mtbtyper

A tool for genotyping Mycobacterium tuberculosis (Mtb) isolates from whole-genome sequencing (WGS) data

requirements

python3 with numpy, pandas and scikit-allel

input & output

Inputs: vcf files (<sample>.vcf.gz) in one directory, one file per sample.

Output: lineage.csv has the form

sample_id	genotype	genotype_specific_snp	..
sample_1	L2.2.M4.1	L2.2 (24/24), L2.2.M4.1 (21/21), L2.2.Modern (6/6), L2 (6/6), L2.2.M4 (3/3)	..
sample_2	L2.2.M4	L2.2 (24/24), L2.2.1 (10/10), L2.2.Modern (6/6), L2 (6/6), L2.2.M4 (3/3)	..
sample_3	L1.2.2.2	L1.2 (56/56), L1.2.2 (53/53), L1.2.2.2 (19/19), L1 (15/15)	..
..	..	..	..

The column genotype_specific_snp contains a list of counts of genotype-specific SNPs found in the sample according to the default scheme (see below). For instance, L2.2 (24/24) means the sample has 24 SNPs out of 24 L2.2-specific SNPs in this scheme.

The most specific sublineage level with a high proportion of genotype-specific SNPs present is returned as the predicted genotype in the column genotype.

Other columns are specific schemes from published studies (optional).

usage

mtbtyper.py vcf_dir [options]

option	description
-o, --out	output directory (default: current working directory)
-f, --fout	output file name (default: lineage.csv)
-e, --vcf_end	ending pattern of vcf file (default: vcf.gz)
--all_schemes	add prediction from all available SNP schemes (default: false)
--snpdb	path to genotyping SNP schemes (default: snpdb)
--quiet	suppress screen output (default: false)

example

mtbtyper.py vcf -o lineage --all_schemes

In this example, the input vcf files are placed in a directory called vcf. The program outputs to lineage/lineage.csv, which include all available schemes (see below).

SNP-typing schemes

The default scheme is a combination of the best scheme for each group of Mtb. It contains over 130 genotypes at different levels of classification hierarchy; see e.g. Coll et al. (2014).

genotypes	source
L1 sublineages	Netikul et al. (2022)
L2 sublineages	Thawornwattana et al. (2021)
L4.5.1	Mokrousov et al. (2017)
L4.5.2, L4.5.3	Ajawatanawong et al. (2019)
Animal-adapted lineages	Lipworth et al. (2019)
Animal-adapted lineages + L6	Unpublished
L8	Napier et al. (2020)
Other L1-L7 lineages	Coll et al. (2014) (diagnostic SNPs)

Other schemes are based on individual published studies. Use --all_schemes flag to also output SNP counts from these schemes.

scheme	description	reference
coll2014	L1-L7 from a global collection	Coll et al. (2014), available here
coll2014_diag	Diagnostic subset of coll2014	Coll et al. (2014)
coll2014_barcode	Barcoding subset of coll2014_diag	Coll et al. (2014), Table S3
cr1	L1-L4 from Chiang Rai, Thailand	Ajawatanawong et al. (2019)
freschi2021	L1-L4; implemented in fast-lineage-caller	Freschi et al. (2021)
freschi2021_hierarchical	Same as freschi2021 but with different genotype names	Freschi et al. (2021)
l1	L1 from a globally representative collection	Netikul et al. (2022), Table S6
l1_barcode	Barcoding subset of l1	Netikul et al. (2022), Table S6
l2	L2 from a globally representative collection	Thawornwattana et al. (2021), Table S7
l2_barcode	Barcoding subset of l2	Thawornwattana et al. (2021), Table S8
lipworth2019	L1-L6 and animal-adapted strains from a UK collection, implemented in snpit	Lipworth et al. (2019)
merker2015	L2 from a global collection	Merker et al. (2015), Table S8
napier2020	Revised scheme of coll2014	Napier et al. (2020), Table S2
napier2020_barcode	Barcoding subset of napier2020	Napier et al. (2020), Table S3
shitikov2017	L2 from a global collection	Shitikov et al. (2017), Table S6