7 Juno_SNP

The goal of this pipeline is to perform SNP analysis on bacterial genomes. As input, it requires clean fastq files (forward or R1 and reverse or R2, filtered and trimmed) for each sample you want to analyze with the ‘.fastq’, ‘.fq’, ‘.fastq.gz’ or ‘.fq.gz’ extensions. It also needs an assembly (.fasta file) per sample. Importantly, this pipeline works directly on output generated from the Juno-assembly pipeline.

You can provide your own reference genome or the tool can choose one from RefSeq. The pipeline follows the following steps: 1. Find best reference genome: This step is only done if no reference genome was provided. It uses the referenceseeker tool to find the reference genome in the RefSeq database that is closest to your genome. The distance to reference genomes is calculated with a combination of Mash distances and ANI (average nucleotide identity). 2. SNP analysis: It uses the tool Snippy to calculate the SNP differences with the reference genome for ever sample. 3. Calculating SNP core: It puts together the results of the SNP analysis of all samples into one VCF file. 4. Draw tree: It makes a phylogenetic tree of the samples ran. The default is to use the UPGMA (Unweighted pair group method with Arithmetic mean) algorithm for the tree but you can also use NJ (neighbor joining) if preferred.

7.1 Handbook

7.1.1 Requirements and preparation

See the General Instructions for all pipelines first.

  • This pipeline needs two pre-processed (filtered and trimmed) .fastq files (forward and reverse) and an assembly (.fasta file) per sample as input. ALL THE INPUT FILES SHOULD BE IN THE SAME FOLDER, NOT IN SUBFOLDERS. The only exception is if you use the Juno-assembly pipeline to pre-process your data. In that case, the pipeline will recognize the subfolders where the clean fastq and fasta files should be.

7.1.2 Download the pipeline

YOU NEED TO DOWNLOAD THE PIPELINE ONCE OR EVERY TIME YOU WANT TO UPDATE IT

Make sure to have followed the instructions to set up conda before installing any of our pipelines!

Please follow the instructions to download pipelines from the Juno team of the IDS-bioinformatics group. The Juno_SNP pipeline can be found in this link.

7.1.3 Install conda environment

YOU NEED TO REINSTALL THE MASTER ENVIRONMENT EVERY TIME YOU UPDATE THE PIPELINE (everytime you download the code)

  1. Open a terminal. (Applications>terminal).

  2. Enter the folder of the pipeline using:

cd /mnt/scratch_dir/<my_folder>/Juno_snp
  1. If you already had a juno_snp environment before you need to delete the old one by using the command:
conda env remove -n juno_snp

If you had never created a juno_snp environment before, you can skip this step and go to step 4 instead.

  1. Create a new environment for running Juno_SNP by using the command:
conda env create -f envs/master_env.yaml

This step will take some time (few minutes).

Note: If this step would take more than 1 hour, please kill the process (using Ctrl + C or Ctrl + Z) and refer to the section General Troubleshooting. The first issue written there (Failure when installing master environment) often solves the problem. If, however, the problem persists, please contact me by email.

7.1.4 Start the analysis. Basics

  1. Open a terminal. (Applications>terminal).

  2. Enter the folder of the pipeline using:

cd /mnt/scratch_dir/<my_folder>/Juno_snp
  1. Activate juno_snp environment
conda activate juno_snp

If you run in trouble please see the troubleshooting section for conda activate.

  1. Run the pipeline

You can run the pipeline just giving your input directory. The pipeline will automatically search for an appropriate reference genome in RefSeq that is as close as possible to the samples in your dataset.

python juno_snp.py -i [path/to/input_directory]

If you have a reference genome that you would like to use instead of letting the pipeline choose it for you, you can give it to the pipeline

python juno_snp.py -i [path/to/input_directory] --reference [path/to/reference.fasta]

Note: Do not keep all your data (including results) on the scratch_dir partition. You are allowed to keep 400GB max and with sequencing data, this can get full quite fast.

7.1.5 Output

A folder called output/, inside the folder of the pipeline, will be created. This folder will contain all the results and logging files of your analysis. Please refer to the manual of ChewBBACA to interpret the results.

Note: If you want your output to be stored in a folder with a different name or location, you can use the option -o (‘o’ from output).

python juno_snp.py -i my_input_files -o my_results

Another very important output from the pipeline are the logging files and audit trail that contain information of the software versions used, the parameters used, the error messages, etc. They could be important for you if you want to publish or reproduce the analysis at a later time point and also to get help from the bioinformatics team if you were to run into trouble with the pipeline. Please read about these files here.

7.1.6 Troubleshooting for this pipeline

Please read first the General Troubleshooting section!

7.1.6.1 Other problems or failing rules

The Juno_SNP pipeline is still in development which means that sometimes the process can fail.

Before contacting for help, try these two steps:

  1. Re-run the pipeline again and see if the process continues. If it does, please keep re-running the pipeline until your analysis is finished or there is no longer progress. In this case, send an email after the pipeline is finished so I can troubleshoot the problem.

  2. Download the pipeline again and start from the beginning of this handbook. Sometimes there is an issue that has been resolved in newer versions of the pipeline.

If the pipeline still fails after these two steps, please inform me about the problem. Send an e-mail with the following content:

  • The log and error files that can be found in the output folder
  • The path to your input directory
  • The path to where the pipeline is installed

Note: I cannot help you without this information, if information is missing there will be a delay in troubleshooting the problem.