## Abstract

We report here the results of a systematic search for the existence and prevalence of potential intramolecular G-quadruplex forming sequences in the human genome. We have also examined the tendency for particular sequences of ‘loop’ regions to occur in particular positions with respect to the G-tracts in a quadruplex. Using arithmetic ratio and probability techniques we have discovered frequent and systematic occurrence of certain sequence types, the most prominent being a potential quadruplex containing CCTGT in the first ‘loop’ position. Being able to highlight types of potential quadruplex sequences in G-rich regions is an important step in searching for biologically relevant sequences and finding their function.

## INTRODUCTION

Four-stranded G-quadruplex structures are the resultant of the folding of guanine-rich nucleic acid sequences ( 13 ) into higher-order structures. They can form most readily from a single strand of nucleic acid, as at the 3′ end of telomeric DNA ( 49 ). They can also be extruded from double stranded DNA ( 10 ), especially under the influence of a small-molecule ligand such as the porphyrin molecule TMPyP, which binds preferably to some quadruplexes rather than duplex DNA, pushing the equilibrium to the former structure ( 1113 ). Some of these quadruplex sequences have been considered as potential therapeutic targets for small molecules since they have been reported to occur within the regulatory regions of several oncogenes ( 1 ). A well-studied example is the G-rich promoter element of the c- myc oncogene, for which G-quadruplex formation has been suggested as a molecular switch for gene expression ( 1116 ). This quadruplex is exceptionally stable, and is readily formed in preference to remaining in a duplex structure, at least within short DNA sequences.

In this paper, we have started to address the use of bioinformatics tools, and in the accompanying one from our collaborators ( 17 ), the more general question of the number and nature of putative quadruplex sequences within the human (and other) genome(s). Such sequences, and the individual quadruplex structures, may be novel targets for therapeutic intervention, analogous to the selective interference with telomere maintenance by molecules that bind to and stabilize telomeric DNA quadruplexes ( 1822 ). Structural data on quadruplexes is as yet relatively sparse; however, those structures that are known, from X-ray crystallography and NMR studies, show a wide diversity of features ( 7 , 9 , 16 , 2326 ).

We have carried out a survey of all possible short quadruplex sequences in the human genome and have attempted to identify some of the most commonly occurring sequences. Our analysis of these sequences has highlighted some motifs, which stand out as being in a separate class from the rest of the potential quadruplexes, and therefore may have an important function. Categorization of short sequences like quadruplexes within the human genome is not straightforward. Unlike conventional gene sequences, the differences between the sequences is not large but is rather a continuum where, for example, trying to isolate a sequence or family of sequences on the grounds of uniqueness is difficult since there are always many very similar sequences that occur with similar frequencies. The number of combinations of bases that are possible for loop sequences of the size that we are considering is similar to the number of distinct loop sequences that exist ( Table 1 ). Because there are no islands of unique sequence as such, finding correlations between possible quadruplex and function is made more difficult.

It has been demonstrated that the stability and the folding topology of a quadruplex is dependent on the sequence of the loop regions ( 2729 ). Therefore, we would expect trends in the sequence of the loops derived from a genome-wide survey of potential quadruplex sequences to reflect the relative stability and possibly the functionality of a particular sequence. Trends in loop sequence were discovered here through inequalities in the distribution of sequences across each of the three loop regions within a quadruplex sequence, i.e. examining whether a particular sequence occurs more in the first, second or third loops. It is important to emphasize that in the absence of appropriate biophysical, biochemical and structural data, we can only assign sequences as being putative quadruplex-forming. Indeed the available evidence ( 28 ) strongly suggests that many such sequences do not actually form stable quadruplexes.

## METHODS

We define a potential quadruplex sequence as a sequence with four runs of guanine between three and five bases long, separated by regions of DNA, which we will call here loop regions L1, L2 and L3, containing between one and seven bases that may or may not themselves contain guanines. The lengths of each of these were restrained for practical reasons (an arbitrary cut-off of a maximum loop-length of 7 nt had to be applied because a loop unrestrained in length would make searching for sequences difficult) and also because of the evidence to date ( 29 ) that quadruplexes exist as short nucleic acid sequences.

