Characterization of relationships between transcriptional units and operon structures in Bacillus subtilis and Escherichia coli
© Okuda et al; licensee BioMed Central Ltd. 2007
Received: 31 August 2006
Accepted: 13 February 2007
Published: 13 February 2007
Operon structures play an important role in transcriptional regulation in prokaryotes. However, there have been fewer studies on complicated operon structures in which the transcriptional units vary with changing environmental conditions. Information about such complicated operons is helpful for predicting and analyzing operon structures, as well as understanding gene functions and transcriptional regulation.
We systematically analyzed the experimentally verified transcriptional units (TUs) in Bacillus subtilis and Escherichia coli obtained from ODB and RegulonDB. To understand the relationships between TUs and operons, we defined a new classification system for adjacent gene pairs, divided into three groups according to the level of gene co-regulation: operon pairs (OP) belong to the same TU, sub-operon pairs (SOP) that are at the transcriptional boundaries within an operon, and non-operon pairs (NOP) belonging to different operons. Consequently, we found that the levels of gene co-regulation was correlated to intergenic distances and gene expression levels. Additional analysis revealed that they were also correlated to the levels of conservation across about 200 prokaryotic genomes. Most interestingly, we found that functional associations in SOPs were more observed in the environmental and genetic information processes.
Complicated operon strucutures were correlated with genome organization and gene expression profiles. Such intricately regulated operons allow functional differences depending on environmental conditions. These regulatory mechanisms are helpful in accommodating the variety of changes that happen around the cell. In addition, such differences may play an important role in the evolution of gene order across genomes.
Genes in prokaryotes are often organized into operon structures. Each operon is a series of genes transcribed in a single mRNA, often identified by the presence of promoters and terminators. It has been reported that genes transcribed in a single operon are functionally related and make up a part of a metabolic pathway [1–3]. Therefore, understanding the operon organization of a genome will lead to better understanding of the functions of genes and the genome.
The information of known TUs in B. subtilis and E. coli
Number of TUs
Number of overlapped TUs
Number of ORFs
Classification of adjacent gene pairs based on operon structures
Statistics of OPs, SOPs and NOPs in B. subtilis and E. coli
Number of pairs
Median of intergenic distances (bp)
Median of correlation coefficients of co-expression
Number of co-occurrence on the same pathway maps
Number of co-occurrence on the different pathway maps
Genomic properties of operons
Furthermore, we would like to point out that OPs and SOPs have different distributions (p < 1E-11, chi-squared test) despite the fact that both gene pairs are contained within the same TU at least once. These results are more clearly shown in the median values in Table 2 and in the box plots in Figure 2. For example, the medians of intergenic distances for OPs (17 bp and 9 bp) are smaller than SOPs (72 bp and 54 bp) in B. subtilis and E. coli, respectively. And both values are much smaller than the values for NOPs (376 bp and 467 bp) in both species (Table 2). A schematic view of these contrasts in median values among OP, SOP and NOP are shown in Figure 2B (B. subtilis) and Figure 2D (E. coli). Since the distributions were so similar even between distantly related organisms such as B. subtilis and E. coli, in terms of operon organization, we expect the differences in the intergenic distances to be evolutionarily conserved and similar across a broad range of prokaryotic genomes.
Conservation of adjacent gene pairs
Co-expression levels of adjacent gene pairs
Operons in biological pathways
Co-occurence on biological pathway maps
To determine the functional similarities at the level of biological pathway maps in the three groups of adjacent gene pairs, we measured the frequency of co-occurrence on pathway maps in KEGG, which contains information about metabolic and regulatory pathways and molecular complexes. KEGG has about 300 diagrams of molecular interactions or reactions. The number of times that both genes in an adjacent gene pair in B. subtilis and E. coli appear on the same and different KEGG pathway maps is shown in Table 2. If either gene of an adjacent gene pair was not mapped to a KEGG pathway, it was counted as being assigned to different maps. If both genes were not assigned to any maps, we ignored them. In particular, NOPs were dominated by gene pairs occurring in different pathway maps. Adjacent gene pairs in OPs frequently appeared on the same pathway maps, in contrast with NOPs (p < 1E-15, Fisher's exact test). Additionally, although only a small number of gene pairs in SOPs and NOPs co-occurred on the same pathways, SOPs significantly co-occured more often (p < 1E-6 for Fisher's exact test). Furthermore, OPs are co-occured less than expected in different pathway maps (p < 1E-7, Fisher's exact test). On the other hand, SOPs are not significantly different from NOPs using Fisher's exact test.
