GAMMA

Introduction

GAMMA (Gene Allele Mutation Microbial Assessment) is a command line tool that finds gene matches in microbial genomic data using protein coding (rather than nucleotide) identity, and then translates and annotates the match by providing the type (i.e., mutant, truncation, etc.) and a translated description (i.e., Y190S mutant, truncation at residue 110, etc.). Because microbial gene families often have multiple alleles and existing databases are rarely exhaustive, GAMMA is helpful in both identifying and explaining how unique alleles differ from their closest known matches.

Quick Installation via Conda:

GAMMA (and all the dependencies) can be installed via Conda:

https://bioconda.github.io/recipes/gamma/README.html

To create a new conda environment (called GAMMA) and install GAMMA into the environment:

conda create -n GAMMA gamma -y

Activate the Conda environment using the following command:

conda activate GAMMA

Run the GAMMA.py script to see usage:

GAMMA.py -h

Deactivate the Conda environment after you are finished (to protect the environment from clashing with other programs):

conda deactivate

If you download directly via Git, GAMMA requires Python 3+, the Biopython package (https://github.com/biopython), and Blat (http://hgdownload.soe.ucsc.edu/admin/exe/), which has to be in your $PATH.

Usage:

The input for GAMMA is a genome or assembly in fasta format and a multifasta database of the coding sequence of genes. GAMMA was tested using AR gene databases from AMRFinderPlus (https://ftp.ncbi.nlm.nih.gov/pathogen/Antimicrobial_resistance/AMRFinderPlus/database/), ARG-ANNOT (http://backup.mediterranee-infection.com/arkotheque/client/ihumed/_depot_arko/articles/2041/arg-annot-v4-aa-may2018_doc.fasta), and ResFinder (https://bitbucket.org/genomicepidemiology/resfinder_db/src/master/).

The default output is a ".gamma" file that includes non-overlapping matches to any genes from your database found in your genome. It is run with this command:

GAMMA.py my_genome.fasta gene_db.fasta output_name [optional arguments]

There are seven optional arguments:

-a,--all: Returns all (including overlapping) gene matches

-e, --extended: Returns all gene mutations, otherwise if there are more than 10 mutations present the count is given

-f, --fasta: Writes out a multifasta file of the gene matches

-g, --gff: Generates a general feature format (.gff) file of the output gene matches

-n, --name: Writes the output name in front of each gene match line in the .gamma output file

-l, --headless: Removes the column headers in the .gamma output file

-i, --identity: The minimum nucleotide sequence identity % used by the Blat search, input as an integer (i.e., "-i 95" for a 95% threshold), default is 90

Output:

The default output of GAMMA is a tab-delimited file with a “.gamma” extension with 15 columns:

1. Gene – The name of the closest matching gene (target) from the database. If there are ambiguous gene matches (i.e., multiple target matches with the same number of non-degenerate codon changes, basepair changes, and transversions), the gene match will be appended with a "‡".
2. Contig – The name of the contig on which the match was found.
3. Start – The start position of the sequence matching the gene on the contig.
4. Stop – The end position of the sequence matching the gene on the contig.
5. Match_Type – The type of the gene match based on the translation of the sequence (i.e., the protein sequence). Can be native (for identical amino acid sequences to the target), mutant (for nonsynonymous mutations), truncation (for nonsense mutations), indels (for insertions/deletions), nonstop (for a missing stop codon), contig edge (for matches that are truncated at the start or stop of a contig), or a combination of multiple types (i.e., indel truncation).
6. Description – A short description of the match type.
7. Codon_Changes – The count of the non-degenerate codon changes in the sequence versus the closest match from the datbase.
8. BP_Changes - The count of the basepair changes in the sequence versus the closest match from the datbase.
9. Transversions - The count of basepair changes that are transversions (i.e., purine to pyrimidine or vice versa, such as an A -> C or a T -> G)
10. Codon_Percent – The percent (expressed as a decimal value) of the degenerate codon similarity between the query and match sequence. Gene matches with large insertions may show a negative value.
11. BP_Percent - The percent (expressed as a decimal value) of the basepair similarity between the query and match sequence. Gene matches with large insertions may show a negative value.
12. Percent_Length - The percent (expressed as a decimal value) of the length of the target covered by the matching sequence, maximum of 1.
13. Match_Length – The length (in basepairs) of the matching sequence.
14. Target_Length - The length (in basepairs) of the target sequence.
15. Strand – The sense of the strand (+ or -) on which the match is found.

Additional outputs in the .gff format and a fasta of the gene matches (in the positive sense) can be generated using the -g and -f options, respectively.

The sample GAMMA output shown below was generated from running GAMMA on a drug resistant Klebsiella pneumoniae (Accession: SAMN11054834) using a combination of all of the ResFinder AR gene databases (https://bitbucket.org/genomicepidemiology/resfinder_db/src/master/) downloaded on 05-06-2020. Your install of GAMMA can be tested using the following command with the test genome and gene database included (the output file will be called "GAMMA_Test.gamma"):

GAMMA.py DHQP1701672_complete_genome.fasta ResFinderDB_Combined_05-06-20.fsa GAMMA_Test

Gene	Contig	Start	Stop	Match_Type	Description	Codon_Changes	BP_Changes	Transversions	Codon_Percent	BP_Percent	Percent_Length	Match_Length	Target_Length	Strand
blaSHV-11_1_X98101	DHQP1701672_chromosome	2783310	2784171	Native	No coding mutations	0	8	3	1	0.9907	1	861	861	+
oqxA_1_EU370913	DHQP1701672_chromosome	1175518	1176694	Native	No coding mutations	0	10	3	1	0.9915	1	1176	1176	-
oqxB_1_EU370913	DHQP1701672_chromosome	1172342	1175495	Mutant	G148N,G540S,D749E,	3	39	14	0.9971	0.9876	1	3153	3153	-
fosA_6_ACZD01000244	DHQP1701672_chromosome	4658498	4658918	Native	No coding mutations	0	12	2	1	0.9714	1	420	420	-
blaTEM-1A_1_HM749966	pDHQP1701672_amr_plasmid	24988	25849	Native	No coding mutations	0	1	1	1	0.9988	1	861	861	-
blaOXA-9_1_KQ089875	pDHQP1701672_amr_plasmid	26548	27373	Truncation	truncation at codon 112 (of 275 codons),1 coding mutations	1	1	0	0.9964	0.9988	1	825	825	-
blaKPC-2_1_AY034847	pDHQP1701672_amr_plasmid	37034	37916	Native	No coding mutations	0	0	0	1	1	1	882	882	-

GAMMA-S:

GAMMA-S (Gene Allele Mutation Microbial Assessment-Sequence) finds best matches from a gene database without translating them--so it will find the best match by nucleotides, rather by the translated protein sequence. However, it can perform protein-protein sequence matching as well, which requires two protein fastas as the input. Details on the usage, arguments, and outputs of GAMMA-S are described in a seperate GAMMA-S_README.md included in this package.

Citing GAMMA:

Stanton RA, Vlachos N, Halpin AL. GAMMA: a tool for the rapid identification, classification, and annotation of translated gene matches from sequencing data. Bioinformatics. 2021 Aug 20:btab607. doi: 10.1093/bioinformatics/btab607. Epub ahead of print. PMID: 34415321.