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Let’s imagine you are a beginner in the field of plant science, and glutamine synthetase (GS) is on the focus of your interest. You have been dealing with the literature and you have come across with a paper that awaken your interest: “Atomic Structure of Plant Glutamine Synthetase. A Key Enzyme for Plant Productivity” (J. Biol. Chem 29: 29287-29296).
In this paper, the authors reports the crystal structure of maize GS. However, as the author acknowledge, higher plants have several isoenzymes of GS differing in heat stability and catalytic properties. The author refer to the isoenzyme they are characterizing as GS1a, being Ile-161 a key residue responsible for the heat stability of this protein. Your aim is to find, if they exist, orthologs of this maize protein in the model plant Arabidopsis thaliana. You may be a beginner, but you don’t ignore that both maize and arabidopsis genomes have been shown to contain six GS genes each one.
At first you’re slightly bewildered: none of the corn isoforms is called GS1a! But, everybody knows that GS1 implies a cytosolic form of the enzyme (while GS2 refer to the chloroplastic one), so our protein should be one of the five cytosolic isoenzymes present in maize. Taking into consideration that isoleucine should be present at position 161 and other information related to the sequence that is provided in the above mentioned paper, we conclude that GS1a match with GS1-4 from UniProt (or Zm_GS1b_4 using the phylo identifier of orthGS).
As a side note, point that the choice of GS1a to name this maize isoform was somewhat unfortunate. Indeed, among researcher in the gymnosperm field, GS1a is a term used to describe a set of evolutionary close cytosolic proteins whose expression and function is related to photosynthesis and photorespiration, reminiscent of GS2 in angiosperms.
In any case, we have already identified in maize the protein of interest (Zm_GS1b_4). Now, we have to look for orthologs in arabidopsis. Of course, the quickest and easiest way to do that is with the R package orthGS, but let’s pretend for a moment that we don’t know this resource. Thus, we are going to download all the arabidopsis and maize sequences, align them and build a phylogenetic tree, to see it this approach suggests something to us.
The function subsetGS()
takes as argument the species of
interest and return a dataframe with the sequences and information
regarding the GS isoforms found in these species.
Afterward, we can proceed with the alignment and phylogenetic tree construction. Ensure the ‘muscle’ package is installed. If necessary, uncomment the following lines of code.
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("muscle")
aln <- msa(sequences = maize_ara$prot, ids = maize_ara$phylo_id, inhouse = FALSE)
