Pseudomonas 273 taxonomy
Robert W. Murdoch
December 4, 2018
Taxonomic placement
- Analysis of 16S SSU gene by placement in Silva SSU database = unclassified Pseudomonas
- Whole genome-based taxonomy at MiGA = unclassified Pseudomonas
SSU taxonomy
Longest SSU is extracted via MiGA and placed in the Silva SSU database:
000000F|quiver_6902612_6904145___ CTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGATGAAGGGAGCTTG CTCCCTGATTCAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGACAACGTTCCGAAAGGGACGC TAATACCGCATACGTCCTACGGGAGAAAGTGGGGGATCTTCGGACCTCACGCTATCAGATGAGCCTAGGTCGGATTAGCT AGTAGGTGGGGTAATGGCTCACCTAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACA CGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGT GAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCTGCAAGTTAATACCTTGTAGTTTTGACGTTACC AACAGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGTGCAAGCGTTAATCGGAATTACTGGGC GTAAAGCGCGCGTAGGTGGTTTGGTAAGATGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCCATAACTGCCTG ACTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAA GGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACG CCGTAAACGATGTCGACTAGCCGTTGGGATCCTTGAGATCTTAGTGGCGCAGCTAACGCGATAAGTCGACCGCCTGGGGA GTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAA CGCGAAGAACCTTACCTGGCCTTGACATGTCCGGAATCCTGCAGAGATGTGGGAGTGCCTTCGGGAATCGGAACACAGGT GCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACC AGCGGGTTATGCCGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGC CCTTACGGCCAGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCACAA AACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGTGAATCAGAATGTCA CGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCTCCAGAAGTAGCTAGTCTAAC CTTCGGGGGGACGGTTACCACGGAGTGATTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCGTAGGGGAACCTGCGGCT GGATCACCTCCTT
silva.class <- read.csv("MiGA/P273_silva_classify.csv", sep = ";", stringsAsFactors = FALSE)
cat(paste("#### SilvaSSU classification:", silva.class$lca_tax_slv))SilvaSSU classification: Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas;
Closest Neighbors in Silva SSU database as determined by RaxML
library(stringr)
silva.neighbors <- read.csv("MiGA/P273_silva_neighbors.csv", sep = ";")
silva.neighbors.tax <- data.frame(stringr::str_split_fixed(silva.neighbors$full_name, ";", 7), stringsAsFactors = F)
kable(unique(silva.neighbors.tax$X7))| x |
|---|
| uncultured Pseudomonas sp. |
| Pseudomonas citronellolis |
| Pseudomonas sp. wust-c |
| Pseudomonas sp. EGD-AKN5 |
| Pseudomonas citronellolis NBRC 103043 |
| Pseudomonas sp. ADP |
| Pseudomonas knackmussii B13 |
| Pseudomonas sp. CCA 1 |
Analysis at MiGA
Genome quality assessment
103 of 111 typcially essential genes are detected:
Whole genome taxonomic placement:
This is most likley an uncharacterized species of Pseudomonas
Phylogeny based on average amino acid identity (AAI)
MiGA calculate AAI scores for the most closely related genomes in the chosen database. I used NCBI Prok, which is the largest database available at MiGA (~9000 genomes). AAI is an identity score among homologous proteins (it ignores non-homologous, non-shared proteins)
kable(read.csv("MiGA/P273_aai.csv", header =T))| Dataset | AAI…. | Standard.deviation..AAI.. | Fraction.of.proteins.shared…. |
|---|---|---|---|
| Pseudomonas citronellolis NZ CP014158 | 94.55 | 9.33 | 85 |
| Pseudomonas knackmussii B13 NZ HG322950 | 85.88 | 13.14 | 75.79 |
| Pseudomonas sp. ATCC 13867 NC 020829 | 81.87 | 13.83 | 82.18 |
| Pseudomonas aeruginosa PAO581 NZ CP006705 | 76.80 | 15.25 | 59.79 |
