Last updated: 2022-03-11

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Knit directory: Amphibolis_Posidonia_Comparison/

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Rmd e9128f4 Philipp Bayer 2022-03-11 fix MSA
html 56d032a Philipp Bayer 2022-03-11 Build site.
Rmd 76a65cd Philipp Bayer 2022-03-11 finalised MSA!
html a994174 Philipp Bayer 2022-03-10 Build site.
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Rmd 02708fd Philipp Bayer 2022-03-10 New: fancy MSAs!!

I found some interesting gene clusters. Let’s look at them

library(tidyverse)
library(ggmsa)
library(Biostrings)
library(ape)
library(ggtree)
library(treeio)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
ggmsa('data/OG0000189.RiceAraSeagrasses.aln.fasta', start = 221, end = 260, char_width = 0.5, seq_name = T) + geom_seqlogo() + geom_msaBar()
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

x <- readAAStringSet('data/OG0000189.RiceAraSeagrasses.aln.fasta')
d <- as.dist(stringDist(x, method = "hamming")/width(x)[1])
tree <- bionj(d)
p <- ggtree(tree) + geom_tiplab()
ggtree(tree) + geom_tiplab()

data <- tidy_msa('data/OG0000189.RiceAraSeagrasses.aln.fasta', 221, 260)
p + geom_facet(geom = geom_msa, data = data,  panel = 'Multiple Sequence Alignment (221-260AA)',
               font = NULL, color = "Chemistry_AA") +
    xlim_tree(1)
Warning: Unknown or uninitialised column: `name`.

Let’s use a RAXML made tree

Commands run, after I shortened protein IDs manually. I pulled out the rice/Arabidopsis/seagrass proteins manually from the OG0000189.fa Orthofinder produced, and shortened their names so they fit into Phylip format.

muscle -in OG0000189.RiceAraSeagrasses.fa -out OG0000189.RiceAraSeagrasses.phy -phyi

raxmlHPC -p12345 -m PROTGAMMAAUTO -s OG0000189.RiceAraSeagrasses.phy -n AUTO

treestring <- '((3O.sativa:0.11386470583955717040,4O.sativa:0.03615896391829190315):0.07379737996482453599,(5O.sativa:0.33751444720146173140,((7A.thalian:0.45696796911334131019,((8Z.mueller:0.33996861614583556710,15Z.marina:0.16088168380872580610):0.31923212677234219514,10P.austra:0.57486248434538900209):0.22324965676023655892):0.10039129162924786964,((13Z.marina:0.11346816437532522559,(12Z.muelle:0.00973855109814669891,10Z.muelle:0.02645894329604922546):0.09840311662577182206):0.35332170475064506032,((14Z.marina:0.12821969530885793387,(9Z.mueller:0.06883167458475099310,11Z.muelle:0.02355162378347611801):0.15839770535961147924):0.63099939044074537797,(1A.amphibo:0.53760770644490452064,6P.austral:0.07001018412366488697):0.08839123570898912985):0.05074368132159561007):0.09141392275039983417):0.12967268161775510893):0.11320488597472283532,2O.sativa:1.70657194075952300949):0.0;'
plot(ape::read.tree(text=treestring))

p2 <- ggtree(ape::read.tree(text=treestring)) + geom_tiplab()

That’s very different from the above dendrogram!

data2 <- data %>% mutate(name = str_sub(name, 1, 10), 
                         name = str_trim(name))
p2 + geom_facet(geom = geom_msa, data = data2,  panel = 'Multiple Sequence Alignment (221-260AA)',
               font = NULL, color = "Chemistry_AA") +
    xlim_tree(2)
Warning: Unknown or uninitialised column: `name`.

Good! I used blastp with Swissprot to see whether I could get ‘official’ gene names for some of these, especially the O. sativa. I uploaded the fasta in data/OG000189.fa to blastp/swissprot, and pulled out new names where available.

