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Utilities for analysis of antibody NGS data

Project Description

# AbTools

AbTools is a package of several tools for performing common antibody repertoire analysis functions: AbCorrect corrects sequencing errors using either molecular barcodes or homology-based clustering AbFinder identifies sequences from NGS datasets with similarity to known antibody sequences AbPhylogeny generates publication-quality phylogenetic trees from antibody sequences AbCompare provides methods for repertoire-level comparison of antibody sequence data

### install pip install abtools

### requirements Python 2.7 (3.x probably doesn’t work, but hasn’t been tested) biopython celery ete2 matplotlib numpy pandas seaborn pymongo

All of the above dependencies can be installed with pip, and will be installed automatically when installing AbTools 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.

## AbCorrect

To calculate centroid sequences using unique antibody IDs (UAIDs) that have already been parsed by AbStar: abcorrect -d <database_name> -c <collection_name> -t <temp_dir> -o <output_dir>

Same as above, but using a remote MongoDB server: abcorrect -d <database_name> -c <collection_name> -i <ip> -p <port> -u <username> -p <password> -t <temp_dir> -o <output_dir>

Calculate consensus sequences using UAIDs (20 nucleotides long) that haven’t been pre-parsed with AbStar:

abcorrect -d <database_name> -c <collection_name> -t <temp_dir> -o <output_dir> --parse-uaids 20 --consensus

Same as above, but UAID is 8 nucleotides long and is at the end of each sequencing read:

abcorrect -d <database_name> -c <collection_name> -t <temp_dir> -o <output_dir> --parse-uaids -8 --consensus

Calculate consensus sequences using homology-based clustering, at a threshold of 96% identity:

abcorrect -d <database_name> -c <collection_name> -t <temp_dir> -o <output_dir> --identity 0.96 --no-uaids --consensus

## AbFinder

To generate an identity/divergence plot using known antibody sequences in a file named ‘known.fasta’:

abfinder -d <database_name> -c <collection_name> -t <temp_dir> -o <output_dir> -d <path/to/known.fasta>

Same as above, but without updating the MongoDB database with identity information:

abfinder -d <database_name> -c <collection_name> -t <temp_dir> -o <output_dir> -d <path/to/known.fasta> --no-update

If known.fasta contains nucleotide sequences (default is amino acid):

abfinder -d <database_name> -c <collection_name> -t <temp_dir> -o <output_dir> -d <path/to/known.fasta> --no-update --nucleotide

## AbCompare

To compare two MongoDB collections of sequences (collection1 and collection2):

abcompare -d <database_name> -1 collection1 -2 collection2 -o <output_dir>

To iteratively compare collection1 to every other collection in the database:

abcompare -d <database_name> -1 collection1 -o <output_dir>

To iteratively compare collection1 to every other collection in the database that starts with ‘abcd’:

abcompare -d <database_name> -1 collection1 --collection-prefix abcd -o <output_dir>

To compare two collections (collection1 and collection2) using Jensen-Shannon similarity (default is Marisita-Horn):

abcompare -d <database_name> -1 collection1 -2 collection2 -o <output_dir> --similarity-method jensen-shannon

## AbPhylogeny

Generate a phylogenic tree from a FASTA-formatted file of sequences (input.fasta), a root sequence (root.fasta):

abphylogeny -i <path/to/input.fasta> -r <path/to/root.fasta> -o <output_dir>

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