Co-occurence in functional categories
Properties of operons from a genomic perspective
The intergenic regions were clearly shorter in OPs and SOPs than in NOPs (Figure 2). Genes co-transcribed as an operon are likely to be compactly arranged on the genome. It is suggested that short intergenic regions would help to allow efficient transcription. Interestingly, we found that the distributions of the intergenic regions of OPs and SOPs also appear to have different shapes (Figure 2). This observation suggests the possibility of the presence of regulatory elements such as internal promoters and internal terminators in the intergenic regions of SOPs. Actually, there are known cases where such regulatory elements cause variations in the length of transcriptional units. For example, the sig B and res ABCDE operons in B. subtilis have upstream and internal promoters, resulting in two TUs [22, 23], and transcriptional terminations of operons such as the bmr and bio operons are also experimentally verified to be transcribed from the upstream promoter to the internal and external terminators, resulting in two different sizes of TUs [24, 25]. The sig B operon consists of eight genes, rsb R-S-T-U-V-W-sig B-rsb X, and is transcribed from an upstream sigma A dependent promoter and from an internal heat-inducible sigma B dependent promoter . The eight genes are usually co-transcribed by sigma A. When sigma B is activated in response to heat stress, it promotes transcription of the sig B regulon from the internal promoter, resulting in a shorter TU, rsb V-W-sigB-rsb X. The intergenic distance at the internal transcriptional boundary between rsb U and rsb V is 64 bp, whereas those of the others are 7, 6, 14, -1, -38 and 2, and their average is -1.7. Thus, the presence of regulatory elements seems to correspond to expanded intergenic regions. When the alternative transcripts are produced, most of them are caused by transcriptional regulatory elements located in the intergenic region at the boundary of the TU. Therefore, the longer intergenic regions of SOPs compared to OPs imply the presence of regulatory elements such as internal promoters and internal terminators. In addition, transcription can also be regulated by the presence of readthrough terminators which void specific termination signals, or by regulatory mechanisms such as riboswitches. Even if the specific promoters or terminators in a SOP region have not been identified, other transcriptional mechanisms may have an effect on the transcription.
Properties of operons from transcriptomic perspective
According to our microarray expression analysis, OPs clearly showed high correlation in contrast to NOPs (Figure 4). It is quite reasonable that gene pairs within a TU are highly correlated. In addition, the correlations of OPs and SOPs also appear to be differently distributed according to the range of the quartiles (Figure 4). Hence, the gene expressions of these groups showed similar relationships to the intergenic distances. As shown in our results, the three groups differed in both genome organization and transcriptomic profiles. The differences would suggest different regulatory mechanisms of transcription and the functions of these genes in cellular processes.
Complicated operon structures
From a practical viewpoint, various situations may occur: (i) all genes within an operon have strong correlation with each other; (ii) there are internal terminators within an operon; (iii) there are internal promoters within an operon; (iv) there are other regulatory mechanisms such as readthrough terminators. For example, Figure 6B is the correlation matrix for the sig B operon described in the previous section (rsb V in this operon was not measured in the microarray experiments, so the region of this gene is not colored), and shows a similar pattern in the schematic model in Figure 6A. The image correctly suggests two different sized transcripts in the operon.
Figure 6C shows another example. The clp C operon in B. subtilis is transcribed as a six gene operon including cts R, yac H, yac I, clp C, sms and yac K . This operon is related to the control of competence and survival under various stress conditions. Two promoters are mapped upstream of this operon. One is a sigma A-like promoter and the other is dependent on sigma B [22, 32]. In addition, it was reported that the last two genes of this operon might be also a part of operons regulated by sigma M [22, 33]. As suggested by the image, these reports imply that there are longer transcripts comprised of six genes and another transcript including just the last two genes.
