Finalizing Part B: alignment score matrix and traceback matrix initialization
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.Rhistory
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1,2)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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#g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.breaks <- g.hist$breaks # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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alpha.LM
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
|
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# Noah Schrick - 1492657
|
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
|
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# Noah Schrick - 1492657
|
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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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$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
|
||||
plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
# Lab 9 for the University of Tulsa's CS-6643 Bioinformatics Course
|
||||
# Pairwise Sequence Alignment with Dynamic Programming
|
||||
# Professor: Dr. McKinney, Fall 2022
|
||||
# Noah L. Schrick - 1492657
|
||||
## Set Working Directory to file directory - RStudio approach
|
||||
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
|
||||
#### Part A: Specifying the Input
|
||||
## Score Rules and Seqs
|
||||
x_str <- "ATAC" # side sequence
|
||||
y_str <- "GTGTAC" # top sequence
|
||||
match_score <- 3
|
||||
mismatch_score <- -1
|
||||
gap_penalty <- -4
|
||||
## Substitution Matrix
|
||||
dna.letters<-c("A","C","G","T")
|
||||
num.letters <- length(dna.letters)
|
||||
S<-data.frame(matrix(0,nrow=num.letters,ncol=num.letters)) # data frame
|
||||
rownames(S)<-dna.letters; colnames(S)<-dna.letters
|
||||
for (i in 1:4){
|
||||
for (j in 1:4){
|
||||
if(dna.letters[i]==dna.letters[j]){
|
||||
S[i,j]<- match_score
|
||||
}
|
||||
else{
|
||||
S[i,j]<- mismatch_score
|
||||
}
|
||||
}
|
||||
}
|
||||
len(S)
|
||||
size(S)
|
||||
nrows(S)
|
||||
nrow(S)
|
||||
col(S)
|
||||
S
|
||||
S[A][T]
|
||||
S[A,T]
|
||||
S
|
||||
S[A]
|
||||
S.A
|
||||
S.at(A)
|
||||
S[1.1]
|
||||
S[1,1]
|
||||
S["A", "T"]
|
||||
dna.letters("A")
|
||||
dna.letters
|
||||
?index()
|
||||
match("A", dna.letters)
|
||||
match("T", dna.letters)
|
||||
S[1,4]
|
||||
@ -31,3 +31,21 @@ for (i in 1:4){
|
||||
}
|
||||
}
|
||||
|
||||
#### Part B: Alignment Score Matrix (F) and Traceback Matrix (T)
|
||||
x <- unlist(strsplit(x_str, ""))
|
||||
y <- unlist(strsplit(y_str, ""))
|
||||
x.len <- length(x)
|
||||
y.len <- length(y)
|
||||
|
||||
Fmat<-matrix(0,nrow=x.len+1,ncol=y.len+1)
|
||||
Tmat<-Fmat # 0's to start
|
||||
|
||||
rownames(Fmat)<-c("-",x); colnames(Fmat)<-c("-",y)
|
||||
rownames(Tmat)<-c("-",x); colnames(Tmat)<-c("-",y)
|
||||
|
||||
# create first row and column
|
||||
Fmat[,1]<- seq(from=0,len=x.len+1,by=-abs(gap_penalty))
|
||||
Fmat[1,]<- seq(from=0,len=y.len+1,by=-abs(gap_penalty))
|
||||
Tmat[,1]<- rep(2,x.len+1) # 2 means align with a gap in the upper seq
|
||||
Tmat[1,]<- rep(3,y.len+1) # 3 means align with a gap in the side seq
|
||||
|
||||
|
||||
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Reference in New Issue
Block a user