Drilling k-wires , what about the osteocytes ? An experimental study in rabbits Introduction The function of osteocytes regarding osteonecrosis has been underestimated for a long time . Recently it has been suggested that apoptosis of osteocytes results in strong osteoclastic bone resorption . Death of osteocytes due to drilling may therefore increase the risk of K-wire loosening . The purposes of our in vivo study were to assess the minimal drill time needed to notice disappearance of osteocytes and to measure the distance of the empty osteocyte lacunae surrounding the drill tract in relation with the insertion time , directly and 4 weeks after drilling Kirschner -LRB- K - -RRB- wires into the femur and tibia of rabbits . Introduction Experiments concerning thermal damage to bone tissue due to drilling Kirschner wires -LRB- K-wires -RRB- show several variables interfering with heat generation -LSB- 7 , 9 -- 14 , 32 -RSB- . These variables can be categorized into three groups : drilling technique , K-wire characteristics and external factors . The drilling technique is a subtle balance between drilling speed , insertion time and pressure -LSB- 1 , 2 , 5 , 30 , 31 , 34 -RSB- . The characteristics of K-wires differ in diameter and K-wire tip -LSB- 17 , 23 , 29 -RSB- . The main external factor is irrigation with a water spray during drilling -LSB- 3 , 20 , 24 , 36 -RSB- . It is believed that critical temperature for bone injury is around 56 °C because alkaline phosphatase denaturates at this temperature . Osteocytes , however , may even be more sensitive to heat -LSB- 23 , 24 -RSB- . Eriksson -LSB- 9 , 10 , 12 -RSB- observed that a temperature of 47 °C for only 1 min results in bone resorption . Bone is capable of remodeling by bone resorbing osteoclasts , bone forming osteoblasts and osteocytes -LSB- 15 , 19 , 22 , 26 -RSB- . The latter differentiate out of osteoblasts which have ceased bone production and have become embedded in the bone matrix , in vacuoles cushioned by fluid and large molecules , forming a network -LSB- 8 , 15 , 19 , 22 , 25 -RSB- . Despite the fact that the osteocytes are the major constituents of bone , their role in bone resorption regulation has remained controversial for a long time -LSB- 4 , 16 , 21 , 22 , 27 -RSB- . In the past decade , however , understanding of osteocyte physiology has increased dramatically and it has become clear that osteocytes play an important role in bone remodeling -LSB- 6 , 15 , 18 , 25 , 26 , 35 -RSB- . Now we know that micro damage to bone , e.g. by drilling , is associated with an increase in osteocyte death by apoptosis -LSB- 25 -RSB- . This process triggers local bone resorption resulting in K-wire loosening , because death of osteocytes turns off the inhibition of osteoclasts -LSB- 6 , 15 , 16 , 22 , 25 , 35 -RSB- . The status of osteocytes after drilling into bone has been investigated before . Thompson -LSB- 33 -RSB- describes the absence of osteocytes as an acute cellular reaction , which increases in severity with increase of drilling speed . Pallan et al. -LSB- 28 -RSB- continued Thompson 's -LSB- 33 -RSB- work and describes the delayed cellular changes in bone after pin insertion which were left in situ for maximum 10 weeks . He also concluded that higher speeds produce relatively higher temperatures and increased tissue response after a long time period -LSB- 28 -RSB- . It is well known that in clinical daily practice K-wires are often drilled without proper cooling because most drilling devices do not have an incorporated cooling system and therefore cooling has to be done manually . In case percutaneous drilling is performed , the cooling effect on bone is minimal because of the surrounding soft tissues . An in vivo study was therefore designed which simulated daily practice concerning K-wire drilling to analyze the absence of osteocytes directly -LRB- t = 0 -RRB- and 4 weeks -LRB- t = 4 -RRB- after insertion . Our first aim was to measure the minimal drill time needed to notice disappearance of osteocytes . The second aim was to measure the distance of the disappeared osteocytes around the periphery of the drill holes in relation to drilling time . Materials and methods Animals and anesthesia A total of 14 healthy , New Zealand white rabbits of female sex weighing a mean of 2.81 kg -LRB- 2.66 -- 3.09 kg -RRB- were used in this investigation . The rabbits were solely housed on a 12 h/12 h -LRB- light/dark -RRB- cycle and provided with standard diet food and water ad libitum . All animals were housed in the Central Animal Laboratory , Utrecht University , Utrecht , The Netherlands and received care in compliance with the European Convention Guidelines . The animals were pre-anesthetized with a combination of methadone -LRB- 10 mg/ml at a dose of 2.5 -- 5.0 mg i.m. -RRB- , ventraquil -LRB- 10 mg/ml at a dose of 2.5 -- 5.0 mg i.m. -RRB- and etomidaat -LRB- 2 mg/ml at a dose of 2.0 -- 8.0 mg i.v. -RRB- . After introduction of anesthesia the rabbits were cuffed and mechanically ventilated with O2 : N2O , proportion 1:1 , and 2 % isoflurane . During the surgical procedure methadone -LRB- 2.0 -- 5.0 mg i.v. -RRB- was given . At the end of the operation nalbuphine -LRB- 10 mg/ml at a dose of 1.0 -- 2.0 mg/kg i.v. -RRB- was administered . After surgery the rabbits were housed at the intensive care for the rest of the day and night . At the end of the study the rabbits were euthanized by an i.v. overdose of pentobarbital . Surgical technique An operation device was created by the first author -LRB- Fig. 1 -RRB- . The operation device consists of a base plate . On this base plate , a dynamic plate was fixed which could be moved up or down . In front of the entire length of this dynamic plate a sideways moving slide was fixed on the base plate . On this sideways moving slide another back-forward slide including the drill was fixed . Forward movement of the drill was initiated using a 1.5 kg weight . Fig. 1Operation device consisting of a base plate with a slide for sideways movement on top of this . On this slide another back-forward slide with a drill was fixed . The forward movement was initiated by a weight of 1.5 kg . Another dynamic plate was fixed on the base of the plate to move up and down . During surgery , the rabbit was fixed on this dynamic plate During surgery the rabbit was fixed on the dynamic plate . This plate made it possible that the femur or tibia were on the same height as the K-wire . The sideways moving slide was responsible for the exact position of the K-wire in front of the femur or tibia . After the rabbits were preanesthetized , X-rays were made to exclude deformities . Thereafter the animal 's hind limb was carefully shaved and prepared with a povidone-iodine solution . After this procedure , the hind limb was fixed on the testing machine . With the animal surgically draped , a straight-line skin incision was made on the lateral aspect of the femur extending from just below the anterior -- inferior spine to the distal femur , followed by a straight-line skin incision on the lateral aspect of the tibia extending from just below the joint line proximally to about the joint line distally . Dissection was carried out down to the periostium . Synthes Trocar tipped K-wires of 70 mm length and 0.6 mm thickness were drilled through the diaphysis . One K-wire was drilled into the femur and one into the tibia . Drilling was performed by a rotary engine fixed at 1,200 rpm . This is the maximum drilling speed used in our daily practice . Cooling was not performed . After insertion , the K-wires were cut short and the K-wire ends were bent to the cortex . After the wounds were closed in layers , X-rays were made to check the position of the K-wires and the condition of the bone . Insertion time could be measured very accurately by analyzing the operations recorded on video camera . All the experiments were performed by the same investigator . Histological technique After termination , the femur and tibia were removed from the hind limb and fixed in 4 % formaldehyde solution . They were then decalcified , cut transversely next to the K-wire hole , after the K-wire was removed gently by a pair of tweezers , and embedded in paraffin according to standard procedures . Four micrometer thick serial sections were cut until the drill hole was visible , stained with hematoxylin and eosin and evaluated under a light microscope at 400 × magnification for the presence or absence of osteocytes in the osteocyte lacunae surrounding the drill holes by a single investigator . The best section was used for evaluation . The distance over which the osteocytes had disappeared perpendicular to the drill holes was measured with an interactive morphometry device -LRB- Q-PRODIT , Leica , Cambridge , UK -RRB- . In each section , four distances from the drill hole to the first osteocyte bearing lacuna were measured and averaged . Statistics Pearson 's Chi-square test was used to determine the drilling time needed for osteocytes to disappear . Pearson correlation was used to highlight any significant correlation between the drilling time and the distance of the disappeared osteocytes surrounding the drill holes . A P-value < 0.05 was considered statistically significant . The data were analyzed using SPSS 12.0.1 for windows . Results After surgery -LRB- t = 0 -RRB- six rabbits were terminated . Both hind limbs were used resulting in 24 assessments . From the remaining eight rabbits only one hind limb was operated . Four weeks later at termination this resulted in 16 assessments -LRB- t = 4 -RRB- . Two t = 0 and one t = 4 assessments were lost because no sections could be produced showing the drill hole , leaving 22 t = 0 and 15 t = 4 assessments . Histological response to drilling was seen in most sections next to the drill hole -LRB- Fig. 2 -RRB- . At t = 0 and t = 4 , all osteocyte lacunae next to the drill holes were empty in 11/22 -LRB- 50.0 % -RRB- and 13/15 -LRB- 86.7 % -RRB- of bones , respectively . At t = 4 , a drilling time longer than 27 s resulted in a significant loss of osteocytes surrounding the drill holes -LRB- P = 0.008 -RRB- . At t = 0 , a drilling time longer than 37 s resulted in a significant loss of osteocytes surrounding the drill holes -LRB- P = 0.011 -RRB- . Fig. 2Status of osteocytes due to K-wire drilling at 1,200 rpm . Disappeared osteocytes due to drilling are marked by a dot . The osteocytes are encircled . The dotted line indicates the border between the present and disappeared osteocytes -LRB- hematoxylin and eosin -RRB- A statistically significant positive correlation was seen between the distance of the empty osteocyte lacunae surrounding the drill hole in relation with drilling time , directly -LRB- P = 0.008 , Fig. 3a -RRB- and 4 weeks after K-wire insertion -LRB- P < 0.000 , Fig. 3b -RRB- . Fig. 3a Distance of empty osteocyte lacunae , in micrometers , surrounding the drill hole in correlation with drilling time , in seconds , at t = 0 . b Distance of empty osteocyte lacunae , in micrometers , surrounding the drill hole in correlation with drilling time , in seconds , at t = 4 Discussion The two most important findings of this experiment are that -LRB- 1 -RRB- osteocytes disappear especially beyond a drilling time of 27 s and -LRB- 2 -RRB- that there is a positive correlation between the distance of the empty osteocyte lacunae in relation with drilling time . As far as we know there are no experiments studying the presence/absence of osteocytes while using drilling time as a variable . Therefore it is interesting to notice that our results , representing daily practice by using a drilling speed of 1,200 rpm , clearly indicate that osteocytes disappear after a drilling time above 27 s in this animal model , when no cooling is used . Pallan -LSB- 28 -RSB- and Thompson -LSB- 33 -RSB- however , noticed absence of osteocytes already after a drilling time varying from 5 to 12 s . The limitation of their experiments is that the insertion force was not standardized . That could be a possible factor explaining the difference in insertion time with matching histological changes . The effect on osteocytes in our study was more pronounced 4 weeks after insertion compared to the effect on osteocytes directly after surgery . This confirms the results from Thompson -LSB- 33 -RSB- where the effect on osteocytes was more pronounced 72 h after the operation than at either 24 or 48 h. Apparently , not all damage to osteocytes is acute and microscopically visible , directly after K-wire insertion . This can be explained by the study of Eriksson et al. -LSB- 13 -RSB- . They compared histology and histochemistry for detection of bone viability directly after surgery . The histological sections revealed a 200 μm wide zone of empty osteocyte lacunae around the periphery of the drill hole while histochemistry of the same bone specimens showed a zone of 500 μm lacking diaphorase enzyme activity . This indicates that the empty osteocyte lacunae directly after surgery underestimates the extent of the drilling trauma and becomes clear as time passes . Neovascularisation is probably a reason for the increase of osteocytes disappearance in time . During heating , due to drilling , blood flow will stop in minor vessels preventing the clearance of the osteocyte lacunae -LSB- 10 , 13 -RSB- . After neovascularisation the osteocyte lacunae can be cleared finally . Our study also showed an interesting positive correlation between the distance of the empty osteocyte lacunae in relation with time . The zones of disappeared osteocytes varied from 106 to 1,285 μm and the drilling times from 41 to 262 s . A correlation between drill time and distance was not shown before . What we do know is that Eriksson et al. showed a 200 μm wide zone of empty osteocyte lacunae around the periphery of the hole after drilling into femurs of New Zealand white rabbits while conventional irrigation was administered -LSB- 13 -RSB- . These results are difficult to compare because Eriksson et al. did not measure time , irrigated with saline , did not standardize the insertion force and drilled with a running speed of 20,000 rpm . Furthermore , Pallan -LSB- 28 -RSB- showed that the histological changes , including absence of osteocytes , were never apparent more than 250 to 500 μm from the pins -LSB- 28 -RSB- . Like Eriksson they did not standardize insertion force and drilling time varied from 5 to 12 s. No osteoclastic activity was noticed in the already existing cortex . This was expected as Pallan -LSB- 28 -RSB- hardly noticed osteoclastic activity after 6 weeks , and only slight osteoclastic activity after 8 and 10 weeks and our experiments covered a time span of only 4 weeks . We are convinced that delayed tissue response corresponds with late K-wire loosening seen in daily practice . Although we did not compare drilling , with and without cooling , this study demonstrates the need for a short drill time , less than 27 s to prevent the disappearance of osteocytes and to limit the bone resorption cascade .

B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O 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animal
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Assessment of clusters of transcription factor binding sites in relationship to human promoter , CpG islands and gene expression Background Gene expression is regulated mainly by transcription factors -LRB- TFs -RRB- that interact with regulatory cis-elements on DNA sequences . To identify functional regulatory elements , computer searching can predict TF binding sites -LRB- TFBS -RRB- using position weight matrices -LRB- PWMs -RRB- that represent positional base frequencies of collected experimentally determined TFBS . A disadvantage of this approach is the large output of results for genomic DNA . One strategy to identify genuine TFBS is to utilize local concentrations of predicted TFBS . It is unclear whether there is a general tendency for TFBS to cluster at promoter regions , although this is the case for certain TFBS . Also unclear is the identification of TFs that have TFBS concentrated in promoters and to what level this occurs . This study hopes to answer some of these questions . Background Understanding the regulation of gene expression is a crucial issue in molecular biology . Since gene expression is mainly regulated by transcription factors -LRB- TFs -RRB- , the elucidation of relationships among TFs , their binding sites -LRB- TFBS -RRB- and their controlling genes , is of great importance . Although TFBS can be predicted by computer searches on DNA sequences , false positives -LRB- FP -RRB- are often produced . Several computer programs use position weight matrices -LRB- PWMs -RRB- -LSB- 1 -RSB- to predict TFBS in silico , including MatInspector -LSB- 2 -RSB- , MATCH -LSB- 3 -RSB- , and TFBS perl modules -LSB- 4 -RSB- . PWMs represent positional base preferences or frequencies constructed by a set of experimentally determined TFBS , and typically correspond to a single TF . The transcription factor database , known as TRANSFAC , is a widely used collection of PWMs -LSB- 5 -RSB- . TRANSFAC provides several PWMs for single TFs