#>
#> MUSCLE v3.8.31 by Robert C. Edgar
#>
#> http://www.drive5.com/muscle
#> This software is donated to the public domain.
#> Please cite: Edgar, R.C. Nucleic Acids Res 32(5), 1792-97.
#>
#> file1293911ac3c53 12 seqs, max length 430, avg length 369
#> 424 MB(5%)00:00:00 Iter 1 1.28% K-mer dist pass 1424 MB(5%)00:00:00 Iter 1 100.00% K-mer dist pass 1
#> 424 MB(5%)00:00:00 Iter 1 1.28% K-mer dist pass 2424 MB(5%)00:00:00 Iter 1 100.00% K-mer dist pass 2
#> 424 MB(5%)00:00:00 Iter 1 9.09% Align node 425 MB(5%)00:00:00 Iter 1 18.18% Align node426 MB(5%)00:00:00 Iter 1 27.27% Align node426 MB(5%)00:00:00 Iter 1 36.36% Align node426 MB(5%)00:00:00 Iter 1 45.45% Align node426 MB(5%)00:00:00 Iter 1 54.55% Align node426 MB(5%)00:00:00 Iter 1 63.64% Align node426 MB(5%)00:00:00 Iter 1 72.73% Align node426 MB(5%)00:00:00 Iter 1 81.82% Align node427 MB(5%)00:00:00 Iter 1 90.91% Align node427 MB(5%)00:00:00 Iter 1 100.00% Align node427 MB(5%)00:00:00 Iter 1 100.00% Align node
#> 427 MB(5%)00:00:00 Iter 1 8.33% Root alignment427 MB(5%)00:00:00 Iter 1 16.67% Root alignment427 MB(5%)00:00:00 Iter 1 25.00% Root alignment427 MB(5%)00:00:00 Iter 1 33.33% Root alignment427 MB(5%)00:00:00 Iter 1 41.67% Root alignment427 MB(5%)00:00:00 Iter 1 50.00% Root alignment427 MB(5%)00:00:00 Iter 1 58.33% Root alignment427 MB(5%)00:00:00 Iter 1 66.67% Root alignment427 MB(5%)00:00:00 Iter 1 75.00% Root alignment427 MB(5%)00:00:00 Iter 1 83.33% Root alignment427 MB(5%)00:00:00 Iter 1 91.67% Root alignment427 MB(5%)00:00:00 Iter 1 100.00% Root alignment427 MB(5%)00:00:00 Iter 1 100.00% Root alignment
#> 427 MB(5%)00:00:00 Iter 2 10.00% Refine tree 427 MB(5%)00:00:00 Iter 2 20.00% Refine tree427 MB(5%)00:00:00 Iter 2 30.00% Refine tree427 MB(5%)00:00:00 Iter 2 40.00% Refine tree427 MB(5%)00:00:00 Iter 2 50.00% Refine tree427 MB(5%)00:00:00 Iter 2 60.00% Refine tree427 MB(5%)00:00:00 Iter 2 100.00% Refine tree
#> 427 MB(5%)00:00:00 Iter 2 8.33% Root alignment427 MB(5%)00:00:00 Iter 2 16.67% Root alignment427 MB(5%)00:00:00 Iter 2 25.00% Root alignment427 MB(5%)00:00:00 Iter 2 33.33% Root alignment427 MB(5%)00:00:00 Iter 2 41.67% Root alignment427 MB(5%)00:00:00 Iter 2 50.00% Root alignment427 MB(5%)00:00:00 Iter 2 58.33% Root alignment427 MB(5%)00:00:00 Iter 2 66.67% Root alignment427 MB(5%)00:00:00 Iter 2 75.00% Root alignment427 MB(5%)00:00:00 Iter 2 83.33% Root alignment427 MB(5%)00:00:00 Iter 2 91.67% Root alignment427 MB(5%)00:00:00 Iter 2 100.00% Root alignment427 MB(5%)00:00:00 Iter 2 100.00% Root alignment
#> 427 MB(5%)00:00:00 Iter 2 100.00% Root alignment
