Expositor is a solution that breaks a DNA sequence into fragments using a sliding windows of 58 nucleotides, encodes them using pc3mer and k-mers, and feeds them to the classifier. The multilayer perceptron classifier outputs the probability for each input sequence to be a promoter. The probabilities are smoothed, so a nucleotide's neighbors' probabilities impact on its probabilitiy that is filtered to limit the minimum length of the promoter predictions. As its last step, the solution outputs a list of the promoter predictions and their position in the DNA sequence.
If you have used Expositor in your research, please kindly cite the following publication:
M. Bernardino and R. Beiko, "Genome-scale prediction of bacterial promoters," 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2021, pp. 01-08, doi: 10.1109/CIBCB49929.2021.9562938
To train the classifier, we used the following samples:
- 1005 experimentally verified promoter samples from RegulonDB, Release 9.4 from 05-08-2017, that were associated with a single σ factor: 65 samples of σ24, 9 σ28, 58 σ32, 103 σ38, 17 σ54 and 753 σ70 promoters. For all these samples, the sequences including both binding sites and the spacer between them have at most 58 nucleotides. Based on this observation, we subsampled each input sequence to encompass 58 nucleotides taken centered on the spacer to ensure that the whole pair of binding sites are contained in the new sample.
- 1008 non-promoter samples obtained by mapping the promoter sequences to the E. coli K-12 MG1655 genome using the Basic Local Alignment Search Tool (BLASTN; [1], [2]). All segments in the DNA with length greater than 58 nucleotides and without a match to an identified promoter sequence were considered for the negative data set, which included both protein-coding genes and non-coding intergenic regions. Then, we used a sliding window with 58 bp to create sequences of fixed length and selected 1008 samples that were equally distant from each other along the genome.
All the samples, raw and encoded sequences, are available in /data_sets. The different types are explained below. The negative samples are identified as Sigma00, everything else are positive samples such as Sigma24, Sigma70, etc.
- Raw sequences (raw_samples.fasta). Examples:
>Sigma00
CCGATTTTTTGCGAAACGTTCCGCCTGGCATCAGGATAGTTTGTTCGTTATCCAGTTC
>Sigma00
GCTGGTTTGTACTACCACAAAGAGATTGGCTCCCGGATTAAAAAAAGTAATCACGAAC
- Sequences encoded with pc3mer. There are twelve physicochemical properties per 3-mers: Bendability-DNAse, Bendability-consensus, Trinucleotide GC Content, Nucleosome positioning, Consensus_roll, Consensus_Rigid, Dnase I, Dnase I-Rigid, MW-Daltons, MW-kg, Nucleosome, and Nucleosome-Rigid [3].
We generated one file for each properties, for instance Bendability-consensus.md. Example:
-0.71222323,0.99978754,-0.39122121,-2.58473502,-2.74523603,-2.74523603,-2.74523603,-2.74523603,-0.2307202,0.83928653,0.38453367,0.99978754,-0.60522256,-2.74523603,-1.35422727,0.06353165,0.06353165,-1.35422727,-0.60522256,-0.09696936,-0.71222323,0.38453367,2.09654444,-0.07021919,0.91953704,-1.14022593,2.09654444,0.83928653,1.34753973,-0.39122121,1.34753973,0.91953704,-0.07021919,-0.09696936,-0.39122121,0.57178485,-0.09696936,-0.68547306,-1.35422727,-2.74523603,-0.2307202,0.17053232,-1.35422727,-0.60522256,0.99978754,0.06353165,-1.35422727,-0.28422054,0.57178485,-0.39122121,-0.09696936,-1.14022593,0.91953704,-0.68547306,-1.35422727,-0.60522256,Sigma00