with different quality levels . Computer programs predict TFBS from DNA sequences , which are the same or similar to known TFBS . The low information contents in the matrices leads to many false positives , due to the weak preference or shortness of the site length -LRB- 6 -- 30 bp -RRB- . Various strategies have been proposed to allow correct identification of true positives -LRB- TPs -RRB- from predicted TFBS . One approach is to employ information from conserved regions in DNA sequences between different species , known as phylogenetic footprinting . Bayes block aligner -LRB- BBA -RRB- is a tool used to extract conserved regions from an alignment of two DNA sequences -LSB- 6 -RSB- . It was demonstrated that it could identify binding sites of muscle-specific transcription factors -LSB- 6 -RSB- . Another approach is to identify multiple TFBS that form a structural cluster on a DNA sequence coordinate . This seems a reasonable technique because the density of predicted TFBS in promoter sequences is reported to be higher than non-promoter sequences , especially in the region 300 bp upstream from the transcription start site -LSB- 7 -RSB- . Genes are regulated by interactions with multiple functional TFs in metazoans -LSB- 8 -RSB- . Therefore , many promoter prediction programs , such as promoterscan -LSB- 9 -RSB- , TSSG , and TSSW -LSB- 10 -RSB- , have been developed based on the density of TFBS . The identification of genuine TFBS by searching clusters of predicted TFBS has been successful ; however , these studies were evaluated with only specific genes and TF sets , such as those found in Yeast -LSB- 11 -RSB- , Drosophila -LRB- early developmental enhancer -RRB- -LSB- 12-14 -RSB- , liver -LSB- 15 -RSB- , LSF and muscle specific regulatory regions -LSB- 16,17 -RSB- . It is unknown whether this method is applicable to other species , or genes . Although many vertebrate promoter sequences have CpG islands -LRB- CGI -RRB- , the relationship between clusters of predicted TFBS and CGI is often underestimated -LSB- 18 -RSB- . Another strategy for the identification of putative TFBS includes a combinatorial approach that uses both phylogenetic footprinting and cluster analysis -LSB- 12,15,19 -RSB- . The program rVISTA utilizes information from conserved regions between human and mouse , in addition to clusters of TFBS predicted by the MATCH -LRB- BIOBASE -RRB- program -LSB- 19 -RSB- . This approach was evaluated using several known TFs -LRB- AP-1 , NFAT , and GATA-3 -RRB- and genes from the cytokine gene cluster . It remains unclear whether the properties used for clusters of TFBS are general and can be applied to other TFs or regulatory regions . Several reports have described methods for determining the statistical significance of predicted TFBS -LSB- 11,12,17,20-22 -RSB- . These studies assume the use of appropriate PWMs to identify clustered TFBS . To determine if a particular cluster is genuinely related to the promoter , it is important to assess clusters of predicted TFBS for each individual PWM . This is done using real non-promoter sequences for the appropriate selection of the PWM and for the interpretation of clusters of predicted TFBS . Most of these studies use specific sets of coregulated genes to identify common predicted TFBS clusters , and therefore can not be applied directly to the study of general properties of promoters . In this study , we developed a measure that evaluates the degree of concentration of predicted TFBS to clarify whether predicted TFBS have a tendency to cluster in human promoter sequences rather than in non-promoter sequences for each PWM . We identified some PWMs in which predicted TFBS clusters occur more significantly in promoter than non-promoter sequences and vice versa . Using partial correlations among three properties -LRB- promoters , CGI and clusters of predicted TFBS -RRB- , we identified two PWM groups , -LRB- 1 -RRB- those in which TFBS cluster in promoters as a result of the presence of CpG islands , and -LRB- 2 -RRB- those in which TFBS cluster in promoters independent of CpG islands . We show that transcription factors corresponding to the latter PWM group tend to be tissue-specific . In summary , this analysis is useful for the interpretation of predicted TFBS in regulatory regions . Results Divergent preferences of TFBS for promoter sequences We determined whether predicted TFBS formed clusters in human promoter sequences or in non-promoter sequences for each PWM using the cluster score described in the Method section . The higher the cluster score -LRB- derived from a logarithm of the p-value -RRB- , the stronger the cluster of predicted TFBS is related to the promoter sequence . The threshold T , used to determine whether a cluster of predicted TFBS is found on a sequence , was calculated simultaneously . Since a prediction for the presence of a TFBS was performed for each PWM , an assessment for TFBS clusters was performed using the cluster score for each PWM . As a result , a number of PWMs do not tend to have clusters of TFBS in the promoter sequence . We observed a divergence of cluster scores . Of the 199 vertebrate PWMs in TRANSFAC , 94 -LRB- 47 % -RRB- PWMs had significantly high cluster scores , while 22 -LRB- 11 % -RRB- PWMs had significantly low cluster scores . The remaining 83 -LRB- 42 % -RRB- PWMs did not show significant cluster scores . A p-value of 1.0 % was used to identify the above PWM set with Bonferroni correction for multiple testing -LRB- -LSB- 23 -RSB- Section 3.8 -RRB- . Figure 1 shows a histogram of cluster scores . Although these results were derived from genes on chromosome 20 , the results from other chromosomes were similar as described in the following subsection . PWMs with high cluster scores are shown in Table 1 . Some of the PWMs have thresholds T -LRB- of accumulated score C -RRB- equal to or less than 1.0 . This indicates that the occurrence of single predicted TFBS is more discriminative than clusters . Sequence logo -LSB- 24 -RSB- of the top three PWMs are depicted in Fig. 2 - -LRB- a -RRB- . See additional file 1 ` PWMs sorted by cluster score ' for the entire PWM list . Figure 1 A histogram of cluster scores for PWMs.Each number of X-axis indicates the maximum score of PWMs in the bin . Table 1 Top 50 PWMs for chromosome 20 sorted by cluster score S in descending order . Each column represents rank number , accession number in TRANSFAC , identifier in TRANSFAC , cluster score , and threshold . Rank ACCESSION ID S T 1 M00736 E2F1DP1_01 189.3 2.75 2 M00332 WHN_B 176.0 1.90 3 M00652 NRF1_Q6 122.0 0.93 4 M00649 MAZ_Q6 117.2 4.35 5 M00491 MAZR_01 111.4 1.78 6 M00739 E2F4DP2_01 103.8 0.93 7 M00737 E2F1DP2_01 103.6 0.94 8 M00108 NRF2_01 81.4 0.92 9 M00665 SP3_Q3 72.1 2.39 10 M00706 TFIII_Q6 61.4 4.23 11 M00740 E2F1DP1RB_01 58.4 0.90 12 M00324 MINI20_B 58.2 1.61 13 M00032 CETS1P54_01 57.3 3.70 14 M00743 CETS168_Q6 51.1 1.75 15 M00341 GABP_B 48.6 0.88 16 M00055 NMYC_01 41.1 0.90 17 M00329 PAX9_B 39.2 0.73 18 M00243 EGR1_01 37.3 0.87 19 M00072 CP2_01 36.5 1.66 20 M00054 NFKAPPAB_01 35.5 0.85 21 M00056 MYOGNF1_01 35.1 1.34 22 M00694 E4F1_Q6 35.0 0.86 23 M00738 E2F4DP1_01 34.9 0.91 24 M00143 PAX5_01 34.7 0.84 25 M00235 AHRARNT_01 34.6 0.92 26 M00698 HEB_Q6 33.6 0.91 27 M00039 CREB_01 33.6 1.00 28 M00514 ATF4_Q2 33.1 1.71 29 M00650 MTF1_Q4 31.4 0.88 30 M00194 NFKB_Q6 30.8 0.82 31 M00007 ELK1_01 30.0 0.85 32 M00733 SMAD4_Q6 29.7 0.81 33 M00261 OLF1_01 28.8 0.84 34 M00017 ATF_01 26.7 0.98 35 M00053 CREL_01 25.6 0.81 36 M00691 ATF1_Q6 25.5 0.89 37 M00244 NGFIC_01 25.2 0.88 38 M00041 CREBP1CJUN_01 24.9 1.00 39 M00086 IK1_01 24.2 0.90 40 M00287 NFY_01 24.0 1.95 41 M00466 HIF1_Q5 22.7 0.90 42 M00634 GCM_Q2 22.6 0.84 43 M00273 R_01 21.8 0.85 44 M00373 PAX4_01 21.7 2.57 45 M00097 PAX6_01 21.5 1.15 46 M00134 HNF4_01 21.1 0.64 47 M00670 TCF1P_Q6 21.1 0.80 48 M00057 COMP1_01 21.1 0.59 49 M00035 VMAF_01 21.0 1.32 50 M00222 HAND1E47_01 20.3 0.81 Figure 2 Sequence logos . -LRB- a -RRB- Top three PWMs from Table 1 , -LRB- b -RRB- representative PWMs from Table 2 , -LRB- c -RRB- representative PWMs from Table 3 . Cluster scores for different datasets To assess the robustness of the cluster score we compared cluster scores for different datasets from chromosomes 20 , 21 and 22 , respectively . Fig. 3 shows the correlation of cluster scores between chromosomes 20 and 21 -LRB- a -RRB- , and between chromosomes 20 and 22 -LRB- b -RRB- . Some PWMs , the matches of which were detected on less than 50 subsequences , are not shown . The correlation coefficient points were 0.91 -LRB- a -RRB- and 0.93 -LRB- b -RRB- . Figure 3 Title : Correlation of cluster scores -LRB- a -RRB- between chromosomes 20 and 21 , -LRB- b -RRB- chromosomes 20 and 22 . Each dot represents a distinct PWM -LRB- defined by the TRANSFAC matrix -RRB- . The correlation coefficients were -LRB- a -RRB- 0.91 and -LRB- b -RRB- 0.93 . Correlations among promoter sequences , CpG islands , and clusters About half of the human coding genes have a compositional bias for CGI over transcription start sites -LSB- 25 -RSB- . It is possible that some of the predicted TFBS clusters might be the consequence of the existence of a CGI . To investigate this possibility , we computed partial correlation coefficients of three categories of promoters , CGI and predicted TFBS clusters . In general , a partial correlation coefficient measures the correlation between any pair of variables when other , specified variables , have been held constant . For example , a partial correlation coefficient rIC.P is the correlation between I and C while controlling for P , where I denotes CGI , C denotes accumulated score -LRB- strength of predicted TFBS clusters , see Methods -RRB- and P denotes promoters . If we calculate simple correlation coefficients , rPI = 0.69 , rIC ranged from -0.25 to 0.57 for various PWMs , and rPC ranged from -0.24 to 0.53 for various PWMs . These correlation coefficients are apparent ones . The partial correlation coefficients provide essential information and pure correlations , without the effect of the third variable . Fig. 4 shows a plot of rIC.P against rPC.I for various PWMs . For most of the PWMs , rPC.I is positive , although not particularly high -LRB- < 0.3 -RRB- . This implies a correlation between clusters of these PWM matches and promoter sequences , separate to the effect of CGI . For the PWMs in the right circle in Fig. 4 , rPC.I is high and rIC.P is approximately zero , where the cluster is more correlated with the promoter than the CGI . Some PWMs have a negative rPC.I , implying the absence of promoter sequences for these PWM matches . For the PWMs in the top circle in Fig. 4 , rIC.P is high and rPC.I is approximately zero , suggesting that the correlation between promoters and clusters for these PWM matches is attributable to the presence of the CGI . While these promoters and clusters do not correlate directly , they appear to correlate because both are associated with CGI . Figure 4 Title : Plot of rIC.P against rPC.I for various PWMs . The top circle is the area where rPC.I is around zero and rIC.P is high . The right circle is the area where rIC.P is around zero and rPC.I is high . The two circles were drawn manually . Ideal CGI-related and CGI-independent PWMs are to be plotted in the top and right circles , respectively . Using these two values , we identified two PWM sets , -LRB- 1 -RRB- a CGI-related set -LRB- 37 PWMs , Table 2 -RRB- in which TFBS clusters are correlated with CGI -LRB- independent of promoter -RRB- , and -LRB- 2 -RRB- a CGI-independent set -LRB- 54 PWMs , Table 3 -RRB- in which clusters of TFBS are correlated with promoters -LRB- independent of CGI -RRB- . These sets were used for the following analysis . Table 2 CGI-related PWMs in descending order of rIC.P = Y . The columns are : rank number , accession number , Identifier in TRANSFAC , rPC.I -LRB- = X -RRB- , rIC.P -LRB- = Y -RRB- , cluster score and threshold . Rank ACCESSION ID X Y S T 1 M00332 WHN_B 0.09 0.43 158.4 1.9 2 M00736 E2F1DP1_01 0.06 0.39 151.8 2.6 3 M00739 E2F4DP2_01 0.09 0.29 91.4 0.9 4 M00737 E2F1DP2_01 0.06 0.27 81.9 0.9 5 M00108 NRF2_01 0.09 0.25 72.6 0.9 6 M00055 NMYC_01 0.05 0.25 34.6 0.9 7 M00235 AHRARNT_01 0.02 0.23 26.8 0.9 8 M00740 E2F1DP1RB_01 0.04 0.23 48.1 0.9 9 M00652 NRF1_Q6 0.05 0.22 105.3 0.9 10 M00466 HIF1_Q5 0.01 0.22 19.7 0.9 11 M00341 GABP_B 0.1 0.19 46.6 0.9 12 M00738 E2F4DP1_01 0.02 0.19 28.6 0.9 13 M00538 HTF_01 0 0.16 9.7 0.8 14 M00694 E4F1_Q6 0.03 0.16 23.6 0.9 15 M00743 CETS168_Q6 0.13 0.14 47.1 1 16 M00650 MTF1_Q4 0.04 0.14 22.6 0.9 17 M00243 EGR1_01 0.07 0.12 32.4 0.9 18 M00251 XBP1_01 0.01 0.12 7.8 0.9 19 M00691 ATF1_Q6 0.07 0.12 17.3 0.9 20 M00236 ARNT_01 0.02 0.11 6.5 1 21 M00143 PAX5_01 0.09 0.11 25.7 0.8 22 M00273 R_01 0.06 0.11 23.8 0.8 23 M00244 NGFIC_01 0.06 0.1 23 0.9 24 M00280 RFX1_01 0.06 0.1 11.1 0.9 25 M00121 USF_01 0.03 0.1 7.6 1 26 M00287 NFY_01 0.04 0.1 21.3 1.9 27 M00039 CREB_01 0.04 0.09 23.2 1 28 M00309 ACAAT_B 0.04 0.09 6.8 0.9 29 M00651 NFMUE1_Q6 0.03 0.09 13 1.8 30 M00017 ATF_01 0.06 0.08 19.2 1 31 M00481 AR_01 0.05 0.08 7.5 0.8 32 M00041 CREBP1CJUN_01 0.04 0.08 20.4 1 33 M00040 CREBP1_01 0.03 0.08 4.7 0.9 34 M00114 TAXCREB_01 0.02 0.06 7.3 0.9 35 M00279 MIF1_01 0.02 0.06 10.9 1.8 36 M00246 EGR2_01 0.04 0.06 9.7 0.9 37 M00085 ZID_01 0.05 0.06 8 0.8 Table 3 CGI-independent PWMs in descending order of rPC.I -LRB- = X -RRB- . The columns are : rank number , accession number , Identifier in TRANSFAC , rPC.I -LRB- = X -RRB- , rIC.P -LRB- = Y -RRB- , cluster score and threshold . Rank ACCESSION ID X Y S T 1 M00491 MAZR_01 0.27 0.15 117.4 1.8 2 M00706 TFIII_Q6 0.24 0.06 52.7 3.5 3 M00324 MINI20_B 0.22 0.1 53.2 0.8 4 M00056 MYOGNF1_01 0.22 0 31.6 1.3 5 M00649 MAZ_Q6 0.21 0.19 114.4 3.7 6 M00665 SP3_Q3 0.2 0.14 67.7 1.7 7 M00032 CETS1P54_01 0.19 0.1 47.7 1.8 8 M00053 CREL_01 0.19 0.04 26.9 0.8 9 M00054 NFKAPPAB_01 0.19 0.06 33.5 0.9 10 M00632 GATA4_Q3 0.19 0.04 25.1 0.6 11 M00373 PAX4_01 0.19 0.05 26.1 0.6 12 M00072 CP2_01 0.19 0.08 32 0.9 13 M00733 SMAD4_Q6 0.18 0.05 26.3 0.8 14 M00134 HNF4_01 0.18 0.06 25.7 0.6 15 M00194 NFKB_Q6 0.18 0.02 28.5 0.8 16 M00445 XVENT1_01 0.17 0.01 19.9 0.7 17 M00057 COMP1_01 0.17 0.05 24.1 0.5 18 M00097 PAX6_01 0.17 0.06 24.1 0.5 19 M00104 CDPCR1_01 0.17 0.03 21.3 0.6 20 M00222 HAND1E47_01 0.17 0.02 20.4 0.8 21 M00626 EFC_Q6 0.17 0.05 22.6 0.6 22 M00745 LEF1_Q6 0.16 -0.02 15.9 0.8 23 M00707 TFIIA_Q6 0.16 0.03 20.2 0.7 24 M00086 IK1_01 0.16 0.06 24.1 0.9 25 M00329 PAX9_B 0.16 0.1 33.7 0.7 26 M00478 CDC5_01 0.15 0.03 19 0.6 27 M00670 TCF1P_Q6 0.15 0.06 22.7 0.8 28 M00257 RREB1_01 0.15 -0.02 15.8 0.8 29 M00007 ELK1_01 0.15 0.08 31 0.8 30 M00698 HEB_Q6 0.15 0.08 28.7 0.9 31 M00052 NFKAPPAB65_01 0.14 -0.05 9.4 0.9 32 M00514 ATF4_Q2 0.14 0.05 21.8 1.7 33 M00191 ER_Q6 0.14 -0.03 11 0.8 34 M00003 VMYB_01 0.14 0.05 18 0.8 35 M00261 OLF1_01 0.14 0.07 24.6 0.8 36 M00490 BACH2_01 0.13 -0.03 9.3 0.7 37 M00001 MYOD_01 0.13 -0.03 10.4 0.9 38 M00634 GCM_Q2 0.12 0.05 19.8 0.8 39 M00035 VMAF_01 0.12 0.06 17.5 0.7 40 M00340 ETS2_B 0.12 -0.08 5 0.8 41 M00005 AP4_01 0.12 0.01 14.1 0.8 42 M00701 SMAD3_Q6 0.11 0.03 11.4 0.8 43 M00531 NERF_Q2 0.1 -0.08 4.8 0.9 44 M00339 ETS1_B 0.1 -0.07 5.7 0.9 45 M00657 PTF1BETA_Q6 0.1 0 7.5 0.9 46 M00254 CAAT_01 0.1 -0.01 6.6 0.9 47 M00118 MYCMAX_01 0.09 -0.02 6.2 0.9 48 M00693 E12_Q6 0.09 -0.01 6.5 0.9 49 M00004 CMYB_01 0.08 0 7.1 0.9 50 M00238 BARBIE_01 0.08 0.02 9.4 0.9 51 M00648 MAF_Q6 0.07 0.01 5.8 0.8 52 M00002 E47_01 0.06 0.02 5.3 0.9 53 M00262 STAF_01 0.05 0 9.2 0.9 54 M00119 MAX_01 0.05 0.03 4.9 1 Correlation between clusters of predicted TFBS and gene expression Since all widely expressed , or housekeeping , genes have CGI -LSB- 25 -RSB- , it is possible that clusters of PWM matches for CGI-independent sets are associated with tissue specific promoters . For this reason we examined the relationship between clusters of PWM matches and the tissue specificity of the associated genes using published gene expression data -LRB- -LSB- 26,27 -RSB- -RRB- . The two resources used for this analysis are not consistent . Some genes annotated as housekeeping genes in one resource are referred to as tissue specific in another resource . We refer to these genes as mixed annotated genes . Genes with associated expression data were analysed and of these 72 were identified among the gene set covering the three chromosomes used in this study . They included 12 housekeeping genes , 9 mixed annotated genes , and 51 tissue specific genes . With the CGI-independent PWM sets we detected promoters with clusters of PWM matches . These clusters have significantly high Z-scores -LRB- see Methods -RRB- based on the accumulated score C in randomly generated DNA sequences -LRB- as control -RRB- with the same dinucleotide frequency of each promoter sequence . Table 4 shows the 40 genes detected , the DCC score -LRB- described below -RRB- , their tissue specificity and start_p score . These genes are sorted according to the DCC score indicating the extent of association with CGI-independent PWMs over CGI-related PWMs . Results show that tissue specific genes tend to have high DCC scores and that transcription factors corresponding to CGI-independent PWMs are related to tissue specific genes . If we extract 20 genes which have DCC scores equal to or higher than -0.03 , 18 -LRB- 90 % -RRB- of these are tissue specific genes . The 72 genes with known gene expressions data included 51 tissue specific genes -LRB- 71 % -RRB- . The p-value of the event of extraction was 0.04 under cumulative hypergeometric distribution . The p-value of the ranking of the two groups -LRB- 11 housekeeping and 29 tissue-specific -RRB- in Table 4 was 0.01 by Wilcoxon rank test . Note that DCC is not correlated with the CGI score -LRB- start_p -RRB- . Table 4 The gene list sorted by DCC score . The genes , in which clusters of TFBS are found on promoters using CpG-related/independent PWMs , and tissue specificity , have been previously identified . HK denotes housekeeping . Tissue specific genes can be selected independent of CpG islands -LRB- start_p -RRB- using DCC score . 