pegi3s/pss-genome-fs

This image belongs to a larger project called Bioinformatics Docker Images Project (http://pegi3s.github.io/dockerfiles)

(Please note that the original software licenses still apply)

GenomeFastScreen https://img.shields.io/badge/license-MIT-brightgreen](https://github.com/pegi3s/pss-genome-fs) https://img.shields.io/badge/hub-docker-blue](https://hub.docker.com/r/pegi3s/pss-genome-fs) https://img.shields.io/badge/hub-compi-blue]([***]

GenomeFastScreen is a Compi pipeline that automates the process of identifying genes that likely show PSS, and thus, should be studied in detail, starting from a set of FASTA files, one per genome, containing all coding sequences. To do this, it uses internally our previous FastScreen pipeline¹ (available here). GenomeFastScreen automatically removes problematic sequences such as those showing ambiguous positions and identifies orthologous gene sets. It is also possible to identify the orthologous genes in an external reference species, which is useful to compare results across different species, or conduct gene ontology enrichment analyses when there is no data for the species being analysed. A Docker image is available for this pipeline in https://hub.docker.com/r/pegi3s/pss-genome-fs.

GenomeFastScreen repositories

• https://github.com/pegi3s/pss-genome-fs
• https://hub.docker.com/r/pegi3s/pss-genome-fs
• CompiHub

Using the GenomeFastScreen image in Linux

In order to use the GenomeFastScreen image, create first a directory in your local file system (working_dir in the example) with the following structure:

bash
working_dir/
├── input
│   ├── 1.fasta
│   ├── 2.fasta
│   ├── .
│   ├── .
│   ├── .
│   └── n.fasta
├── global
│   └── global-reference-file.fasta
└── pss-genome-fs.params

Where:

• The input FASTA files to be analized must be placed in the working_dir/input directory.
• The global reference FASTA file is located at working_dir/global/global-reference-file.fasta.
• The Compi parameters file is located at working_dir/pss-genome-fs.params.

Using such folder structure, the Compi parameters file must look like:

input_data_dir=input
reference_file=1.fasta
blast_type=tblastx
global_reference_file=global/global-reference-file.fasta

Where:

• input_data_dir is set to input, which is the name of the directory that contains the input FASTA files to be analyzed (it is possible to use a different name than input as long as it is properly set in the Compi parameters file).
• reference_file is set to 1.fasta, which is the name input FASTA file to use as reference for finding the orthologous gene sets.
• blast_type is set to the type of BLAST to use for finding the orthologous gene sets (either blastn or tblastx).
• global_reference_file is set to the location (relative to the working_dir directory) of the global reference FASTA file. Note that this file is optional and it can be ommited. When provided, the last task of the pipeline (orthologs-reference-species) is executed using it in order to identify the orthologous genes in the short lists produced by FastScreen.

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 WORKING_DIR to the right path in your local file system and COMPI_NUM_TASKS to the maximum number of parallel tasks that can be run.

bash
WORKING_DIR=/path/to/pss-genome-fs/working_dir/
COMPI_NUM_TASKS=8

mkdir -p ${WORKING_DIR}/logs

docker run --rm -v /tmp:/tmp -v /var/run/docker.sock:/var/run/docker.sock -v ${WORKING_DIR}:/working_dir --rm pegi3s/pss-genome-fs --logs /working_dir/logs --params /working_dir/pss-genome-fs.params --num-tasks ${COMPI_NUM_TASKS} -- --host_working_dir ${WORKING_DIR} --compi_num_tasks ${COMPI_NUM_TASKS}

The following section explains the main pipeline outputs as well as how to proceed with the analysis of the results.

Pipeline outputs

The main pipeline outpus created in the working_dir are:

• /orthologs: this directory contains one FASTA file, one for each gene, with the sequences from the analysed genomes (problematic sequences such as those containing in-frame stop codons and ambiguous positions are excluded).
• /fast-screen: results generated by the FastScreen pipeline after analyzing all genes. The main output from this directory is the short_list file, which contains the names of the FASTA files that likely show evidence for PSS and that have been copied to the /short_list directory. Six other output files are generated in this directory:
  • FUBAR_short_list: a file containing the names of the files where evidence for positive selection has been found by FUBAR.
  • to_be_reevaluated_by_codeML: a file containing the names of the files that where re-evaluated by CodeML.
  • codeML_random_list: a file containing the names of the files from which a random sequence sample was taken because they were too large to be analysed by CodeML.
  • codeML_short_list: a file containing the names of the files where PSS were detected by CodeML model M2a.
  • negative_list: a file containing the names of the files where no evidence for positive selection was found by either FUBAR or CodeML.
  • files_requiring_attention: a file containing the names of the files that could not be processed without error (usually because they have in frame stop codons that were introduced during the nucleotide alignment step). These files are copied to the /files_to_re_run directory each time the fast-screen step is executed.
• /short_list_dir: contains the FASTA files that likely show evidence for PSS and, therefore, should be analysed in detail.
• /files_to_re_run: this directory contains the FASTA files listed in the /fast-screen/files_requiring_attention file after each execution of the fast-screen step. The following subsection explains how to re-analyze this files.
• orthologous_gene_list: a file containing the orthologous genes in the external reference species.
• orthologous_codeML_short_list: file containing the orthologous gene names listed in the /fast-screen/codeML_short_list.
• orthologous_FUBAR_short_list: file containing the orthologous gene names listed in the /fast-screen/FUBAR_short_list.
• orthologous_gene_short_list: file containing the orthologous gene names listed in the /fast-screen/short_list.

