################# Max-Log-Likelihood #################
n <- length(g.breaks.clean)
kmin <- g.breaks.clean[1]
alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
alpha.ML
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
alpha.LM
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
# Degree Distribution
# Professor: Dr. McKinney, Spring 2022
# Noah Schrick - 1492657
library(igraph)
library(igraphdata)
data(yeast)
g <- yeast
g.netname <- "Yeast"
################# Set up Work #################
g.vec <- degree(g)
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
" Network"))
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
"#006CD1", "#E66100", "#D35FB7"))
g.mean <- mean(g.vec)
g.seq <- 0:max(g.vec) # x-axis
################# Guessing Alpha #################
alpha.guess <- 1.5
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
################# Poisson #################
g.pois <- dpois(g.seq, g.mean, log=F)
lines(g.seq, g.pois, col="#006CD1", lty=2)
################# Linear model: Least-Squares Fit #################
g.breaks <- g.hist$breaks[-c(1)] # remove 0
g.probs <- g.hist$density[-1] # make lengths match
# Need to clean up probabilities that are 0
nz.probs.mask <- g.probs!=0
g.breaks.clean <- g.breaks[nz.probs.mask]
g.probs.clean <- g.probs[nz.probs.mask]
#plot(log(g.breaks.clean), log(g.probs.clean))
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
summary(g.fit)
alpha.LM <- coef(g.fit)[2]
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
################# Max-Log-Likelihood #################
n <- length(g.breaks.clean)
kmin <- g.breaks.clean[1]
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
alpha.ML
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
# Degree Distribution
# Professor: Dr. McKinney, Spring 2022
# Noah Schrick - 1492657
library(igraph)
library(igraphdata)
data(yeast)
g <- yeast
g.netname <- "Yeast"
################# Set up Work #################
g.vec <- degree(g)
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
" Network"))
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
"#006CD1", "#E66100", "#D35FB7"))
g.mean <- mean(g.vec)
g.seq <- 0:max(g.vec) # x-axis
################# Guessing Alpha #################
alpha.guess <- 1.5
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
################# Poisson #################
g.pois <- dpois(g.seq, g.mean, log=F)
lines(g.seq, g.pois, col="#006CD1", lty=2)
################# Linear model: Least-Squares Fit #################
g.breaks <- g.hist$breaks[-c(1)] # remove 0
g.probs <- g.hist$density[-1] # make lengths match
# Need to clean up probabilities that are 0
nz.probs.mask <- g.probs!=0
g.breaks.clean <- g.breaks[nz.probs.mask]
g.probs.clean <- g.probs[nz.probs.mask]
#plot(log(g.breaks.clean), log(g.probs.clean))
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
summary(g.fit)
alpha.LM <- coef(g.fit)[2]
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
################# Max-Log-Likelihood #################
n <- length(g.breaks.clean)
kmin <- g.breaks.clean[1]
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
alpha.ML
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
# Degree Distribution
# Professor: Dr. McKinney, Spring 2022
# Noah Schrick - 1492657
library(igraph)
library(igraphdata)
data(yeast)
g <- yeast
g.netname <- "Yeast"
################# Set up Work #################
g.vec <- degree(g)
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
" Network"))
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
"#006CD1", "#E66100", "#D35FB7"))
g.mean <- mean(g.vec)
g.seq <- 0:max(g.vec) # x-axis
################# Guessing Alpha #################
alpha.guess <- 1.5
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
