AGAIN standalone is for users who want to run it locally in a command-line environment (also in GitHub).

Current Version: 1

Programs:

AGAIN_VCF.py
AGAIN_VCF_batch.py

Dependency:

The code is written in python3, and requires bedtools installed.

Reference Datasets:

Download them and put them in the same folder with the AGAIN programs.

Data_AGAIN_coding_map_GRCh37.txt

Data_AGAIN_detection_GRCh37_all.bed

Data_AGAIN_detection_GRCh37_canonical.bed

Data_AGAIN_coding_map_GRCh38.txt

Data_AGAIN_detection_GRCh38_all.bed

Data_AGAIN_detection_GRCh38_canonical.bed

File Format:

Input: Variants in VCF format, with 5 mandatory and tab-delimited fields (CHROM, POS, ID, REF, ALT).

Output: AGAIN-detected variants will be output with the following annotations.

SAMPLE (only for AGAIN_VCF_batch.py)

CHROM, POS, ID, REF, ALT (exactly the same as input)

STRAND

VAR_TYPE (snv, x nt-del, x nt-ins)

GENE

TRANSCRIPT_IVS (e.g., ENST123456789_IVS10)

CANONICAL (canonical transcript_IVS)

AGAIN_ZONE (ZONE1/ZONE2, ZONE1 is from 1st BP to ACC, ZONE2 is from 2nd BP to 1st BP)

AGAIN_YAG (YES/NO, if the AG-gain variant also fits YAG)

AGAIN_BP_DIST (the distance from the created AG to BP)

AGAIN_ACC_DIST (the distance from the created AG to ACC)

AGAIN_HIGHRISK (YES/NO, if the AG-gain variant falls inside high-risk [BP+8, ACC-4] region)

AGAIN_SCORE (score of the AG-gain variant (suggested cutoff >= 3, max = 5))

PROT_SEQ_WT (wild-type protein sequence)

PROT_SEQ_NEW_ACC (consequent protein sequence if new acceptor site is created)

HGVS_NEW_ACC (protein-level HGVS annotation if new acceptor site is created)

PROT_SEQ_EXON_SKIP (consequent protein sequence if exon skipping occurs)

HGVS_EXON_SKIP (protein-level HGVS annotation if exon skipping occurs)

Command:

    python AGAIN_VCF.py -i variants.vcf

    python AGAIN_VCF.py -g GRCh37 -t all -i variants.vcf

    arguments:
      -h, --help         show help message
      -g, --genome       human genome assembly {GRCh37, GRCh38}, default: GRCh37
      -t, --transcript   all/canonical transcript? {all, canonical}, default: all
      -i, --input        input variants in VCF format file
    

    python AGAIN_VCF_batch.py -d directory -s samplelist.txt -o output.txt
    
    python AGAIN_VCF_batch.py -g GRCh37 -t all -d directory -s samplelist.txt -o output.txt

    arguments:
      -h, --help         show help message
      -g, --genome       human genome assembly {GRCh37, GRCh38}, default: GRCh37
      -t, --transcript   all/canonical transcript? {all, canonical}, default: all
      -d, --dir          directory of VCF files
      -s, --sample       sample list (without .vcf extension) in the above directory
      -o, --output       output filename
    

AGAIN Scoring Scheme:

We recommend to focus on the AG-gain variants with AGAIN_HIGHRISK = 'YES', and then prioritize by AGAIN_SCORE.