Note that the orthologous_* lists are only created when the global reference file is provided as parameter to the pipeline.

Re-analyzing the files requiring attention

After running the entire pipeline, the /files_to_re_run directory contains the FASTA files listed in the /fast-screen/files_requiring_attention file. These files could not be analyzed by the FastScreen pipeline after each execution of the fast-screen step, usually because they have in frame stop codons that were introduced during the nucleotide alignment step.

To re-analyze them, you should first fix the problems and put the updated FASTA files in the same directory. Then, you should execute only the fast-screen step by running the following command:

bash
docker run --rm -v /tmp:/tmp -v /var/run/docker.sock:/var/run/docker.sock -v ${WORKING_DIR}:/working_dir --rm pegi3s/pss-genome-fs --logs /working_dir/logs --params /working_dir/pss-genome-fs.params --num-tasks ${COMPI_NUM_TASKS} --single-task fast-screen -- --host_working_dir ${WORKING_DIR} --compi_num_tasks ${COMPI_NUM_TASKS}

This command simply introduces the --single-task fast-screen parameter to ask Compi to only run this step. When this step finishes, the new results will be added to the existing ones in the /fast-screen and, like before, the /files_to_re_run directory will contain the FASTA files that could not be analzyed in this new run.

Once you are done analyzing the problematic files, you can re-run the final two steps of the pipeline to copy the FASTA files that likely show evidence for PSS and to re-generate the orthologous gene lists. To do this, you should execute the following command:

bash
docker run --rm -v /tmp:/tmp -v /var/run/docker.sock:/var/run/docker.sock -v ${WORKING_DIR}:/working_dir --rm pegi3s/pss-genome-fs --logs /working_dir/logs --params /working_dir/pss-genome-fs.params --num-tasks ${COMPI_NUM_TASKS} --from get-short-list-files -- --host_working_dir ${WORKING_DIR} --compi_num_tasks ${COMPI_NUM_TASKS}

This command uses the --from get-short-list-files parameter to ask Compi to run this and the subsequent orthologs-reference-species step.

Test data

The sample data is available https://github.com/pegi3s/pss-genome-fs/raw/master/resources/pss-genome-fs.zip. Download, uncompress it and move to the pss-genome-fs directory, where you will find:

• A directory called working_dir, that contains the structure described previously.
• A file called run.sh, that contains the following commands (where you should adapt the WORKING_DIR path) to test the pipeline:

bash
WORKING_DIR=/path/to/pss-genome-fs/working_dir/
COMPI_NUM_TASKS=8

mkdir -p ${WORKING_DIR}/logs

docker run --rm -v /tmp:/tmp -v /var/run/docker.sock:/var/run/docker.sock -v ${WORKING_DIR}:/working_dir --rm pegi3s/pss-genome-fs --logs /working_dir/logs --params /working_dir/pss-genome-fs.params --num-tasks ${COMPI_NUM_TASKS} -- --host_working_dir ${WORKING_DIR} --keep_temporary_dir --compi_num_tasks ${COMPI_NUM_TASKS}

Running times

• ≈ 158 minutes - 16 parallel tasks - Ubuntu 18.04.3 LTS, 6 cores (AMD Ryzen 5 2600 @ 3.40GHz),16GB of RAM and SSD disk.
• ≈ 760 minutes - 10 parallel tasks - Ubuntu 16.04.3 LTS, 4 cores (Intel(R) Core(TM) i5-5200U CPU @ 2.20GHz), 16GB of RAM and SSD disk.

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/pss-genome-fs: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. https://img.shields.io/badge/DOI-10.1007%2Fs***--021--00439--2-blue](https://doi.org/10.1007/s***-021-00439-2)
• H. López-Fernández; C.P. Vieira; F. Fdez-Riverola; M. Reboiro-Jato; J. Vieira (2020) Inferences on Mycobacterium leprae host immune response escape and antibiotic resistance using genomic data and GenomeFastScreen. 14th International Conference on Practical Applications of Computational Biology & Bioinformatics: PACBB 2020. L'Aquila, Italy. 7 - October https://img.shields.io/badge/DOI-https%3A%2F%2Fdoi.org%2F10.1007%2F978--3--030--54568--0__5-green](https://doi.org/10.1007/978-3-030-54568-0_5)
• H. López-Fernández; P. Duque; N. Vázquez; F. Fdez-Riverola; M. Reboiro-Jato; C.P. Vieira; J. Vieira (2019) Inferring Positive Selection in Large Viral Datasets. 13th International Conference on Practical Applications of Computational Biology & Bioinformatics: PACBB 2019. Ávila, Spain. 26 - June https://img.shields.io/badge/DOI-10.1007%2F978--3--030--23873--5__8-green](https://doi.org/10.1007/978-3-030-23873-5_8)