Functional relationships of operon structures
Gene clusters obtained by comparative genomics are likely to be operons, and they also tend to cluster on metabolic pathways [1–3]. We measured the relationship between OPs, SOPs and NOPs with KEGG biological pathway maps. As shown in Table 2, gene pairs in OPs tend to appear in the same KEGG pathway maps. Therefore, genes within an operon are more often closely located on metabolic pathways. On the other hand, almost all gene pairs in NOPs occurred on different pathway maps (Table 2). This suggests that the boundaries between operons are clearly split according to functional relations. 11 NOPs in B. subtilis were, however, mapped to the same pathways. For example, roc A constitutes an operon with roc B and roc C, among four consecutively located genes: genes roc G, roc A, roc B and roc C. This operon is not coregulated with roc G due to the presence of a specific enhancer located between roc G and roc A. So the gene pair, roc G-roc A, is regarded as a NOP, while both genes belong to glutamate metabolism. This gene pair is also assigned to other pathways: nitrogen metabolism (roc G) and arginine and proline metabolism (roc A). The other NOPs that appear on the same map and also appear on alternative pathway maps are, his C-trp A (phenylalanine, tyrosine and tryptophan biosynthesis), spo VD-mur E (peptidoglycan biosynthesis), trp E-aro H (phenylalanine, tyrosine and tryptophan biosynthesis), and men C-men E (ubiquinone biosynthesis). On the other hand, the remaining six pairs, hxl A-hxl B (pentose and glucuronate interconversions), yfl S-yfl R (two-component system), puc E-puc H (purine metabolism), yly B-pyr R (pyrimidine metabolism), pbp B-spo VD (peptidoglycan biosynthesis), and yvr P-fhu C (ABC transporters), are assigned to only the same pathway map. In E. coli, 35 NOPs were mapped to the same pathways. Of these pairs, 18 NOPs are assigned to the same map and the rest of them are assigned to multiple maps. These functionally related NOPs can be regarded as gene pairs similar to SOPs in the sense that the gene order indicates their operon structure, but they are not directly co-regulated. In addition, from the comparative analysis, NOPs were either not adjacently conserved or lost in the other genomes. If an adjacent gene pair is not co-regulated in the same manner even if they are related to the same process, their gene order would be less conserved. Thus, a small fraction of NOPs are very similar to SOPs. Furthermore, the fact that SOPs occur more on different pathway maps is a similar tendency to NOPs, compared with those of OPs. Therefore, SOPs and NOPs may be relatively close in functional relationships. This implies that SOPs as well as NOPs may also play a role in the functional boundaries that produce a suitable set of proteins in a certain environment by alternative promoters or terminators, although such functional differences of SOPs are not as clear as those of NOPs.
Because almost half of all OPs were distributed in different biological pathway maps in Table 2, we can speculate that these genes in the same operon can have diverse functions. However, distribution of broader functional categories in Figure 5 and Additional file 1 (statistical distribution based on chi-square value) clearly show that the functional relationships of OPs are quite significant. The map-based analysis may be too specific to see the general trends in functional relationships of OPs. It is also interesting that gene pairs in SOPs share more functions related to genetic information and environmental responses such as transcription, translation and signal transduction, compared to the other two groups, OPs, and NOPs (Figure 5 and Additional file 1). This suggests that such functions are associated with the regulatory changes causing the transcription of alternative transcriptional units. As described in the previous section, it has been observed that some environmental factors trigger transcriptional unit changes. Therefore, it is understandable that some SOPs have a bias to these functions.
In addition, we have shown that SOPs are less conserved than OPs from the comparison of about 200 prokaryotes (Figure 3). Although it has been reported that operon structures are not stable throughout the evolutionary process , our result suggests that the collapse of operon structures has occurred frequently at the region of regulatory boundaries including SOPs and, in particular, NOPs. Recently, Price et al. have reported that, during operon evolution, a new gene is more likely to append to the end of a pre-existing operon and it is often a functionally unrelated gene . The facts found by them suggest that these appending genes may be the origin of SOPs. Therefore, these SOPs and functionally related NOPs described above would play an important role in the evolution of operons. Moreover, it has been observed that even if genes found in an operon in a given genome are split in another genome, they can be co-regulated by a single regulon in the given genome [17, 36]. Therefore, we suggest that complicated operon structure and regulon structures in different organisms, although they have different regulatory mechanisms, are evolutionary associated with each other. To clarify these relationships, highly reliable operon and regulon predictions are required. However, the intricate transcriptional regulation we have shown here makes this difficult. Our on-going project is to improve such predictions using the operon features that we have shown here and to uncover gene regulatory mechanisms across a variety of genomes. In this study, we found that there are the interesting differences among OPs, SOPs and NOPs. However, it still remain that higher statistical analysis could solve the inter-dependence among genomic, transcriptomic and functional features of gene pairs.
