Package: abstar
abstarVDJ assignment and antibody sequence annotation. Scalable from a single sequence to billions of sequences.Source code:github.com/briney/abstarDocumentation:abstar.readthedocs.orgDownload:pypi.python.org/pypi/abstarDocker:hub.docker.com/r/briney/abstar/installpip install abstaruseTo 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-dataWhen 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.When using BaseSpace as the input data source, you can optionally provide all of the required directories:abstar -i <input-directory> -o <output-directory> -t <temp-directory> -bOr you can simply provide a single project directory, and all required directories will be created in the project directory:abstar -p <project_directory> -badditional options-l LOG_LOCATION, --log LOG_LOCATIONChange the log directory location. Default is the parent directory of<output_directory>.-m, --mergeInput directory should contain paired FASTQ (or gzipped FASTQ) files. Paired files will be merged with PANDAseq prior to processing with abstar. Note that when using the BaseSpace option (-b, --basespace), this option is implied.-b, --basespaceDownload a sequencing run from BaseSpace, which is Illumina's cloud storage environment. Since Illumina sequencers produce paired-end reads,--mergeis implied.-u N, --uaid NSequences 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, --species SPECIESSelect the species from which the input sequences are derived. Supported options are 'human', 'mouse', and 'macaque'. Default is 'human'.-c, --clusterRuns abstar in distributed mode on a Celery cluster.-h, --helpPrints detailed information about all runtime options.-D --debugMuch more verbose logging.apiMost core abstar functions are available through a public API, making it easier to run abstar as a component of integrated analysis pipelines. See the abstardocumentationfor more detail about the API.helper scriptsA few helper scripts are included with abstar:batch_mongoimportautomates the import of multiple JSON output files into a MongoDB database.build_abstar_germline_dbcreates abstar germline databases from IMGT-gapped FASTA files of V, D and J gene segments.make_basespace_credfilemakes 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 explainedheretestingTo run the test suite, clone or download the repository and runpytest ./from the top-level directory.requirementsPython 3.8+abutilsbiopythoncelerynwalign3pymongopytestscikit-bioAll 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 theAnaconda Python distribution, which includes pip as well as a ton of useful scientific Python packages.sequence merging requiresPANDAseqbatch_mongoimport requiresMongoDBBaseSpace downloading requires theBaseSpace Python SDK