#> 427 MB(5%)00:00:00 Iter 3 9.52% Refine biparts427 MB(5%)00:00:00 Iter 3 14.29% Refine biparts427 MB(5%)00:00:00 Iter 3 19.05% Refine biparts427 MB(5%)00:00:00 Iter 3 23.81% Refine biparts427 MB(5%)00:00:00 Iter 3 28.57% Refine biparts427 MB(5%)00:00:00 Iter 3 33.33% Refine biparts427 MB(5%)00:00:00 Iter 3 38.10% Refine biparts427 MB(5%)00:00:00 Iter 3 42.86% Refine biparts427 MB(5%)00:00:00 Iter 3 47.62% Refine biparts427 MB(5%)00:00:00 Iter 3 52.38% Refine biparts427 MB(5%)00:00:00 Iter 3 57.14% Refine biparts427 MB(5%)00:00:00 Iter 3 61.90% Refine biparts427 MB(5%)00:00:00 Iter 3 66.67% Refine biparts427 MB(5%)00:00:00 Iter 3 71.43% Refine biparts427 MB(5%)00:00:00 Iter 3 76.19% Refine biparts427 MB(5%)00:00:00 Iter 3 80.95% Refine biparts427 MB(5%)00:00:00 Iter 3 85.71% Refine biparts427 MB(5%)00:00:00 Iter 3 90.48% Refine biparts427 MB(5%)00:00:00 Iter 3 95.24% Refine biparts427 MB(5%)00:00:00 Iter 3 100.00% Refine biparts427 MB(5%)00:00:00 Iter 3 104.76% Refine biparts427 MB(5%)00:00:00 Iter 3 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 4 9.52% Refine biparts427 MB(5%)00:00:00 Iter 4 14.29% Refine biparts427 MB(5%)00:00:00 Iter 4 19.05% Refine biparts427 MB(5%)00:00:00 Iter 4 23.81% Refine biparts427 MB(5%)00:00:00 Iter 4 28.57% Refine biparts427 MB(5%)00:00:00 Iter 4 33.33% Refine biparts427 MB(5%)00:00:00 Iter 4 38.10% Refine biparts427 MB(5%)00:00:00 Iter 4 42.86% Refine biparts427 MB(5%)00:00:00 Iter 4 47.62% Refine biparts427 MB(5%)00:00:00 Iter 4 52.38% Refine biparts427 MB(5%)00:00:00 Iter 4 57.14% Refine biparts427 MB(5%)00:00:00 Iter 4 61.90% Refine biparts427 MB(5%)00:00:00 Iter 4 66.67% Refine biparts427 MB(5%)00:00:00 Iter 4 71.43% Refine biparts427 MB(5%)00:00:00 Iter 4 76.19% Refine biparts427 MB(5%)00:00:00 Iter 4 80.95% Refine biparts427 MB(5%)00:00:00 Iter 4 85.71% Refine biparts427 MB(5%)00:00:00 Iter 4 90.48% Refine biparts427 MB(5%)00:00:00 Iter 4 95.24% Refine biparts427 MB(5%)00:00:00 Iter 4 100.00% Refine biparts427 MB(5%)00:00:00 Iter 4 104.76% Refine biparts427 MB(5%)00:00:00 Iter 4 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 5 9.52% Refine biparts427 MB(5%)00:00:00 Iter 5 14.29% Refine biparts427 MB(5%)00:00:00 Iter 5 19.05% Refine biparts427 MB(5%)00:00:00 Iter 5 23.81% Refine biparts427 MB(5%)00:00:00 Iter 5 28.57% Refine biparts427 MB(5%)00:00:00 Iter 5 33.33% Refine biparts427 MB(5%)00:00:00 Iter 5 38.10% Refine biparts427 MB(5%)00:00:00 Iter 5 42.86% Refine biparts427 MB(5%)00:00:00 Iter 5 47.62% Refine biparts427 MB(5%)00:00:00 Iter 5 52.38% Refine biparts427 MB(5%)00:00:00 Iter 5 57.14% Refine biparts427 MB(5%)00:00:00 Iter 5 61.90% Refine biparts427 MB(5%)00:00:00 Iter 5 66.67% Refine biparts427 MB(5%)00:00:00 Iter 5 71.43% Refine biparts427 MB(5%)00:00:00 Iter 5 76.19% Refine biparts427 MB(5%)00:00:00 Iter 5 80.95% Refine biparts427 MB(5%)00:00:00 Iter 5 85.71% Refine biparts427 MB(5%)00:00:00 Iter 5 90.48% Refine biparts427 MB(5%)00:00:00 Iter 5 95.24% Refine biparts427 MB(5%)00:00:00 