L. plantarum DSM20174_tryptic

The extended simplified human intestinal microbiota (SIHUMIx) consists of eight bacterial members (Anaerostipes caccae (DSMZ 14662); Bacteroides thetaiotaomicron (DSMZ 2079); Bifidobacterium longum (NCC 2705); Blautia producta (DSMZ 2950); Clostridium butyricum (DSMZ 10702); Clostridium ramosum (DSMZ 1402); Escherichia coli K-12 (MG1655); Lactobacillus plantarum (DSMZ 20174)) of the human intestine and thus represents a model community to analyze such microbial interactions [1].

A tryptic iPtgxDB of Lactobacillus plantarum (DSM 20174) was created by hierarchically integrating protein coding sequences from the following annotation resources:

Hierarchy	Resource	Link
1	NCBI RefSeq	CP039121.1 / CP039122.1 from 10-APR-2019 (NCBI Prokaryotic Genome Annotation Pipeline (PGAP v.4.7)
2	Prodigal [2]	ab initio gene predictions from Prodigal (v2.6)
3	ChemGenome [3]	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)
4	in silico ORFs	in silico ORF annotations were generated as described by Omasits and Varadarajan et al., 2017 (v2.0, Only ORFs above a selectable length threshold (here 18 aa) were considered.)

The iPtgxDB was created using the hierarchy RefSeq > 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. Becker, N., Kunath, J., Loh, G. & Blaut, M. Human intestinal microbiota: Characterization of a simplified and stable gnotobiotic rat model. Gut Microbes 2, 25-33, doi:10.4161/gmic.2.1.14651 (2011).
2. Hyatt, D., Chen, G.L., Locascio, P.F., Land, M.L., Larimer, F.W., and Hauser, L.J. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11: 119.
3. Singhal, P., Jayaram, B., Dixit, S.B., and Beveridge, D.L. 2008. Prokaryotic gene finding based on physicochemical characteristics of codons calculated from molecular dynamics simulations. Biophys J 94: 4173-4183.
4. Omasits, U., Varadarajan, A. R., Schmid, M., Goetze, S., Melidis, D., Bourqui, M., Nikolayeva, O., Quebatte, M., Patrignani, A., Dehio, C., Frey, J. E., Robinson, M. D., Wollscheid, B., and Ahrens., C. H. An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics. bioRxiv, Cold Spring Harbor Labs Journals, 2017.