0.91953704,0.91953704,-1.14022593,0.06353165,-1.35422727,-2.74523603,-0.2307202,0.17053232,-0.07021919,-0.07021919,-0.68547306,-0.09696936,-0.07021919,0.06353165,-1.14022593,0.7857862,0.17053232,-0.2307202,-2.74523603,-0.25747037,-0.1504697,0.43803401,-0.1504697,-0.39122121,-2.58473502,-0.2307202,-1.14022593,2.09654444,0.91953704,0.43803401,-0.09696936,0.3577835,-0.71222323,-0.71222323,-0.09696936,-0.39122121,-2.58473502,-0.28422054,-0.28422054,-2.74523603,-2.74523603,-2.74523603,-2.74523603,-2.74523603,-0.25747037,-0.68547306,-0.07021919,-0.28422054,-2.58473502,-0.39122121,1.34753973,0.7857862,0.06353165,0.99978754,-0.60522256,-1.35422727,Sigma00
-0.09696936,0.43803401,-0.07021919,0.06353165,0.17053232,1.34753973,0.7857862,0.06353165,-1.14022593,0.91953704,0.91953704,0.83928653,0.91953704,-0.07021919,-0.09696936,-0.60522256,-2.74523603,-0.25747037,-0.07021919,0.3577835,0.3577835,2.09654444,0.38453367,0.06353165,0.17053232,1.34753973,0.7857862,0.06353165,-1.14022593,-0.2307202,-2.58473502,-0.39122121,0.99978754,0.38453367,2.09654444,-0.07021919,0.91953704,0.17053232,-1.35422727,-2.74523603,-0.60522256,1.34753973,0.7857862,-0.68547306,0.91953704,0.83928653,0.91953704,0.91953704,0.83928653,0.83928653,0.91953704,0.91953704,0.91953704,-0.25747037,-0.60522256,-0.1504697,Sigma00
-0.07021919,0.3577835,0.3577835,0.06353165,0.7857862,1.34753973,0.43803401,0.91953704,0.38453367,0.99978754,-0.60522256,-2.74523603,-1.35422727,0.06353165,-0.07021919,0.91953704,-1.14022593,0.3577835,0.06353165,-1.35422727,-0.2307202,0.17053232,0.17053232,-0.09696936,-0.07021919,0.43803401,1.34753973,-0.2307202,-2.74523603,-1.35422727,0.17053232,0.7857862,0.06353165,-0.71222323,0.3577835,2.09654444,0.91953704,-0.09696936,-0.07021919,0.06353165,0.06353165,-0.07021919,-0.28422054,-1.35422727,0.06353165,0.38453367,0.38453367,0.99978754,-0.39122121,1.34753973,1.34753973,-0.39122121,-0.39122121,-0.09696936,0.3577835,-0.71222323,Sigma00
- Sequences encoded with k-mers (k-mers.md). Example:
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- Download Expositor and DNA_encoders into the same directory. Add its path to the system's variables.
Some of the files in Expositor folder are explained below:
- GCF_000005845.2_ASM584v2_genomic.fna has Escherichia coli k12 MG1655's chromosome,
- conda_environment.yml has the conda environment setting to run Expositor and DNA_encoders.
- expositor.py is the main code.
- Create a conda environment using the conda_environment.yml file in Expositor by doing:
Then activate the environment with:
conda env create -f conda_environment.ymlconda activate expositor - To run Expositor on your data, use the following command line:
Expositor will evaluate the genome in path_to_the_genome and make predictions saving intermediate and final results in the same folder.
python expositor.py -p path_to_the_genome -l number_of_nucleotides_in_the_genome
[1] S. F. Altschul, W. Gish, W. Miller, E. W. Myers and D. J. Lipman, ”Basic local alignment search tool.” J. Mol. Biol. 215:403-410, 1990.
[2] C. Camacho et al., “BLAST+: Architecture and applications,” BMCBioinformatics, vol. 10, p. 421, Dec. 2009, doi: 10.1186/1471-2105-10-421.
[3] W. Chen, T. Y. Lei, D. C. Jin, H. Lin, and K. C. Chou, “PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition,” Anal. Biochem., vol. 456, no. 1, pp. 53–60, 2014, doi: 10.1016/j.ab.2014.04.001.