Last updated: 2022-03-23

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Knit directory: Amphibolis_Posidonia_Comparison/

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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/SOS3_OG0000189/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/SOS3_OG0000189/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/SOS3_OG0000189/OG0000189.RiceAraSeagrasses.aln.fasta', 221, 300)
p + geom_facet(geom = geom_msa, data = data,  panel = 'Multiple Sequence Alignment (221-300AA)',
               font = NULL, color = "Clustal", by_conversation= TRUE) +
    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()
p2

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 = "Clustal") +
    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)

rename_df <- tibble::tribble(
                       ~old,        ~new,
                "3O.sativa",    "OsCBL8",
                "4O.sativa",  "OsCBL7.1",
                "5O.sativa",    "OsCBL4",
               "7A.thalian",    "AtSOS3",
               "8Z.mueller",   "ZmuSOS3",
               "15Z.marina",   "ZmaSOS3",
               "10P.austra",    "PaSOS3",
               "13Z.marina", "ZmaSOS3.2",
               "12Z.muelle", "ZmuSOS3.2",
               "10Z.muelle", "ZmuSOS3.3",
               "14Z.marina", "ZmaSOS3.2",
               "9Z.mueller", "ZmuSOS3.4",
               "11Z.muelle", "ZmuSOS3.5",
               "1A.amphibo",    "AaSOS3",
               "6P.austral",  "PaSOS3.2",
                "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 <- rename_df$new
names(replace_vector) <- rename_df$old
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

Phylogeny for the SOS3 cluster


 # 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


# 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 

#yes, after 550 - close one!

# let's make another 400 so we can have a nice 1000 bootstraps - different seed again!

raxml-ng --msa OG0000189.RiceAraSeagrasses.aln.fasta.raxml.reduced.phy --model JTT-DCMUT+G4 --prefix T14 --threads 8 --seed 1234 --bs-trees 400  --bootstrap

cat T8.raxml.bootstraps T11.raxml.bootstraps T14.raxml.bootstraps > allbootstraps
raxml-ng --support --tree T3.raxml.bestTree --bs-trees allbootstraps --prefix T15 

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/SOS3_OG0000189/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`.

msaplot(p3, 'data/SOS3_OG0000189/OG0000189.RiceAraSeagrasses.aln.NamesFixed.fasta',
        offset = 0.5) + theme(legend.position='none')

Phylogeny for the EIN3 cluster

First, to align the sequences again:

modeltest-ng reports JTT+I+G4+F

raxml-ng --msa OG0000629.RiceAraSeagrasses.aln.fa --model JTT+I+G4+F


 # to get fixed fasta - all good!!
raxml-ng --msa OG0000629.RiceAraSeagrasses.aln.fa --model JTT+I+G4+F --check

# to make a regular tree
raxml-ng --msa OG0000629.RiceAraSeagrasses.aln.fa --model JTT+I+G4+F --prefix T3 --threads 2 --seed 2 

# make 1000 bootstrap trees
raxml-ng --msa OG0000629.RiceAraSeagrasses.aln.fa --model JTT+I+G4+F --prefix T8 --threads 16 --seed 2 --bootstrap --bs-trees 1000 

# check whether they converge with <3% WRF cutoff
raxml-ng --bsconverge --bs-trees T8.raxml.bootstraps --prefix T12 --seed 2 --threads 1 --bs-cutoff 0.03

# it actually converged with 50 trees. haha.

# to make the final trees with bootstrap values

raxml-ng --support --tree T3.raxml.bestTree --bs-trees allbootstraps --prefix T13 

There we go:

tree3 <- ape::read.tree(text='(((LOC_Os07g48630.1:0.641123,(LOC_Os03g20780.1:0.007239,LOC_Os03g20790.1:0.001048)100:0.214718)100:0.418001,(((Zosma02g24210:0.119273,(maker-1834_92013--0.52-mRNA-1:0.074816,augustus_masked-5412_21708--0.0-mRNA-1:0.082668)97:0.073791)100:0.640955,augustus_masked-scf7180003860583-processed-gene-0.3-mRNA-1:0.182720)89:0.114788,(maker-3356_51779--0.18-mRNA-1:0.661393,Zosma01g42670:0.320772)100:0.468870)67:0.098845)100:0.660268,AT3G20770.1:0.149857,AT2G27050.1:0.250165)100:0.0;', branch.label='support')

