https://github.com/beiko-lab/expositor

https://github.com/beiko-lab/expositor

Repository

https://github.com/beiko-lab/Expositor/blob/main/

# Expositor

Expositor is a solution that breaks a DNA sequence into fragments using a sliding windows of 58 nucleotides, encodes them using [_pc3mer_](https://github.com/beiko-lab/DNA_encoders#pc3mer) and [_k-mers_](https://github.com/beiko-lab/DNA_encoders#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.


## Citation
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

## Samples used for training

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  factor: 65 samples of 24, 9 28, 58 32, 103 38, 17 54 and 753 70 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](www.ncbi.nlm.nih.gov/assembly/GCF_000005845.2/) using the Basic Local Alignment Search Tool (BLASTN; [[1]](#1), [[2]](#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.

### Data sets

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.

  1) Raw sequences (**raw_samples.fasta**). Examples:
  ```
     >Sigma00
     CCGATTTTTTGCGAAACGTTCCGCCTGGCATCAGGATAGTTTGTTCGTTATCCAGTTC
     >Sigma00
     GCTGGTTTGTACTACCACAAAGAGATTGGCTCCCGGATTAAAAAAAGTAATCACGAAC
  ```

  2) 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
   ```
  
  3) Sequences encoded with _k-mers_ (**k-mers.md**). Example:
  ```
    10.0,14.0,13.0,21.0,2.0,1.0,3.0,4.0,3.0,4.0,5.0,1.0,3.0,3.0,2.0,5.0,2.0,5.0,3.0,11.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,2.0,1.0,2.0,0.0,1.0,0.0,0.0,2.0,1.0,1.0,0.0,2.0,1.0,2.0,1.0,0.0,2.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,1.0,1.0,1.0,2.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,3.0,2.0,5.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0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  ```

## How to apply Expositor to your data

1. Download [_Expositor_](https://github.com/beiko-lab/Expositor) and [_DNA_encoders_](https://github.com/beiko-lab/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.
2. Create a conda environment using the _conda_environment.yml_ file in _Expositor_ by doing:
    ```
    conda env create -f conda_environment.yml
    ```
   Then activate the environment with:
    ```
    conda activate expositor
    ```
3. To run _Expositor_ on your data, use the following command line:
    ```
    python expositor.py -p path_to_the_genome -l number_of_nucleotides_in_the_genome
    ```
   _Expositor_ will evaluate the genome in _path_to_the_genome_ and make predictions saving intermediate and final results in the same folder.


## References
[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. 5360, 2014, doi: 10.1016/j.ab.2014.04.001.

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