What is PlasticEnz?

PlasticEnz offers a streamlined and accessible solution for identifying plastic-degrading enzymes in metagenomic data by combining homology-based and machine learning approaches. It accepts contigs, genomes, MAGs and proteins and screens them for potential plastic degrading homologous enzymes.

Downloading PlasticEnz

Clone the repositiory and navigate into the tool main folder (where setup.py is located)

git clone https://github.com/akrzyno/PlasticEnz.git

Set Up the Conda Environment with External Tools

conda create -n plasticenz_env --no-channel-priority -c bioconda -c conda-forge -c defaults python=3.11 libffi=3.4.2 prodigal hmmer diamond bowtie2 samtools

Activate the environment

conda activate plasticenz_env

Install Python Package Dependencies With your conda environment activated, navigate to the package folder and install the remaining python packages:

cd PlasticEnz
pip install -r requirements.txt

Install the package

pip install .

Test if it runs correctly

To see all the options:

 plasticenz

or

 plasticenz --help

Running a test-case

Please before using the PlasticEnz on your dataset run the test-case (data included within the package) to ensure all is sound. To do so run:

  plasticenz --test --outdir .

Wait until you see "✅PlasticEnz analysis completed successfully!" and check the outdir folder for the output folder. If you see three these files there: Abundances_table.tsv Proteins_unique.fa Summary_table.tsv,you are good to go.

All options

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      / _ | | __ _ ___| |_(_) ___  /___ __  ____
     / /_)| |/ _` / __| __| |/ __|/_\| '_ \|_  /
    / ___/| | (_| \__ | |_| | (__//__| | | |/ / 
    \/    |_|\__,_|___/\__|_|\___\__/|_| |_/___|
                                                
    
    #####################################
    #        Welcome to PlasticEnz      #
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Please remember to cite following tools:
- Prodigal: Hyatt et al., 2010. BMC Bioinformatics. DOI: 10.1186/1471-2105-11-119
- HMMER: Eddy, 2011. PLoS Comput Biol. DOI: 10.1371/journal.pcbi.1002195
- DIAMOND: Buchfink et al., 2015. Nat Methods. DOI: 10.1038/nmeth.3176
- Bowtie2: Langmead & Salzberg, 2012. Nat Methods. DOI: 10.1038/nmeth.1923
- Samtools: Danecek et al., 2021. Gigascience. DOI: 10.1093/gigascience/giab008
- ProtTrans: Elnaggar et al., 2022. IEEE TPAMI. DOI: 10.1109/TPAMI.2021.3095381


usage: plasticenz [-h] [-c CONTIGS] [-1 READS_FORWARD] [-2 READS_REVERSE] [-p PROTEINS] [-g GENOME] [--cores CORES] [--polymer POLYMER] [--outdir OUTDIR]
                  [--use_gpu] [--evalue_hmmer EVALUE_HMMER] [--bitscore_hmmer BITSCORE_HMMER] [--evalue_diamond EVALUE_DIAMOND]
                  [--bitscore_diamond BITSCORE_DIAMOND] [--test] [--sensitive]

PlasticEnz: A tool for detecting plastic-degrading enzymes from sequence data.

options:
  -h, --help            show this help message and exit
  -c CONTIGS, --contigs CONTIGS
                        Path to contigs file (FASTA). (default: None)
  -1 READS_FORWARD, --reads_forward READS_FORWARD
                        Path to forward reads file (FASTQ). (default: None)
  -2 READS_REVERSE, --reads_reverse READS_REVERSE
                        Path to reverse reads file (FASTQ). (default: None)
  -p PROTEINS, --proteins PROTEINS
                        Path to protein file (FASTA). (default: None)
  -g GENOME, --genome GENOME
                        Path to genome or MAG file (FASTA). (default: None)
  --cores CORES         Number of CPU cores to use. (default: 1)
  --polymer POLYMER     Polymer(s) to screen for. Use 'all' for all available. (default: None)
  --outdir OUTDIR       Output directory. (default: None)
  --use_gpu             Attempt to use GPU for accelerated computations. (default: False)
  --evalue_hmmer EVALUE_HMMER
                        E-value threshold for HMMER search. (default: 1e-05)
  --bitscore_hmmer BITSCORE_HMMER
                        Bitscore value for HMMER search. (default: 20)
  --evalue_diamond EVALUE_DIAMOND
                        E-value threshold for DIAMOND search. (default: 1e-05)
  --bitscore_diamond BITSCORE_DIAMOND
                        Minimum alignment quality for DIAMOND search. (default: 20)
  --test                Run the tool with a predefined test dataset. (default: False)
  --sensitive           Use neural network model (nn_model.pkl) for sensitive predictions. (default: False)

❓Troubleshooting

PlasticEnz requires several external tools. If you encounter issues with Conda installation, you can install them manually:

conda install -c bioconda prodigal=2.6.3
conda install -c bioconda hmmer=3.4
conda install -c bioconda diamond=2.1.8
conda install -c bioconda bowtie2=2.5.4
conda install -c bioconda samtools=1.21