Iter 5 100.00% Refine biparts427 MB(5%)00:00:00 Iter 5 104.76% Refine biparts427 MB(5%)00:00:00 Iter 5 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 6 9.52% Refine biparts427 MB(5%)00:00:00 Iter 6 14.29% Refine biparts427 MB(5%)00:00:00 Iter 6 19.05% Refine biparts427 MB(5%)00:00:00 Iter 6 23.81% Refine biparts427 MB(5%)00:00:00 Iter 6 28.57% Refine biparts427 MB(5%)00:00:00 Iter 6 33.33% Refine biparts427 MB(5%)00:00:00 Iter 6 38.10% Refine biparts427 MB(5%)00:00:00 Iter 6 42.86% Refine biparts427 MB(5%)00:00:00 Iter 6 47.62% Refine biparts427 MB(5%)00:00:00 Iter 6 52.38% Refine biparts427 MB(5%)00:00:00 Iter 6 57.14% Refine biparts427 MB(5%)00:00:00 Iter 6 61.90% Refine biparts427 MB(5%)00:00:00 Iter 6 66.67% Refine biparts427 MB(5%)00:00:00 Iter 6 71.43% Refine biparts427 MB(5%)00:00:00 Iter 6 76.19% Refine biparts427 MB(5%)00:00:00 Iter 6 80.95% Refine biparts427 MB(5%)00:00:00 Iter 6 85.71% Refine biparts427 MB(5%)00:00:00 Iter 6 90.48% Refine biparts427 MB(5%)00:00:00 Iter 6 95.24% Refine biparts427 MB(5%)00:00:00 Iter 6 100.00% Refine biparts427 MB(5%)00:00:00 Iter 6 104.76% Refine biparts427 MB(5%)00:00:00 Iter 6 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 7 9.52% Refine biparts427 MB(5%)00:00:00 Iter 7 14.29% Refine biparts427 MB(5%)00:00:00 Iter 7 19.05% Refine biparts427 MB(5%)00:00:00 Iter 7 23.81% Refine biparts427 MB(5%)00:00:00 Iter 7 28.57% Refine biparts427 MB(5%)00:00:00 Iter 7 33.33% Refine biparts427 MB(5%)00:00:00 Iter 7 38.10% Refine biparts427 MB(5%)00:00:00 Iter 7 42.86% Refine biparts427 MB(5%)00:00:00 Iter 7 47.62% Refine biparts427 MB(5%)00:00:00 Iter 7 52.38% Refine biparts427 MB(5%)00:00:00 Iter 7 57.14% Refine biparts427 MB(5%)00:00:00 Iter 7 61.90% Refine biparts427 MB(5%)00:00:00 Iter 7 66.67% Refine biparts427 MB(5%)00:00:00 Iter 7 71.43% Refine biparts427 MB(5%)00:00:00 Iter 7 76.19% Refine biparts427 MB(5%)00:00:00 Iter 7 80.95% Refine biparts427 MB(5%)00:00:00 Iter 7 85.71% Refine biparts427 MB(5%)00:00:00 Iter 7 90.48% Refine biparts427 MB(5%)00:00:00 Iter 7 95.24% Refine biparts427 MB(5%)00:00:00 Iter 7 100.00% Refine biparts427 MB(5%)00:00:00 Iter 7 104.76% Refine biparts427 MB(5%)00:00:00 Iter 7 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 8 9.52% Refine biparts427 MB(5%)00:00:00 Iter 8 14.29% Refine biparts427 MB(5%)00:00:00 Iter 8 19.05% Refine biparts427 MB(5%)00:00:00 Iter 8 23.81% Refine biparts427 MB(5%)00:00:00 Iter 8 28.57% Refine biparts427 MB(5%)00:00:00 Iter 8 33.33% Refine biparts427 MB(5%)00:00:00 Iter 8 38.10% Refine biparts427 MB(5%)00:00:00 Iter 8 42.86% Refine biparts427 MB(5%)00:00:00 Iter 8 47.62% Refine biparts427 MB(5%)00:00:00 Iter 8 52.38% Refine biparts427 MB(5%)00:00:00 Iter 8 57.14% Refine biparts427 MB(5%)00:00:00 Iter 8 61.90% Refine biparts427 MB(5%)00:00:00 