We thus define a general quadruplex sequence as

${\hbox{ G }}_{3-5}\hbox{ }{\hbox{ N }}_{\hbox{ L }1}\hbox{ }{\hbox{ G }}_{3-5}\hbox{ }{\hbox{ N }}_{\hbox{ L }2}\hbox{ }{\hbox{ G }}_{3-5}\hbox{ }{\hbox{ N }}_{\hbox{ L }3}\hbox{ }{\hbox{ G }}_{3-5},$
where N L1–3 are loops of unknown length, although within the limits 1 < N L1–3 < 7 nt.

The examination of the distribution of loop sequence was carried out in several different ways:

• The total number of times that a particular loop sequence appears.

• The distribution of loop sequences with respect to loop position, by taking a particular loop sequence and examining the number of times that it occurred in each loop position, N L1 , N L2 or N L3 . We then looked at the ratio between the highest and lowest values for these populations.

• The probability of a given distribution in each of the three loops occurring, given an equal likelihood of each sequence occurring in each loop region L1, L2 or L3.

It is possible for a single sequence to have a number of different quadruplex topologies ( Figure 1 ) and several different isomers of a sequence fold may lend stability to the system. Not only are there a number of distinct quadruplex fold motifs that have been identified by X-ray crystallography and NMR, but there can be a number of choices about which nucleotides in a quadruplex sequence are members of the G-quartet and which are within loop regions. This complicates the analysis of the loop distribution since not only is it impossible to determine the ‘correct’ choice of bases for each loop region from just sequence data but the same sequence could at least in principle be involved in different alternative and dynamic structures. To overcome some of these difficulties we have used two distinct sets of data. In the first instance, we include all the sequences that could be considered as belonging to loop regions. In some cases this can include many overlapping sequences. However this data will contain some loop sequences, which may otherwise be missed out of the second dataset. In the second case, we have included only sequences that do not overlap with one another. In order to overcome some of the ambiguity illustrated in Figure 1a , we have removed leading and trailing guanines, and loops that consisted of guanines only were reduced to a single G.

### Obtaining and preparing the data

Version 20.34c of the Ensembl human genome database ( 30 ) was downloaded from the Ensembl website in the form of SQL dumps of the Ensembl MySQL database, as were the software tools to access the database using the Perl scripting language (Perl API). The Ensembl tables were then compiled into the relational database program MySQL.

The database was searched for quadruplex sequences in two steps. First, Ensembl Perl API was used to extract assembled lengths of 2 000 000 bases, which were then searched for potential quadruplex sequences using a C++ program developed by A. K. Todd. A list of all combinations of loop length (1–7 nt) and guanine run length (3–5 nt) was generated and each was compared against the total genomic sequence. The results were broken down into the following fields: (i) individual chromosome, (ii) position in the chromosome, (iii) function (intron, exon or other), (iv) sequence of the extracted loop regions and (v) strand on which the hit occurred. The results were then compiled into mySQL tables and added to our local implementation of the Ensembl database. This set of tables included all potential quadruplex sequences including those where a region of DNA could contain more than one potential quadruplex sequence. This raw data is available on request from alan.todd@ulsop.ac.uk

As demonstrated in Figure 1 , a single sequence may have more than one possible quadruplex folding topology. Also more than one loop sequence for a particular loop position L1, L2 or L3 may be possible. We will refer to these problems as quadruplex fold ambiguity. An arbitrary choice of a quadruplex sequence will bias the results of many types of analyses of quadruplex. However, it may also be necessary for finding the number of times a particular motif occurs. Therefore, we have examined our data in two ways. First, a list of loop sequences was compiled, which included overlapping sequences and all possible choices of loop region. We will refer to this as the un-restricted dataset. A second list was also generated in which the quadruplex motif was found but this time, if overlapping sequences occurred, only the first one encountered would be considered. This list was further modified by removing any leading or trailing guanines from the loop sequence, as these would otherwise lead to ambiguity in the loop sequence. This also prevented the inclusion of a particular loop region more than once in the list. Where loop regions were made up entirely of guanines, these were reduced to a single guanine base. This will be referred to as the arbitrary dataset.

