1.5) was selected to optimize the sensitivity-specificity tradeoff in detecting empirically defined regulatory elements in the SCL locus. Gap characters in the alignment are assigned a weighted average of mismatch and match scores: SG = p NS0 + (1 - p N)S1.

3. Each alignment column is scored as a sum of pairwise log-odds scores. The resulting conservation score fulfills the requirements of Karlin-Altschul statistics, in that positive column scores are possible, though the average column score is negative [33].

4. Conserved regions appear as stretches of alignment columns with a high aggregate score.

5. The aggregate score of the alignment columns in each conserved region is translated into a P-value using Karlin-Altschul statistics. As is the case with the BLAST algorithm [34], the P-value of a given conserved element varies with the size of the search space, since one is more likely to find a given degree of conservation by random chance in a long alignment than in a short alignment. To make the P-values comparable across alignments of different lengths, Gumby normalizes them to refer to a fictitious fixed-length alignment with the same statistical properties as the true alignment. The 10-kb P-value is related to the expected number of false positives in a 10-kb region (i.e. the 10-kb E-value) as follows: P = 1-exp(-E). When P << 1, PE. Thus, the P-value also doubles as an estimate of the false-positive rate.

Intervals with P-value threshold of 0.01 produced a set of 144,165 highly conserved sequences that totaled 49 Mb in length. We eliminated all conserved regions that coincide with the coding evidence provided by the UCSC data sets of mRNA, human spliced EST and human EST. We excluded CNSs located within (-1000, +1000) from the start and end of transcription.

Non-CNSs were defined as regions that have human/mouse alignment, conserved below 50% in a 100 bp window and not containing repeats and coding evidences. Random sequences were generated using standard C library pseudo-random generator. Overrepresentation of motifs in different random sequences was calculated using DME [14] (see Additional File 1). DME identifies motifs, represented as position weight matrices that are overrepresented in one set of sequences relative to another set. The ability to directly optimize relative overrepresentation is a unique feature of DME, making DME an ideal tool for comparing two sets. In all of studies we compared 8-mers (parameter w = 8) and bits/column bound was set to 1.6 (parameter i = 1.6).

DME motifs were compared to the TRANSFAC® database with the m2transfac program [16]. The program retrieves all non-overlapping pairwise ungapped alignments of a query matrix and a TRANSFAC matrix satisfying a given threshold. The primary similarity measure is an alignment score which combines Kullback-Leibler divergence with a scoring system that was previously applied successfully to comparison of Hidden Markov Models [35]

S(p, q) = C(p, q) - D(p, q)

with
C ( p , q ) = log 2 i = 1 4 p i q i r i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGdbWqcqGGOaakcqWGWbaCcqGGSaalcqWGXbqCcqGGPaqkcqGH9aqpcyGGSbaBcqGGVbWBcqGGNbWzdaWgaaWcbaGaeGOmaidabeaakmaaqahabaWaaSaaaeaacqWGWbaCdaWgaaWcbaGaemyAaKgabeaakiabdghaXnaaBaaaleaacqWGPbqAaeqaaaGcbaGaemOCai3aaSbaaSqaaiabdMgaPbqabaaaaaqaaiabdMgaPjabg2da9iabigdaXaqaaiabisda0aqdcqGHris5aaaa@48E5@
(2)
D ( p , q ) = 1 2 ( i = 1 4 p i log p i q i + i = 1 4 q i log q i p i ) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=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@5FCB@
(3)

In equation (2), r is background model which is set to the uniform distribution. Equation (2) is based on the column score derived in [33]. The term assigns a positive score to similar distributions and tends towards zero for less conserved positions. Equation (3) is a symmetrized relative entropy or Kullback-Leibler (KL) divergence. Relative entropy was used previously in applications for classification of protein as well as nucleotide patterns [36, 37]. The m2transfac scoring system combines the advantages of both measures. The KL divergence directly assesses the difference of two distributions and therefore increases specificity for similar distributions, but makes no distinction on the basis of their conservation, which is however a property of the column score.

