Visualsing MSAs
Philipp Bayer
10 March 2022
Last updated: 2022-03-10
Checks: 7 0
Knit directory: Amphibolis_Posidonia_Comparison/
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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', 'ZmaSOS3.3', 'ZmuSOS3.2', 'ZmuSOS3.4', 'AaSOS3', 'PaSOS3', '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 ZmaSOS3.3
12 9Z.mueller ZmuSOS3.2
13 11Z.muelle ZmuSOS3.4
14 1A.amphibo AaSOS3
15 6P.austral PaSOS3
16 2O.sativa OsCBL7.2trees <- 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)
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