We classified adjacent gene pairs into three groups (OP, SOP and NOP) according to the levels of gene co-regulation in operon structures including substructures such as alternative TUs. Consequently, we found that the levels of gene co-regulation are correlated with genome organization, gene expression profiles and conservation across genomes. Interestingly, we found that functional associations of SOPs are often observed in the environmental and genetic information processing functional classes in KEGG. This is the first report of these relationships between operon organization and transcriptional units including substructures in operons, and we suggest that the strength of gene associations in an operon play an important role in environmental accommodation and in evolution of gene order across genomes.
The genome information for B. subtilis and E. coli was prepared from the KEGG GENES database [37, 38]. By using the information of the positions of genes, we classified adjacent gene pairs into the following three groups (Figure 1): OPs, SOPs and NOPs. We also calculated the intergenic distances between all the gene pairs. The distance was defined as the number of bases separating adjacent gene pairs on the same strand on the genome. If they have an overlapped region, the distance is negative.
We have obtained the information on TUs from ODB and RegulonDB. A summary of the known TUs in B. subtilis and E. coli is shown in Table 1. ORFs in Table 1 mean the total number of genes organizing the known TUs in ODB and RegulonDB. We obtained 688 TUs in B. subtilis and 396 TUs in E. coli from ODB and 693 TUs in E. coli from RegulonDB. If two TUs overlap but contain different sets of genes, we regarded these units as different TUs. These overlapped TUs share same genes with different TUs.
Evaluation of conservation of adjacent gene pairs
We obtained the genomic data of 185 prokaryotic organisms from KEGG [37, 38]. We summarize the organisms used in this comparative analysis in Additional file 2. To identify orthologs, we used OC [37, 38], which is an ortholog clustering using the results of homology searches done by the Smith-Waterman algorithm [39, 40]. In each adjacent gene pair, we counted the number of the gene pairs that the orthologs to both genes are on the other genomes. When both genes in a gene pair in a given genome are conserved in other genomes, the ratio is defined by dividing the number of genomes in which they are adjacently conserved by the number of total genomes in which they are conserved. In each measurement, we removed organisms closely related to B. subtilis and E. coli in the same taxonomic group, which are defined in KEGG.
Similarity of gene expression profiles
We used 150 microarray experiments for B. subtilis performed under 10 different experimental growth conditions in BSORF  (Additional file 3). Gene expression intensities were obtained by subtracting background intensities [42–45]. If the intensity was less than the standard deviation of the backgrounds, it was treated as a missing value. A ratio of expression intensity was obtained by dividing the target intensity by the control intensity [42–45], which was transformed into a logarithm of base 2. Normalization was carried out by subtracting the median value in each experiment [45, 46]. In addition, 140 microarray experiments for E. coli from Gene Expression Omnibus  (Additional file 4) with lowess normalization were collected. We then calculated the Pearson's correlation coefficients between all gene expression profiles.
Evaluation of adjacent gene pairs in biological pathways
We obtained the KEGG PATHWAY information from the GenomeNet database [37, 38]. KEGG PATHWAY is a knowledge base for molecular interaction networks, including metabolic pathways, regulatory pathways and molecular complexes. KEGG has about 300 diagrams of molecular interactions or reactions. In this study, 127 biological pathway maps for B. subtilis and 121 maps for E. coli were used. We extracted the set of the genes that belong to each pathway. We counted the genes that matched with those in OPs, SOPs and NOPs. In each group, we counted the number of genes that appeared in the same and different pathway maps. In addition, these maps are classified into hierarchical categories. We used 22 categories at the second level of the hierarchy (e.g. Carbohydrate metabolism) that are related to prokaryotes, in which 12 are included in metabolisms, 4 in genetic information processing and 6 in environmental information processing. We counted the number of the corresponding gene pairs for each category pair.
