bacillus subtilis is one of the best - studied gram - positive bacteria , and hence serves as a model organism , much like escherichia coli for gram - negative bacteria . bacillus subtilis databases , such as subtilist ( 1,2 ) or the database of transcriptional regulation in b. subtilis ( dbtbs ) ( 3,4 ) , are therefore used as a reference not only by b. subtilis researchers , but also by researchers focusing on more or less distant organisms . indeed , it is assumed that at least part of the knowledge gained on the model organism can be extended to these other organisms ; the amount of this extension being related to the distance between both organisms . we previously presented dbtbs , which offers detailed information about the b. subtilis transcription system . besides increasing the amount of b. subtilis data contained in dbtbs , we added a new section to the database aimed at helping wet - lab researchers assess the relevance of extending b. subtilis knowledge to the bacteria they study . this section presents upstream intergenic region conservation profiles for homologous proteins of a same gram - positive genus , as well as hexameric motifs conserved between different profiles . since release 3 of dbtbs in 2004 ( 4 ) , a significant increase in the number of referenced publication , from 378 to 947 , has occurred ( table 1 ) . this increase resulted in the inclusion of six new transcription factors , bringing their number to 120 . at the same time , the number of promoters rose from 633 to 1475 and the number of regulated operons went from 525 to 736 . indeed , the regulated genes were reorganized in regulated operons , and all the regulated genes are now reported , in contrast to only the first gene of the operon . in addition to the extension of the existing data , 463 experimentally validated b. subtilis operons ( 5 ) and their terminators have been included as well ( 6 ) . as previously , researchers worldwide are encouraged to report outdated , incorrect or missing information in order to make dbtbs as complete and accurate as possible . table 1.summary of the updated datacategoryrelease 3release 5referenced publications378947transcription factors114120position - specific scoring matrices4545promoters6331475regulated operons525736terminators0463the number of items of each category present in release 3 ( november 2004 ) and release 5 ( september 2007 ) of dbtbs are given . summary of the updated data the number of items of each category present in release 3 ( november 2004 ) and release 5 ( september 2007 ) of dbtbs are given . to facilitate the identification of regulatory elements , two new tools have been added . first , a matrix search function allows users to identify which transcription factors correspond to the position - specific weighted matrix they submitted by querying dbtbs for the top 10 weight matrices similar to it . second , following a user request , a b. subtilis motif location search tool was added as a remedy for the disappearance of the grasp - dna tool developed by schilling et al . this tool allows a user to input a list of binding site sequences and returns the b. subtilis genome locations matched by the position - specific weighted matrix calculated from them . for each location , furthermore , the generated position - specific weighted matrix can be directly used to search for the 10 most similar dbtbs weighted matrices matrices by using the provided link . in order to provide upstream intergenic region conservation information , groups of homologous proteins from 40 gram - positive bacteria ( supplementary table 1 ) were built . homologies between the proteins were determined by all - against - all protein blast ( 8) searches , where a protein a was considered homologous to a protein b if an identity higher than 40% on more than 50% of the length of a was found . each group was then divided into subgroups based on genus , and each subgroup further divided based on the lengths of the upstream intergenic region of its members . although orthologous and paralogous genes are first grouped together , subdividing the genus - specific groups based on the length of the upstream intergenic regions is expected to separate paralogous genes that are differently regulated . the upstream intergenic regions of each of the subgroups containing more than two members were aligned with clustalw ( 9 ) , and the last 300 positions of the alignment , representing the nucleotides directly upstream of the gene starts , were kept for further analysis . for each subgroup , a conservation profile was calculated based on information content , thus giving the degree of conservation of each position and allowing the determination of conserved regions . in our analysis , conserved regions were determined by setting the threshold for the degree of conservation to 75% , while allowing at most three consecutive positions to have lower values . all the possible 6-bases - long position - specific weighted matrices were then created from the determined conserved regions and clustered using the quality cluster algorithm ( 10 ) and a kullback  leibler divergence ( 11 ) of 0.3 as the maximum cluster diameter . matrices clustering together were merged to yield the hexameric motif matrices available from dbtbs . through this process , 29 520 hexameric motif matrices were created ; 5652 of them were specific to bacillus , 1516 to staphylococcus and 184 to streptococcus ( table 2 and supplementary table 2 ) . these numbers are largely influenced by the grouping method , which results in only few groups for genus with few members , and hence lowers the potential number of motifs specific to that genus . table 2.repartition of the clusters and motifsgenusbacteriagenesspecific sub- groups%specific motifs%bacillusbac77450158179.0565222.5carboxydothermuscab172100.000.0clostridiumclo32714653.5110.4enterococcusent111133.300.0geobacillusgeo1194100.000.0lactobacilluslab32992425.5130.4lactococcuslac1457100.000.0listerialis251964.300.0mycoplasmamyc92984058.0240.8oceanobacillusocb16150.000.0phytoplasmaoyp1921885.700.0staphylococcussta4343555560.515168.3streptococcusstr5157319646.31841.9thermoanaerobactertab15910100.000.0genuses are listed followed by the three - letter abbreviation used for cluster names . bacteria : number of bacteria species used for the given genus ; genes : total number of genes reported in the bacteria of the given genus ; specific subgroups : number of subgroups for which no