# *Pseudomonas aeruginosa* PAO1_tryptic

| iPtgxDB Release     ||
| ------: | :--------: |
| Version | 1          |
| Date    | 04.03.2020 |

## Downloaded from
[/database/annotations/p-aeruginosa-pao1-tryptic](/database/annotations/p-aeruginosa-pao1-tryptic)

*Pseudomonas aeruginosa* strain PAO1, also referred to as PAO1-UW (Genbank #[NC_002516](https://www.ncbi.nlm.nih.gov/nuccore/NC_002516.2)) [1], is an opportunistic human pathogen that belongs to the notorious group of Gram-negative ESKAPE pathogens [2]. *Pseudomonas aeruginosa* is extensively studied for its antimicrobial resistance (AMR) traits and resistance against different antibiotics used in the clinics, including the last-resort antibiotic colistin [3]. 
In our study [4], we compared the *P. aeruginosa* PAO1 reference strain against *P. aeruginosa* strain MPAO1, the parental strain of the widely used transposon (Tn) insertion mutant library from the University of Washington [5] (from Prof. Colin Manoil). We re-sequenced *P. aeruginosa* strain MPAO1 and assembled its complete genome *de novo* as part of establishing and validating a model system to identify genes involved in biofilm growth and antibiotic resistance[4]. The complete genome represents an optimal basis for studies on the evolution of antimicrobial resistance in cells grown as biofilm or when exposed to combinations of antibiotics and biocides. Here, we release an iPtgxDB for the PAO1 reference strain as a resource for the Pseudomonas research community. In addition, you can also find the iPtgxDB for #[MPAO1] (https://iptgxdb.expasy.org/database/) strain in our webserver.

An iPtgxDB was created by hierarchically integrating protein coding sequences from these annotation resources:

| Hierarchy | Resource | Link |
| :---: | :--- | :--- |
| 1 | NCBI RefSeq 2019 | [GCF_000006765.1_ASM676v1](ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/006/765/GCF_000006765.1_ASM676v1); from 16/05/2014 |
| 2 | NCBI RefSeq 2017 | [GCA_000006765.1_ASM676v1](tp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/006/765/GCA_000006765.1_ASM676v1); from 31/01/2014 |
| 3 | Genoscope [6] | v2.7.3, accessed 14/02/2020 |
| 4 | Prodigal [7] | *Ab initio* gene predictions from Prodigal (v2.6.3) |
| 5 | ChemGenome [8] | *Ab initio* gene predictions from ChemGenome (v2.0, http://www.scfbio-iitd.res.in/chemgenome/chemgenomenew.jsp; with parameters: method, Swissprot space; length threshold, 70 nt; initiation codons, ATG, CTG, TTG, GTG) |
| 6 | *in silico* ORFs |  The *in silico* ORF annotations were generated as described by Omasits and Varadarajan et al., 2017 [9] |

Only ORFs above a selectable length threshold (here 18 aa) were considered. The iPtgxDB was created using the hierarchy RefSeq 2019 > RefSeq 2017 > Genoscope > Prodigal > ChemGenome > *in silico*. Files were parsed to extract the identifier, coordinates and sequences of bona fide protein-coding sequences (CDS) and pseudogene entries.

### References
1. Stover, C. K. et al. 2000. Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen. Nature. 406: 959-964.
2. Boucher, H.W., et al. 2009. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin Infect Dis. 48(1): 1-12.
3. Livermore, D. M. 2002. Multiple mechanisms of antimicrobial resistance in Pseudomonas aeruginosa: our worst nightmare? Clin Infect Dis. 34: 634-640.
4. Varadarajan, A. R., Raymond, N. A., Valentin, J., Castañeda Ocampo, O. E., Somerville, V., Pietsch, F., Buhmann, M. T., Skipp, P., van der Mei, C. H., Ren, Q., Schreiber, F., Webb, S. W., Ahrens, C. H. 2020. An integrated model system to study biofilm-associated adaptation to antimicrobials and resistance evolution in Pseudomonas aeruginosa MPAO1. https://doi.org/10.1101/2020.02.06.936690.
5. Jacobs, M. A., et al. 2003. Comprehensive transposon mutant library of Pseudomonas aeruginosa. Proc Natl Acad Sci U S A. 100(24): 14339–44.
6. Vallenet, D., et al. 2013. MicroScope--an integrated microbial resource for the curation and comparative analysis of genomic and metabolic data. Nucleic Acids Res 41: D636-647.
7. Hyatt, D., et al. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11: 119.
8. Singhal, P., et al. 2008. Prokaryotic gene finding based on physicochemical characteristics of codons calculated from molecular dynamics simulations. Biophys J 94: 4173-4183.
9. Omasits, U., Varadarajan, A. R., et al. 2017. An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics. Genome Research. 27: 2083-2095.