################# Poisson #################
g.pois <- dpois(g.seq, g.mean, log=F)
lines(g.seq, g.pois, col="#006CD1", lty=2)
################# Linear model: Least-Squares Fit #################
g.breaks <- g.hist$breaks[-c(1)] # remove 0
g.probs <- g.hist$density[-1] # make lengths match
# Need to clean up probabilities that are 0
nz.probs.mask <- g.probs!=0
g.breaks.clean <- g.breaks[nz.probs.mask]
g.probs.clean <- g.probs[nz.probs.mask]
#plot(log(g.breaks.clean), log(g.probs.clean))
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
summary(g.fit)
alpha.LM <- coef(g.fit)[2]
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
################# Max-Log-Likelihood #################
n <- length(g.breaks.clean)
kmin <- g.breaks.clean[1]
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
alpha.ML
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
# Degree Distribution
# Professor: Dr. McKinney, Spring 2022
# Noah Schrick - 1492657
library(igraph)
library(igraphdata)
data(yeast)
g <- yeast
g.netname <- "Yeast"
################# Set up Work #################
g.vec <- degree(g)
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
" Network"))
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
"#006CD1", "#E66100", "#D35FB7"))
g.mean <- mean(g.vec)
g.seq <- 0:max(g.vec) # x-axis
################# Guessing Alpha #################
alpha.guess <- 1.5
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
################# Poisson #################
g.pois <- dpois(g.seq, g.mean, log=F)
lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
################# Linear model: Least-Squares Fit #################
g.breaks <- g.hist$breaks[-c(1)] # remove 0
g.probs <- g.hist$density[-1] # make lengths match
# Need to clean up probabilities that are 0
nz.probs.mask <- g.probs!=0
g.breaks.clean <- g.breaks[nz.probs.mask]
g.probs.clean <- g.probs[nz.probs.mask]
#plot(log(g.breaks.clean), log(g.probs.clean))
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
summary(g.fit)
alpha.LM <- coef(g.fit)[2]
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
################# Max-Log-Likelihood #################
n <- length(g.breaks.clean)
kmin <- g.breaks.clean[1]
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
alpha.ML
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
plot(yeast)
hist(yeast)
hist(g.vec)
g.pois
g.mean
alpha.LM
alpha.ML
degree(g)
sort(degree(g))
sort(degree(g),decreasing=FALSE)
sort(degree(g),decreasing=F)
sort(degree(g),decreasing=false)
sort(degree(g), decreasing = TRUE)
head(sort(degree(g), decreasing = TRUE))
stddev(degree(g))
sd(degree(g))
tail(sort(degree(g), decreasing = TRUE))
plot(log(g.breaks.clean), log(g.probs.clean))
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
# Degree Distribution
# Professor: Dr. McKinney, Spring 2022
# Noah Schrick - 1492657
library(igraph)
library(igraphdata)
data(yeast)
g <- yeast
g.netname <- "Yeast"
################# Set up Work #################
g.vec <- degree(g)
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
" Network"))
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
"#006CD1", "#E66100", "#D35FB7"))
g.mean <- mean(g.vec)
g.seq <- 0:max(g.vec) # x-axis
################# Guessing Alpha #################
alpha.guess <- 1.5
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
################# Poisson #################
g.pois <- dpois(g.seq, g.mean, log=F)
lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
################# Linear model: Least-Squares Fit #################
g.breaks <- g.hist$breaks[-c(1)] # remove 0
g.probs <- g.hist$density[-1] # make lengths match
# Need to clean up probabilities that are 0
nz.probs.mask <- g.probs!=0
