c-SSTAR

CLI-Sequence Search Tool for Antimicrobial Resistance

System Requirements

• Linux or Mac OS X platform
• BLAST+ (blastn and makeblastdb)
• Python 2.7 or 3 with BioPython

Usage

c-SSTAR -g <genome_file> -d <database_file>

Input

1. FastA formatted genome
2. FastA database of antimicrobial resistance (AR) gene sequences from SSTAR. Two databases are available (ARG-ANNOT or ResFinder), which are formatted according to Kat Holt's clustering approach for SRST2. A combination of these two databases also exists "ResGANNOT"

Output

I) Summary output (to stdout)

A tab-delimited summary is printed to standard out with the following fields:

1. AR gene family (from database)
2. AR gene variant (from database)
3. sequence defline/header where the AR gene is located (from genome)
4. % nucleotide identity (from blastn output)
5. bp length of alignment (from blastn output)
6. bp length of AR gene (from blastn output)

Columns 1 and 2 will have suffixes appended to denote special interest:

• * indicates the best scoring allele is full-length but has >=1 mismatch (SNP). This often means you have a novel allele.
• ? indicates uncertainty in the result due to incomplete length alignment
• TR indicates truncation due to an internal stop codon being present
• $ indicates gene detected at edge of contig

II) Raw alignment output (OUTDIR/BASENAME.blastn.tsv)

The tab-delimited outfmt 6 of blast is saved with three columns added to the right. Column 13 is the query (AR gene database) length, column 14 is the subject (contig) length, and column 15 is the subject (contig's AR gene) sequence.

III) Log output (OUTDIR/c-SSTAR_BASENAME.log)

A text file is generated to log the date and time of execution, user ID, shell environment, python version, blastn binary location, and blastn version.

Example Install

pip install biopython
git clone https://github.com/chrisgulvik/c-SSTAR.git $HOME
echo 'export PATH="$PATH:$HOME/c-SSTAR"' >> $HOME/.bash_profile

Example Usage

Run c-SSTAR on several genomes with the combo database

for F in *.fna; do
  B=$(basename $F .fna)
  c-SSTAR -g $F -d ~/c-SSTAR/db/ResGANNOT_srst2.fasta.gz -o $B > "$B"_ResGANNOT.tab
done

Example Summary Output

c-SSTAR -g ~/c-SSTAR/tests/data/SRR3112344.fa.gz \
 -d ~/c-SSTAR/db/ResGANNOT_srst2.fasta.gz

AR_Family	AR_Variant	Query_Defline	Identity	Aln_Len	DB_Gene_Len
aac(3)*	aac(3)-IId*	tig093	99.884%	861	861
aac(3)?	aac(3)-Ib-aac(6')-Ib'?	tig123	99.097%	554	1005
aac(6')	aac(6')-Ib-cr	tig123	100.0%	600	600
aac(6')?	aac(6')-30-aac(6')-Ib'?	tig123	99.309%	579	987
aadA2?	aadA2?	tig104	99.875%	802	819
ampH*	ampH*	tig003	98.88%	1161	1161
aph(3'')*	aph(3'')-Ib*	tig096	99.876%	804	804
aph(6)*	aph(6)-Id*	tig096	99.881%	837	837
blaCTX	blaCTX-M-15	tig089	100.0%	876	876
blaOXA	blaOXA-1	tig123	100.0%	831	831
blaSHV	blaSHV-11	tig016	100.0%	861	861
blaSHV*	blaSHV-100*	tig016	95.111%	900	900
blaTEM	blaTEM-1B	tig108	100.0%	861	861
catA2*	catA2*	tig127	96.106%	642	642
catB3?$	catB3?$	tig123	100.0%	440	633
catB4?$	catB4?$	tig123	100.0%	440	549
dfrA12	dfrA12	tig104	100.0%	498	498
dfrA14*	dfrA14*	tig134	99.586%	483	483
fosA6?$	fosA6?$	tig026	98.81%	420	433
mph(A)?TR	mph(A)?TR	tig110	99.675%	922	921
oqxA	oqxA	tig024	100.0%	1176	1176
oqxB	oqxB	tig024	100.0%	3153	3153
qnrB1*TR	qnrB1*TR	tig080	99.853%	681	681
sul1*	sul1*	tig104	99.885%	867	867
sul2	sul2	tig096	100.0%	816	816
tet(D)	tet(D)	tig108	100.0%	1185	1185

Literature References

c-SSTAR: Cunningham SA, Limbago B, Traczewski M, Anderson K, Hackel M, Hindler J, Sahm D, Alyanak E, Lawsin A, Gulvik CA, de Man TJB, Mandrekar JN, Schuetz AN, Jenkins S, Humphries R, Palavecino E, Vasoo S, Patel R. 2017. Multicenter Performance Assessment of Carba NP Test. J Clin Microbiol 55(6):1954-1960. doi: 10.1128/JCM.00244-17

SSTAR: de Man TJB, Limbago BM. 2016. SSTAR, a stand-alone easy-to-use antimicrobial resistance gene predictor. mSphere 1(1): e00050-15. doi: 10.1128/mSphere.00050-15

ARG-ANNOT database: Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, Rolain J-M. 2014. ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation), a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrobial Agents and Chemotherapy 58:212–220. doi: 10.1128/AAC.01310-13

ResFinder database: Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup F, Larsen MV. 2012. Identification of acquired antimicrobial resistance genes. Journal of Antimicrobial Chemotherapy 67:2640–2644. doi: 10.1093/jac/dks261