Antigen Receptor Classifier
Project description
ARC (Antigen Receptor Classifier)
@authors: Swapnil Mahajan, Austin Crinklaw
Requirements:
- Linux OS
- HMMER3
- NCBI Blast+
- Python 3+
- Python packages: Pandas, BioPython
- Git
How to use:
Installation:
The easiest way to use the software is to download and utilize the Dockerfile.
ARC can also be downloaded through PyPI using the following pip command.
pip install bio-arc
Input
- A fasta format file with one or more protein sequences.
>1WBZ_A_alpha I H2-Kb
MVPCTLLLLLAAALAPTQTRAGPHSLRYFVTAVSRPGLGEPRYMEVGYVDDTEFVRFDSDAENPRYEPRARWMEQEGPEYWERETQKAKGNEQSFRVDLRTLLGYYNQSKGGSHTIQVISGCEVGSDGRLLRGYQQYAYDGCDYIALNEDLKTWTAADMAALITKHKWEQAGEAERLRAYLEGTCVEWLRRYLKNGNATLLRTDSPKAHVTHHSRPEDKVTLRCWALGFYPADITLTWQLNGEELIQDMELVETRPAGDGTFQKWASVVVPLGKEQYYTCHVYHQGLPEPLTLRWEPPPSTVSNMATVAVLVVLGAAIVTGAVVAFVMKMRRRNTGGKGGDYALAPGSQTSDLSLPDCKVMVHDPHSLA
>1WBZ_B_b2m I H2-Kb
MARSVTLVFLVLVSLTGLYAIQKTPQIQVYSRHPPENGKPNILNCYVTQFHPPHIEIQMLKNGKKIPKVEMSDMSFSKDWSFYILAHTEFTPTETDTYACRVKHASMAEPKTVYWDRDM
Commands
- A list of commands can be found via the -h flag
python -m ARC -h
- Specific commands are explained in a similar manner
python -m ARC <command> -h
- HMMs come by default but can be updated. If they are missing for some they can be added via the install command
python -m ARC update -archive
Note: Archive is an optional parameter that will create an archive folder with HMMs stored by date. You will find this in the data folder wherever you have installed ARC.
- Using Fasta file as an input:
python -m ARC classify -i /path/to/input.fasta -o /path/to/output.csv
Output
- Output file has 4 columns in CSV format.
- First column named 'ID' is the description provoded in the fasta for each sequence.
- Second column named 'class' is the assigned molecule class for each sequence.
- e.g. MHC-I, MHC-II, BCR or TCR.
- The third column named 'chain_type' is the assigned chain type for each sequence.
- e.g. alpha, beta, heavy, lambda, kappa, scFv, TscFv or construct.
- The fourth column named 'calc_mhc_allele' is the MHC allele identified using groove domain similarity to MRO alleles.
ID | class | chain_type | calc_mhc_allele |
---|---|---|---|
1WBY_A_alpha I H2-Db | MHC-I | alpha | |
1WBY_B_b2m I H2-Db | |||
1HQR_A_alpha II HLA-DRA01:01/DRB501:01 | MHC-II | alpha | HLA-DRA*01:01 |
1HQR_B_beta II HLA-DRA01:01/DRB501:01 | MHC-II | beta | HLA-DRB5*01:01 |
2CMR_H_heavy | BCR | heavy | |
2CMR_L_light | BCR | kappa | |
4RFO_L_light | BCR | lambda | |
3UZE_A_heavy | BCR | scFv | |
1FYT_D_alpha | TCR | alpha | |
1FYT_E_beta | TCR | beta | |
3TF7_C_alpha | TCR | TscFv |
How it works:
- BCR and TCR chains are identified using HMMs. A given protein sequence is searched against HMMs built using BCR and TCR chain sequences from IMGT. HMMER is used to align an input sequence to the HMMs.
- MHC class I (alpha1-alpha2 domains) and MHC class I alpha and beta chain HMMs are downloaded from Pfam website. An input protein sequence is searched against these HMMs. A HMMER bit score threshold of 250 was used to identify MHC chain sequences. DTU uses 250 as a score cutoff which can exclude MHC like molecules such as Human and Mouse CD1d molecules. -To identify MHC alleles, MRO repository is downloaded every time the script is run. Groove domains (G-domains) are assigned to new MRO allles and stored in a CSV file. If this file does not exist then G-domains are assigned to all the MRO alleles (which may slow down the script).
References:
Several pieces of code, including the IMGT ripping / HMM generation, was sourced from ANARCI.
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