### Data analysis

The contents of datasets were ranked in the following way:

• By overall loop composition for each hit.

• By the number of times each loop sequence occurs.

• By population of loop position:

• By looking at the number of times each particular loop sequence occurs in each loop position and finding the ratio between the maximum and minimum of these populations. Where there was a population of 0 this was counted as 1.

• By probability of loop distribution, given an equal likelihood that a quadruplex sequence can occur in each of the loops.

### Calculating probability scores

Given an equal likelihood that a particular loop sequence can be found in any of the three loop positions, the probability that a loop distribution [ a , b or c ] occurs is given by the equation

$P=\frac{(a+b+c)!}{a!b!c!{3}^{a+b+c}},$
where a,b and c represent the observed populations of a particular sequence in loop positions L1, L2 and L3, respectively. Because of the impracticality of working with the very large numbers that are generated when using factorials we need to work in log space, so for our probability score the negative log of the probability was calculated as in Equation 2 :
$\begin{array}{c}-logP=(a+b+c)log3+{\displaystyle \sum _{n=1}^{a}}logn+{\displaystyle \sum _{n=1}^{b}}logn\\ +{\displaystyle \sum _{n=1}^{c}}logn-{\displaystyle \sum _{n=1}^{a+b+c}}logn.\end{array}$

Therefore the higher the score, the less probable the distribution. Derivations of Equations 1 and 2 are based on an exercise in reference ( 31 ), and are given in the Supplementary Material.

Two types of quadruplex sequences which stood out, those which contained CCTGTT and CCTGTCA in the first loop, were selected from the database and multiple sequence alignment of these two quadruplex sequence types were carried out using CLUSTAL W version 1.85 ( 32 ). Figure 2 , which shows the consensus sequences containing them, was generated with the program MakeLogo ( 33 ). The data used in constructing Figure 2 is available in the Supplementary Material.

## RESULTS AND DISCUSSION

Table 1 gives a summary of the number of quadruplexes in both datasets. A large number of potential quadruplex sequences were found on the initial search, which was reduced by ∼15-fold when the overlapping sequences were rejected (5713900 → 375157). The number of distinct (i.e. unique) quadruplexes is similarly reduced, from 3166800 to 226157; each quadruplex sequence occurs only once in this category. Table 1 also shows that some loop sequence combinations were not detected since the number of unique sequences that were observed is less than the total number possible. Overall, 375157 putative quadruplex sequences have been located in the genome. This agrees remarkably well with the estimate of 376 000 by a distinct approach ( 17 ). Tables 2 and 3 represent two ways in which the arbitrary dataset has been examined to search for inequalities in the distribution of sequences by loop position, ranked by ratio and probability respectively, and show the top 40 occurrences in each set. Tables 4 and 5 are the corresponding tables for the un-restricted dataset.

### Distribution of loop sequence by position

Unusual distributions are easier to spot for longer loop sequences. This is because there is a much lower probability that these longer sequences would occur by chance than a short one or two base sequences. Since low populations have a major effect on the ratio, simply looking at the ratio between the populations in each loop position highlights sequences that have a low population in one of the positions ( Table 2 ).

The differences between Tables 2 and 4 show the merits of using both data sets. e.g. the highest ratio in Table 4 (TAGCATT) occurs in 14th place in Table 2 . This difference is because the sequences in which TAGCATT occur are very G-rich and have many possible choices of loop sequence so although the un-restricted dataset is an unbiased choice of loop sequence Table 4 artificially raises the population of some sequences. In both tables the most commonly occurring motifs contain CCTGT or CCTAT in the first loop position with CCTGTCA the most frequent and CCTGTT the next most frequent.