rename_df <- tibble::tribble(
                                                       ~old,        ~new,
                                         "LOC_Os07g48630.1",    "OsEIL2",
                                         "LOC_Os03g20780.1",    "OsEIL1",
                                         "LOC_Os03g20790.1",  "OsEIL1.2",
                                            "Zosma02g24210", "ZmaEIN3.1",
                            "maker-1834_92013--0.52-mRNA-1", "ZmuEIN3.1",
                   "augustus_masked-5412_21708--0.0-mRNA-1", "ZmuEIN3.2",
"augustus_masked-scf7180003860583-processed-gene-0.3-mRNA-1",   "PaEIN3",
                            "maker-3356_51779--0.18-mRNA-1", "ZmuEIN3.3",
                                            "Zosma01g42670", "ZmaEIN3.2",
                                              "AT3G20770.1",    "AtEIN3",
                                              "AT2G27050.1",    "AtEIL1"
               )

tree3 <- rename_taxa(tree3, rename_df, old, new)

p3 <-  ggtree(tree3) + geom_tiplab(fontface='italic')
p3 <- p3 + geom_nodelab(aes(subset=label>80))
p3 + xlim_tree(2.1)

msaplot(p3, './data/EIN3_OG0000629/OG0000629.RiceAraSeagrasses.aln.NamesFixed.fa',
        offset = 0.5)  + theme(legend.position='none')

domains <- tibble::tribble(
                                                                    ~Gene,     ~Pfam, ~Start, ~End,   ~Evalue,
                                                          "Zosma01g42670", "PF04873",     3L, 246L,  2.9e-117,
                                                            "AT2G27050.1", "PF04873",    49L, 298L,  2.6e-130,
                                                       "LOC_Os03g20780.1", "PF04873",    78L, 332L,  1.4e-132,
             "augustus_masked-scf7180003860583-processed-gene-0.3-mRNA-1", "PF04873",    76L, 325L,  1.9e-131,
                                          "maker-1834_92013--0.52-mRNA-1", "PF04873",   136L, 408L,    2e-123,
                                                       "LOC_Os07g48630.1", "PF04873",    81L, 338L,  3.7e-129,
                                                            "AT3G20770.1", "PF04873",    49L, 296L,  4.8e-129,
                                 "augustus_masked-5412_21708--0.0-mRNA-1", "PF04873",    16L, 285L,  2.2e-123,
                                          "maker-3356_51779--0.18-mRNA-1", "PF04873",    85L, 125L,     2e-12,
                                                       "LOC_Os03g20790.1", "PF04873",    78L, 332L,  1.4e-132,
                                                          "Zosma02g24210", "PF04873",     1L, 206L,  1.9e-113
             )
domains$length <- domains$End - domains$Start
replace_vector <- rename_df$new
names(replace_vector) <- rename_df$old
domains <- domains %>% mutate(Gene = str_replace_all(Gene, replace_vector))
domains
# A tibble: 11 x 6
   Gene      Pfam    Start   End   Evalue length
   <chr>     <chr>   <int> <int>    <dbl>  <int>
 1 ZmaEIN3.2 PF04873     3   246 2.9e-117    243
 2 AtEIL1    PF04873    49   298 2.6e-130    249
 3 OsEIL1    PF04873    78   332 1.4e-132    254
 4 PaEIN3    PF04873    76   325 1.9e-131    249
 5 ZmuEIN3.1 PF04873   136   408 2  e-123    272
 6 OsEIL2    PF04873    81   338 3.7e-129    257
 7 AtEIN3    PF04873    49   296 4.8e-129    247
 8 ZmuEIN3.2 PF04873    16   285 2.2e-123    269
 9 ZmuEIN3.3 PF04873    85   125 2  e- 12     40
10 OsEIL1.2  PF04873    78   332 1.4e-132    254
11 ZmaEIN3.1 PF04873     1   206 1.9e-113    205
g <- domains %>% left_join(rename_df, by=c('Gene'='new')) %>%  ggplot(aes(y = Gene, yend=Gene, x=Start,  xend=End,  color=Evalue)) + geom_segment(size=2) + theme_minimal() + xlab(expression(paste("Position of ", italic("EIN3"))))
g

library(aplot)
Warning: package 'aplot' was built under R version 4.1.2
g <- g + theme(axis.title.y=element_blank(),
               axis.text.y = element_blank(),
                legend.position='none')