homologous subgroup is found in other genuses ; specific motifs : number of hexameric motif matrices found to bind exclusively in a given genus . percentages are given based on the total number of subgroups , respectively motifs , found in a given genus . repartition of the clusters and motifs genuses are listed followed by the three - letter abbreviation used for cluster names . bacteria : number of bacteria species used for the given genus ; genes : total number of genes reported in the bacteria of the given genus ; specific subgroups : number of subgroups for which no homologous subgroup is found in other genuses ; specific motifs : number of hexameric motif matrices found to bind exclusively in a given genus . percentages are given based on the total number of subgroups , respectively motifs , found in a given genus . users can then search the data by submitting a gene name , a genus - subgroup name or a motif number . also , because of the large number of hexameric motif matrices available , the desired ranges for the information content and the number of occurrences of a motif can be selected in order to filter the displayed motifs . submitting a gene name will return a table indicating which organisms contain a gene labeled with the given name , as well as in which genus - subgroup this gene is included and which motifs are found in that subgroup . genus - subgroups and motifs are linked to the same pages that those obtained by directly searching with a genus - subgroup name or a motif number . the result of a genus - subgroup name search is a page presenting the conservation profile of the subgroup , with the conserved regions and motifs positions . the upstream intergenic sequence alignment used to calculate the conservation profile is shown under it ( figure 1 ) . following the graphical display of the subgroup is a list of the genes included in the subgroup , and a list of the motifs present . this last list shows the motif logo ( 12 ) and indicates in which other groups the same motif is found . again each genus - subgroup name and motif number in this list is linked to the same page as the one obtained by a direct search . the upper part shows the search entry box , with the criteria selected for filtering of the displayed hexameric motifs . the lower part is the resulting figure , showing the conservation profile as a black line and the alignment used to obtain it . the blue line represents the calculated conserved region and the red boxes the found hexameric motifs . the upper part shows the search entry box , with the criteria selected for filtering of the displayed hexameric motifs . the lower part is the resulting figure , showing the conservation profile as a black line and the alignment used to obtain it . the blue line represents the calculated conserved region and the red boxes the found hexameric motifs . a motif number search shows first the motif logo , and then a list of genus - subgroups where the motif is found . in this list , the position of the motif in each subgroup is shown graphically , and for each subgroup , the list of the included genes and of the other motifs found in it is given , once again linked to the relevant pages . in order to provide upstream intergenic region conservation information , groups of homologous proteins from 40 gram - positive bacteria ( supplementary table 1 ) were built . homologies between the proteins were determined by all - against - all protein blast ( 8) searches , where a protein a was considered homologous to a protein b if an identity higher than 40% on more than 50% of the length of a was found . each group was then divided into subgroups based on genus , and each subgroup further divided based on the lengths of the upstream intergenic region of its members . although orthologous and paralogous genes are first grouped together , subdividing the genus - specific groups based on the length of the upstream intergenic regions is expected to separate paralogous genes that are differently regulated . the upstream intergenic regions of each of the subgroups containing more than two members were aligned with clustalw ( 9 ) , and the last 300 positions of the alignment , representing the nucleotides directly upstream of the gene starts , were kept for further analysis . for each subgroup , a conservation profile was calculated based on information content , thus giving the degree of conservation of each position and allowing the determination of conserved regions . in our analysis , conserved regions were determined by setting the threshold for the degree of conservation to 75% , while allowing at most three consecutive positions to have lower values . all the possible 6-bases - long position - specific weighted matrices were then created from the determined conserved regions and clustered using the quality cluster algorithm ( 10 ) and a kullback  leibler divergence ( 11 ) of 0.3 as the maximum cluster diameter . matrices clustering together were merged to yield the hexameric motif matrices available from dbtbs . through this process , 29 520 hexameric motif matrices were created ; 5652 of them were specific to bacillus , 1516 to staphylococcus and 184 to streptococcus ( table 2 and supplementary table 2 ) . these numbers are largely influenced by the grouping method , which results in only few groups for genus with few members , and hence lowers the potential number of motifs specific to that genus . table 2.repartition of the clusters and motifsgenusbacteriagenesspecific sub- groups%specific motifs%bacillusbac77450158179.0565222.5carboxydothermuscab172100.000.0clostridiumclo32714653.5110.4enterococcusent111133.300.0geobacillusgeo1194100.000.0lactobacilluslab32992425.5130.4lactococcuslac1457100.000.0listerialis251964.300.0mycoplasmamyc92984058.0240.8oceanobacillusocb16150.000.0phytoplasmaoyp1921885.700.0staphylococcussta4343555560.515168.3streptococcusstr5157319646.31841.9thermoanaerobactertab15910100.000.0genuses are listed followed by the three - letter abbreviation used for cluster names . bacteria : number of bacteria species used for the given genus ; genes : total number of genes reported in the bacteria of the given genus ; specific subgroups : number of subgroups for which no homologous subgroup is found in other genuses ; specific motifs : number of hexameric motif matrices found to bind exclusively in a given genus . percentages are given based on the total number of subgroups , respectively motifs , found in a