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Routh-Lab / ViReMaMinimap2

Docker file for a version of ViReMa that can use minimap2
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ViReMaMinimap2

Docker file for a version of ViReMa that can use minimap2. Currently still buggy.

ViReMa Setting When Using Minimap2

This has not been rigorously evaluated but the command being used to run minimap2 on nanopore data is: python ./ViReMa.py ./FHV_Genome.txt ./FHV_nanopore.fastq FHV_recombinations.SAM --Seed 50 --MicroInDel_Length 5 -BED --Output_Dir FHV_Test_1 --Aligner minimap2 --ErrorDensity 25,200

Docker Setup (~10 minutes)

  1. Download Docker Desktop.
  2. Download this GitHub repository's files as a .zip file and unzip it as a new folder.
  3. Open the command line/prompt on your operating system. The next three steps involve entering lines into the command line.
  4. Change the current directory using the command line to the folder you just unzipped with the file "virema" and the folder "src". This command looks something like cd C:\Users\user\Documents\GitHub\ViReMaDocker replacing "C:\Users\user\Documents\GitHub\ViReMaDocker" with your folder path.
  5. Now, build the image with docker build -t virema . -f virema. If you want to add your own data first, add appropriate files to the "src/TestData" folder before building the image. Messages should indicate if everything builds smoothly. Here, the "virema" after "-t" can be replaced with whatever text you would like to name/tag the container with.
  6. You can now make a container using docker run -d -t virema or by going to the "Images" tab in Docker Desktop and hitting "Run" when hovering over the "virema" image.
  7. You can either (a) continue in the operating system command line or (b) use Docker Desktop to open the container's command line. For (b), navigate to the "Containers/Apps" tab with Docker Desktop open. If the container is running, hit "CLI". If the container is stopped, hit the "Start" button first.
  8. (a) Type docker exec -it <container-id> python ./ViReMa.py ./FHV_Genome_padded ./FHV_10k.txt FHV_recombinations.SAM --Seed 20 --MicroInDel_Length 5 -BED --Output_Dir FHV_Test into the operating system command line/prompt replacing "<container-id>" with your container id (use docker container ls and copy the container id for the "virema" container). (b) Type python ./ViReMa.py ./FHV_Genome_padded ./FHV_10k.txt FHV_recombinations.SAM --Seed 20 --MicroInDel_Length 5 -BED --Output_Dir FHV_Test into the "CLI". This command runs the ViReMa algorithm on flock house virus example data. If everything worked, it should produce a folder called "FHV_Test" with a file called "FHV_recombinations.SAM" inside. You can check that this worked with the commands ls followed by cd FHV_Test and one more ls.
  9. If you've been doing option (b), go back to the command line on your operating system. The next two steps involve entering lines into the command line.
  10. With the container still running, use docker container ls and copy the container id for the "virema" container.
  11. Export your files from the container to your computer. Use the command cd C:\Users\user\Documents\Results replacing "C:\Users\user\Documents\Results" with where you want the folder to show up on your local machine. Run docker cp <container-id>:/FHV_Test ., replacing "<container-id>" with the container id from Step 10. The "." puts the files into the local directory we just used "cd" to get into.
  12. When finished, stop the container in Docker Desktop or using the command line (docker stop <container-id>).

Running ViReMa On Your Own Data

  1. You don't have to rebuild the image everytime you use ViReMa. Start the existing container either through Docker Desktop in the "Containers/Apps" tab or using the command line.
  2. To get additional input data files into the container, we will use the "cd" and "cp" commands again in the operating system command line (not the container's command line). Use the command cd C:\Users\user\Documents\Data replacing "C:\Users\user\Documents\Data" with the folder path to your data. Then, run docker cp ./<data.fileext> <container-id>:., replacing "<data.fileext>" with the name of the file including its file extension and "<container-id>" with the container id.
  3. Run Step 8 from the "Setup" section with appropriate changes.

Analysis

You can then create figures and visualizations with the newly created files by following tutorials here.

Details

Click to expand ## ViReMa Version 0.25 ### Last Modified: Jun-21 Test Data - FHV (8 files) - FHV_10k.txt Contains ten thousand reads from the Flock House Virus Dataset: SRP013296 - FHV_Genome_padded.txt Contains reference genes for Flock House Virus with long 3' terminal A residues. - FHV_Genome_padded.*.ebwt These are the built index sequences for the FHV padded genome using Bowtie-Build v0.12.9 - FHV_P7R2_rep2_100k.txt Contains raw data from Jaworski et al PLoS Path paper - FHV_P7R2_rep2_100k_virema.bam Contains example mapping of data using ViReMa2 Compiler_Module.py Module or Stand-alone script used to compile output results from ViReMa.py. Runs from Command-line. ConfigViReMa.py This script carries the global variables used by both ViReMa.py and Compiler_Module.py README.txt Includes instructions to run ViReMa. ViReMa.py Runs ViReMa (Viral-Recombination-Mapper) from commmand line. ViReMa_GUI.py Runs ViReMa (Viral-Recombination-Mapper) from GUI (requires GOOEY). Before you Start: ViReMa is a simple python script and so should not require any special installation. ViReMa requires python version 3.7 and Bowtie version 0.12.9. ViReMa only uses modules packaged as a standard with Python version 2.7. Bowtie and Bowtie-Inspect must be in your $PATH. Indexes for reference genomes must be built with Bowtie-Build. For maximum sensitivity, please add a terminal pad using 'A' nucleotides to the end of your genome sequence before creating virus reference indexes using Bowtie-Build. This pad must be longer than the length of the reads being aligned. Without these pads, ViReMa will fail to detect recombination events occuring at the edges of the viral genome. ViReMa is run from the command line: >python ViReMa.py Virus_Index Input_Data Output_Data [args] Example using test data: >home/ViReMa0.1/python ViReMa.py Test_Data/FHV_Genome_padded Test_Data/FHV_10k.txt FHV_recombinations.txt --Seed 20 --MicroInDel_Length 5 ViReMa will take read data and attempt to align it to the reference genomes (Virus first, Host second). If the Seed of the read successfully aligns to a reference genome, bowtie will continue to align the remaining nucleotides after the Seed.Alignment() will extract all the successfully aligned nucleotides and the remaining unaligned nucleotides will be written to a new temporary read file. If there is no succesful alignment, Alignment() will trim one nucleotide from the beginning of the read and report. Again, the remaining nucleotides will be written to a new temporary file which will be used for subsequent alignment. Required arguments: Virus_Index Virus genome reference. e.g. FHV_Genome.txt Enter full path if the index is not in the current working directory, even when that index is stored in your Bowtie-0.12.9/indexes folder. E.g.: ../../Desktop/Bowtie-0.12.9/indexes/FHV_Genome Input_Data File containing single reads in FASTQ or FASTA format. Output_Data Destination file for results. This is be saved in the current working directory.


Read the updated ViReMa paper with vignettes:
Sotcheff S, Zhou Y, Sun Y, Johnson JE, Torbett B, Routh AL. ViReMa: A Virus Recombination Mapper of Next-Generation Sequencing data characterizes diverse recombinant viral nucleic acids. bioRxiv. 2022:2022.03.12.484090. doi:10.1101/2022.03.12.484090

Read the original ViReMa paper:
Andrew Routh, John E. Johnson, Discovery of functional genomic motifs in viruses with ViReMa-a Virus Recombination Mapper-for analysis of next-generation sequencing data, Nucleic Acids Research, Volume 42, Issue 2, 1 January 2014, Page e11, https://doi.org/10.1093/nar/gkt916