bio-arc 0.0.7

Antigen Receptor Classifier

ARC (Antigen Receptor Classifier)

@authors: Swapnil Mahajan, Austin Crinklaw

Requirements:

• Linux OS
• HMMER3
• NCBI Blast+
• Python 3+
  • Python packages: Pandas, BioPython

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 25 was used to identify MHC chain sequences. -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.

Dunbar J and Deane CM. ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics (2016)