We thank Mitsuteru Nakao and Akiyasu. C. Yoshizawa. for helpful discussions and Alex Gutteridge, Kiyoko F. Aoki-Kinoshita and J. B. Brown for critical reading of our manuscript. We also thank Yoshinori Yamanishi for helpful advice in statistical analysis. This work was supported by grants from the Ministry of Education, Culture, Sports, Science and Technology, and the Japan Science and Technology Agency. The computational resources were provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University and the Super Computer System, Human Genome Center, The Institute of Medical Science, The University of Tokyo.
- Ogata H, Fujibuchi W, Goto S, Kanehisa M: A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Res. 2000, 28 (20): 4021-4028. 10.1093/nar/28.20.4021.PubMed CentralPubMedView ArticleGoogle Scholar
- Overbeek R, Fonstein M, D'Souza M, Pusch GD, Maltsev N: The use of gene clusters to infer functional coupling. Proc Natl Acad Sci USA. 1999, 96 (6): 2896-2901. 10.1073/pnas.96.6.2896.PubMed CentralPubMedView ArticleGoogle Scholar
- Zheng Y, Szustakowski JD, Fortnow L, Roberts RJ, Kasif S: Computational identification of operons in microbial genomes. Genome Res. 2002, 12 (8): 1221-1230. 10.1101/gr.200601.PubMed CentralPubMedView ArticleGoogle Scholar
- Yada T, Nakao M, Totoki Y, Nakai K: Modeling and predicting transcriptional units of Escherichia coli genes using hidden Markov models. Bioinformatics. 1999, 15 (12): 987-993. 10.1093/bioinformatics/15.12.987.PubMedView ArticleGoogle Scholar
- Craven M, Page D, Shavlik J, Bockhorst J, Glasner J: A probabilistic learning approach to whole-genome operon prediction. Proc Int Conf Intell Syst Mol Biol. 2000, 8: 116-127.PubMedGoogle Scholar
- Salgado H, Moreno-Hagelsieb G, Smith TF, Collado-Vides J: Operons in Escherichia coli: genomic analyses and predictions. Proc Natl Acad Sci USA. 2000, 97 (12): 6652-6657. 10.1073/pnas.110147297.PubMed CentralPubMedView ArticleGoogle Scholar
- Ermolaeva MD, White O, Salzberg SL: Prediction of operons in microbial genomes. Nucleic Acids Res. 2001, 29 (5): 1216-1221. 10.1093/nar/29.5.1216.PubMed CentralPubMedView ArticleGoogle Scholar
- Sabatti C, Rohlin L, Oh MK, Liao JC: Co-expression pattern from DNA microarray experiments as a tool for operon prediction. Nucleic Acids Res. 2002, 30 (13): 2886-2893. 10.1093/nar/gkf388.PubMed CentralPubMedView ArticleGoogle Scholar
- Bockhorst J, Craven M, Page D, Shavlik J, Glasner J: A Bayesian network approach to operon prediction. Bioinformatics. 2003, 19 (10): 1227-1235. 10.1093/bioinformatics/btg147.PubMedView ArticleGoogle Scholar
- de Hoon M, Imoto S, Kobayashi K, Ogasawara N, Miyano S: Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pac Symp Biocomput. 2003, 17-28.Google Scholar
- de Hoon M, Imoto S, Kobayashi K, Ogasawara N, Miyano S: Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information. Pac Symp Biocomput. 2004, 276-287.Google Scholar
- Chen X, Su Z, Dam P, Palenik B, Xu Y, Jiang T: Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome. Nucleic Acids Res. 2004, 32 (7): 2147-2157. 10.1093/nar/gkh510.PubMed CentralPubMedView ArticleGoogle Scholar
- Romero P, Karp P: Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases. Bioinformatics. 2004, 20 (5): 709-717. 10.1093/bioinformatics/btg471.PubMedView ArticleGoogle Scholar
- Westover B, Buhler J, Sonnenburg J, Gordon J: Operon prediction without a training set. Bioinformatics. 2005, 21 (7): 880-888. 10.1093/bioinformatics/bti123.PubMedView ArticleGoogle Scholar