The number of possible sequences for a stretch of DNA is equal to 4 N where N is the number of bases. This means that there are fewer sequences for a population to be distributed across for shorter loops. The shorter the sequence, the fewer the number of changes required to distribute the population across the spectrum of possible sequences. As a result the only sequences that occur infrequently in any given loop position are the longer sequences. We find therefore that using a ratio of populations does not highlight short sequences. The probability technique that we used, greatly reduces this bias towards long sequences, with Tables 3 and 5 showing that guanine-rich sequences with single A and T bases have the least probable distribution.

Several sequences occur in both the ratio tables ( Tables 2 and 4 ) and the probability tables ( Tables 3 and 5 ); ATCTCCA and CCTTGGC tend to occur in the third loop position and many of the CCTGT which tend to occur on the first loop position.

The most frequently occurring loops are those containing single A and single T bases, as seen in Table 6 , which one derived from the arbitrary dataset. Study of these quadruplex sequences and surrounding DNA reveals that there are large G-rich regions interspersed with single A or T bases. The c- myc sequence ( 11 , 13 ) is a member of this type.

### CCTGT sequences

The CCTGT sequences were examined in more detail because they were the logical choice to illustrate certain aspects of the sequences that come to light when looking at quadruplexes in such a way. Although, it may not necessarily be representative of the sequences that our methods have flagged, it is useful in an illustrative capacity. Although some sequences may be rigidly conserved throughout, the CCTGT type, shows a degree of variability. In order to determine whether the rest of the sequences that had the CCTGT motif in the first loop were consistent in the rest of the quadruplex we extracted all of the CCTGT sequences from the arbitrary dataset and ranked them by population. Table 7 shows the top 40 sequences. There were 3524 sequences that contained CCTGT in one of the loop regions. The most common loop sequences were T for the second loop and CTA for the third loop, both of which occur in Table 5 . Looking at the whole table we see a large variability in quadruplex sequences that contain CCTGT. Only the top 526 sequences occur more than once, which leaves 2998 unique sequences. This variability makes it difficult to find a consensus sequence that contains non-guanines in the second loop. However, the most commonly occurring sequences are very similar.

Consensus sequences were generated from the multiple sequence alignments of the quadruplex sequences that contained CCTGTT and CCTGTCA in the first loop ( Figures 2a and b , respectively). The variability of the second loop and the length of the G-runs surrounding it result in a somewhat incoherent result for the consensus sequence. The consensus sequences for both of these types have only two regions that do not contain guanines. For the CCTGTT type sequences the third loop has the sequence CTA, which is consistent with the most commonly occurring CCTGT type sequence shown in Figure 2a . For the CCTGTCA type sequence the third loop has a similar sequence, CT, which also features highly in the most frequently occurring overall sequences.

We have also examined where the CCTGTT and CCTGTCA sequences occurred with respect to DNA function ( Table 8 ). The relative distributions of CCTGTT and CCTGTCA appear to be similar, whereas the distribution of these two subsets is different from the distribution when all quadruplex sequences are considered. Not only is the proportion of CCTGTT and CCTGTCA quadruplex sequences within genes markedly lower than for the overall quadruplex population but also there seems to be a larger number on the minus strand, suggestive that these sequences could form RNA secondary structures ( 34 ) which would, in some cases be undesirable.

Despite variability in the loop sequences that the CCTGT-type potential quadruplex structures show, they frequently occur in the context of quadruplex sequence and this may be evidence of quadruplex structure. Our analysis shows that there are a large number of sequences in the human genome, many of which occur systematically, which could potentially form G-quadruplexes. We have demonstrated that it is possible to use sequence data alone to isolate unique sequence types within these. Further sequence analyses are possible and with the knowledge we can begin to interpret experimental evidence, e.g. correlate location of quadruplex sequences with RNA expression levels. We may also be able to correlate the occurrence of particular quadruplex sequence types by proximity to particular families of proteins.

