diff --git a/.Rhistory b/.Rhistory
index 8dc0731..0b49493 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,218 +1,3 @@
-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)
-################# 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$breaks[-c(1,2)] # remove 0
-g.probs <- g.hist$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.breaks[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)
-################# 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$breaks[-c(1,2,3)] # remove 0
-g.probs <- g.hist$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.breaks[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)
-################# 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.breaks[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)
-################# 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$breaks[-c(1)] # remove 0
-g.breaks <- g.hist$breaks # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.breaks[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
 ################# Max-Log-Likelihood #################
 n <- length(g.breaks.clean)
 kmin <- g.breaks.clean[1]
@@ -452,20 +237,10 @@ plot(log(g.breaks.clean), log(g.probs.clean))
 g.breaks.clean <- g.breaks[nz.probs.mask]
 g.probs.clean <- g.probs[nz.probs.mask]
 plot(log(g.breaks.clean), log(g.probs.clean))
-# Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
-# PDB
-# Professor: Dr. McKinney, Fall 2022
-# Noah L. Schrick - 1492657
-## Set Working Directory to file directory - RStudio approach
-setwd(dirname(rstudioapi::getActiveDocumentContext()$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")
-## Visualize the B and C chains
-B_chain_pdb <- subset(x$atoms, x$atoms$chainid=="B")
+if (!require("BiocManager")) install.packages("BiocManager")
+library(BiocManager)
+if (!require("Biostrings")) BiocManager::install("Biostrings")
+library(snpStats)
 # Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
 # PDB
 # Professor: Dr. McKinney, Fall 2022
@@ -490,7 +265,6 @@ BC_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" |
 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)
-rgl.postscript("BC_chains.pdf","pdf",drawText=TRUE)
 ## Visualize B-C and A Chains
 A_chain_pdb <- subset(x$atoms, x$atoms$chainid=="A")
 # remove water
@@ -500,7 +274,7 @@ BCA_chains_pdb <- subset(x$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)
-rgl.postscript("full_complex.pdf","pdf",drawText=TRUE)
+#### 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)
@@ -508,5 +282,231 @@ 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 = "")
-C_chain_sequence_messy
-C_chain_sequence
+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
diff --git a/.~lock.pdb_lab.docx# b/.~lock.pdb_lab.docx#
deleted file mode 100644
index 184ce00..0000000
--- a/.~lock.pdb_lab.docx#
+++ /dev/null
@@ -1 +0,0 @@
-,noah,NovaArchSys,27.10.2022 18:54,file:///home/noah/.config/libreoffice/4;
\ No newline at end of file
diff --git a/Schrick-Noah_CS-6643_Lab-7_Report.pdf b/Schrick-Noah_CS-6643_Lab-7_Report.pdf
new file mode 100644
index 0000000..972569b
Binary files /dev/null and b/Schrick-Noah_CS-6643_Lab-7_Report.pdf differ
diff --git a/Schrick-Noah_CS-6643_Lab7.R b/Schrick-Noah_CS-6643_Lab7.R
index 925a834..6e4451b 100644
--- a/Schrick-Noah_CS-6643_Lab7.R
+++ b/Schrick-Noah_CS-6643_Lab7.R
@@ -93,4 +93,29 @@ CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("lightblue", natom(A_cha
 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)
\ No newline at end of file
+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)[[1]][1]
+fasta_DNAString <- DNAString(myfasta)
+fasta.dna.pals <- findPalindromes(fasta_DNAString, min.armlength=5)
+toString(fasta.dna.pals)
+
+## Reverse and complement 
+# Convert pal to str, split on each char, rev. Leave broken up for %in%
+fasta.dna.pals.rev <- rev(strsplit(toString(fasta.dna.pals), 
+                                         split = "")[[1]])
+fasta.dna.pals.rev
+# 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.rc
diff --git a/pdb_lab.docx b/pdb_lab.docx
index faf4869..dfac8f8 100644
Binary files a/pdb_lab.docx and b/pdb_lab.docx differ