Iter 8 66.67% Refine biparts427 MB(5%)00:00:00 Iter 8 71.43% Refine biparts427 MB(5%)00:00:00 Iter 8 76.19% Refine biparts427 MB(5%)00:00:00 Iter 8 80.95% Refine biparts427 MB(5%)00:00:00 Iter 8 85.71% Refine biparts427 MB(5%)00:00:00 Iter 8 90.48% Refine biparts427 MB(5%)00:00:00 Iter 8 95.24% Refine biparts427 MB(5%)00:00:00 Iter 8 100.00% Refine biparts427 MB(5%)00:00:00 Iter 8 104.76% Refine biparts427 MB(5%)00:00:00 Iter 8 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 9 9.52% Refine biparts427 MB(5%)00:00:00 Iter 9 14.29% Refine biparts427 MB(5%)00:00:00 Iter 9 19.05% Refine biparts427 MB(5%)00:00:00 Iter 9 23.81% Refine biparts427 MB(5%)00:00:00 Iter 9 28.57% Refine biparts427 MB(5%)00:00:00 Iter 9 33.33% Refine biparts427 MB(5%)00:00:00 Iter 9 38.10% Refine biparts427 MB(5%)00:00:00 Iter 9 42.86% Refine biparts427 MB(5%)00:00:00 Iter 9 47.62% Refine biparts427 MB(5%)00:00:00 Iter 9 52.38% Refine biparts427 MB(5%)00:00:00 Iter 9 57.14% Refine biparts427 MB(5%)00:00:00 Iter 9 61.90% Refine biparts427 MB(5%)00:00:00 Iter 9 66.67% Refine biparts427 MB(5%)00:00:00 Iter 9 71.43% Refine biparts427 MB(5%)00:00:00 Iter 9 76.19% Refine biparts427 MB(5%)00:00:00 Iter 9 80.95% Refine biparts427 MB(5%)00:00:00 Iter 9 85.71% Refine biparts427 MB(5%)00:00:00 Iter 9 90.48% Refine biparts427 MB(5%)00:00:00 Iter 9 95.24% Refine biparts427 MB(5%)00:00:00 Iter 9 100.00% Refine biparts427 MB(5%)00:00:00 Iter 9 104.76% Refine biparts427 MB(5%)00:00:00 Iter 9 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 10 9.52% Refine biparts427 MB(5%)00:00:00 Iter 10 14.29% Refine biparts427 MB(5%)00:00:00 Iter 10 19.05% Refine biparts427 MB(5%)00:00:00 Iter 10 23.81% Refine biparts427 MB(5%)00:00:00 Iter 10 28.57% Refine biparts427 MB(5%)00:00:00 Iter 10 33.33% Refine biparts427 MB(5%)00:00:00 Iter 10 38.10% Refine biparts427 MB(5%)00:00:00 Iter 10 42.86% Refine biparts427 MB(5%)00:00:00 Iter 10 47.62% Refine biparts427 MB(5%)00:00:00 Iter 10 52.38% Refine biparts427 MB(5%)00:00:00 Iter 10 57.14% Refine biparts427 MB(5%)00:00:00 Iter 10 61.90% Refine biparts427 MB(5%)00:00:00 Iter 10 66.67% Refine biparts427 MB(5%)00:00:00 Iter 10 71.43% Refine biparts427 MB(5%)00:00:00 Iter 10 76.19% Refine biparts427 MB(5%)00:00:00 Iter 10 80.95% Refine biparts427 MB(5%)00:00:00 Iter 10 85.71% Refine biparts427 MB(5%)00:00:00 Iter 10 90.48% Refine biparts427 MB(5%)00:00:00 Iter 10 95.24% Refine biparts427 MB(5%)00:00:00 Iter 10 100.00% Refine biparts427 MB(5%)00:00:00 Iter 10 104.76% Refine biparts427 MB(5%)00:00:00 Iter 10 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 11 9.52% Refine biparts427 MB(5%)00:00:00 Iter 11 14.29% Refine biparts427 MB(5%)00:00:00 Iter 11 19.05% Refine biparts427 MB(5%)00:00:00 Iter 11 23.81% Refine biparts427 MB(5%)00:00:00 Iter 11 28.57% Refine