g.breaks.clean <- g.breaks[nz.probs.mask]
g.probs.clean <- g.probs[nz.probs.mask]
plot(log(g.breaks.clean), log(g.probs.clean))
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
summary(g.fit)
alpha.LM <- coef(g.fit)[2]
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
################# Max-Log-Likelihood #################
n <- length(g.breaks.clean)
kmin <- g.breaks.clean[1]
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
alpha.ML
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
plot(log(g.breaks.clean), log(g.probs.clean))
g.breaks.clean <- g.breaks[nz.probs.mask]
g.probs.clean <- g.probs[nz.probs.mask]
plot(log(g.breaks.clean), log(g.probs.clean))
if (!require("BiocManager")) install.packages("BiocManager")
library(BiocManager)
if (!require("Biostrings")) BiocManager::install("Biostrings")
library(snpStats)
# Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
# PDB
# Professor: Dr. McKinney, Fall 2022
# Noah L. Schrick - 1492657
## Set Working Directory to file directory - RStudio approach
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#### Part A: Obtaining PDB - no supporting R Code
#### Part B: Visualize the 3D structure
## Install Rpdb and load the pdb
if (!require("Rpdb")) install.packages("Rpdb")
library(Rpdb)
x<-read.pdb("1TGH.pdb")
natom(x)
visualize(x,type="l")
## Visualize the B and C chains
B_chain_pdb <- subset(x$atoms, x$atoms$chainid=="B")
C_chain_pdb <- subset(x$atoms, x$atoms$chainid=="C")
# remove water:
C_chain_pdb <- subset(C_chain_pdb,C_chain_pdb$resname!="HOH")
# visualize chains B and C
BC_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" | 				                             x$atoms$chainid=="C")
color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)))
visualize(BC_chains_pdb,col=color.vec)
addResLab(BC_chains_pdb)
## Visualize B-C and A Chains
A_chain_pdb <- subset(x$atoms, x$atoms$chainid=="A")
# remove water
A_chain_pdb <- subset(A_chain_pdb, A_chain_pdb$resname!="HOH")
# visualize complex complex
BCA_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" |
x$atoms$chainid=="C" | x$atoms$chainid=="A")
BCA.color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)),rep("blue",natom(A_chain_pdb)))
visualize(BCA_chains_pdb,col=BCA.color.vec)
#### Part C: Primary structure and DNA Palindromes
# get coordinates of C1' atoms of the C-chain DNA molecule
C_chain_pdb$resname
C_chain_resids<-unique(C_chain_pdb$resid)
C_chain_C1prime <- subset(C_chain_pdb, C_chain_pdb$elename=="C1'")
# get chain C DNA sequence
C_chain_sequence_messy <- C_chain_C1prime$resname
C_chain_sequence <- paste(sapply(C_chain_sequence_messy,function(x) {unlist(strsplit(x,""))[2]}),collapse = "")
if (!require("BiocManager")) install.packages("BiocManager")
library(BiocManager)
if (!require("Biostrings")) BiocManager::install("Biostrings")
library(snpStats)
C_chain_DNAString <- DNAString(C_chain_sequence)
dna.pals <- findPalindromes(C_chain_DNAString, min.armlength=3,
max.looplength=5, max.mismatch = 0)
dna.pals
# Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
# PDB
# Professor: Dr. McKinney, Fall 2022
# Noah L. Schrick - 1492657
## Set Working Directory to file directory - RStudio approach
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#### Part A: Obtaining PDB - no supporting R Code
#### Part B: Visualize the 3D structure
## Install Rpdb and load the pdb
if (!require("Rpdb")) install.packages("Rpdb")
library(Rpdb)
x<-read.pdb("1TGH.pdb")
natom(x)
visualize(x,type="l")
## Visualize the B and C chains
B_chain_pdb <- subset(x$atoms, x$atoms$chainid=="B")
C_chain_pdb <- subset(x$atoms, x$atoms$chainid=="C")
# remove water:
C_chain_pdb <- subset(C_chain_pdb,C_chain_pdb$resname!="HOH")