Table 1

Number of quadruplex sequences occurring in human genomic DNA

Number of quadruplexes Number of unique quadruplexes Number of unique loop sequences (number observed/number possible)
Un-restricted dataset 5 713 900 3 166 800 20 492/21 844
Arbitrary dataset 375 157 226 157 10 551/12 289
Number of quadruplexes Number of unique quadruplexes Number of unique loop sequences (number observed/number possible)
Un-restricted dataset 5 713 900 3 166 800 20 492/21 844
Arbitrary dataset 375 157 226 157 10 551/12 289

Figure 1

Ways in which quadruplex-fold ambiguity can occur. ( a ) Shaded regions represent the guanines contributing to the G-quartets and the unshaded regions the loops. Regions of high guanine density tend to have more quadruplex hits which in some cases lead to many hits for a single region of DNA. ( b ) Overlapping quadruplexes. In the first (un-restricted) dataset, the above sequence would produce five possible quadruplex folds, and in the second (arbitrary) dataset, this sequence would only have been counted as two distinct quadruplexes.

Figure 1

Ways in which quadruplex-fold ambiguity can occur. ( a ) Shaded regions represent the guanines contributing to the G-quartets and the unshaded regions the loops. Regions of high guanine density tend to have more quadruplex hits which in some cases lead to many hits for a single region of DNA. ( b ) Overlapping quadruplexes. In the first (un-restricted) dataset, the above sequence would produce five possible quadruplex folds, and in the second (arbitrary) dataset, this sequence would only have been counted as two distinct quadruplexes.

Figure 2

Consensus sequences for ( a ) CCTGTCA and ( b ) CCTGTT sequence types. Diagrams were generated with the program MakeLogo ( 33 ). A total of 1956 sequences were used to find the consensus sequence for CCTGTCA and 2361 sequences for the CCTGTT type. The height of each letter is proportional to the number of times each base appeared in that position.

Figure 2

Consensus sequences for ( a ) CCTGTCA and ( b ) CCTGTT sequence types. Diagrams were generated with the program MakeLogo ( 33 ). A total of 1956 sequences were used to find the consensus sequence for CCTGTCA and 2361 sequences for the CCTGTT type. The height of each letter is proportional to the number of times each base appeared in that position.

Table 2

Top 20 loop sequence by maximum ratio of population in loop position, for the arbitrary dataset

Sequence Ratio of max and min populations Population in loop a Population in loop b Population in loop c
CCTGTCA 309 1239
CCTGTT 140 1266 18
CCTGTC 139 836
CCTGTTA 90 90
ATCTCCA 74 74
TGGTCTT 58 58
CCTATCA 53 53
TCTGTCA 51 51
TAGCACA 42 42
10 CCTATC 38 38
11 CCTATT 37 75
12 CCTTTCA 37 37
13 CTTGGC 36 13 16 471
14 TAGCATT 34 34
15 CCTGTCC 30 30
16 CCTGTTT 29 58
17 CTTGTCA 29 58
18 CCTGTGA 28 28
19 CCAGTC 28 28
20 ACCTGTC 27 27
Sequence Ratio of max and min populations Population in loop a Population in loop b Population in loop c
CCTGTCA 309 1239
CCTGTT 140 1266 18
CCTGTC 139 836
CCTGTTA 90 90
ATCTCCA 74 74
TGGTCTT 58 58
CCTATCA 53 53
TCTGTCA 51 51
TAGCACA 42 42
10 CCTATC 38 38
11 CCTATT 37 75
12 CCTTTCA 37 37
13 CTTGGC 36 13 16 471
14 TAGCATT 34 34
15 CCTGTCC 30 30
16 CCTGTTT 29 58
17 CTTGTCA 29 58
18 CCTGTGA 28 28
19 CCAGTC 28 28
20 ACCTGTC 27 27

