Running the Example Pipeline

This page serves to familiarize new users with the basic process of running the ImmuneDB pipeline. Example input FASTQ files are provided which contain human B-cell heavy chain sequences.

Commands are listed as either being run in either the Docker container or on the host. All immunedb_* commands have a --help flag which will show all arguments and their descriptions. It is recommended for each command you run the help flag to see options not listed in this documentation.

To begin, run the Docker container as documented:

Run on Host
 $ docker run -v $HOME/immunedb_share:/share \
      -p 8080:8080 -it arosenfeld/immunedb:v0.29.11

Metadata Specification

Before ImmuneDB can be run, metadata must be specified for each input file. For this example, one has already been created for you. To learn how to create a metadata file for your own data, see Creating a Metadata Sheet.

ImmuneDB Instance Creation

Next, we create a database for the data with:

Run in Docker
 $ immunedb_admin create example_db /share/configs

This creates a new database named example_db and stores its configuration in /share/configs/example_db.json.

Identifying or Importing Sequences

Data can be added to the new ImmuneDB database either by importing annotated sequencing data in AIRR format, or via a built-in gene assignment method based on Zhang, et al., 2015.

For this example, there are two input FASTQ files in /example/fastq along with an associated metadata file. These will be used regardless of the method you choose. There are also germline files for human and mouse included.

Option 2: Annotating FASTA/FASTQ Files via Anchoring

Alternatively, if you’d prefer to use the built-in annotation method on FASTA/FASTQ files, you can use the immunedb_identify command. Note this method is more sensitive to high mutation rates in the regions flanking the CDR3.

Run in Docker
 $ immunedb_identify /share/configs/example_db.json \
      /root/germlines/anchor/human/IGHV.gapped.fasta \
      /root/germlines/anchor/human/IGHJ.gapped.fasta \

Sequence Collapsing

After data are imported or annotated on a sample-level basis, ImmuneDB determines the subject-level unique sequences; that is, the set of unique sequences across all samples in each subject. Because sequences may contain the ambiguous N symbol, the process is not trivial string equality checking. It is implemented in the immunedb_collapse command.

To collapse sequences, run:

Run in Docker
 $ immunedb_collapse /share/configs/example_db.json

Clonal Assignment

After collapsing unique sequences across each subject they can be grouped into clones which are aggregations of sequences likely deriving from a common progenitor cell.

ImmuneDB offers two clonal inference methods, similarity and cluster. The cluster method is recommended and documented here as it more robust than similarity.

For both methods, clones are inferred in two steps: grouping sequences and then merging similar clones. Both steps are run together with the immunedb_clones command

By default, only sequences with a subject-level copy number greater than 1 are included in clones. This can be changed with the --min-copy parameter.

In the first step of clonal inference, sequences meeting the above copy number criteria are hierarchically clustered together such that any two sequences in a clone must (1) have the same CDR3 length and (2) share at least 85% amino-acid similarity in the CDR3. The similarity can be changed with --min-similarity X parameter where X is the minimum similarity between 0 and 1. If nucleotide similarity should be used, --level nt can be passed.


For T-cells it is recommended the --min-similarity 1 parameter be set but the --level parameter by left at the default amino-acid setting. Using both --min-similarity 1 --level nt may lead to the creation of spurious clones due to sequencing error. Only pass both if you’re quite certain your sequencing error has been eliminated (e.g. by barcoding).

After this step is complete, sequences have been assigned to clones. In some cases clones may have the same CDR3 nucleotide sequence but different gene calls. This can indeed occur biologically but frequently due to mutation and sequencing error causing incorrect gene calls.

To rectify this, a second step in clonal inference is to collapse merge clones that have the same CDR3 nucleotide sequences. In cases where this occurs, the highest copy clone absorbs the lower copy clones. This second step can be configured in two ways via the --reduce-difference flag. Setting it to a negative number (e.g. --reduce-difference -1) disables the step entirely. Setting it to a positive number (e.g. --reduce-difference 2) will alter the step’s behavior to combine clones differing by at most that number of nucleotides. The default value is 0, so only clones with exactly the same CDR3 nucleotide sequences will be combined.

Run in Docker
 $ immunedb_clones /share/configs/example_db.json cluster

Statistics Generation

Two sets of statistics can be calculated in ImmuneDB:

  • Clone Statistics: For each clone and sample combination, statistics on the clone’s size, mutation level, and top copy sequence

  • Sample Statistics: Distribution of sequence and clone features on a per-sample basis, including gene usage, mutation level, copy number, CDR3 length.

These are calculated with the immunedb_clone_stats and immunedb_sample_stats commands and must be run in that order.

Run in Docker
 $ immunedb_clone_stats /share/configs/example_db.json
 $ immunedb_sample_stats /share/configs/example_db.json

Selection Pressure (Optional)


Selection pressure calculations are time-consuming, so you can skip this step if time is limited.

Selection pressure of clones can be calculated with Baseline. To do so run:

Run in Docker
 $ immunedb_clone_pressure /share/configs/example_db.json \

Note, this process is relatively slow and may take some time to complete.

Clone Trees (Optional)

Lineage trees for clones is generated with the immunedb_clone_trees command. The only currently supported method is neighbor-joining as provided by Clearcut.

There are many parameters that can be changed for tree construction:

  • --min-seq-copies (default 0): The minimum number copy number required for a sequence to be included in the tree.

  • --min-seq-samples (default 0): The minimum number samples in which a sequence must appear for it to be included in the tree.

  • --min-mut-copies (default 0): The minimum number of copies in which a mutation must occur to be included in the tree.

  • --min-mut-samples (default 0): The minimum number of samples in which a mutation must occur to be included in the tree.

  • --exclude-stops (default False): Exclude sequences with a stop codon.

  • --full-seq (default False): By default only the V-region of each sequence (the portion 5’ of the CDR3) is included in the tree construction. Setting this flag will use the entire sequence.

Generally we recommend using --min-seq-copies 2.

Run in Docker
 $ immunedb_clone_trees /share/configs/example_db.json --min-seq-copies 2

Web Interface

ImmuneDB has a web interface to interact with a database instance. The Docker container automatically makes this available at http://localhost:8080/frontend/example_db

When you create more databases, simply replace example_db with the proper database name.

Next Steps

Now that the basic workflow has been covered, instructions to run ImmuneDB on your own data can be found at Running the Pipeline on Your Data.