The m2transfac output provides E-values, the number of alignments with greater or equal score expected from searching a database with 1000 matrices. These are derived for each TRANSFAC PWM from score distribution estimates based on large-scale searches of a random matrix library. Furthermore, we apply the transcription factor classification that was developed in our group [38, 39] to gather matrices according to DNA-binding domain classes of their binding factors and derive factor class-specific score thresholds. We define 57 matrix groups, 15 of which comprise matrices which cannot be associated with a particular factor class, e.g. the barbiturate-inducible element, or whose binding factors are so far not assigned to a protein-structural class. Some matrices occur in more than one class if TFs of different classes are annotated as binding factors, or binding factors possess multiple DNA-binding domain types. For each PWM, score thresholds are defined at three levels of stringency above the score of the first observed false positive in a search of the TRANSFAC database.

Declarations

Acknowledgements

We are grateful to Andrew Smith for providing us with the DME software. Research was conducted at the E.O. Lawrence Berkeley National Laboratory, supported by grant HL066681 Berkeley-PGA (SM and ID), under the Programs for Genomic Applications, funded by National Heart, Lung, & Blood Institute and by HG003988 (L.A.P.) and performed under Department of Energy Contract DE-AC02-05CH11231, University of California. MB was supported by the NSERC Discovery grant.

Authors’ Affiliations

(1)
Genomics Division, Lawrence Berkeley National Laboratory
(2)
BIOBASE GmbH
(3)
Life Sciences Institute and Department of Ecology and Evolutionary Biology, University of Michigan
(4)
DOE Joint Genome Institute

