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Auto-PSS-Genome (Automatic Positively Selected Sites Genome) is a Compi pipeline to automatically identify positively selected amino acid sites using three different methods, namely CodeML, omegaMap, and FUBAR in complete genomes (FASTA files containing all coding sequences).

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Auto-PSS-Genome (Automatic Positively Selected Sites Genome) is a Compi pipeline to automatically identify positively selected amino acid sites using three different methods, namely CodeML, omegaMap, and FUBAR in complete genomes (FASTA files containing all coding sequences). A Docker image is available for this pipeline in this Docker Hub repository.

Auto-PSS-Genome repositories

What does Auto-PSS-Genome do?

Auto-PSS-Genome (Automatic Positively Selected Sites Genome) is a Compi pipeline to automatically identify positively selected amino acid sites (PSS) using three different methods, namely CodeML, omegaMap, and FUBAR in complete genomes (FASTA files containing all coding sequences).

This process comprises the following steps:

  1. Use the GenomeFastScreen pipeline to quickly identify genes that likely show PSS.
  2. Apply the CheckCDS to the files that failed to be analyzed by GenomeFastScreen in order to try to convert them into valid CDS files.
  3. Reanalyze such files using GenomeFastScreen.
  4. Finally, perform a more detailed analysis of all the genes that likely show PSS using the IPSSA pipeline.

Using the Auto-PSS-Genome image in Linux

In order to use the Auto-PSS-Genome image, create first a directory in your local file system (auto_pss_genome_project in the example) with the following structure:

auto_pss_genome_project/
├── input
│   ├── 1.fasta
│   ├── 2.fasta
│   ├── .
│   ├── .
│   ├── .
│   └── n.fasta
│
├── global
│   └── global-reference-file.fasta
│
├── pss-genome-fs.params
├── ipssa-project.params
└── check-cds.params

Where:

  • The input FASTA files to be analized must be placed in the auto_pss_genome_project/input directory.
  • Optionally, the global reference FASTA file for the GenomeFastScreen pipeline must be placed at auto_pss_genome_project/global/global-reference-file.fasta.
  • The pss-genome-fs.params file contains the Compi parameters file for the GenomeFastScreen pipeline.
  • The ipssa-project.params file contains the Compi parameters file for the IPSSA pipeline.
  • The check-cds.params file contains the Compi parameters file for the CheckCDS pipeline.

You can populate the Auto-PSS-Genome project directory, including sample Compi parameter files with default values, running the following command (here, you only need to set AUTO_PSS_GENOME_PD to the right path in your local file system):

AUTO_PSS_GENOME_PD=/path/to/auto_pss_genome_project

mkdir ${AUTO_PSS_GENOME_PD}

docker run --user "$(id -u):$(id -g)" --rm -v ${AUTO_PSS_GENOME_PD}:/working_dir pegi3s/auto-pss-genome init-working-dir.sh /working_dir

Now, you should:

  1. Put the input FASTA files in the auto_pss_genome_project/input directory.
  2. If required, put the global reference FASTA file in the auto_pss_genome_project/global directory.
  3. Edit the parameters of the GenomeFastScreen pipeline in the pss-genome-fs.params file. Here it is mandatory to set the reference_file to be the name of a file in the auto_pss_genome_project/input directory and blast_type. Optionally, set the global_reference_file value (and remove the # at the beginning of the line).
  4. Edit the parameters of the CheckCDS pipeline in the check-cds.params file. Here you only need to provide the reference word (case insensitive) in the sequence headers to identify the reference sequences when trying to create valid CDS files.
  5. Check the values of the parameters of the IPSSA pipeline in the ipssa-project.params file. This file contains the default recommended values for this pipeline and may need to be adjusted.

Once this structure and files are ready, you should run and adapt the following commands to run the entire pipeline. Here, you only need to set AUTO_PSS_GENOME_PD to the right path in your local file system and COMPI_NUM_TASKS to the maximum number of parallel tasks that can be run. Note that the --host_working_dir is mandatory and must point to the pipeline working directory in the host machine.

AUTO_PSS_GENOME_PD=/path/to/auto_pss_genome_project
COMPI_NUM_TASKS=6

docker run --rm -v /tmp:/tmp -v /var/run/docker.sock:/var/run/docker.sock -v ${AUTO_PSS_GENOME_PD}:/working_dir --rm pegi3s/auto-pss-genome /compi run -o --logs /working_dir/logs --num-tasks ${COMPI_NUM_TASKS} -- --host_working_dir ${AUTO_PSS_GENOME_PD} --compi_num_tasks ${COMPI_NUM_TASKS}

Test data

The sample data is available here. Download and uncompress it, and move the directory named auto-pss-genome-m-haemophylum, where you will find:

  • A directory called auto-pss-genome-project, that contains the structure described previously.
  • A file called run.sh, that contains the following commands (where you should adapt the AUTO_PSS_GENOME_PD path) to test the pipeline:
AUTO_PSS_GENOME_PD=/path/to/auto-pss-genome-project
COMPI_NUM_TASKS=8

docker run --rm -v /tmp:/tmp -v /var/run/docker.sock:/var/run/docker.sock -v ${AUTO_PSS_GENOME_PD}:/working_dir --rm pegi3s/auto-pss-genome /compi run -o --logs /working_dir/logs --num-tasks ${COMPI_NUM_TASKS} -- --host_working_dir ${AUTO_PSS_GENOME_PD} --compi_num_tasks ${COMPI_NUM_TASKS}

Running times

  • ≈ 11.5 hours - 50 parallel tasks - Ubuntu 18.04.2 LTS, 96 CPUs (AMD EPYC™ 7401 @ 2GHz), 1TB of RAM and SSD disk.

Preparing B+ submission files

Since pegi3s/auto-pss-genome:1.11.0 there are included several scripts to help in preparing B+ submission files. Check out this section to discover how to use them.

For Developers

Building the Docker image

To build the Docker image, compi-dk is required. Once you have it installed, simply run compi-dk build from the project directory to build the Docker image. The image will be created with the name specified in the compi.project file (i.e. pegi3s/auto-pss-genome:latest). This file also specifies the version of compi that goes into the Docker image.

References

  • H. López-Fernández; C. P. Vieira; P. Ferreira; P. Gouveia; F. Fdez-Riverola; M. Reboiro-Jato; J. Vieira (2021) On the identification of clinically relevant bacterial amino acid changes at the whole genome level using Auto-PSS-Genome. Interdisciplinary Sciences: Computational Life Sciences. Volume 13, pp. 334–343. DOI

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Auto-PSS-Genome (Automatic Positively Selected Sites Genome) is a Compi pipeline to automatically identify positively selected amino acid sites using three different methods, namely CodeML, omegaMap, and FUBAR in complete genomes (FASTA files containing all coding sequences).

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