KaKs_Calculator-3.0)

• Fork of the KaKs_Calculator-3.0 project https://ngdc.cncb.ac.cn/biocode/tools/BT000001.
• Calculating selective pressure on coding and non-coding sequences

Building KaKs_Calculator-3.0

git clone https://github.com/Chenglin20170390/KaKs_Calculator-3.0.git
cd KaKs_Calculator-3.0/bin;make

Run KaKs_Calculator-3.0

• for coding aligned sequences

./KaKs_Calculator-3.0/bin/KaKs -h

• for non-coding aligned sequences

./KaKs_Calculator-3.0/bin/KnKs -h

Example

./bin/KaKs -i examples/coding.axt -o test.kaks

Tutorial from the fasta file

input cds and pep sequence

• mafft alignment

sample=ID
mafft --auto  test.pep.fa   >  $sample.pep.mafft.fa

• check alignment from pep and convert alignment to axt format (https://github.com/liaochenlanruo/PAL2NAL/blob/master/pal2nal.pl)

pal2nal.pl -nogap -nomismatch $sample.pep.mafft.fa  cds.list.$sample.fa  -output clustal > cds.$sample.aln.clustal.fa
AXTConvertor cds.$sample.aln.clustal.fa cds.$sample.aln.clustal.aux

• Ka/Ks analysis

KaKs -i cds.$sample.aln.clustal.aux -o cds.$sample.aln.clustal.aux.kaka.txt

##format to R
python 22.1.kaks.matrix.py cds.$sample.aln.clustal.aux.kaka.txt $sample.kaka.txt

R code (example of repeat genes)

library(ggplot2)
library(pheatmap)

dat <-read.table('$sample.kaks.txt',header=T)  ## read input file

### heatmap figure
p<-pheatmap(dat, color = colorRampPalette(c( "white","blue"))(10),
            legend_labels = c("low", "median", "high"),
            border=T,display_numbers = F,
            clustering_distance_rows = "euclidean",
            cluster_cols=T,
            cluster_rows=T,
            clustering_method="ward.D")
p
ggsave(p, filename= "Kaks.heatmap.pdf" , width=30, height=30,  units =c( "cm" ),colormodel= "srgb" )  ##save pdf figure

References

Zhang Z. KaKs_Calculator 3.0: calculating selective pressure on coding and non-coding sequences[J]. Genomics, Proteomics and Bioinformatics, 2022, 20(3): 536-540. https://doi.org/10.1016/j.gpb.2021.12.002