## 2 Methods
### Tfbs Retrieval Experiments  
In these experiments we regard the power spectra of the tfbs as fingerprints and the set of power spectra of an entire chromosome as a database of fingerprints to be searched. We have mapped the spectra of a set of tfbs to their position in the entire chromosome spectra in the following manner. The sequence of the entire chromosome, from BUILD 35 of the human genome [2] was obtained from UCSF [32]. The spectrum of each consecutive 1031-base pair block of the chromosome was obtained as described above, giving 33,369 power spectra for chromosome 21 and 33,949 spectra for chromosome 22. The sequences of the transcription tfbs obtained from Affymetrix were actually from BUILD 32. This was a problem, since BUILD 32 is no longer available, and the addition of new sequence data in later builds means that a position in the chromosome from BUILD 32 no longer corresponds to the tfbs in BUILD 35. The spectrum of a tfbs was therefore mapped to the equivalent chromosomal spectrum using a similarity search, where the similarity, d, between two spectra was measured using the complement of the cosine coefficient. Given two spectra (we used roll3 spectra), S=(s\({}_{1}\), s\({}_{2}\),.., s\({}_{n}\)), C=(c\({}_{1}\), c\({}_{2}\),.., c\({}_{n}\)), the cosine distance d between them is:  
\[\textrm{d}=1-\frac{\sum_{i}^{n}\textrm{S}_{i}\textrm{c}_{i}}{\sqrt{\sum_{i}^{n }\textrm{s}_{i}^{2}\sum_{i}^{n}\textrm{c}_{i}^{2}}}\]  
We calculated the cosine distances between a tfbs power spectrum and each of the power spectra of the entire chromosome, sorted the distances, and in most cases the most similar spectrum by some distance was that of the 'correct' block of the chromosome. NB this is not always the case since a tfbs will almost certainly overlap two consecutive 1,031 bp chromosome sequences, and then, if whatever structural pattern it contains is similarly divided over two blocks, it may not be found by the similarity search. However this occurred in only 10% of cases. In these cases, BLAT [33] sequence searches were carried out to determine which block contained the larger portion of the tfbs sequence. Given a set of transcription tfbs, S, and a particular structural property, p, the fingerprint for each member of S was used to search the entire set of fingerprints of its chromosome using a similarity search as described above, and the number of members of S in the top fraction was recorded. We used two different methods in order to get an estimate of the likelihood of the retrieval being achieved by chance. Firstly, we chose blocks at random from the chromosome and used these as fingerprints to search the chromosomal spectra. Secondly, the set of fingerprints, R, of the randomised versions of the sequences in S, was also used to search the chromosomal spectra and the count of members of S again recorded. Since a member of S has an advantage if it is allowed to find itself, this was disallowed. This actually gives members of S an inbuilt disadvantage, since their maximum possible retrieval is one less than for the randomised sequences. Initial experiments used Sp1 tfbs in chromosome 21. Sp1 tfbs retrieval was measured at the 1% level. Thus if the search results were completely random, the expected value would be 0.88, since there are 89 Sp1 tfbs, but a tfbs is not allowed to recall itself. The significance of any difference in retrieval rates was measured by a one-sided Mann-Whitney test. A z-score of 1.96 is significant at the 95% level. Data fusion is a technique used in searching databases of small molecules, with the aim of increasing the number of active molecules retrieved by a target molecule[34, 35] and also in consensus scoring for protein-ligand docking [36]. Our aim, in using data fusion, is to increase the number of true positives in the top fraction of the database. [PERSON] _et al_ found that a sum fusion method, based on the ranking produced by the similarity searches, was the most effective of those studied [34], and we have thus adopted this approach.