1 NM006272 0.43 brain 0 2 NM007341 0.4 muscle 0 3 NM002592 0.37 brain 0.86 4 NM001819 0.27 brain 0.68 5 NM004414 0.23 kidney 0.89 6 NM002999 0.19 kidney 0.73 7 NM003195 0.16 brain 0.73 8 NM002591 0.14 liver 0 9 NM000454 0.11 HK.liver 0.87 10 NM003312 0.1 liver 0.72 11 NM004339 0.09 brain 0.9 12 NM020708 0.08 brain 0.64 13 NM006870 0.05 HK 0.7 14 NM003277 0.04 lung 0.74 15 NM005194 0.04 brain 0.86 16 NM003610 0.01 brain 0.76 17 NM000355 -0.03 kidney 0 18 NM002430 -0.03 muscle 0.75 19 NM006767 -0.03 brain 0.74 20 NM005137 -0.03 muscle 0.76 21 NM003279 -0.05 muscle 0 22 NM004535 -0.05 brain 0 23 NM007019 -0.05 HK 0.72 24 NM013236 -0.07 HK 0.69 25 NM004175 -0.07 brain 0.72 26 NM001958 -0.07 muscle 0 27 NM001338 -0.13 vulva 0.84 28 NM002676 -0.14 HK 0.63 29 NM003098 -0.16 muscle 0.71 30 NM002854 -0.17 brain 0 31 NM002305 -0.23 HK 0 32 NM005080 -0.25 HK 0.84 33 NM001024 -0.25 HK 0.76 34 NM021974 -0.26 HK 0.63 35 NM014876 -0.3 HK 0.95 36 NM001098 -0.34 muscle 0.65 37 NM000071 -0.37 liver 0.8 38 NM006198 -0.37 brain 0 39 NM001675 -0.39 HK.muscle 0.8 40 NM005423 -0.68 brain 0 Discussion Clusters of TFBS are an important property of regulatory regions -LSB- 7,8,19,28 -RSB- . To determine if this is a general tendency for PWM matches and all protein coding genes , we have developed a measure that evaluates the correlation between predicted TFBS concentrations and promoter sequences . We then examined the correlation for individual PWMs using an unbiased sequence set . Our results show that not all TFBS are clustered in promoter sequences . We found that TFBS clusters corresponding to 47 % of PWMs are positively correlated with promoter sequences , and that TFBS clusters corresponding to around 11 % of PWMs are negatively correlated with promoter sequences . It is important to ascertain the relationship between cluster scores of PWMs and CGI , because CGI are a prominent feature of promoter sequences . The consensus sequences of the top-ranked PWMs -LRB- Table 1 -RRB- are , ` ANNGACGCTNN ' -LRB- WHN_B -RRB- , ` TTTCSCGC ' -LRB- E2F1DP1_Q6 -RRB- , ` NSGGGGGGGGMCN ' -LRB- MAZR_01 -RRB- , and ` GGGGAGGG ' -LRB- MAZ_Q6 -RRB- , where S represents C or G , M represents A or C , and N represents any bases . The sequence logos of the PWMs are depicted in Fig. 2 - -LRB- a -RRB- . The G+C % of base composition of each matrix is 70 % , 56 % , 91 % , and 86 % , respectively . The sequences with high cluster scores appear to be GC-rich . Larsen et al found that 57 % of human genes are associated with CGI , that all housekeeping genes have CGI covering transcription start sites -LRB- TSS -RRB- , and that 40 % of tissue specific genes have CGI -LSB- 25 -RSB- . Therefore , the association of PWM-PCP with CGI may be significant , and CGI-related PWMs may play important roles in housekeeping regulation . To evaluate the relationship between PWM-PCP and CGI , we calculated the partial correlation coefficient for each PWM . In general , if a correlation coefficient rXY is not small and rXY.Z -LRB- defined in Methods -RRB- ≈ 0 , the probable hypotheses concerning cause and effect will be either 1 -RRB- the correlation of X and Y is a consequence of Z , or 2 -RRB- Z intervenes between X and Y. For the PWMs in the top circle in Fig. 4 , rIC.P is high and rPC.I is approximately zero . This suggests that the correlation between promoters and TFBS clusters is attributable to the presence of the CGI and that while they do not directly correlate they appear to because both independently correlate with CGI . The characteristic PWMs in Table 2 are NMYC_01 -LRB- M00055 : 0.25 -RRB- , AHRARNT_01 -LRB- M00235 : 0.23 -RRB- , and HIF1_Q5 -LRB- M00466 : 0.22 -RRB- , where parentheses include the accession number referred to in TRANSFAC and the recorded rIC.P -LRB- Y-value in Fig. 4 -RRB- . Sequence logos are depicted in Fig. 2 - -LRB- b -RRB- . The PWMs in the middle right circle in Fig. 4 have an rIC.P of approximately zero and a high rPC.I rPC.I value showing that the cluster is correlated with promoters independent of CGI . The predicted TFBS clusters corresponding to these PWMs could not be explained by the presence of CGI . Some of these PWMs have thresholds T less than 1.0 indicating that even the single occurrence of a predicted TFBS is more discriminative than clusters . Particular examples with high recorded rPC.I values and values for rIC.P < 0.1 are TFIII_Q6 -LRB- M000706 -RRB- , MYOGNF1_01 -LRB- M00056 -RRB- and CREL_01 -LRB- M00053 -RRB- . Sequence logos are depicted in Fig. 2 - -LRB- c -RRB- . TFIII_Q6 is a matrix associated with a general transcription factor II-I with the consensus sequence RGAGGKAGG , where the K represents G or T . The matrix TFIII_Q6 contains many ` G ' , and ` C ' is allowed only the fourth position with low frequency . MYOGNF1_01 is a matrix associated with myogenin , nuclear factor 1 or related factors , and is therefore involved in the regulation of differentiation . CREL_01 is a matrix associated with the C-Rel proto-oncogene protein -LRB- C-Rel protein -RRB- . An understanding of the function of these factors is important to this study . The PWM groups described above may be involved in tissue-specific gene regulation . If all housekeeping genes have CGI -LSB- 25 -RSB- then genes without CGI can be assumed to be tissue-specific or rarely expressed . Thus , genes with a cluster of predicted TFBS not associated with CGI might be associated with tissue-specific regulation . Further analysis of extractions of tissue specific genes , shown in Results , supports the hypothesis . Results from this analysis provide a solution to the promoter prediction problem . Hannenhalli et al. used additional information , including profiles of TF binding sites , for promoter prediction based on CGI -LSB- 29 -RSB- , with no significant improvement to prediction performance . The report using 7 manually selected PWMs confirmed that CGI are the most dominant feature . Our results show that Sp1 and ATF have a strong correlation with CGI ; a result consistent with their result that information including both PWM did not improve prediction accuracy . This observation is consistent with other PWMs . More stringent selection of PWMs is required for an improved accuracy of promoter prediction . One strategy is to utilise the CGI-independent PWMs identified in this study . Another problem is exemplified by the under-representation of Oct-1 -LRB- M00138 -RRB- in the -LRB- -600:600 -RRB- region of the human promoter and the absence of positional preferences -LSB- 29 -RSB- . This under-representation was not expected but is observed in 10 % of known PWMs . OCT1_04 -LRB- M00138 -RRB- is not in the high quality list of TRANSFAC , OCT1_01 -LRB- M00135 -RRB- and OCT1_C -LRB- M00210 -RRB- was found to have minus cluster scores -LRB- -0.63 and -2.89 -RRB- in our table -LRB- additional file 1 -RRB- . It is noteworthy that Fig. 5 shows TFBS -LRB- AP2_Q6 -RRB- in non-promoters to occur randomly under a certain distribution . This distribution can be modelled by a binomial probability distribution . A model of Poisson distribution , which is an approximation of binomial probability distribution for a certain condition , was proposed in -LSB- 11 -RSB- as the probability distribution of TFBS density . Although we have not tested the goodness-of-fit , our observation does not contradict the Poisson distribution model . Figure 5 Title : Distribution of accumulated score C for promoters and non-promoters for AP2_Q6 To assess the robustness of the cluster score , we compared cluster scores for different datasets from chromosomes 20 , 21 and 22 -LRB- Fig. 3 -RRB- . The correlation coefficients were 0.91 -LRB- a -RRB- and 