- Jacob E, Sasikumar R, Nair K: A fuzzy guided genetic algorithm for operon prediction. Bioinformatics. 2005, 21 (8): 1403-1407. 10.1093/bioinformatics/bti156.PubMedView ArticleGoogle Scholar
- Snel B, Bork P, Huynen MA: The identification of functional modules from the genomic association of genes. Proc Natl Acad Sci USA. 2002, 99 (9): 5890-5895. 10.1073/pnas.092632599.PubMed CentralPubMedView ArticleGoogle Scholar
- Snel B, van Noort V, Huynen MA: Gene co-regulation is highly conserved in the evolution of eukaryotes and prokaryotes. Nucleic Acids Res. 2004, 32 (16): 4725-4731. 10.1093/nar/gkh815.PubMed CentralPubMedView ArticleGoogle Scholar
- Huynen M, Snel B, Lather W, Bork P: Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome Res. 2000, 10 (8): 1204-1210. 10.1101/gr.10.8.1204.PubMed CentralPubMedView ArticleGoogle Scholar
- Dandekar T, Snel B, Huynen M, Bork P: Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci. 1998, 23: 324-328. 10.1016/S0968-0004(98)01274-2.PubMedView ArticleGoogle Scholar
- Price MN, Huang KH, Arkin AP, Alm EJ: A novel method for accurate operon predictions in all sequenced prokaryotes. Nucleic Acids Res. 2005, 33: 880-892. 10.1093/nar/gki232.PubMed CentralPubMedView ArticleGoogle Scholar
- de Hoon M, Makita Y, Imoto S, Kobayashi K, Ogasawara N, Nakai K, Miyano S: Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data. Bioinformatics. 2004, 20 Suppl 1 (): I101-I108. 10.1093/bioinformatics/bth927.PubMedView ArticleGoogle Scholar
- Helmann JD, Wu MF, Kobel PA, Gamo FJ, Wilson M, Morshedi MM, Navre M, Paddon C: Global transcriptional response of Bacillus subtilis to heat shock. J Bacteriol. 2001, 183 (24): 7318-7328. 10.1128/JB.183.24.7318-7328.2001.PubMed CentralPubMedView ArticleGoogle Scholar
- Sun G, Sharkova E, Chesnut R, Birkey S, Duggan MF, Sorokin A, Pujic P, Ehrlich SD, Hulett FM: Regulators of aerobic and anaerobic respiration in Bacillus subtilis. J Bacteriol. 1996, 178 (5): 1374-1385.PubMed CentralPubMedGoogle Scholar
- Petersohn A, Antelmann H, Gerth U, Hecker M: Identification and transcriptional analysis of new members of the sigmaB regulon in Bacillus subtilis. Microbiology. 1999, 145: 869-880.PubMedView ArticleGoogle Scholar
- Perkins JB, Bower S, Howitt CL, Yocum RR, Pero J: Identification and characterization of transcripts from the biotin biosynthetic operon of Bacillus subtilis. J Bacteriol. 1996, 178 (21): 6361-6365.PubMed CentralPubMedGoogle Scholar
- MacDaniel BA, Grundy FJ, Artsimovitch TMI, Henkin : Transcription termination control of the S box system: Direct measurement of S-adenosylmethionine by the leader RNA. Proc Natl Acad Sci USA. 2003, 100 (6): 3083-3088. 10.1073/pnas.0630422100.View ArticleGoogle Scholar
- Rodionov DA, Vitreschak AG, A MA, Gelfand MS: Regulation of lysine biosynthesis and transport genes in bacteria: yet another RNA riboswitch?. Nucleic Acids Res. 2003, 31 (23): 6748-6757. 10.1093/nar/gkg900.PubMed CentralPubMedView ArticleGoogle Scholar
- Salgado H, Santos-Zavaleta A, Gama-Castro S, Millan-Zarate D, Diaz-Peredo E, Sanchez-Solano F, Perez-Rueda E, Bonavides-Martinez C, Collado-Vides J: RegulonDB (version 3.2): transcriptional regulation and operon organization in Escherichia coli K-12. Nucleic Acids Res. 2001, 29: 72-74. 10.1093/nar/29.1.72.PubMed CentralPubMedView ArticleGoogle Scholar
- Kunst F, Ogasawara N, Moszer I, Albertini AM, Alloni G, Azevedo V, Bertero MG, Bessieres P, Bolotin A, Borchert S, Borriss R, Boursier L, Brans A, Braun M, Brignell SC, Bron S, Brouillet S, Bruschi CV, Caldwell B, Capuano V, Carter NM, Choi SK, Codani JJ, Connerton IF, Danchin A et al.: The complete genome sequence of the gram-positive bacterium Bacillus subtilis. Nature. 1997, 390 (6657): 249-256. 10.1038/36786.PubMedView ArticleGoogle Scholar