# visualize chains B and C
BC_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" | 				                             x$atoms$chainid=="C")
color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)))
visualize(BC_chains_pdb,col=color.vec)
addResLab(BC_chains_pdb)
## Visualize B-C and A Chains
A_chain_pdb <- subset(x$atoms, x$atoms$chainid=="A")
# remove water
A_chain_pdb <- subset(A_chain_pdb, A_chain_pdb$resname!="HOH")
# visualize complex complex
BCA_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" |
x$atoms$chainid=="C" | x$atoms$chainid=="A")
BCA.color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)),rep("blue",natom(A_chain_pdb)))
visualize(BCA_chains_pdb,col=BCA.color.vec)
#### Part C: Primary structure and DNA Palindromes
# get coordinates of C1' atoms of the C-chain DNA molecule
C_chain_pdb$resname
C_chain_resids<-unique(C_chain_pdb$resid)
C_chain_C1prime <- subset(C_chain_pdb, C_chain_pdb$elename=="C1'")
# get chain C DNA sequence
C_chain_sequence_messy <- C_chain_C1prime$resname
C_chain_sequence <- paste(sapply(C_chain_sequence_messy,function(x) {unlist(strsplit(x,""))[2]}),collapse = "")
## Find palindromes
if (!require("BiocManager")) install.packages("BiocManager")
library(BiocManager)
if (!require("Biostrings")) BiocManager::install("Biostrings")
library(snpStats)
C_chain_DNAString <- DNAString(C_chain_sequence)
dna.pals <- findPalindromes(C_chain_DNAString, min.armlength=3,
max.looplength=5, max.mismatch = 0)
visualize(x,type="l")
#### Part D: Find the binding site
## Get size of C chain coords
dim(C_chain_C1prime_coords)
#### Part D: Find the binding site
## Get Coordinates
C_chain_C1prime_coords <- coords(C_chain_C1prime)
dim(C_chain_C1prime_coords)
?coords
rownames(C_chain_C1prime_coords)
colnames(C_chain_C1prime_coords)
C_chain_C1prime_coords[1][1]
C_chain_C1prime
# get coordinates of CA atoms of the A-chain protein molecule
A_chain_sequence_3letter <- A_chain_pdb$resname
A_chain_resids<-unique(A_chain_pdb$resid)
A_chain_CA <- subset(A_chain_pdb, A_chain_pdb$elename=="CA")
A_chain_CA_coords <- coords(A_chain_CA)
dim(A_chain_CA_coords)
outer(1:nrow(chain1),
1:nrow(chain2),
Vectorize(function(i,j) {
dist(rbind(chain1[i,],chain2[j,]))
}
))}
outer(1:nrow(chain1),
1:nrow(chain2),
Vectorize(function(i,j) {
dist(rbind(chain1[i,],chain2[j,]))
}))}
outer(1:nrow(chain1),
1:nrow(chain2),
Vectorize(function(i,j) {
dist(rbind(chain1[i,],chain2[j,]))
}))}
dist(rbind(chain1[i,],chain2[j,]))}))}
outer(1:nrow(chain1),
1:nrow(chain2), Vectorize(function(i,j) {dist(rbind(chain1[i,],chain2[j,]))}))}
# create distance matrix between chains
pair.dist <- function(chain1,chain2){outer(1:nrow(chain1),1:nrow(chain2),Vectorize(function(i,j) {dist(rbind(chain1[i,],chain2[j,]))}))}
prot2DNAdistMat <- pair.dist(A_chain_CA_coords,C_chain_C1prime_coords)
dim(prot2DNAdistMat)
rownames(prot2DNAdistMat)
prot2DNAdistMat[1]
prot2DNAdistMat
vectorize
Vectorize
dim(A_chain_CA_coords)
colnames(A_chain_CA_coords)
rownames(A_chain_CA_coords)
A_chain_CA_coords[1]
A_chain_CA
nrow(A_chain_CA_coords)
# ij location of min in current matrix (2-elt vector)
min_dist <- min(prot2DNAdistMat)
min_dist
min_ij <- which(prot2DNAdistMat == min_dist, arr.ind = TRUE)
min_ij
A_chain_sequence_3letter[min_ij[1]]  # closest A-chain residue
strsplit(C_chain_sequence,"")[[1]][min_ij[2]]  # closest C-chain residue
?visualize
# color binding residues
CA_chains_pdb <- subset(x$atoms, x$atoms$chainid == "C" | x$atoms$chainid == "A")
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("blue", natom(A_chain_pdb)))
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "purple"
visualize(CA_chains_pdb, col=CA.color.vec)
# color binding residues
CA_chains_pdb <- subset(x$atoms, x$atoms$chainid == "C" | x$atoms$chainid == "A")