0.93 -LRB- b -RRB- , proving that the significance would be similar if we utilized the whole human genome dataset in the analysis . The scale of the figures between the Y-axis and X-axis are different because of the different number of sequences taken from each chromosome . Conclusions We have developed a measure that statistically evaluates the degree of concentration of predicted TFBS in promoter sequences . Using this strategy to analyse various PWMs we have determined that predicted TFBS tend to cluster in human promoter sequences rather than in non-promoter sequences . Our results show that local concentrations of predicted TFBS in human promoter sequences are not a general characteristic of PWMs . Only a portion of identified PWM matches corresponded to TFBS occurring in clusters in promoter sequences . By computing partial correlation coefficients , we identified PWM sets associated with CGI and others that are independent of CGI . Transcription factors and binding sites associated with CGI-independent PWMs are likely to be involved in tissue-specific gene regulation . Indeed , using the CGI-related/dependent PWM sets , we extracted tissue-specific genes with high accuracy by detecting clusters of predicted TFBS . These results will be useful to interpret predicted transcription factor binding sites and to further understand the role of their formation into clusters . Ultimately , these findings will further elucidate the various functions of promoters , genes and transcription factors . Methods Data DNA sequences from the fully sequenced chromosomes -LRB- chromosomes 20 , 21 and 22 -RRB- were taken from the November , 2002 GenBank freeze -LRB- build 31 -RRB- and assembled by NCBI , in accordance with the annotation of the UCSC genome browser -LSB- 30 -RSB- . RefSeq -LSB- 31 -RSB- genes were used as they have been reviewed by NCBI staff , are well studied , and are unlikely to be spurious . Some genes in the human genome have alternative promoters -LSB- 32 -RSB- , complicating our analysis . For this reason , overlapping genes identified using the UCSC annotation were discarded . This check of RefSeq genes reduced the number of genes in the analysis from 527 to 373 for chromosome 20 , 224 to 142 for chromosome 21 , and 449 to 294 for chromosome 22 . The resultant gene set U consists of 809 genes . To increase the accuracy of the annotation of transcriptional start sites , we modified the annotation of RefSeq according to DBTSS -LRB- version 2 , Mar 2002 -RRB- -LSB- 33 -RSB- , a database of transcriptional start sites for 5 ' end mRNA sequences . Suzuki et al. reported that a certain portion of sequences in DBTSS were longer -LRB- extended -RRB- toward 5 ' end of mRNA sequences than those in RefSeq -LSB- 33 -RSB- . We describe how the modification improved the first gene set U. Fig. 6 shows the composition of different gene collections . The RefSeq database is updated daily by increasing the number of entries and correcting others . For illustration purpose , two versions of RefSeq are shown in Fig. 6 . The old RefSeq is the version analysed by Suzuki et al. . The new RefSeq is the current version used in this study . Of the 217,402 sequences contained in DBTSS 7,889 correspond to sequences in RefSeq and are referred to as cloned RefSeq sequences . The extension rate , defined as the rate of extension of mRNAs sequences from cloned RefSeq by DBTSS , was 0.34 . Therefore , | -LCB- D , G -RCB- | -LRB- the number of gene set -LCB- D , G -RCB- -RRB- = 7,889 genes , | -LCB- Dex , Gex -RCB- | = 2,683 genes and | -LCB- Dex , Gex -RCB- | / | -LCB- D , G -RCB- | = 0.34 . The ftp site of DBTSS provides the set of extended mRNA sequences -LRB- Iftp = -LCB- Dex , Gex -RCB- -RRB- . The gene set from chromosomes 20 , 21 and 22 is a partial set U = -LCB- Cu , Eu , Fu , , -RCB- from New RefSeq . We can identify the genes in set as the conjunction of Iftp and U . The number of was counted -LRB- 273 genes -RRB- . If the extension rate of mRNAs sequences for -LCB- , -RCB- is also 0.34 , then | -LCB- -RCB- | / | -LCB- , -RCB- | = 0.34 . Therefore , -LCB- | , -RCB- | is estimated to be 802.9 genes . As | U | = 809 , the number of genes in V = -LCB- Cu , Eu , Fu -RCB- is estimated to be 6.1 genes . If all human genes are cloned by the cap-targeted selection method called oligo-capping , a greater number will have extended 5 ' ends . If this were the case then 34 % -LRB- or 2.1 genes -RRB- of V would have extended 5 ' ends . Thus , any further correction of TSS for our gene set is expected to be quite small . Figure 6 A Venn diagram of three gene sets -LRB- DBTSS , old RefSeq , and new RefSeq -RRB- . Gene sets from A to G -LRB- Bold alphabet -RRB- consist of genes in the regions bounded by the thick lines . D consists of Dn -LRB- genes whose 5 ' end sequences were not extended from the old RefSeq sequences with DBTSS data -RRB- and Dex -LRB- genes whose 5 ' end sequences were extended -RRB- . G consists of Gn -LRB- genes whose 5 ' end sequences were not extended from the old RefSeq sequences with DBTSS data -RRB- and Gex -LRB- genes whose 5 ' end sequences were extended -RRB- . Namely D = -LCB- Dn , Dex -RCB- and G = -LCB- Gn , Gex -RCB- . Genes in chromosomes 20 , 21 , 22 were denoted by U = -LCB- Cu , Eu , Fu , , -RCB- . Gene sets Cu , Eu , Fu , and are parts of C , E , F , Gn and Gex , respectively . Some of the numbers of the sets are given in -LSB- 33 -RSB- , that is , | -LCB- D , G -RCB- | = 7889 , | -LCB- Dex , Gex -RCB- | = 2683 and | -LCB- Dex , Gex -RCB- | / | -LCB- D , G -RCB- | = 0.34 , where | -LCB- D , G -RCB- | denotes the number of genes in set -LCB- D , G -RCB- . We identified the conjunction set -LRB- 273 genes -RRB- of the above collected 809 RefSeq genes and 2,683 genes in DBTSS . The set was examined to determine if extended sequences existed on human genome sequences and if they are registered in the new RefSeq . Of this set , the BLAT program -LSB- 34 -RSB- identified 30 genes in which the 5 ' end sequences could not be detected . Due to the uncertainty of TSS these genes were not used in this study . Forty-one DBTSS mRNA sequences from were shorter than corresponding sequences in the new RefSeq with regard to 5 ' end sequences . It is assumed that these RefSeq sequences were corrected following the old RefSeq release independent of DBTSS and were used as they were . Finally , we modified the exon annotation of 202 genes according to DBTSS . We extracted promoter sequences at relative positions -LRB- -600 : -1 -RRB- from the TSS , and intron subsequences 600 bp in length from genome sequences . Only intron sequences were used for the non-promoter sequence data sets as exon sequences are known to have preferences in their oligomer statistics , such as G+C % and codon bias -LSB- 35 -RSB- . The first intron was not included in the data set as although regulatory elements are rare in introns , intron 1 occasionally contains regulatory elements such as enhancers . We investigated the frequency of enhancers in human introns by searching NCBI PubMed -LSB- 36 -RSB- with the keywords ` human ' , ` first intron ' , and ` enhancer ' . This search yielded 194 papers . Replacing the keyword ` first intron ' with ` second ' , ` third ' , ` fourth intron ' , ` fifth ' or ` last intron ' yielded 40 , 15 , 1 , 1 and 6 papers , respectively . Replacing ` enhancer ' with ` silencer ' resulted in 281 , 6 , 3 , 0 , 0 and 0 papers , respectively . Removal of intron 1 from the data set greatly reduces the overall occurrence of regulatory elements in human intron sequences and allows our statistical analysis to be performed without significant interference from intronic regulatory sequences . Intergenic sequences are left out of the non-promoter dataset due the unknown occurrence of regulatory sequences . Prediction of TFBS Each promoter or non-promoter sequence was scanned by the MATCH program using 423 matrices in TRNASFAC version 6.3 -LRB- a transcription factor database -RRB- with options including ` vertebrate ' , ` minimize false negatives ' -LRB- in cut-off selection -RRB- and ` use high quality matrices only ' . As Kel et al. described , the cut-off was determined so that the false negative rate is 10 % -LSB- 3 -RSB- . The option ` use high quality matrices only ' uses approximately 70 % of matrice -LSB- 3 -RSB- . Any PWM in the ` high quality ' PWMs meet the criteria ; When the PWM is used with a cut-off value which allows a false negative rate of 50 % , then the match rate dropped below 1 match/kb in exon2 sequences -LSB- 3 -RSB- . If more than one matrix was matched to same