biparts427 MB(5%)00:00:00 Iter 11 33.33% Refine biparts427 MB(5%)00:00:00 Iter 11 38.10% Refine biparts427 MB(5%)00:00:00 Iter 11 42.86% Refine biparts427 MB(5%)00:00:00 Iter 11 47.62% Refine biparts427 MB(5%)00:00:00 Iter 11 52.38% Refine biparts427 MB(5%)00:00:00 Iter 11 57.14% Refine biparts427 MB(5%)00:00:00 Iter 11 61.90% Refine biparts427 MB(5%)00:00:00 Iter 11 66.67% Refine biparts427 MB(5%)00:00:00 Iter 11 71.43% Refine biparts427 MB(5%)00:00:00 Iter 11 76.19% Refine biparts427 MB(5%)00:00:00 Iter 11 80.95% Refine biparts427 MB(5%)00:00:00 Iter 11 85.71% Refine biparts427 MB(5%)00:00:00 Iter 11 90.48% Refine biparts427 MB(5%)00:00:00 Iter 11 95.24% Refine biparts427 MB(5%)00:00:00 Iter 11 100.00% Refine biparts427 MB(5%)00:00:00 Iter 11 104.76% Refine biparts427 MB(5%)00:00:00 Iter 11 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 12 9.52% Refine biparts427 MB(5%)00:00:00 Iter 12 14.29% Refine biparts427 MB(5%)00:00:00 Iter 12 19.05% Refine biparts427 MB(5%)00:00:00 Iter 12 23.81% Refine biparts427 MB(5%)00:00:00 Iter 12 28.57% Refine biparts427 MB(5%)00:00:00 Iter 12 33.33% Refine biparts427 MB(5%)00:00:00 Iter 12 38.10% Refine biparts427 MB(5%)00:00:00 Iter 12 42.86% Refine biparts427 MB(5%)00:00:00 Iter 12 47.62% Refine biparts427 MB(5%)00:00:00 Iter 12 52.38% Refine biparts427 MB(5%)00:00:00 Iter 12 57.14% Refine biparts427 MB(5%)00:00:00 Iter 12 61.90% Refine biparts427 MB(5%)00:00:00 Iter 12 66.67% Refine biparts427 MB(5%)00:00:00 Iter 12 71.43% Refine biparts427 MB(5%)00:00:00 Iter 12 76.19% Refine biparts427 MB(5%)00:00:00 Iter 12 80.95% Refine biparts427 MB(5%)00:00:00 Iter 12 85.71% Refine biparts427 MB(5%)00:00:00 Iter 12 90.48% Refine biparts427 MB(5%)00:00:00 Iter 12 95.24% Refine biparts427 MB(5%)00:00:00 Iter 12 100.00% Refine biparts427 MB(5%)00:00:00 Iter 12 104.76% Refine biparts427 MB(5%)00:00:00 Iter 12 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 13 9.52% Refine biparts427 MB(5%)00:00:00 Iter 13 14.29% Refine biparts427 MB(5%)00:00:00 Iter 13 19.05% Refine biparts427 MB(5%)00:00:00 Iter 13 23.81% Refine biparts427 MB(5%)00:00:00 Iter 13 28.57% Refine biparts427 MB(5%)00:00:00 Iter 13 33.33% Refine biparts427 MB(5%)00:00:00 Iter 13 38.10% Refine biparts427 MB(5%)00:00:00 Iter 13 42.86% Refine biparts427 MB(5%)00:00:00 Iter 13 47.62% Refine biparts427 MB(5%)00:00:00 Iter 13 52.38% Refine biparts427 MB(5%)00:00:00 Iter 13 57.14% Refine biparts427 MB(5%)00:00:00 Iter 13 61.90% Refine biparts427 MB(5%)00:00:00 Iter 13 66.67% Refine biparts427 MB(5%)00:00:00 Iter 13 71.43% Refine biparts427 MB(5%)00:00:00 Iter 13 76.19% Refine biparts427 MB(5%)00:00:00 Iter 13 80.95% Refine biparts427 MB(5%)00:00:00 Iter 13 85.71% Refine biparts427 MB(5%)00:00:00 Iter 13 90.48% Refine biparts427 MB(5%)00:00:00 Iter 13 95.24% Refine biparts427 MB(5%)00:00:00 Iter 13 100.00% Refine biparts427 MB(5%)00:00:00 Iter 13 104.76% Refine biparts427 MB(5%)00:00:00 Iter 13 100.00% Refine biparts
