Finalizing primary structure and DNA palindromes

.Rhistory

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+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$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+alpha.LM
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$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$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+# Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
+# PDB
+# Professor: Dr. McKinney, Fall 2022
+# Noah L. Schrick - 1492657
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$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$chainid=="B")
+# Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
+# PDB
+# Professor: Dr. McKinney, Fall 2022
+# Noah L. Schrick - 1492657
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$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)
+rgl.postscript("BC_chains.pdf","pdf",drawText=TRUE)
+## Visualize B-C and A Chains
+A_chain_pdb <- subset(x$atoms, x$atoms$chainid=="A")
+# remove water
+A_chain_pdb <- subset(A_chain_pdb, A_chain_pdb$resname!="HOH")
+# visualize complex complex
+BCA_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" |
+x$atoms$chainid=="C" | x$atoms$chainid=="A")
+BCA.color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)),rep("blue",natom(A_chain_pdb)))
+visualize(BCA_chains_pdb,col=BCA.color.vec)
+rgl.postscript("full_complex.pdf","pdf",drawText=TRUE)
+# get coordinates of C1' atoms of the C-chain DNA molecule
+C_chain_pdb$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 = "")
+C_chain_sequence_messy
+C_chain_sequence

.~lock.pdb_lab.docx#

@@ -1 +1 @@
-,noah,NovaArchSys,27.10.2022 13:09,file:///home/noah/.config/libreoffice/4;
+,noah,NovaArchSys,27.10.2022 15:21,file:///home/noah/.config/libreoffice/4;

Schrick-Noah_CS-6643_Lab7.R

@@ -40,3 +40,24 @@ BCA_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" |
 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)
+