References

  1. Shabalina SA, Kondrashov AS: Pattern of selective constraint in C. elegans and C. briggsae genomes. Genet Res. 1999, 74 (1): 23-30. 10.1017/S0016672399003821.PubMedView ArticleGoogle Scholar
  2. Dermitzakis ET, Reymond A, Scamuffa N, Ucla C, Kirkness E, Rossier C, Antonarakis SE: Evolutionary discrimination of mammalian conserved non-genic sequences (CNGs). Science. 2003, 302 (5647): 1033-1035. 10.1126/science.1087047.PubMedView ArticleGoogle Scholar
  3. Margulies EH, Blanchette M, Haussler D, Green ED: Identification and characterization of multi-species conserved sequences. Genome Res. 2003, 13 (12): 2507-2518. 10.1101/gr.1602203.PubMed CentralPubMedView ArticleGoogle Scholar
  4. Drake JA, Bird C, Nemesh J, Thomas DJ, Newton-Cheh C, Reymond A, Excoffier L, Attar H, Antonarakis SE, Dermitzakis ET: Conserved noncoding sequences are selectively constrained and not mutation cold spots. Nat Genet. 2006, 38 (2): 223-227. 10.1038/ng1710.PubMedView ArticleGoogle Scholar
  5. Halligan DL, Keightley PD: Ubiquitous selective constraints in the Drosophila genome revealed by a genome-wide interspecies comparison. Genome Res. 2006, 16 (7): 875-884. 10.1101/gr.5022906.PubMed CentralPubMedView ArticleGoogle Scholar
  6. Andolfatto P: Adaptive evolution of non-coding DNA in Drosophila. Nature. 2005, 437 (7062): 1149-1152. 10.1038/nature04107.PubMedView ArticleGoogle Scholar
  7. Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P: Initial sequencing and comparative analysis of the mouse genome. Nature. 2002, 420 (6915): 520-562. 10.1038/nature01262.PubMedView ArticleGoogle Scholar
  8. Shabalina SA, Ogurtsov AY, Kondrashov VA, Kondrashov AS: Selective constraint in intergenic regions of human and mouse genomes. Trends Genet. 2001, 17 (7): 373-376. 10.1016/S0168-9525(01)02344-7.PubMedView ArticleGoogle Scholar
  9. Dermitzakis ET, Reymond A, Antonarakis SE: Conserved non-genic sequences – an unexpected feature of mammalian genomes. Nat Rev Genet. 2005, 6 (2): 151-157. 10.1038/nrg1527.PubMedView ArticleGoogle Scholar
  10. Hardison RC: Conserved noncoding sequences are reliable guides to regulatory elements. Trends Genet. 2000, 16 (9): 369-372. 10.1016/S0168-9525(00)02081-3.PubMedView ArticleGoogle Scholar
  11. Smith AD, Sumazin P, Xuan Z, Zhang MQ: DNA motifs in human and mouse proximal promoters predict tissue-specific expression. Proc Natl Acad Sci USA. 2006, 103 (16): 6275-6280. 10.1073/pnas.0508169103.PubMed CentralPubMedView ArticleGoogle Scholar
  12. Bernat JA, Crawford GE, Ogurtsov AY, Collins FS, Ginsburg D, Kondrashov AS: Distant conserved sequences flanking endothelial-specific promoters contain tissue-specific DNase-hypersensitive sites and over-represented motifs. Hum Mol Genet. 2006, 15 (13): 2098-2105. 10.1093/hmg/ddl133.PubMedView ArticleGoogle Scholar
  13. Kondrashov AS, Shabalina SA: Classification of common conserved sequences in mammalian intergenic regions. Hum Mol Genet. 2002, 11 (6): 669-674. 10.1093/hmg/11.6.669.PubMedView ArticleGoogle Scholar
  14. Smith AD, Sumazin P, Zhang MQ: Identifying tissue-selective transcription factor binding sites in vertebrate promoters. Proc Natl Acad Sci USA. 2005, 102 (5): 1560-1565. 10.1073/pnas.0406123102.PubMed CentralPubMedView ArticleGoogle Scholar
  15. Liu Y, Wei L, Batzoglou S, Brutlag DL, Liu JS, Liu XS: A suite of web-based programs to search for transcriptional regulatory motifs. Nucleic Acids Res. 2004, W204-207. 10.1093/nar/gkh461. 32 Web Server
  16. Stegmaier P, Kel A, Wingender E: [http://www.gene-regulation.com/pub/programs.html#m2transfac]
  17. Walter K, Abnizova I, Elgar G, Gilks WR: Striking nucleotide frequency pattern at the borders of highly conserved vertebrate non-coding sequences. Trends Genet. 2005, 21 (8): 436-440. 10.1016/j.tig.2005.06.003.PubMedView ArticleGoogle Scholar
  18. Hwang DG, Green P: Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution. Proc Natl Acad Sci USA. 2004, 101 (39): 13994-14001. 10.1073/pnas.0404142101.PubMed CentralPubMedView ArticleGoogle Scholar
  19. Kondrashov FA, Ogurtsov AY, Kondrashov AS: Selection in favor of nucleotides G and C diversifies evolution rates and levels of polymorphism at mammalian synonymous sites. J Theor Biol. 2006, 240 (4): 616-626. 10.1016/j.jtbi.2005.10.020.PubMedView ArticleGoogle Scholar