Table 3

Top 20 loop sequence by probability for the arbitrary dataset. Sequences 5,6,7 and 10 also feature in Table 2

Sequence −Log probability Population in loop a Population in loop b Population in loop c
3277 53 234 37 657 30 515
2873 51 361 63 872 78 523
AGGT 2413 1516 6448 1470
1319 7183 8375 14 065
CCTGTCA 1313 1239
CCTGTT 1275 1266 18
CCTGTC 855 836
TT 494 7437 5530 4122
TC 458 3181 1774 1283
10 CTTGGC 421 13 16 471
11 TCTGA 412 737 115 85
12 AGGA 405 1932 3559 3972
13 CTA 316 769 2068 1481
14 AGT 295 2767 4447 2682
15 TGGA 287 2573 1379 1282
16 CAA 175 1035 1928 1876
17 ACTCA 175 428 79 108
18 AGC 173 973 1826 1042
19 ACTT 173 674 225 223
20 AAAT 152 324 299 781
Sequence −Log probability Population in loop a Population in loop b Population in loop c
3277 53 234 37 657 30 515
2873 51 361 63 872 78 523
AGGT 2413 1516 6448 1470
1319 7183 8375 14 065
CCTGTCA 1313 1239
CCTGTT 1275 1266 18
CCTGTC 855 836
TT 494 7437 5530 4122
TC 458 3181 1774 1283
10 CTTGGC 421 13 16 471
11 TCTGA 412 737 115 85
12 AGGA 405 1932 3559 3972
13 CTA 316 769 2068 1481
14 AGT 295 2767 4447 2682
15 TGGA 287 2573 1379 1282
16 CAA 175 1035 1928 1876
17 ACTCA 175 428 79 108
18 AGC 173 973 1826 1042
19 ACTT 173 674 225 223
20 AAAT 152 324 299 781

Table 4

Top 20 loop sequence by maximum ratio of population in loop position for un-restricted dataset

Sequence Ratio of max and min populations Population in loop a Population in loop b Population in loop c
TAGCATT 1058 1058
CCTGTTG 990 10 897 79 11
CCTGTCG 949 7592 40
CCTGTCA 714 12 138 39 17
CCTATCA 467 467
CCTATCG 352 352
GCCTATT 336 336
CCTGTT 332 12 308 113 37
CCTTTCA 310 310
10 GCCTGTT 303 6373 61 21
11 TCTGTCG 287 287
12 CCTATTG 268 537 10
13 CCTGTC 267 8553 104 32
14 CCTGTTA 221 885
15 CCTATC 203 407
16 GCCTATC 203 203
17 GACTCAA 190 190
18 GCCTGTC 179 4679 89 26
19 ACTGTCA 173 173 10
20 CCAGTTG 165 165
Sequence Ratio of max and min populations Population in loop a Population in loop b Population in loop c
TAGCATT 1058 1058
CCTGTTG 990 10 897 79 11
CCTGTCG 949 7592 40
CCTGTCA 714 12 138 39 17
CCTATCA 467 467
CCTATCG 352 352
GCCTATT 336 336
CCTGTT 332 12 308 113 37
CCTTTCA 310 310
10 GCCTGTT 303 6373 61 21
11 TCTGTCG 287 287
12 CCTATTG 268 537 10
13 CCTGTC 267 8553 104 32
14 CCTGTTA 221 885
15 CCTATC 203 407
16 GCCTATC 203 203
17 GACTCAA 190 190
18 GCCTGTC 179 4679 89 26
19 ACTGTCA 173 173 10
20 CCAGTTG 165 165

Table 5

Top 20 loop sequence by probability, for the unrestricted dataset. Sequences 11, 12, 14 and 19 also feature in Table 4