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("blue", natom(A_chain_pdb)))
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "red"
visualize(CA_chains_pdb, col=CA.color.vec)
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("teal", natom(A_chain_pdb)))
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "red"
visualize(CA_chains_pdb, col=CA.color.vec)
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("lightblue", natom(A_chain_pdb)))
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "red"
visualize(CA_chains_pdb, col=CA.color.vec)
rgl.postscript("binding_site.pdf", "pdf", drawText=TRUE)
#### Part E: Palindromes in other organisms
## Load associated supportive libraries
if (!require("seqinr")) install.packages("seqinr")
library(seqinr)
## Load in the fasta file as a string
myfasta <- read.fasta(file="sequence.fasta", as.string= TRUE)
myfasta
## Load in the fasta file as a string
myfasta <- read.fasta(file="sequence.fasta", as.string= TRUE)[[1]][1]
myfasta
fasta_DNAString <- DNAString(myfasta)
dna.pals <- findPalindromes(fasta_DNAString, min.armlength=5)
fasta.dna.pals <- findPalindromes(fasta_DNAString, min.armlength=5)
fasta.dna.pals
rc
BiocManager::install("insect")
BiocManager::remove("insect")
BiocManager::uninstall("insect")
BiocManager::delete("insect")
remove.packages("insect")
## Reverse and complement with the "rc" function from insect
fasta.dna.pals.rev <- rev(fasta.dna.pals)
dnachars <- strsplit("ACGT", split = "")[[1]]
comps <- strsplit("TGCA", split = "")[[1]]
fasta.dna.pals.rev
fasta.dna.pals.rev[1]
fasta.dna.pals.rev[4
]
fasta.dna.pals.rev[1][4]
fasta.dna.pals.rev[1][1]
fasta.dna.pals.rev$views
class(fasta.dna.pals.rev)
?Biostrings
toString(fasta.dna.pals.rev)
## Reverse and complement with the "rc" function from insect
fasta.dna.pals.rev <- rev(toString(fasta.dna.pals))
dnachars <- strsplit("ACGT", split = "")[[1]]
comps <- strsplit("TGCA", split = "")[[1]]
fasta.dna.pals.rev
fasta.dna.pals
toString(fasta.dna.pals)
toString(fasta.dna.pals)
## Reverse and complement with the "rc" function from insect
fasta.dna.pals.rev <- rev(toString(fasta.dna.pals))
fasta.dna.pals.rev
## Reverse and complement with the "rc" function from insect
rev(strsplit(toString(fasta.dna.pals), split = "")[[1]])
paste(rev(toString(fasta.dna.pals)),collapse='')
?rev
## Reverse and complement with the "rc" function from insect
paste(rev(strsplit(toString(fasta.dna.pals), split = "")[[1]]), collapse='')
fasta.dna.pals.rev
dnachars <- strsplit("ACGT", split = "")[[1]]
comps <- strsplit("TGCA", split = "")[[1]]
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dchars]
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
fasta.dna.pals.rc
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
fasta.dna.pals.rc
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
fasta.dna.pals.rc
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
fasta.dna.pals.rc
## Reverse and complement
#Convert pal to str, split on each char, rev, then join back as a single str
fasta.dna.pals.rev <- rev(strsplit(toString(fasta.dna.pals),
split = "")[[1]])
fasta.dna.pals.rev
dnachars <- strsplit("ACGT", split = "")[[1]]
comps <- strsplit("TGCA", split = "")[[1]]
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
fasta.dna.pals.rev
fasta.dna.pals.rc
# From the rc function in the insect package. Modified for these variables
dnachars <- strsplit("ACGT", split = "")[[1]]
comps <- strsplit("TGCA", split = "")[[1]]
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
fasta.dna.pals.rc
toString(fasta.dna.pals)
fasta.dna.pals.rev
fasta.dna.pals.rc
fasta.dna.pals == fasta.dna.pals.rc
toString(fasta.dna.pals) == fasta.dna.pals.rc