transcription factor -LRB- prefix of `` Identifier '' -RRB- , we selected a representative matrix with the highest quality and smallest suffix number according to the TRANSFAC definition . After scanning the sequences by MATCH , we set consecutive sampling windows -LRB- 600 bp -RRB- in introns and promoter sequences , and then recorded corresponding TFBS predictions . To prevent double counting of palindromic binding sites , two matches for the same matrix at the same position was regarded as a single match and the match with the higher score was taken . Before MATCH ran , repeat sequences were masked to 'N' according to the annotation by RepeatMasker in the UCSC genome browser . From the above analysis we extracted 361 , 129 , and 278 promoter sequences from chromosomes 20 , 21 and 22 , respectively . The promoter sequences identified contained repeat sequences -LRB- e.g. ALU , L1 -RRB- and simple repeats with low complexity , as observed in intron sequences . These sequences account for about 20 % of all bases . To balance the rate of repeats between promoters and introns , we discarded intron sequences with high rates of repeats , so that the average rate of repeats in the intron samples was at the same level as in promoter sequences . The number of 600 bp intron sequences included in the analysis was 6,589 -LRB- chromosome 20 -RRB- , 4,324 -LRB- chromosome 21 -RRB- and 4,531 -LRB- chromosome 22 -RRB- . Accumulated scores of TFBS When predicted TFBS occur many times in a sequence there is a high probability that it contains functional regulatory regions or promoters -LSB- 7,8,19,28 -RSB- . We tested this hypothesis for individual PWMs . The degree of concentration of predicted TFBS in a sequence was defined as the accumulated score C , which is a summation of the MATCH score for PWMs in the subsequence and is calculated for each PMW and corresponding sequence . C is assumed to be almost proportional to the frequency of predicted TFBS for the corresponding PWM . Many sequences generate different C values although some are identical . We then generated a series of Cj -LRB- j = 1 ... n -RRB- values for a PWM , where n is the number of different C values . Fig. 5 shows the histogram of C for promoters and non-promoters respectively , using the TRANSFAC matrix of identifier ` AP2_Q6 ' as an example . Since C reflects the number of predicted TFBS found in a sequence , the figure shows the density of predicted TFBS in a sequence . This result is similar to the density plot described by Pestridge and Burks -LSB- 7 -RSB- , although our figure -LRB- Fig. 5 -RRB- is not a plot of predicted TFBS density for mixed PWMs , but instead is a plot of predicted TFBS density for individual PWMs . Also , the X-axis in our plot does not indicate the number of predicted TFBS but instead indicates the accumulated score C . The Y-axis is smoothed by averaging for the width of 5 in C value . Cluster score and statistical significance for a PWM Significance values for an individual PWM from a series of Cj can be determined from a contingency table . Table 5 shows a contingency table for the number of promoters and non-promoters above and below the threshold Cj for a given PWM . From this table , χ2 value for a given Cj is defined as Table 5 A contingency table when predicted TFBS and a threshold T are given . Sequences where TFBS clusters found Sequences where TFBS clusters not found Sum # of promoter A 1 A 2 A # of non-promoter B 1 B 2 B where described in -LSB- 37 -RSB- . From the χ2 value , we computed the probability P that the χ2 value or greater is obtained by chance . The probability P was calculated from the χ2 . Since P is calculated for many PWMs , we must deal with the problem of multiple testing . Using the Bonferroni correction -LSB- 23 -RSB- , Pn was calculated using the formula 1 - Pn = -LRB- 1 - P -RRB- n , approximately Pn = P × n for small P × n . The n is the number of PWMs . When we determine the set of significant PWMs , Pn were compared with the significance level -LRB- i.e. 0.01 -RRB- . We also defined the statistical significance Qj as Qj = - log10 -LRB- Pn -RRB- if Rprom > Rnonprom and Qj = + log10 -LRB- Pn -RRB- otherwise , where Rprom = A1/A -LRB- a rate of sequences in promoters where clusters found -RRB- , Rnonprom = B1/B -LRB- a rate of sequences in non-promoters where clusters found -RRB- . Although the P is an indicator of the difference between the occurrence of promoters and non-promoters , the probability P itself does not represent the preferences of PWMs for promoters . To represent the preference of predicted TFBS for or against promoters , we add signs for statistical significance Qj . Positive Qj indicates that predicted TFBS tend to appear frequently in promoters , while negative Qj indicates that predicted TFBS tend to avoid promoters . We studied how statistical significance Qj varies with the threshold of C j. Fig. 7 shows the presence of a peak of Qj when we change the threshold . We define the cluster score S of a PWM in such a way that the significance is the maximum , namely Figure 7 Title : significant score Qj of matrix AP2_Q6 for different thresholds . We simultaneously define a unique threshold T of the PWM by For the all-vertebrate TRANSFAC PWMs , we determined thresholds T and calculated significance scores -LRB- or cluster scores -RRB- S . The highest scoring PWMs are listed in Table 1 . Correlations among promoter sequences , CpG islands , and clusters For every 600 bp sequence , three numerical features -LRB- promoter , CGI , and clusters -RRB- were annotated . CGI were identified using the CpGProD program -LSB- 38 -RSB- for original long sequences -LRB- not for short sequences of 600 bp -RRB- . Regions larger than 500 bp with a G+C % equal to or greater than 50 % and ` observed CpG / expected CpG ' equal to or greater than 0.60 were classified as CGI -LSB- 38,39 -RSB- . The CpGProD program outputs ` start_p ' scores for the predicted CGI . This score indicates the probability that the region is a CGI located over a transcription start site -LRB- start CGI -RRB- . Short 600 bp sequences sampled from long sequences containing CGI were annotated as CGI if the overlapping CGI region was longer than 300 bp . The accumulated score C was used for cluster annotation . From sequences with feature annotation , the correlation coefficients between every two of the three features were computed for each PWM by the statistical language R -LSB- 40 -RSB- . We use P to denote whether the sequences is promoter or not , namely P = -LCB- 1,0 -RCB- , and I to the denote ` start_p ' score for CGI calculated using the CpGProD program -LSB- 38 -RSB- . A partial correlation coefficient for each PWM was calculated using the subsequences . For example , a partial correlation coefficient rPC.I is the correlation between P and C while controlling for I , defined by where rPI is a correlation coefficient between variable P and I , rPC is a correlation coefficient between variable P and C and rCI is a correlation coefficient between variable C and I . A partial correlation coefficient differs from a correlation coefficient . If the correlation between P and C depends entirely on the common cause I , then when I is constant , the correlation between P and C should be zero . The partial correlation rPC.I expresses such a relationship . Even when I varies , rPC.I is expected to be zero in such a situation , while the correlation coefficient rPC may not be zero . See chapter 16.4 in -LSB- 41 -RSB- for details . Fig. 4 shows a plot of rIC.P against rPC.I for various PWMs . Using these two values , we identified two PWM sets including , -LRB- 1 -RRB- a CGI-related set consisting of 37 PWMs in which the clusters are correlated with CGI independent of promoters , and -LRB- 2 -RRB- a CGI-independent set consisting of 54 PWMs , in which the clusters are correlated with promoters independent of CGI . The CGI-related set requires that rIC.P > rPC.I and that the partial correlation coefficient -LRB- PCC -RRB- rIC.P is significantly high -LRB- p < 0.01 -RRB- under the hypothesis that rIC.P is zero -LRB- see below -RRB- . The CGI-independent set requires that rIC.P < rPC.I and that rPC.I is likewise significantly high . To calculate the statistical significance of PCC , a PCC r was subjected to z-transformation defined as The values of z are supposed to be normally distributed and the expected variance is where n is the sample size -LSB- 41 -RSB- . The CGI-related PWMs and CGI-independent PWMs are listed in Tables 2 and 3 . Gene expression data To examine the relationship between