#> 427 MB(5%)00:00:00 Iter 14 9.52% Refine biparts427 MB(5%)00:00:00 Iter 14 14.29% Refine biparts427 MB(5%)00:00:00 Iter 14 19.05% Refine biparts427 MB(5%)00:00:00 Iter 14 23.81% Refine biparts427 MB(5%)00:00:00 Iter 14 28.57% Refine biparts427 MB(5%)00:00:00 Iter 14 33.33% Refine biparts427 MB(5%)00:00:00 Iter 14 38.10% Refine biparts427 MB(5%)00:00:00 Iter 14 42.86% Refine biparts427 MB(5%)00:00:00 Iter 14 47.62% Refine biparts427 MB(5%)00:00:00 Iter 14 52.38% Refine biparts427 MB(5%)00:00:00 Iter 14 57.14% Refine biparts427 MB(5%)00:00:00 Iter 14 61.90% Refine biparts427 MB(5%)00:00:00 Iter 14 66.67% Refine biparts427 MB(5%)00:00:00 Iter 14 71.43% Refine biparts427 MB(5%)00:00:00 Iter 14 76.19% Refine biparts427 MB(5%)00:00:00 Iter 14 80.95% Refine biparts427 MB(5%)00:00:00 Iter 14 85.71% Refine biparts427 MB(5%)00:00:00 Iter 14 90.48% Refine biparts427 MB(5%)00:00:00 Iter 14 95.24% Refine biparts427 MB(5%)00:00:00 Iter 14 100.00% Refine biparts427 MB(5%)00:00:00 Iter 14 104.76% Refine biparts427 MB(5%)00:00:00 Iter 14 100.00% Refine biparts
a <- aln$ali
rownames(a) <- maize_ara$phylo_id
tr <- mltree(a)$tree
#> optimize edge weights: -12202.73 --> -3027.366
#> optimize edge weights: -3027.366 --> -3027.366
#> optimize edge weights: -3027.366 --> -3027.366
plot(phangorn::midpoint(tr), cex = 0.7)
If we want to be rigorous, and of course we want, in view of these
results there is little, if anything, to conclude with respect to
orthology relationships between maize and arabidopsis GS proteins. So,
it’s time to switch to a more suitable approach. Firstly, we will use
the function orthG()
, which takes as argument the set of
species to be included in the analysis and return an orthology graph:
two nodes (two GS proteins) are connected if and only if they are
orthologs (also an adjacency matrix is provided if wished).
Let’s now include the rice in the set of species to be analyzed:
As you can appreciate, the enzyme Osa_GS1b_1 is orthologous to the maize Zm_GS1b_4. Furthermore, rice and maize share a few orthologs, but arabidopsis has no orthologs neither in rice nor maize.
A function slightly different from the one we have just used, which
you might find useful on occasion is orthP()
. Let’s see it
in action:
In red, the GS protein whose ortholgs we are searching for. In blue, the proteins detected as ortholgs in other plant species.
While Zm_GS1b_4 is orthologous of any GS1b from gymnosperms, among the angiosperms we only find three orthologs: Atr_GS1b_2, Sly_GS1b2 and Osa_GS1b_1. To decode which species are Atr, Sly and Osa, we proceed as follows:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.