Hits are to: A. thaliana Calcineurin B-like protein 4 (CBL4, Alternative name: SOS3) O. sativa Calcineurin B-like protein 8 (CBL8) O. sativa Calcineurin B-like protein 4 (CBL4)

Let’s rename the tree and the MSA data table using rename_taxa

trees <- ape::read.tree(text=treestring)
old_names <- trees$tip.label
# [1] "3O.sativa"  "4O.sativa"  "5O.sativa"  "7A.thalian" "8Z.mueller" "15Z.marina" "10P.austra" "13Z.marina"
# [9] "12Z.muelle" "10Z.muelle" "14Z.marina" "9Z.mueller" "11Z.muelle" "1A.amphibo" "6P.austral" "2O.sativa"
new_names  <- c( 'OsCBL8', 'OsCBL7.1', 'OsCBL4', 'AtSOS3', 'ZmuSOS3', 'ZmaSOS3', 'PaSOS3', 'ZmaSOS3.2', 'ZmuSOS3.2', 'ZmuSOS3.3', 'ZmaSOS3.2', 'ZmuSOS3.4', 'ZmuSOS3.5', 'AaSOS3', 'PaSOS3.2', 'OsCBL7.2')
rename_df <- data.frame(old = old_names, new = new_names)
rename_df
          old       new
1   3O.sativa    OsCBL8
2   4O.sativa  OsCBL7.1
3   5O.sativa    OsCBL4
4  7A.thalian    AtSOS3
5  8Z.mueller   ZmuSOS3
6  15Z.marina   ZmaSOS3
7  10P.austra    PaSOS3
8  13Z.marina ZmaSOS3.2
9  12Z.muelle ZmuSOS3.2
10 10Z.muelle ZmuSOS3.3
11 14Z.marina ZmaSOS3.2
12 9Z.mueller ZmuSOS3.4
13 11Z.muelle ZmuSOS3.5
14 1A.amphibo    AaSOS3
15 6P.austral  PaSOS3.2
16  2O.sativa  OsCBL7.2
trees <- rename_taxa(trees, rename_df, old, new)
p3 <- ggtree(trees) + geom_tiplab(fontface='italic')

p3

#str_replace_all takes a named vector
replace_vector <- new_names
names(replace_vector) <- old_names
data2 <- data2 %>% mutate(name = str_replace_all(name, replace_vector))
final_p <- p3 + geom_facet(geom = geom_msa, data = data2,  panel = 'Multiple Sequence Alignment (221-260AA)',
               font = NULL, color = "Chemistry_AA") +
    xlim_tree(2)
Warning: Unknown or uninitialised column: `name`.
final_p

cowplot::save_plot(final_p, filename = 'output/SOS3_phylogeny.png', base_width=10)

“However, apparent photosynthesis is still maintained at a salinity 15% that of normal seawater and at temperatures of 3 and 30°C, consistent with the ecological role of Z. muelleri as an intertidal species.” https://www.sciencedirect.com/science/article/abs/pii/0304377085900634

Now let’s also add support values.

I ran this based on Using RAxML-NG in Practice

 # to get best model
modeltest-ng -i OG0000189.RiceAraSeagrasses.aln.fasta -t ml -d aa -p 8

 # to get fixed fasta
raxml-ng --msa OG0000189.RiceAraSeagrasses.aln.fasta --model JTT-DCMUT+G4 --check

# to make a regular tree
raxml-ng --msa OG0000189.RiceAraSeagrasses.aln.fasta.raxml.reduced.phy --model JTT-DCMUT+G4 --prefix T3 --threads 2 --seed 2 

# make 200 bootstrap trees, does not converge
raxml-ng --msa OG0000189.RiceAraSeagrasses.aln.fasta.raxml.reduced.phy --model JTT-DCMUT+G4 --prefix T8 --threads 8 --seed 2 --bootstrap --bs-trees 200 

# make another 400  with different seed
raxml-ng --msa OG0000189.RiceAraSeagrasses.aln.fasta.raxml.reduced.phy --model JTT-DCMUT+G4 --prefix T11 --threads 8 --seed 333 --bootstrap --bs-trees 400 

# check whether they converge with <3% WRF cutoff
raxml-ng --bsconverge --bs-trees allbootstraps --prefix T12 --seed 2 --threads 1 --bs-cutoff 0.03

#yes, after 550 - close one!