given genus . repartition of the clusters and motifs genuses are listed followed by the three - letter abbreviation used for cluster names . bacteria : number of bacteria species used for the given genus ; genes : total number of genes reported in the bacteria of the given genus ; specific subgroups : number of subgroups for which no homologous subgroup is found in other genuses ; specific motifs : number of hexameric motif matrices found to bind exclusively in a given genus . percentages are given based on the total number of subgroups , respectively motifs , found in a given genus . the gram - positive bacteria upstream intergenic conservation information can be accessed from the  motif conservation link on the main page of dbtbs . users can then search the data by submitting a gene name , a genus - subgroup name or a motif number . also , because of the large number of hexameric motif matrices available , the desired ranges for the information content and the number of occurrences of a motif can be selected in order to filter the displayed motifs . submitting a gene name will return a table indicating which organisms contain a gene labeled with the given name , as well as in which genus - subgroup this gene is included and which motifs are found in that subgroup . genus - subgroups and motifs are linked to the same pages that those obtained by directly searching with a genus - subgroup name or a motif number . the result of a genus - subgroup name search is a page presenting the conservation profile of the subgroup , with the conserved regions and motifs positions . the upstream intergenic sequence alignment used to calculate the conservation profile is shown under it ( figure 1 ) . following the graphical display of the subgroup is a list of the genes included in the subgroup , and a list of the motifs present . this last list shows the motif logo ( 12 ) and indicates in which other groups the same motif is found . again each genus - subgroup name and motif number in this list is linked to the same page as the one obtained by a direct search . the upper part shows the search entry box , with the criteria selected for filtering of the displayed hexameric motifs . the lower part is the resulting figure , showing the conservation profile as a black line and the alignment used to obtain it . the blue line represents the calculated conserved region and the red boxes the found hexameric motifs . the upper part shows the search entry box , with the criteria selected for filtering of the displayed hexameric motifs . the lower part is the resulting figure , showing the conservation profile as a black line and the alignment used to obtain it . the blue line represents the calculated conserved region and the red boxes the found hexameric motifs . a motif number search shows first the motif logo , and then a list of genus - subgroups where the motif is found . in this list , the position of the motif in each subgroup is shown graphically , and for each subgroup , the list of the included genes and of the other motifs found in it is given , once again linked to the relevant pages . because of the high number of hexameric motif matrices , a careful picking of the filtering ranges is necessary to avoid the presence of too many motifs in the genus - subgroup pages . although this might be seen as a drawback , it allows a higher flexibility in the type of searches users can perform . indeed , by changing the range of the number of occurrences of a motif , one could search for conserved regions potentially indicating binding sites of minor or specialized transcription factors , which typically only bind at a few places in a genome , or target - binding sites of global regulators , which occur a lot more frequently additionally , the provided conservation profiles and sequence alignments can by themselves offer valuable information that could not be captured by the matrix conservation analysis . for instance , the shift by a few bases of a binding site might result in sub - optimal sequence alignment , and hence in an apparent absence of conservation which will result in the lack of matrix motif at this position . however , a visual inspection of the alignment will nevertheless allow the identification of the conserved region , despite the failure of the automatic recognition . in fact , many factors influence the quality of the motifs obtained using the method presented here , such as the quality of the homologous genes grouping , the accuracy of the sequence alignment , the distance threshold used in the motif clustering , or the chosen length of the motifs . the exact effects of these parameters have not been investigated so far , and future work in that direction should be carried out to be able to decrease the number of biologically meaningless motifs more efficiently . the prediction of the operon structures for each strain used should also improve our results and will therefore be considered in the future . in addition , an analysis of the co - occurrence of motifs might provide interesting information about co - regulation by several transcription factors . although the current version of this tool already offers a significant amount of information to interested scientists , it should consequently only be considered as a first step in the analysis of the conservation of upstream intergenic regions , setting ground for finer investigations .
dbtbs , first released in 1999 , is a reference database on transcriptional regulation in bacillus subtilis , summarizing the experimentally characterized transcription factors , their recognition sequences and the genes they regulate . since the previous release 
 , the original content was extended by the addition of the data contained in 569 new publications , the total of which now reaches 947 . 
 the number of b. subtilis promoters annotated in the database was more than doubled to 1475 . 
 in addition , 463 experimentally validated b. subtilis operons and their terminators have been included . 
 given the increase in the number of fully sequenced bacterial genomes , we decided to extend the usability of dbtbs in comparative regulatory genomics . 
 we therefore created a new section on the conservation of the upstream regulatory sequences between homologous genes in 40 gram - positive bacterial species , as well as on the presence of overrepresented hexameric motifs that may have regulatory functions . 
 dbtbs can be accessed at : http://dbtbs.hgc.jp .