Running the Pipeline on Your Data¶
This page describes how to run the ImmuneDB pipeline on your own BCR/TCR data. It is assumed that you’ve previously tried the example pipeline and understand the basics of running commands in the Docker container.
Like in the example, each code block has a header saying if the command should be run on the host or in the Docker container.
Copying Your Sequence Data Into Docker¶
Unlike in the example pipeline where sequencing data was provided, you’ll need to copy your own FASTA/FASTQ sequencing data or AIRR-formatted IgBLAST output into the Docker container.
To do so, on the host, we create a new directory in the shared directory
into which we’ll copy your sequencing data. Here we’re calling it
sequences but you’ll probably want to choose a more descriptive name.
PATH_TO_SEQUENCES with the path to your sequencing data.
$ mkdir -p $HOME/immunedb_share/input $ cp PATH_TO_SEQUENCES $HOME/immunedb_share/input
Running IgBLAST (optional)¶
If your data is already in AIRR-compliant IgBLAST format or you are planning on using the built in anchoring method, you can skip this step.
The following command will run IgBLAST on your files. Valid values for species and locus are:
$ run_igblast.sh SPECIES LOCUS /share/input /share/input
For consistency with the commands in the rest of this tutorial, we’ll move the
new IgBLAST output files to
/share/input and move the FASTA/FASTQ files to
$ mkdir -p /share/sequences $ mv /share/input/*.fast[aq] /share/sequences
Creating a Metadata Sheet¶
Next, we’ll use the
immunedb_metadata command to create a template metadata
file for your sequencing data. In the Docker container run:
$ cd /share/input $ immunedb_metadata --use-filenames
This command expects the files to end in .fasta for FASTA, .fastq for FASTQ, or .tsv for AIRR.
This creates a
metadata.tsv file in
/share/input in Docker which is
$HOME/immunedb_share/input on the host.
--use-filenames flag is optional, and simply populates the
sample_name field with the file names stripped of their extension.
Editing the Metadata Sheet¶
On the host open the metadata file in Excel or your favorite spreadsheet
editor. The headers included in the file are required. You may add
additional headers as necessary for your dataset (e.g.
timepoint) so long as they follow the following rules:
- The headers must all be unique
- Each header may only contain lowercase letters, numbers, and underscores
- Each header must begin with a (lowercase) character
- Each header must not exceed 32 characters in length
- The values within each column cannot exceed 64 characters in length
When data is missing or not necessary in a field, leave it blank or set to
Much of the rest of the pipeline follows from the example pipeline’s
instance creation step. To start, create a
database. Here we’ll call it
my_db but you’ll probably want to give it a
more descriptive name:
$ immunedb_admin create my_db /share/configs
Then we’ll identify or import the sequences. For this process the germline
genes must be specified. The germlines are provided FASTA files in the Docker
You can use your own germline files if you desire so long as they are IMGT gapped.
For this segment we’ll assume human B-cell heavy chains, but the process is the same for any dataset. Depending on if you want to use IgBLAST input (recommended) or the built-in annotation method the command will be one of the following:
Option 1: Importing from IgBLAST output (recommended):
$ immunedb_import /share/configs/example_db.json airr \ /root/germlines/igblast/human/IGHV.gapped.fasta \ /root/germlines/igblast/human/IGHJ.gapped.fasta \ /share/input
Option 2: Using anchoring method:
$ immunedb_identify /share/configs/my_db.json \ /root/germlines/anchor/human/IGHV.gapped.fasta \ /root/germlines/anchor/human/IGHJ.gapped.fasta \ /share/input
After importing or identifying sequences, continue running the pipeline from here:
$ immunedb_collapse /share/configs/my_db.json
Then we assign clones. For B-cells we recommend:
$ immunedb_clones /share/configs/my_db.json cluster
For T-cells we recommend:
$ immunedb_clones /share/configs/my_db.json cluster --min-similarity 1
If you have a mixed dataset, you can assign clones in different ways, filtering on V-gene type. For example:
$ immunedb_clones /share/configs/my_db.json cluster --gene IGHV $ immunedb_clones /share/configs/my_db.json cluster --gene TCRB \ --min-similarity 1
The last required step is to generate aggregate statistics:
$ immunedb_clone_stats /share/configs/my_db.json $ immunedb_sample_stats /share/configs/my_db.json
For B-cells, you might want to generate lineages too. The following excludes
mutations that only occur once.
immunedb_clone_trees has many other
parameters for filtering which you can view with the
--help flag or at
Clone Trees (Optional).
$ immunedb_clone_trees /share/configs/my_db.json --min-seq-copies 2
Selection pressure can be run with the following. This process is quite time-consuming, even for small datasets:
$ immunedb_clone_pressure /share/configs/my_db.json \ /apps/baseline/Baseline_Main.r
Finally, the data should be available at http://localhost:8080/frontend/my_db.
Analyzing Your Data¶
After all the above steps are complete, you should have a fully populated database, ready for analysis via Exporting Data to Files, Querying with SQL, and the Python API.