Sequence −Log probability Population in loop a Population in loop b Population in loop c
GA 63 611 117 903 163 870 340 624
GGA 51 459 34 048 67 293 165 738
GGGA 48 837 9892 31 567 102 345
38 358 273 627 300 495 492 842
GTGGG 25 655 55 719 11 578 8617
TGGG 24 126 62 418 15 377 12 614
TGG 22 161 101 363 41 802 32 928
GTGG 22 104 82 252 30 832 22 040
TG 17 189 153 386 86 943 72 114
10 GTG 16 479 114 504 61 257 46 660
11 CCTGTCA 13 009 12 138 39 17
12 CCTGTT 12 795 12 308 113 37
13 GT 12 062 143 082 88 734 75 429
14 CCTGTTG 11 518 10 897 79 11
15 10 975 220 140 154 559 136 752
16 GGAGGG 10 793 4373 25 445 5925
17 GGGAGGG 9174 2588 19 101 4022
18 GGGAGG 8778 4447 22 995 6125
19 CCTGTC 8775 8553 104 32
20 GAGGG 8557 8102 29 589 9460
Sequence −Log probability Population in loop a Population in loop b Population in loop c
GA 63 611 117 903 163 870 340 624
GGA 51 459 34 048 67 293 165 738
GGGA 48 837 9892 31 567 102 345
38 358 273 627 300 495 492 842
GTGGG 25 655 55 719 11 578 8617
TGGG 24 126 62 418 15 377 12 614
TGG 22 161 101 363 41 802 32 928
GTGG 22 104 82 252 30 832 22 040
TG 17 189 153 386 86 943 72 114
10 GTG 16 479 114 504 61 257 46 660
11 CCTGTCA 13 009 12 138 39 17
12 CCTGTT 12 795 12 308 113 37
13 GT 12 062 143 082 88 734 75 429
14 CCTGTTG 11 518 10 897 79 11
15 10 975 220 140 154 559 136 752
16 GGAGGG 10 793 4373 25 445 5925
17 GGGAGGG 9174 2588 19 101 4022
18 GGGAGG 8778 4447 22 995 6125
19 CCTGTC 8775 8553 104 32
20 GAGGG 8557 8102 29 589 9460

Table 6

Most popular loop sequences for the arbitrary dataset

Sequence Population Population in loop a Population in loop b Population in loop c
193 756 51 361 63 872 78 523
121 406 53 234 37 657 30 515
44 020 14 983 14 907 14 130
AA 40 026 12 778 13 717 13 531
CT 32 472 11 637 10 554 10 281
CA 32 070 10 781 10 846 10 443
29 623 7183 8375 14 065
AT 19 957 6789 7242 5926
AGA 19 144 5377 6919 6848
10 TT 17 089 7437 5530 4122
11 TA 12 641 4744 4329 3568
12 CC 10 955 3646 3726 3583
13 AGT 9896 2767 4447 2682
14 AGGA 9463 1932 3559 3972
15 AGGT 9434 1516 6448 1470
16 TGA 9237 3006 2849 3382
17 AAA 7839 2393 2970 2476
18 CCT 7151 2540 2298 2313
19 TGT 6619 2530 2307 1782
20 CCA 6269 2105 2048 2116
Sequence Population Population in loop a Population in loop b Population in loop c
193 756 51 361 63 872 78 523
121 406 53 234 37 657 30 515
44 020 14 983 14 907 14 130
AA 40 026 12 778 13 717 13 531
CT 32 472 11 637 10 554 10 281
CA 32 070 10 781 10 846 10 443
29 623 7183 8375 14 065
AT 19 957 6789 7242 5926
AGA 19 144 5377 6919 6848
10 TT 17 089 7437 5530 4122
11 TA 12 641 4744 4329 3568
12 CC 10 955 3646 3726 3583
13 AGT 9896 2767 4447 2682
14 AGGA 9463 1932 3559 3972
15 AGGT 9434 1516 6448 1470
16 TGA 9237 3006 2849 3382
17 AAA 7839 2393 2970 2476
18 CCT 7151 2540 2298 2313
19 TGT 6619 2530 2307 1782
20 CCA 6269 2105 2048 2116