clusters of predicted TFBS and the tissue specificity of the genes where clusters were found , we generated a list of genes with expression data . This list includes 535 housekeeping or maintenance genes expressed in 11 human adult and foetal tissues from -LSB- 26 -RSB- , and 451 housekeeping or maintenance genes available at HugeIndex database -LSB- 27 -RSB- . We then identified 581 non-redundant housekeeping genes and 't issue-selective ' genes , which are predominantly , but not exclusively , expressed in one tissue type . Tissue-selective genes were expressed in brain -LRB- 589 genes -RRB- , kidney -LRB- 129 genes -RRB- , liver -LRB- 271 genes -RRB- , lung -LRB- 68 genes -RRB- , muscle -LRB- 302 genes -RRB- , prostate -LRB- 45 genes -RRB- and vulva -LRB- 95 genes -RRB- -LSB- 27 -RSB- . These genes corresponded to 2,069 RefSeq entries . Seventy-two of these genes were identified in our gene set covering chromosome 20 , 21 and 22 and were used for further analysis . Tissue specific gene detection based on clusters of predicted TFBS Using the two PWM sets described above , we searched clusters of predicted TFBS in promoter sequences . We calculated C and statistical significance of C as follows . For each promoter sequence , 30 random 600 bp sequences were generated under the first order Markov model , which is based on dinucleotide frequency and can identify promoter CpG bias and G+C % . The MATCH program was run with given matrix thresholds . The accumulated score C of PWMs was computed in every random sequence . A mean and a variance of C for each PWM were estimated from the random sequences . Then , for the given promoter sequence Sp and the PWM -LRB- M -RRB- , we can run MATCH and calculate C and its significance score -LRB- Z-score -RRB- , namely If the Z-score value was above three , the promoter sequence was taken and considered together with the PWM . Table 4 lists genes identified with clusters of predicted TFBS with significant C values for the CGI-related/dependent PWM set . The computation of p-value of cumulative hypergeometric distribution was performed using the AS R77 algorithm -LSB- 42 -RSB- . Authors ' contributions KM designed the study and carried out statistical analysis . TK participated in the design and carried out functional analysis . YS directed the study . All authors read and approved the final manuscript . Supplementary Material Additional File 1 The list of PWM-PCP/NCP sorted by cluster score . Each column represents rank number , accession number in TRANSFAC , identifier in TRANSFAC , cluster score , threshold . Click here for file

O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

tissue-specific gene expression
position weight matrix
regulatory motif


TeCK Database : A comprehensive collection of telomeric and centromeric sequences with their associated proteins Telomeres and centromere are two essential features of all eukaryotic chromosomes . They provide function that is necessary for the stability of chromosomes . We developed a comprehensive database named TeCK , which covers a gamut of sequence and other related information about telomeric patterns , telomere repeat sequences , centromere sequences and centromeric patterns present in chromosomes . It also contains information about telomerase ribo-nucleoprotein complexes , centromere binding protein and centromere DNA-binding protein complexes . The database also includes a collection of all kinetochore-associated proteins including inner , outer and central kinetochore proteins . The database can be searched using a user-friendly web interface . Background Telomeres -LSB- 1 -RSB- are a series of repeated DNA sequences located at the end of chromosomes , with strand asymmetry in GC content , resulting in one G-rich strand and one C-rich strand . They serve to assure that a chromosome is replicated properly during cell division . During this process some of the telomeres are lost . Eventually little or no telomere remains , and the cell dies . -LSB- 2 -RSB- Telomerase is an enzyme that adds specific DNA sequence repeats , -LRB- `` TTAGGG '' in all vertebrates -RRB- to the 3 ' end of DNA strands , in the telomere regions at the ends of chromosomes , thus preventing it from getting shorter . Centromeres -LSB- 3 -RSB- are DNA sequences contained in the heterochromatin , responsible for the segregation of each chromosome into daughter cells during cell division . The kinetochore -LSB- 4 -RSB- assembled on the centromere links the chromosome to the mitotic spindle during mitosis . As a cell tends to become cancerous -LSB- 5 -RSB- , it divides more often , and its telomeres become very short , causing death of cells , which can be prevented by activating the enzyme telomerase . By understanding these regions of chromosomes , cancer and aging can be treated effectively and thus is a potential target of drug design approach . Hence , a database for analyzing and interpreting chromosomal information is essential . So far there had been no attempts to annotate and curate these parts of chromosomes . Methodology TECK is developed using MySQL -LSB- 6 -RSB- a relational database management system that serves as the backend for storing data . IIS -LRB- Internet Information Server -RRB- is used as the web server and PHP5 -LRB- Hypertext preprocessor -RRB- -LSB- 7 -RSB- a widely used scripting language driven by Zend engine is used as the web interface . The nucleotide information is retrieved from NCBI , -LSB- 8 -RSB- a national resource repository for molecular biological information . About 160 entries are obtained for both telomere and centromere . The information about the associated proteins is retrieved from SWISSPROT , -LSB- 9 -RSB- a protein database . About 154 entries are obtained for telomerase , 162 entries are obtained for centromere binding proteins and 170 entries are obtained for kinetochore . Hyperlinks are provided to the corresponding databases for each entry with their IDs displayed . Each entry is given an unique ID called TeCK ID consisting of three parts , the first part indicating the class and the second depicting the organism and the last denoting the position of that particular entry in the table . About 16 tables had been created all of which are linked using their unique TeCK ID . Each protein entry is described using twenty parameters such as protein name , gene name , source , lineage , function , other interacting proteins etc. . The nucleotide information is described using parameters such as chromosome number , keywords , lineage , the articles along with the author details and references . BLAST -LSB- 10 -RSB- is a search tool for comparing biological sequences . A stand alone BLAST Program obtained from NCBI has been installed for searching similar sequences . Accessibility TeCK database can be accessed via internet and the screen shot of the homepage is displayed -LSB- Figure 1A -RSB- . There are three ways by which the user can query the database . The first one is the keyword search in home page that can be performed for five categories -LRB- Telomere , Centromere and its associated proteins along with Kinetochore proteins -RRB- by giving any text data related to organism name , protein name , function etc. to retrieve the required data . The second one is the advanced search option for specific requirements -LSB- Figure 1B -RSB- , where the user can fill the form by specifying the input parameters such as SwissProt , Interpro , Pfam , BLOCKS , PDB , ModBase IDs or TeCk ID . Alternatively , the users can also browse the database -LSB- Figure 1C -RSB- by organisms and category . On selection of the required query , a result page containing the number of hits and the list of all entries ID with a short description is displayed . Detailed information can be obtained by clicking each entry ID -LSB- Figure 1D -RSB- . The sequences are displayed in FASTA format and the retrieval can be done using a separate ` retrieve in FASTA format ' link . BLASTn can be done against TeCK database & NCBI and BLASTp against TeCK database , SwissProt & PDB . Utility As TeCK database includes name , function , sequence , subunits , and other literature information . The data for TeCK database were obtained from various curated databases and hence TeCK database helps end users to study , compare , analyze and interpret them and finds utility in control of cancer and ageing . Caveats It should be noted that the consistency depends on the source of the original data .

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telomere
centromere
chromosome
kinetochore proteins