- Okuda S, Katayama T, Kawashima S, Goto S, Kanehisa M: ODB: a database of operons accumulating known operons across multiple genomes. Nucleic Acids Res. 2006, D358-D362. 10.1093/nar/gkj037. 34 DatabaseGoogle Scholar
- Kruger E, Msadek T, Ohlmeier S, Hecker M: The Bacillus subtilis clpC operon encodes DNA repair and competence proteins. Microbiology. 1997, 143: 1309-1316.PubMedView ArticleGoogle Scholar
- Kruger E, Msadek T, Hecker M: Alternate promoters direct stress-induced transcription of the Bacillus subtilis clpC operon. Mol Microbiol. 1996, 20 (4): 713-723. 10.1111/j.1365-2958.1996.tb02511.x.PubMedView ArticleGoogle Scholar
- Thackray PD, Moir A: SigM, an extracytoplasmic function sigma factor of Bacillus subtilis, is activated in response to cell wall antibiotics, ethanol, heat, acid, and superoxide stress. J Bacteriol. 2003, 185 (12): 3491-3498. 10.1128/JB.185.12.3491-3498.2003.PubMed CentralPubMedView ArticleGoogle Scholar
- Itoh T, Takemoto K, Mori H, Gojobori T: Evolutionary instability of operon structures disclosed by sequence comparisons of complete microbial genomes. Mol Biol Evol. 1999, 16 (3): 332-346.PubMedView ArticleGoogle Scholar
- Price MN, Arkin AP, Alm EJ: The life-cycle of operons. PLoS Genet. 2006, 2 (6): e96-10.1371/journal.pgen.0020096.PubMed CentralPubMedView ArticleGoogle Scholar
- Okuda S, Kawashima S, Goto S, Kanehisa M: Conservation of gene co-regulation between two prokaryotes: Bacillus subtilis and Escherichia coli. Genome Inform. 2005, 16: 116-124.PubMedGoogle Scholar
- Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004, D277-D280. 10.1093/nar/gkh063. 32 DatabaseGoogle Scholar
- KEGG. [http://www.genome.jp/kegg/]
- Smith TF, Waterman MS: Identification of common molecular subsequences. J Mol Biol. 1981, 147: 195-197. 10.1016/0022-2836(81)90087-5.PubMedView ArticleGoogle Scholar
- Pearson WR: Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms. Genomics. 1991, 11: 635-650. 10.1016/0888-7543(91)90071-L.PubMedView ArticleGoogle Scholar
- BSORF. [http://bacillus.genome.jp/]
- Tojo S, Matsunaga M, Matsumoto T, Kang CM, Yamaguchi H, Asai K, Sadaie Y, Yoshida K, Fujita Y: Organization and expression of the Bacillus subtilis sigY operon. J Biochem (Tokyo). 2003, 134 (6): 935-946.View ArticleGoogle Scholar
- Yoshida K, Kobayashi K, Miwa Y, Kang CM, Matsunaga M, Yamaguchi H, Tojo S, Yamamoto M, Nishi R, Ogasawara N, Nakayama T, Fujita Y: Combined transcriptome and proteome analysis as a powerful approach to study genes under glucose repression in Bacillus subtilis. Nucleic Acids Res. 2001, 29 (3): 683-692. 10.1093/nar/29.3.683.PubMed CentralPubMedView ArticleGoogle Scholar
- Quackenbush J: Computational analysis of microarray data. Nat Rev Genet. 2001, 2 (6): 418-427. 10.1038/35076576.PubMedView ArticleGoogle Scholar
- Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30 (4): e15-10.1093/nar/30.4.e15.PubMed CentralPubMedView ArticleGoogle Scholar
- Lercher MJ, Blumenthal T, Hurst LD: Coexpression of neighboring genes in Caenorhabditis elegans is mostly due to operons and duplicate genes. Genome Res. 2003, 13 (2): 238-243. 10.1101/gr.553803.PubMed CentralPubMedView ArticleGoogle Scholar
- Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R: NCBI GEO: mining millions of expression profiles – database and tools. Nucleic Acids Res. 2005, D562-D566. 33 DatabaseGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.