Table 7

Quadruplex sequences containing CCTGT in the first loop

Loop a Loop b Loop c Length of G-run Population
CCTGTCA CTA 39
CCTGTT CTA 38
CCTGTCA CTA 37
CCTGTC CTA 35
CCTGTCA CT 23
CCTGTCA CT 22
CCTGTCA CAA 21
CCTGTC CTA 21
CCTGTT CAA 20
10 CCTGTT CTA 18
11 CCTGTT 18
12 CCTGTC CT 18
13 CCTGTCA CAA 16
14 CCTGTC CAA 16
15 CCTGTT CT 15
16 CCTGTT TT CTA 15
17 CCTGTCA TT CTA 13
18 CCTGTC TT CAA 12
19 CCTGTT 12
20 CCTGTT CT 12
21 CCTGTT AT CAA 11
22 CCTGTC CT 11
23 CCTGTCA TT CTA 11
24 CCTGTCA AT CTA 10
25 CCTGTT TT CT 10
26 CCTGT 10
27 CCTGTCA TGA CTA 10
28 CCTGTT 10
29 CCTGTC CAA 10
30 CCTGTCA AGGCAA
31 CCTGTT AT CTA
32 CCTGTT TGA
33 CCTGTCA TGGA CTA
34 CCTGTCA TT CAA
35 CCTGTT
36 CCTGTCA ACTA
37 CCTGTT CAA
38 CCTGTT AGT CTA
39 CCTGTC AT CAA
40 CCTGTCA ACTA
Loop a Loop b Loop c Length of G-run Population
CCTGTCA CTA 39
CCTGTT CTA 38
CCTGTCA CTA 37
CCTGTC CTA 35
CCTGTCA CT 23
CCTGTCA CT 22
CCTGTCA CAA 21
CCTGTC CTA 21
CCTGTT CAA 20
10 CCTGTT CTA 18
11 CCTGTT 18
12 CCTGTC CT 18
13 CCTGTCA CAA 16
14 CCTGTC CAA 16
15 CCTGTT CT 15
16 CCTGTT TT CTA 15
17 CCTGTCA TT CTA 13
18 CCTGTC TT CAA 12
19 CCTGTT 12
20 CCTGTT CT 12
21 CCTGTT AT CAA 11
22 CCTGTC CT 11
23 CCTGTCA TT CTA 11
24 CCTGTCA AT CTA 10
25 CCTGTT TT CT 10
26 CCTGT 10
27 CCTGTCA TGA CTA 10
28 CCTGTT 10
29 CCTGTC CAA 10
30 CCTGTCA AGGCAA
31 CCTGTT AT CTA
32 CCTGTT TGA
33 CCTGTCA TGGA CTA
34 CCTGTCA TT CAA
35 CCTGTT
36 CCTGTCA ACTA
37 CCTGTT CAA
38 CCTGTT AGT CTA
39 CCTGTC AT CAA
40 CCTGTCA ACTA

Table 8

Sequence distribution by DNA function for the arbitrary dataset

Intergenic regions 223 321 (60%) 1193 (76%) 1490 (77%)
Within genes (plus strand) 75 189 (20%) 170 (11%) 162 (8%)
Within genes (minus strand) 76 647 (20%) 212 (13%) 290 (15%)
Of which within exons 14 009
Intergenic regions 223 321 (60%) 1193 (76%) 1490 (77%)
Within genes (plus strand) 75 189 (20%) 170 (11%) 162 (8%)
Within genes (minus strand) 76 647 (20%) 212 (13%) 290 (15%)
Of which within exons 14 009

The numbers represent the number of quadruplex sequences occuring within the given type of DNA. Number totally within exons 12 393.

We are grateful to Cancer Research UK for support (Programme Grant C129/A4489), and to various colleagues, notably Julian Huppart and Shankar Balasubramanian (Cambridge), for useful discussions and exchange of ideas. Funding to pay the Open Access publication charges for this article was provided by JISC (UK).

Conflict of interest statement . None declared.

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