SsuisChara

SsuisChara is a tool for integrally analyze the characteristics of Streptococcus suis using assemblied genome sequence for:

• Species verification
• Serotype prediction
• MLST sequence type
• Virulence associated factors (vafs) screen
• Human infection potential
• Antimicrobial resistance determinants

External Dependencies

BLAST+

prodigal

Usage

Put the python script and database folder into the folder contains your sequence file

SsuisChara [-i] [-o] [-t] [--min_gene_cov] [--min_gene_id ] [--vf_screen_mode] [--no_heat_map] [-v]
Input and Output:
  -i, --input             Input FASTA file
  -o, --output            Output file
Parameters:
  -t, --threads           Threads to use for BLAST searches
  --min_gene_cov          Minimum percentage coverage to consider a single gene complete. [default: 80.0%]
  --min_gene_id           Minimum percentage identity to consider a single gene complete. [default: 70.0%]
  --vf_screen_mode        The virulence factor screen mode, two modes, "concise" and "full" were provided. "concise" was set as default
  --heat_map              Generate a heatmap file to visualize the prevalence of VFs
  -v, --version           Show version number and exit

Quick usage

python SsuisChara.py -i *.fasta 

Species verification

The 16S rRNA sequence of Streptococcus suis were obtained from public database, through align the 16S rRNA sequence with input sequence, the 16S sequence in input sequence could be found. If the identity of 16S rRNA sequence lower than 97%, we consider the input sequence do not belong to Streptococcus suis species, otherwise, we consider the input sequence belong to Streptococcus suis species.

Serotype prediction

Initially, serotyping was based on serological tests, subsequently, the serotyping determine locus of many bacteria were found in the genome, and a lot of molecular serotyping method were developed based on the difference of serotyping determine locus, such as multiplex PCR. Now the number of acquired sequenced bacteria genome in public databased are fastly increasing, allow us to explore a full locus alignment method to high throughput and precisely determine serotype in silico.

For Streptococcus suis, the serotype determine locus is capsular polysaccharide (cps) locus. In this analysis, the cps locus of 29 classic Streptococcus suis serotype were collected and used as reference to find the best match in input genome to determine the serotype. Dispite a lot of novel capsular polysaccharide loci (NCL) were found, however, the importance and prevalence of these serotype as limited, so we haven't include them, however, we may upload in the future.

To aviod the uncorrected prediction, we displayed the coverage and identity of predicted serotype, if it lower than the threshold (we set as 95% prelimitarily), a "?" will be added to the end of output string of predicted serotype.

MLST sequence type

The MLST database were obtained from pubMLST, update: 2023-01-12

Seven housekeeping genes, aroA, cpn60, dpr, gki, mutS, recA, and thrA, of Streptococcus suis, were screened in input genome and return there allele number, or closest allele number, then determine there Sequence Type (ST), if not every allele number are exact match, a "?" will be added to the end of output string of predicted ST.

Virulence associated factors (vafs) screen

Total 111 vafs of S. suis were collected from published papers and established as database to screen there presence and absence in input genome. 53 vafs distributed in accessory genome of S. suis, 58 vafs distributed in core genome of S. suis, two screen mode are provided, "concise" and "full", "concise" mode was set as default, only screen 53 vafs in accessory genome, "full" mode could screen all vafs.

A table include the virulence prevalence information will be generated, contained the location of matched genes of known VFs. A heatmap based on the presence and absence of vafs could be generated and clustered to visualize the vafs prevalence use '''--heat_map''' command.

Human infection potential

Several vafs are found associated with human S. suis infection in previous study, there prevalence ratio in human S. suis isolated were used as weight of each vafs, we use the prevalence of these vafs to predict the human infection potential of each source isolate of input gneomic sequence.

The summation of weights were named as "zoonotic score", if zoonotic_score >= 70.0, we give human_infection_potential as "high", if 30.0 <= zoonotic_score < 70.0, we give human_infection_potential as "medium", if zoonotic_score < 30.0, we give human_infection_potential as "low".

Antimicrobial resistance determinants

Aminoglycoside, macrolide, and tetracycline are major class of antimicrobial drugs resist by S. suis, we screened the known AMRGs resist these drugs to determine the antimicrobial resistance level of each source isolate of input gneomic sequence, the AMRG_level = The number of these 3 AMR drugs class covered by the AMRG screened in input genome. Then the screened AMRGs will be output in output file.

Output

A simlified result will generated in terminal, inputfile + species + serotype + ST

A detailed table generated in work folder

Also a clusterd heatmap