# to make the final trees with bootstrap values

cat T8.raxml.bootstraps T11.raxml.bootstraps > allbootstraps

raxml-ng --support --tree T3.raxml.bestTree --bs-trees allbootstraps --prefix T13 --redo
newtree <- '((((((12Z.muelleri_maker-6084_47488_1_44441--0.5-mRNA-1:0.009685,10Z.muelleri_maker-8016_47756--0.9-mRNA-1:0.026505)99:0.098293,13Z.marina_Zosma01g02330:0.113268)99:0.354433,(((11Z.muelleri_maker-8016_47756--0.8-mRNA-1:0.023273,9Z.muelleri_maker-2123_50249_30746_38748--0.4-mRNA-1:0.068317)99:0.153634,14Z.marina_Zosma01g02340:0.129762)100:0.616637,(1A.amphibolis_maker-scf7180000542070-augustus-gene-0.0-mRNA-1:0.518908,6P.australis_maker-scf7180003728898-augustus-gene-0.1-mRNA-1:0.074916)72:0.082830)22:0.047398)31:0.080205,((2O.sativa_LOC_Os03g33570.1:1.647537,(3O.sativa_LOC_Os02g18930.1:0.113637,4O.sativa_LOC_Os02g18880.1:0.035299)53:0.061341)66:0.111396,5O.sativa_LOC_Os05g45810.1:0.344545)75:0.137407)42:0.100482,7A.thaliana_AT5G24270.1:0.448151)83:0.209634,(15Z.marina_Zosma06g25260:0.152988,8Z.muelleri_snap_masked-14205_19045--0.3-mRNA-1:0.336420)100:0.314204,10P.australis_maker-scf7180004080271-augustus-gene-0.8-mRNA-1:0.580458)83:0.0;'

tree3 <- ape::read.tree(text=newtree, branch.label='support')

p3 <- ggtree(tree3) + geom_tiplab()
p3

OK the nodes are now named differently due to raxml-ng, time to fix again

old_names <- tree3$tip.label

# [1] "3O.sativa"  "4O.sativa"  "5O.sativa"  "7A.thalian" "8Z.mueller" "15Z.marina" "10P.austra" "13Z.marina"
# [9] "12Z.muelle" "10Z.muelle" "14Z.marina" "9Z.mueller" "11Z.muelle" "1A.amphibo" "6P.austral" "2O.sativa"
#new_names  <- c( 'OsCBL8', 'OsCBL7.1', 'OsCBL4', 'AtSOS3', 'ZmuSOS3', 'ZmaSOS3', 'PaSOS3', 'ZmaSOS3.2',
#                 'ZmuSOS3.2', 'ZmuSOS3.3', 'ZmaSOS3.3', 'ZmuSOS3.2', 'ZmuSOS3.4', 'AaSOS3', 'PaSOS3.2', 'OsCBL7.2')
new_names  <- c('ZmuSOS3.2', 'ZmuSOS3.3', 'ZmaSOS3.2', 'ZmuSOS3.4', 'ZmuSOS3.5', 'ZmaSOS3.3','AaSOS3',  
                'PaSOS3.2', 'OsCBL7.2', 'OsCBL8', 'OsCBL7.1', 'OsCBL4', 'AtSOS3', 'ZmaSOS3', 'ZmuSOS3', 'PaSOS3')
rename_df <- data.frame(old = old_names, new = new_names)


tree3 <- rename_taxa(tree3, rename_df, old, new)
p3 <- ggtree(tree3) + geom_tiplab(fontface='italic')
p3 <- p3 + geom_nodelab(aes(subset=label>80))
p3

replace_vector <- new_names
names(replace_vector) <- old_names

data <- tidy_msa('data/OG0000189.RiceAraSeagrasses.aln.fasta', 221, 260)
data3 <- data %>% mutate(name = str_replace(name, ' ', '_'),
                          name = str_replace_all(name, replace_vector))
p3 + geom_facet(geom = geom_msa, data = data3,  panel = 'Multiple Sequence Alignment (221-260AA)',
               font = NULL, color = "Chemistry_AA") +
    xlim_tree(3)
Warning: Unknown or uninitialised column: `name`.


sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
[3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
[5] LC_TIME=English_Australia.1252    

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] treeio_1.16.2       ggtree_3.0.4        ape_5.5            
 [4] Biostrings_2.60.2   GenomeInfoDb_1.28.4 XVector_0.32.0     
 [7] IRanges_2.26.0      S4Vectors_0.30.2    BiocGenerics_0.38.0
[10] ggmsa_1.1.5         forcats_0.5.1       stringr_1.4.0      
[13] dplyr_1.0.7         purrr_0.3.4         readr_2.0.2        
[16] tidyr_1.1.4         tibble_3.1.5        ggplot2_3.3.5      
[19] tidyverse_1.3.1     workflowr_1.6.2    

loaded via a namespace (and not attached):
 [1] colorspace_2.0-2       ellipsis_0.3.2         rprojroot_2.0.2       
 [4] fs_1.5.0               aplot_0.1.2            rstudioapi_0.13       
 [7] farver_2.1.0           fansi_0.5.0            lubridate_1.8.0       
[10] xml2_1.3.2             extrafont_0.17         knitr_1.36            
[13] polyclip_1.10-0        jsonlite_1.7.2         broom_0.7.9           
[16] Rttf2pt1_1.3.8         dbplyr_2.1.1           ggforce_0.3.3         
[19] compiler_4.1.0         httr_1.4.2             backports_1.2.1       
[22] assertthat_0.2.1       fastmap_1.1.0          lazyeval_0.2.2        
[25] cli_3.2.0              later_1.3.0            tweenr_1.0.2          
[28] htmltools_0.5.2        tools_4.1.0            gtable_0.3.0          
[31] glue_1.4.2             GenomeInfoDbData_1.2.6 maps_3.4.0            
[34] Rcpp_1.0.7             cellranger_1.1.0       jquerylib_0.1.4       
[37] vctrs_0.3.8            ggalt_0.4.0            nlme_3.1-152          
[40] extrafontdb_1.0        xfun_0.27              rvest_1.0.2           
[43] lifecycle_1.0.1        zlibbioc_1.38.0        MASS_7.3-54           
[46] scales_1.1.1           hms_1.1.1              promises_1.2.0.1      
[49] proj4_1.0-11           RColorBrewer_1.1-2     yaml_2.2.1            
[52] R4RNA_1.22.0           ggfun_0.0.5            seqmagick_0.1.5       
[55] yulab.utils_0.0.4      sass_0.4.0             stringi_1.7.5         
[58] highr_0.9              tidytree_0.3.7         rlang_0.4.12          
[61] pkgconfig_2.0.3        bitops_1.0-7           evaluate_0.14         
[64] lattice_0.20-44        labeling_0.4.2         patchwork_1.1.1       
[67] cowplot_1.1.1          tidyselect_1.1.1       magrittr_2.0.1        
[70] R6_2.5.1               generics_0.1.1         DBI_1.1.1             
[73] pillar_1.6.4           haven_2.4.3            whisker_0.4           
[76] withr_2.5.0            RCurl_1.98-1.5         ash_1.0-15            
[79] modelr_0.1.8           crayon_1.4.1           KernSmooth_2.23-20    
[82] utf8_1.2.2             tzdb_0.1.2             rmarkdown_2.11        
[85] grid_4.1.0             readxl_1.3.1           git2r_0.28.0          
[88] reprex_2.0.1           digest_0.6.28          httpuv_1.6.3          
[91] gridGraphics_0.5-1     munsell_0.5.0          ggplotify_0.1.0       
[94] bslib_0.3.1