| Pseudomonas aeruginosa NZ CP027169 | 76.45 | 15.84 | 61.93 |
| Pseudomonas sp. TCU HL1 NZ CP015992 | 74.00 | 15.75 | 67 |
| Pseudomonas stutzeri NZ CP027543 | 67.20 | 0.00 | (estimated) |
| Pseudomonas psychrotolerans NZ CP018758 | 67.11 | 0.00 | (estimated) |
| Pseudomonas sp. LPH1 NZ CP017290 | 66.67 | 0.00 | (estimated) |
| Pseudomonas fluorescens NZ CP028826 | 65.01 | 0.00 | (estimated) |
| Pseudomonas brassicacearum NZ LT629713 | 65.00 | 0.00 | (estimated) |
| Pseudomonas sp. DR 5 09 NZ CP011566 | 65.00 | 0.00 | (estimated) |
| Pseudomonas putida NBRC 14164 NC 021505 | 64.86 | 0.00 | (estimated) |
| Pseudomonas syringae pv. syringae NZ LT962480 | 64.82 | 0.00 | (estimated) |
| Pseudomonas frederiksbergensis NZ CP018319 | 64.46 | 0.00 | (estimated) |
| Pseudomonas fluorescens NZ CP010896 | 64.26 | 0.00 | (estimated) |
| Pseudomonas fragi NZ CP013861 | 64.06 | 0.00 | (estimated) |
| Pseudomonas sp. S 6 2 NZ CP020100 | 61.56 | 0.00 | (estimated) |
| Oblitimonas alkaliphila NZ CP012360 | 60.73 | 0.00 | (estimated) |
| Gammaproteobacteria bacterium ESL0073 NZ CP029478 | 58.51 | 0.00 | (estimated) |
| Microbulbifer thermotolerans NZ CP014864 | 51.52 | 0.00 | (estimated) |
| Cellvibrio sp. PSBB023 NZ CP019799 | 51.52 | 0.00 | (estimated) |
| Marinobacter sp. Arc7 DN 1 CP031848 | 51.44 | 0.00 | (estimated) |
| Halomonas sp. SF2003 NZ CP028367 | 51.44 | 0.00 | (estimated) |
| Halomonas aestuarii NZ CP018139 | 51.44 | 0.00 | (estimated) |
| Halioglobus japonicus NZ CP019450 | 51.41 | 0.00 | (estimated) |
| Halomonas campaniensis NZ CP007757 | 51.05 | 0.00 | (estimated) |
| Bacterioplanes sanyensis NZ CP022530 | 50.81 | 0.00 | (estimated) |
| Saccharospirillum mangrovi NZ CP031415 | 50.30 | 0.00 | (estimated) |
| Kushneria marisflavi NZ CP021358 | 50.17 | 0.00 | (estimated) |
| Marinomonas primoryensis CP016181 | 50.15 | 0.00 | (estimated) |
| Alcanivorax xenomutans NZ CP012331 | 49.68 | 0.00 | (estimated) |
| Citrobacter sp. FDAARGOS 156 NZ CP014030 | 48.79 | 0.00 | (estimated) |
| endosymbiont of unidentified scaly snail isolate Monju NZ AP012978 | 48.50 | 0.00 | (estimated) |
| Halorhodospira halochloris str. A CP007268 | 47.83 | 0.00 | (estimated) |
| Piscirickettsia salmonis LF 89 ATCC VR 1361 NZ CP011849 | 47.77 | 0.00 | (estimated) |
| Acinetobacter lactucae NZ CP020015 | 47.75 | 0.00 | (estimated) |
| Allochromatium vinosum DSM 180 NC 013851 | 47.68 | 0.00 | (estimated) |
| Acinetobacter schindleri NZ CP015615 | 47.63 | 0.00 | (estimated) |
| Acidihalobacter prosperus NZ CP017448 | 47.55 | 0.00 | (estimated) |
| Methylophaga nitratireducenticrescens NC 017857 | 47.13 | 0.00 | (estimated) |
| Methylomonas denitrificans NZ CP014476 | 46.85 | 0.00 | (estimated) |
| Thioploca ingrica NZ AP014633 | 46.72 | 0.00 | (estimated) |
| Moraxella bovoculi NZ CP011377 | 46.65 | 0.00 | (estimated) |
| Thioalkalivibrio paradoxus ARh 1 NZ CP007029 | 46.60 | 0.00 | (estimated) |
| Moraxella osloensis NZ CP024176 | 46.54 | 0.00 | (estimated) |
| Nitrosococcus oceani ATCC 19707 NC 007484 | 46.53 | 0.00 | (estimated) |
| Halothiobacillus sp. LS2 NZ CP016027 | 46.15 | 0.00 | (estimated) |
| Spiribacter curvatus NC 022664 | 46.15 | 0.00 | (estimated) |
| Thioalkalivibrio versutus NZ CP011367 | 46.14 | 0.00 | (estimated) |
MiGA also provides a tree based on the AAI; the methods are not explicit, but I have to assume that it aligns proteins shared across the dataset and builds a tree (BioNJ tree, the program is provided here. BioNJ is not the best, but it is better than NJ and is much faster than ML).