g %>% insert_left(p3, width=2.5)

library(patchwork)
Warning: package 'patchwork' was built under R version 4.1.1
# let's make a patchwork
# PROBLEM: patchwork does not automatically sort, so we'll have to sort the plot `g` manually 

plot_order <- p3[['data']] %>% filter(isTip == TRUE) %>% select(label, y) %>% arrange(desc(y))
g <- domains %>% left_join(rename_df, by=c('Gene'='new')) %>%  ggplot(aes(y = factor(Gene, levels = plot_order$label), yend=Gene, x=Start,  xend=End,  color=Evalue)) + geom_segment(size=2) + theme_minimal() + xlab(expression(paste("Position of ", italic("EIN3"))))+ theme(axis.title.y=element_blank(),
               axis.text.y = element_blank(),
                legend.position='none')
g

msaplot(p3, './data/EIN3_OG0000629/OG0000629.RiceAraSeagrasses.aln.NamesFixed.fa',
        offset = 0.5)  + theme(legend.position='none') + g  + 
  plot_layout(widths = c(3.5, 1)) 


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] patchwork_1.1.1     aplot_0.1.2         treeio_1.16.2      
 [4] ggtree_3.0.4        ape_5.5             Biostrings_2.60.2  
 [7] GenomeInfoDb_1.28.4 XVector_0.32.0      IRanges_2.26.0     
[10] S4Vectors_0.30.2    BiocGenerics_0.38.0 ggmsa_1.1.5        
[13] forcats_0.5.1       stringr_1.4.0       dplyr_1.0.7        
[16] purrr_0.3.4         readr_2.1.2         tidyr_1.1.4        
[19] tibble_3.1.5        ggplot2_3.3.5       tidyverse_1.3.1    
[22] 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               rstudioapi_0.13        farver_2.1.0          
 [7] fansi_0.5.0            lubridate_1.8.0        xml2_1.3.2            
[10] extrafont_0.17         knitr_1.36             polyclip_1.10-0       
[13] jsonlite_1.7.2         broom_0.7.9            Rttf2pt1_1.3.8        
[16] dbplyr_2.1.1           ggforce_0.3.3          compiler_4.1.0        
[19] httr_1.4.2             backports_1.2.1        assertthat_0.2.1      
[22] fastmap_1.1.0          lazyeval_0.2.2         cli_3.2.0             
[25] later_1.3.0            tweenr_1.0.2           htmltools_0.5.2       
[28] tools_4.1.0            gtable_0.3.0           glue_1.6.2            
[31] GenomeInfoDbData_1.2.6 maps_3.4.0             Rcpp_1.0.7            
[34] cellranger_1.1.0       jquerylib_0.1.4        vctrs_0.3.8           
[37] ggalt_0.4.0            nlme_3.1-152           extrafontdb_1.0       
[40] xfun_0.27              rvest_1.0.2            lifecycle_1.0.1       
[43] zlibbioc_1.38.0        MASS_7.3-54            scales_1.1.1          
[46] hms_1.1.1              promises_1.2.0.1       proj4_1.0-11          
[49] RColorBrewer_1.1-2     yaml_2.2.1             R4RNA_1.22.0          
[52] ggfun_0.0.5            seqmagick_0.1.5        yulab.utils_0.0.4     
[55] sass_0.4.0             stringi_1.7.5          highr_0.9             
[58] tidytree_0.3.7         rlang_0.4.12           pkgconfig_2.0.3       
[61] bitops_1.0-7           evaluate_0.14          lattice_0.20-44       
[64] labeling_0.4.2         cowplot_1.1.1          tidyselect_1.1.1      
[67] magrittr_2.0.1         R6_2.5.1               generics_0.1.1        
[70] DBI_1.1.1              pillar_1.6.4           haven_2.4.3           
[73] whisker_0.4            withr_2.5.0            RCurl_1.98-1.5        
[76] ash_1.0-15             modelr_0.1.8           crayon_1.4.1          
[79] KernSmooth_2.23-20     utf8_1.2.2             tzdb_0.1.2            
[82] rmarkdown_2.11         grid_4.1.0             readxl_1.3.1          
[85] git2r_0.28.0           reprex_2.0.1           digest_0.6.28         
[88] httpuv_1.6.3           gridGraphics_0.5-1     munsell_0.5.0         
[91] ggplotify_0.1.0        bslib_0.3.1