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Fig. 2. Normalized values of 31 structural and energy parameters of 1001-nt long sequences having primary promoters (coloured lines) as well as sequences with no promoters i.e. CDSs (black line). Parameters are shown in five graph based on their respective group. (a) Nine parameters of backbone, (b) eight inter-BP parameters, (c) six intra-BP parameters, (d) five BP-axis parameters and (e) three energy parameters. In each graph, each colour line plot represents specific parameter based on legend table, while line plot in black colour represents average of parameters shown in that group for coding sequence. (Color version of this figure is available at Bioinformatics online.)

Figure 2 Normalized values of 31 structural and energy parameters of 1001-nt long sequences having primary promoters (coloured lines) as well as sequences with no promoters i.e. CDSs (black line). Parameters are shown in five graph based on their respective group. (a) Nine parameters of backbone, (b) eight inter-BP parameters, (c) six intra-BP parameters, (d) five BP-axis parameters and (e) three energy parameters. In each graph, each colour line plot represents specific parameter based on legend table, while line plot in black colour represents average of parameters shown in that group for coding sequence. (Color version of this figure is available at Bioinformatics online.)

Related Figures (7)

Fig. 3. Graphical representation of correlation values among 31 parameters (param- eters names for codes used above is available in Supplementary Table $4)
Fig. 3. Graphical representation of correlation values among 31 parameters (param- eters names for codes used above is available in Supplementary Table $4)
Fig. 4. Scatter plot, with best fit line, for correlation coefficient values between the %GC content of genome and sensitivity (the upper one) and specificity (the lower one)
Fig. 4. Scatter plot, with best fit line, for correlation coefficient values between the %GC content of genome and sensitivity (the upper one) and specificity (the lower one)
Table 1. Characteristic features of peaks obtained with SEProm on two promoter datasets *Last nucleotide of peak while moving from 5’ to 3’. Positions downstream to TSS are designated ‘+’, while towards upstream are designated as ‘~’.
Table 1. Characteristic features of peaks obtained with SEProm on two promoter datasets *Last nucleotide of peak while moving from 5’ to 3’. Positions downstream to TSS are designated ‘+’, while towards upstream are designated as ‘~’.
Table 2. Performance of SEProm in prediction E.coli promoters with different sigma specificity Note: The highest values of Precision and F-score are highlighted as bold values.
Table 2. Performance of SEProm in prediction E.coli promoters with different sigma specificity Note: The highest values of Precision and F-score are highlighted as bold values.
Fig. 5. Comparison of SEProm with 5 other tools in prediction of promoters for 12 organisms used in the study; (a) average F-value, (b) F-value for individual organisms, (c wverage precision value and (d) precision value for individual organisms
Fig. 5. Comparison of SEProm with 5 other tools in prediction of promoters for 12 organisms used in the study; (a) average F-value, (b) F-value for individual organisms, (c wverage precision value and (d) precision value for individual organisms
Table 3. Comparison of SEProm with five popular promoter prediction programmes on two test datasets (promoters of Halobacterium sal: narum and Leptospira interrogans) Pepper tool could not be used as its server was not responding. The highest values are highlighted as bold values.
Table 3. Comparison of SEProm with five popular promoter prediction programmes on two test datasets (promoters of Halobacterium sal: narum and Leptospira interrogans) Pepper tool could not be used as its server was not responding. The highest values are highlighted as bold values.
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