  20. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W: Initial sequencing and analysis of the human genome. Nature. 2001, 409 (6822): 860-921. 10.1038/35057062.PubMedView ArticleGoogle Scholar
  21. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K: TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006, D108-110. 10.1093/nar/gkj143. 34 Database
  22. Peterson RS, Lim L, Ye H, Zhou H, Overdier DG, Costa RH: The winged helix transcriptional activator HFH-8 is expressed in the mesoderm of the primitive streak stage of mouse embryos and its cellular derivatives. Mech Dev. 1997, 69 (1–2): 53-69. 10.1016/S0925-4773(97)00153-6.PubMedView ArticleGoogle Scholar
  23. Ormestad M, Astorga J, Landgren H, Wang T, Johansson BR, Miura N, Carlsson P: Foxf1 and Foxf2 control murine gut development by limiting mesenchymal Wnt signaling and promoting extracellular matrix production. Development. 2006, 133 (5): 833-843. 10.1242/dev.02252.PubMedView ArticleGoogle Scholar
  24. Hatini V, Huh SO, Herzlinger D, Soares VC, Lai E: Essential role of stromal mesenchyme in kidney morphogenesis revealed by targeted disruption of Winged Helix transcription factor BF-2. Genes Dev. 1996, 10 (12): 1467-1478. 10.1101/gad.10.12.1467.PubMedView ArticleGoogle Scholar
  25. Levinson RS, Batourina E, Choi C, Vorontchikhina M, Kitajewski J, Mendelsohn CL: Foxd1-dependent signals control cellularity in the renal capsule, a structure required for normal renal development. Development. 2005, 132 (3): 529-539. 10.1242/dev.01604.PubMedView ArticleGoogle Scholar
  26. Ni YG, Berenji K, Wang N, Oh M, Sachan N, Dey A, Cheng J, Lu G, Morris DJ, Castrillon DH: Foxo transcription factors blunt cardiac hypertrophy by inhibiting calcineurin signaling. Circulation. 2006, 114 (11): 1159-1168. 10.1161/CIRCULATIONAHA.106.637124.PubMed CentralPubMedView ArticleGoogle Scholar
  27. Schreiber E, Matthias P, Müller MM, Schaffner W: Identification of a novel lymphoid specific octamer binding protein (OTF-2B) by proteolytic clipping bandshift assay (PCBA). EMBO J. 1988, 7 (13): 4221-4229.PubMed CentralPubMedGoogle Scholar
  28. Scholer HR, Balling R, Hatzopoulos AK, Suzuki N, Gruss P: Octamer binding proteins confer transcriptional activity in early mouse embryogenesis. EMBO J. 1989, 8 (9): 2551-2557.PubMed CentralPubMedGoogle Scholar
  29. Frazer KA, Pachter L, Poliakov A, Rubin EM, Dubchak I: VISTA: computational tools for comparative genomics. Nucleic Acids Res. 2004, W273-279. 10.1093/nar/gkh458. 32 Web Server
  30. Brudno M, Malde S, Poliakov A, Do CB, Couronne O, Dubchak I, Batzoglou S: Glocal alignment: finding rearrangements during alignment. Bioinformatics. 2003, 19 (Suppl 1): i54-62. 10.1093/bioinformatics/btg1005.PubMedView ArticleGoogle Scholar
  31. Kent WJ: BLAT–the BLAST-like alignment tool. Genome Res. 2002, 12 (4): 656-664. 10.1101/gr.229202. Article published online before March 2002.PubMed CentralPubMedView ArticleGoogle Scholar
  32. Prabhakar S, Poulin F, Shoukry M, Afzal V, Rubin EM, Couronne O, Pennacchio LA: Close sequence comparisons are sufficient to identify human cis-regulatory elements. Genome Res. 2006, 16 (7): 855-863. 10.1101/gr.4717506.PubMed CentralPubMedView ArticleGoogle Scholar
  33. Karlin S, Altschul SF: Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc Natl Acad Sci USA. 1990, 87 (6): 2264-2268. 10.1073/pnas.87.6.2264.PubMed CentralPubMedView ArticleGoogle Scholar
  34. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.PubMedView ArticleGoogle Scholar
  35. Soding J: Protein homology detection by HMM-HMM comparison. Bioinformatics. 2005, 21 (7): 951-960. 10.1093/bioinformatics/bti125.PubMedView ArticleGoogle Scholar
  36. Roepcke S, Grossmann S, Rahmann S, Vingron M: T-Reg Comparator: an analysis tool for the comparison of position weight matrices. Nucleic Acids Res. 2005, W438-441. 10.1093/nar/gki590. 33 Web Server
  37. Sjölander K: Phylogenetic inference in protein superfamilies: analysis of SH2 domains. Proc Int Conf Intell Syst Mol Biol. 1998, 6: 165-174.PubMedGoogle Scholar
  38. Stegmaier P, Kel AE, Wingender E: Systematic DNA-binding domain classification of transcription factors. Genome Inform. 2004, 15 (2): 276-286.PubMedGoogle Scholar
  39. Wingender E: [Classification of eukaryotic transcription factors]. Mol Biol (Mosk). 1997, 31 (4): 584-600. [Article in Russian]Google Scholar

Copyright

© Minovitsky et al; licensee BioMed Central Ltd. 2007

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.

Advertisement