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PROMOTECH: A UNIVERSAL TOOL FOR PROMOTER DETECTION IN BACTERIAL GENOMES

Promotech is a machine-learning-based classifier trained to generate a model that generalizes and detects promoters in a wide range of bacterial species. During the study, two model architectures were tested, Random Forest and Recurrent Networks. The Random Forest model, trained with promoter sequences with a binary encoded representation of each nucleotide, achieved the highest performance across nine different bacteria and was able to work with short 40bp sequences and whole bacterial genomes using a sliding window. The selected model was evaluated on a validation set of four bacteria not used during training.

Requirements

  1. Download and Install Anaconda or Miniconda from here.
    • wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh for Ubuntu 20.04
    • wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh for Mac OS Big Sur V11.3
    • bash miniconda.sh
  2. Install conda environment from the prebuilt environment YAML file.
    • conda env create -f promotech_env.yml for Ubuntu 20.04
    • conda env create -f promotech_mac_env.yml for Mac OS Big Sur V11.3
    • Note: The environment was made on Ubuntu 20.04, different versions of the packages could be required for different operating systems.
  3. Activate environment
    • conda activate promotech_env
  4. Download the models from here
  5. Uncompress the two Random Forest models with .zip format and save them at the models folder. The resulting files are models/RF-HOT.model and models/RF-TETRA.model
  6. Make sure all files required to run Promotech are available in promotech's folder

Note: A minimum of 24 GB of RAM memory is recommended to run the RF-HOT, LSTM, and GRU model on a whole-genome. Parsing a whole-genome to the RF-TETRA model's input format can produce the python "Memory Error" due to the high complexity and high RAM-memory demand required to obtain the tetra-nucleotide frequencies for millions of sequences in forward and inverse strand. An example of this process is shown in the examples section below. All models can run on lower-end systems, with at least 8GB of RAM, when predicting FASTA files with hundreds or thousands of sequences, 40 nt in length.

The examples in the section below were tested in a desktop computer with the following specifications:

  • Processor : Intel(R) Core(TM) i5-9300H CPU @ 2.40GHz 2.40 GHz
  • RAM : 24.0 GB (23.8 GB usable)
  • System Type : 64-bit Ubuntu 20.04 LTS
  • Graphic Memory : NVIDIA GeForce RTX 2060 6GB GDDR6
  • Python Version : Python 3.6

Commands

  1. -v, --version - Print Promotech's latest version.
  2. -gui, --gui - Use the interactive GUI interface for predicting 40 nucleotide sequences. The interface does not work for whole-genome prediction.
  3. -f, --fasta - Specify the location of the sequences or whole-genome FASTA file location in disk. This command is used together with the -s, --predict-sequences or -PG, --parse-genome arguments.
  4. -m, --model - Indicates the type of model used for prediction and the target output data type used during the genome parsing stage. The default value is RF-HOT.
    • The available options for 40nt sequences prediction are RF-HOT, RF-TETRA, GRU, LSTM.
    • The available options for whole-genome parsing and prediction are RF-HOT, GRU, LSTM.
  5. -ts, --test-samples - Used for testing purposes during the genome parsing stage. A whole-genome can be made of 4 million+ nucleotides and can take hours, depending on your system configuration to parse and predict. This command limits the number of sequences the sliding window cuts from the genome. It is used only with the -pg, --parse-genome argument.
  6. -pg, --parse-genome - Use a sliding window to cut 40 nucleotide sequences from the whole-genome in forward and reverse strand. The files are then saved to the "results" folder with a "[MODEL-TYPE].data" format, where MODEL-TYPE is the name of the model's desired input format, i.e. "RF-HOT.data" and "RF-HOT-INV.data".
    • The mandatory argument used with this command is -f, --fasta. Note that the fasta file should contain a single sequence.
    • The optional arguments used with this command are -m, --model, and --ts, --test-samples.
  7. -g, --predict-genome - This command uses the files generated using the -pg, --parse-genome argument and located in the "results" folder.
    • The optional argument used with this command is -m, --model. Make sure to match the same model type used during the parsing stage.
  8. -s, --predict-sequences - Used to parse and predict 40nt sequences from a FASTA file.
    • The mandatory argument used with this command is -f, --fasta.
    • Make sure that the FASTA file has only 40-nt sequences as shown in the example below. If you require to use longer sequences, use the -pg, --parse-genome and -g, --predict-genome commands.

  >seq1
  AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
  >seq2
  AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
  >seq3
  AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

Examples

40 Nucleotide Sequences

  1. Parse and predict using the --predict-sequence, -s command and specify the FASTA file using --fasta, -f. The FASTA file should only include 40nt sequences. If you require to predict longer sequences, use the "whole-genome" commands. An example of the command-line output obtained when running this command is found HERE

python promotech.py -s -m RF-HOT -f examples/sequences/test.fasta -o results

Whole-Genome

  1. Parse the whole-genome in the FASTA file (a single sequence is expected) by using --parse-genome, -pg and specifying the file using --fasta, -f . A smaller subset of the sliding window sequences can be used for testing purposes using the --test-samples, -ts parameter. An example of the command-line output obtained when running this command is found HERE

python promotech.py -pg -m RF-HOT -f examples/genome/ECOLI_2.fasta -o results

or

python promotech.py -pg -ts 20000 -m RF-HOT -f examples/genome/ECOLI_2.fasta -o results

  • Note: Running one of the following commands will use a sliding window of 40nt size and 1nt step, pre-processed the sequences to meet the specified model's input requirement and create two files, results/[MODEL_TYPE].data and results/[MODEL_TYPE]-INV.data, for forward and inverse strand, where MODEL_TYPE specifies the type of model that will later be used for assessing the pre-processed 40nt sequences. Do not delete the 'results' folder or the '.data' files, because they will be used in the next step.
  • Note: For comparison, the pipeline configured to generate data for the RF-HOT model, took 35 minutes and 42 seconds to cut 4,639,634 forward and 4,639,634 inverse sequences from the E. coli (NC_000913.2) genome with 4,639,675 nucleotides in length, pre-processed them to hot-encoded binary format, save them to two binary files and each file was 5.8 GB in size. During this time, it maintained around 18.5/24GB of RAM exclusively for the python running process.
  1. Predict promoter sequences using the parsed sequences using --predict-genome, -g, assign a threshold using --threshold, -t, and select a model using --model, -m. The default threshold, and model are 0.5, and RF-HOT, respectively. An example of the command-line output obtained when running this command is found HERE

python promotech.py -g -t 0.6 -i results -o results

  • Note: This command expects the user to have used the --parse-genome, -pg command before to generate the pre-processed sequences from the bacterial genome and stored in the files results/[MODEL_TYPE].data and results/[MODEL_TYPE]-INV.data.
  • Note: For comparison, it took 1 hour, 5 minutes, and 27 seconds to predict both, forward and inverse strand batches, each with 4,639,634 pre-processed sequences, with a total of 9,279,268 sequences as input and an output of 55,002 promoters sequences with a score above the 0.5 threshold.

Cite

If you use Promotech please cite:

Promotech: A general tool for bacterial promoter recognition. Ruben Chevez-Guardado and Lourdes Peña-Castillo. Genome Biol 22(1):318 (2021). PMID: 34789306. [DOI: 10.1186/s13059-021-02514-9 ] (https://doi.org/10.1186/s13059-021-02514-9)