VDJ assignment and antibody sequence annotation. Scalable from a single sequence to billions of sequences.

• Source code: github.com/briney/abstar
• Documentation: abstar.readthedocs.org
• Download: pypi.python.org/pypi/abstar
• Docker: hub.docker.com/r/briney/abstar/

pip install abstar

To run abstar on a single FASTA or FASTQ file:
abstar -i <input-file> -o <output-directory> -t <temp-directory>

To iteratively run abstar on all files in an input directory:
abstar -i <input-directory> -o <output-directory> -t <temp-directory>

To run abstar using the included test data as input:
abstar -o <output-directory> -t <temp-directory> --use-test-data

When using the abstar test data, note that although the test data file contains 1,000 sequences, one of the test sequences is not a valid antibody recombination. Only 999 sequences should be processed successfully.

-l, --log Change the log file location. Default is <output_directory>/mongo.log.

-m, --merge Input directory should contain paired FASTQ (or gzipped FASTQ) files. Paired files will be merged with PANDAseq prior to processing with AbAnalysis.

-b, --basespace Download a sequencing run from BaseSpace, which is Illumina's cloud storage environment. Since Illumina sequencers produce paired-end reads, --merge is also set.

-u N, --uaid N Sequences contain a unique antibody ID (UAID, or molecular barcode) of length N. The uaid will be parsed from the beginning of each input sequence and added to the JSON output. Negative values result in the UAID being parsed from the end of the sequence.

-s, --species Select the species from which the input sequences are derived. Supported options are 'human', 'mouse', and 'macaque'. Default is 'human'.

-c, --cluster Runs abstar in distributed mode on a Celery cluster.

-h, --help Prints detailed information about all runtime options.

-D --debug Much more verbose logging.

Two helper scripts are included:
batch_mongoimport automates the import of multiple JSON output files into a MongoDB database.
make_basespace_credfile makes a credentials file for BaseSpace, which is required if downloading sequences from BaseSpace with abstar. Developer credentials are required, and the process for obtaining them is explained here

Python 3.3+ (2.7.x may work, but isn't well tested)
abutils
biopython
celery
nwalign3 (nwalign for Python 2.7.x)
pymongo
scikit-bio (<=0.4.2 for Python 2.7.x)

All of the above dependencies can be installed with pip, and will be installed automatically when installing abstar with pip.
If you're new to Python, a great way to get started is to install the Anaconda Python distribution, which includes pip as well as a ton of useful scientific Python packages.

sequence merging requires PANDAseq
batch_mongoimport requires MongoDB
BaseSpace downloading requires the BaseSpace Python SDK