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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 58587e05f604590c70550e13fc51b7425e916ed4
author iuc
date Sat, 14 Jan 2023 21:08:33 +0000
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457fd8fd681a planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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1 # VirHunter
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457fd8fd681a planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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3 VirHunter is a deep learning method that uses Convolutional Neural Networks (CNNs) and a Random Forest Classifier to identify viruses in sequening datasets. More precisely, VirHunter classifies previously assembled contigs as viral, host and bacterial (contamination).
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5 ## System Requirements
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6 VirHunter installation requires a Unix environment with [python 3.8](http://www.python.org/).
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7 It was tested on Linux and macOS operating systems.
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8 For now, VirHunter is still not fully compatible with M1 chip MacBook.
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10 In order to run VirHunter you need to have git and conda already installed.
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11 If you are installing conda for the first time, we suggest you to use
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12 a lightweight [miniconda](https://docs.conda.io/en/latest/miniconda.html).
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13 Otherwise, you can use pip for the dependencies' installation.
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457fd8fd681a planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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15 ## Installation
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17 To install VirHunter, you need to download it from github and then to install the dependancies.
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19 First, clone the repository from [github](https://github.com/cbib/virhunter)
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21 git clone https://github.com/cbib/virhunter.git
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23 Go to the VirHunter root folder
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25 cd virhunter/
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27 ### Installing dependencies with Conda
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29 First, you have to create the environment from the envs/environment.yml file.
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30 The installation may take around 500 Mb of drive space.
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32 conda env create -f envs/environment.yml
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34 Second, activate the environment:
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36 conda activate virhunter
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38 ### Installing dependencies with pip
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40 If you don't have Conda installed in your system, you can install python dependencies via pip program:
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42 pip install -r envs/requirements.txt
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44 Then if you have macOS you will need to install wget library to run some scripts (Conda installation already has it). You can do this with brew package manager.
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46 brew install wget
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48 ### Testing your installation of VirHunter
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50 You can test that VirHunter was successfully installed on the toy dataset we provide.
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51 IMPORTANT: the toy dataset is intended only to test that VirHunter has been well installed and all the scripts can be executed.
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52 These modules should not be used for prediction on your owd datasets!
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54 First, you have to download the toy dataset
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56 bash scripts/download_test_installation.sh
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58 Then run the bash script that calls the testing, training and prediction python scripts of VirHunter.
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59 Attention, the training process may take some time (up to an hour).
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61 bash scripts/test_installation.sh
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64 ## Using VirHunter for prediction
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66 To run VirHunter you can use the already pre-trained models or train VirHunter yourself (described in the next section).
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67 Pre-trained model weights are already available for the multiple host plants.
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68 You can download them using the download_weights.sh script.
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70 bash scripts/download_weights.sh
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72 Once the config file is ready, you can start the prediction:
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74 python virhunter/predict.py --test_ds /path/to/test_ds_1
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76 After prediction VirHunter produces two csv files and one optional fasta file:
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78 1. The first file ends with _predicted_fragments.csv
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79 It is an intermediate result containing predictions of the three CNN networks (probabilities of belonging to each of the virus/plant/bacteria class) and of the RF classifier for each fragment of every contig.
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81 2. The second file ends with _predicted.csv.
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82 This file contains final predictions for contigs calculated from the previous file.
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83 - id - fasta header of a contig.
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84 - length - length of the contig.
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85 - # viral fragments, # plant fragments and # bacterial fragments - the number of fragments of the contig that received corresponding class prediction by the RF classifier.
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86 - decision - class given by the VirHunter to the contig.
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87 - # viral / # total - number of viral fragments divided by the total number of fragments of the contig.
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88 - # viral / # total * length - number of viral fragments divided by the total number of fragments of the contig multiplied by contig length. It is used to display the most relevant contigs first.
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90 3. The fasta file ends with _viral.fasta. It contains contigs that were predicted as viral by VirHunter.