上了几天的课，http://ibw2011.fmmu.edu.cn/schedule.htm
今天就上完了，只完成了project 1，想写gibbs sampling，但是没搞明白，汗。
这个纯属练习用，没啥实用价值。
Course Projects:
Project 1: Implementation of a simple gene finder
GOAL
Build a simple codon-usage based gene finder for finding genes in E.coli.
Procedure
Collect 100 gene sequences from the bacterium E. coli in the genbank (http://www.ncbi.nlm.nihh.gov).
Compute the codon usage table based on these genes (and the translated protein sequences from them);
Build a probabilistic model based on the codon usages;
Implement a random sequence model in which the nucleotide frequency is computed from the 100 E. coli genes.
For a given DNA sequence (and one selected reading frame), compare your model with a random sequence model;
Results that you should submit:Two FASTA files for the collected 100 genes and 100 translated protein sequences;
The printed codon usage table;
A program named ECgnfinder, running with the syntax as
ECgnfinder –i inputfileInputfile stands for the name of input file, which should contain one DNA sequence in FASTA file format; the program should be able to report an error message if the input file is in the wrong format.
The output should be printed to the standard output as (xxx stands for the likelihood)
ORF1: xxx
ORF2: xxx
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 | ########################################################### ## ## ## Dragonstar Course Project (IBW 2011) ## ## ## ## Project 1: Implementation of a simple gene finder ## ## Author Guangchuang Yu <guangchuangyu@gmail.com> ## ## Xi'an (2011-07-08) ## ## ## ########################################################### ##' .. content for \description{} (ECgnfinder: An R program to predict prokaryotic genes (e.g. E.coli) using 1st Markov Chain) .. ##' ##' .. content for \details{} .. ##' @title ECgnfinder ##' @param traningSequence : A FASTA file containing multiple sequences for training set ##' @param testingSequence : A FASTA file containing multiple sequences for testing ##' @param backgroundSequence : A FASTA file containing multiple sequences for building the background model ##' @return log likelihood ratio ##' @author Guangchuang Yu ECgnfinder <- function(trainingSequence, testingSequence, backgroundSequence, print=FALSE) { print ("reading training sequence file...") trainingFasta <- readFASTA(trainingSequence) trainSeq <- sapply(trainingFasta, function(i) i$seq) print("building Transition Probability Matrix...") TPM <- getTPM(trainSeq) print("building codon usage table...") codonUsage <- getCodonUsage(trainSeq) print("building NucleoTide table...") NtUsage <- getNtUsage(backgroundSequence) print("reading testing sequence file...") testing <- readFASTA(testingSequence) testingSeq <- sapply(testing, function(i) i$seq) x <- lapply(testingSeq, logLikelihood, transition=TPM, codonusage=codonUsage, background=NtUsage) names(x) <- paste("Sequence", seq_along(testingSeq), sep="") if (print) { x } return(x) } ##' .. content for \description{} (read FASTA file) .. ##' ##' .. content for \details{} .. ##' @title readFASTA ##' @param file: A FASTA file ##' @return a list of list which containing desc and seq. ##' @author Guangchuang Yu readFASTA <- function(file) { x=scan(file, what="", sep="\n") idx <- grep(">", x) fasta <- list() for (i in 1:length(idx)) { desc <- x[idx[i]] if (i == length(idx)) { stop <- length(x) } else { stop <- idx[i+1]-1 } seq.idx <- seq(idx[i]+1, stop) s <- x[seq.idx] s <- paste(s, collapse="") ss <- list(desc=desc, seq=s) fasta[i] <- list(ss) } return(fasta) } ##' Build a transtion probability matrix from the given Sequence set. ##' ##' @title getTPM ##' @param sequences : A vector of Sequences. ##' @return transition probability matrix ##' @author Guangchuang Yu getTPM <- function(sequences) { codonLength <- 3 codons <- getCodon() ## calculate a transition count matrix transition <- matrix(1, nrow=length(codons), ncol=length(codons)) ## pseudo count colnames(transition) <- codons rownames(transition) <- codons for (x in 1:length(sequences)) { s <- sequences[x] len <- nchar(s) ## drop the last alphabets which is too short (<codonLength) to form a codon. ## and -codonLength to eliminate out of boundary. len <- len - len %% codonLength - codonLength idx <- seq(1, len, codonLength) for (i in seq_along(idx)) { ## codonI--codonJ codonI <- substr(s,idx[i], idx[i]+codonLength-1) if (i != length(idx)) { codonJ <- substr(s,idx[i+1], idx[i+1]+codonLength-1) } if (codonI %in% codons & codonJ %in% codons) { transition[codonI, codonJ] <- transition[codonI, codonJ] +1 } else { warning("codon ", codonI, " or ", codonJ, " which does not defined was detected...") } } } ## Calculate Transition Probability Matrix.. rowsum <- rowSums(transition) TPM <- transition/rowsum return(TPM) } getCodon <- function() { codons <- c("TTT", "TTC", "TTA", "TTG", "CTT", "CTC", "CTA", "CTG", "ATT", "ATC", "ATA", "ATG", "GTT", "GTC", "GTA", "GTG", "TCT", "TCC", "TCA", "TCG", "CCT", "CCC", "CCA", "CCG", "ACT", "ACC", "ACA", "ACG", "GCT", "GCC", "GCA", "GCG", "TAT", "TAC", "TAA", "TAG", "CAT", "CAC", "CAA", "CAG", "AAT", "AAC", "AAA", "AAG", "GAT", "GAC", "GAA", "GAG", "TGT", "TGC", "TGA", "TGG", "CGT", "CGC", "CGA", "CGG", "AGT", "AGC", "AGA", "AGG", "GGT", "GGC", "GGA", "GGG" ) return(codons) } ##' .. content for \description{} (estimate codon usage table based on training sequences) .. ##' ##' .. content for \details{} .. ##' @title getCodonUsage ##' @param sequences : A vector of sequence set. ##' @return A data.frame containing two columns of codons and Freq (frequency probabilities) ##' @author Guangchuang Yu getCodonUsage <- function(sequences) { codons <- codons <- getCodon() codon.table <- data.frame">data.frame(codons=codons, Freq=rep(0, length(codons))) rownames(codon.table) = codons for (i in 1:length(sequences)) { codon <- codonCount(sequences[i]) rowname.idx <- as.character(codon[,1]) codon.table[rowname.idx,2] <- codon.table[rowname.idx,2] + codon[,2] } codon.table[,2] <- codon.table[,2]/sum(codon.table[,2]) #print(codon.table) #### print result. return(codon.table) } ##' .. content for \description{} (no empty lines) .. ##' ##' .. content for \details{} .. ##' @title codonCount ##' @param seqStr : A sequence string ##' @return A data.frame containing two columns of codons and their counts ##' @author Guangchuang Yu codonCount <- function(seqStr) { codonLength=3 len <- nchar(seqStr) ## drop the last alphabets which is too short (<codonLength) to form a codon. len <- len - len %% codonLength idx <- seq(1, len,codonLength) codons <- c() for ( i in 1:length(idx)) { codon <- substr(seqStr, idx[i], idx[i]+codonLength-1) codons <- c(codons, codon) } codons.df <- as.data.frame(table(codons)) return(codons.df) } ##' .. content for \description{} (reverse complementary sequence) .. ##' ##' .. content for \details{} .. ##' @title revcom ##' @param seqStr : A sequence string ##' @return A reverse complementary sequence ##' @author Guangchuang Yu revcom <- function(seqStr) { seqStr.str <- unlist(strsplit(seqStr, split="")) idxT <- which(seqStr.str=="T") idxG <- which(seqStr.str=="G") idxA <- which(seqStr.str=="A") idxC <- which(seqStr.str=="C") seqStr.str[idxT] <- "A" seqStr.str[idxA] <- "T" seqStr.str[idxG] <- "C" seqStr.str[idxC] <- "G" seqStr.rc <- rev(seqStr.str) seqStr.rc <- paste(seqStr.rc, collapse="") return(seqStr.rc) } ##' .. content for \description{} (calculate NT frequency probabilities of a given sequence set) .. ##' ##' .. content for \details{use to estimate the background/random NT usage} .. ##' @title getNtUsage ##' @param file : A FASTA file ##' @return A data.frame containing NTs and their corresponding frequency probabilities. ##' @author Guangchuang Yu getNtUsage <- function(file) { print("reading FASTA file") fasta <- readFASTA(file) sequences <- sapply(fasta, function(i) i$seq) x <- sapply(sequences, strsplit, split="") x <- unlist(x) NTprob <- as.data.frame(table(x)) NTprob[,2] <- NTprob[,2]/sum(NTprob[,2]) rownames(NTprob) <- NTprob[,1] return(NTprob) } ##' .. content for \description{} (calculate the log likelihood of the six possible reading frame of a given sequence) .. ##' ##' .. content for \details{} .. ##' @title logLikelihood ##' @param sequence : A sequence string ##' @param transition : Transition Probatility Matrix ##' @param codonusage : Codon Usage data.frame ##' @param background : NT Usage data.frame for background/random model ##' @return Log likelihood ##' @author Guangchuang Yu logLikelihood <- function(sequence, transition, codonusage, background) { codonLength <- 3 orf1 <- sequence orf2 <- substr(sequence, 2, nchar(sequence)) orf3 <- substr(sequence, 3, nchar(sequence)) seq.rc <- revcom(sequence) orf4 <- seq.rc orf5 <- substr(seq.rc, 2, nchar(seq.rc)) orf6 <- substr(seq.rc, 3, nchar(seq.rc)) ORF <- c(orf1, orf2, orf3, orf4, orf5, orf6) names(ORF) <- c("ORF1", "ORF2", "ORF3", "ORF4", "ORF5", "ORF6") result <- data.frame">data.frame(ORF=names(ORF), logLikelihood=rep(0,6)) for (j in seq_along(ORF)) { firstCodon <- substr(ORF[j], 1, 3) if (firstCodon %in% codonusage[,1]) { initProb <- log(codonusage[firstCodon,2]) } else { initProb <- log(1/64) } transitionCount <- matrix(0, nrow=nrow(transition), ncol=ncol(transition)) rownames(transitionCount) <- rownames(transition) colnames(transitionCount) <- colnames(transition) codons <- rownames(transition) len <- nchar(ORF[j]) len <- len - len %% codonLength - codonLength idx <- seq(1, len, codonLength) for (i in seq_along(idx)) { ## codonI--codonJ codonI <- substr(ORF[j],idx[i], idx[i]+codonLength-1) if (i != length(idx)) { codonJ <- substr(ORF[j],idx[i+1], idx[i+1]+codonLength-1) } if (codonI %in% codons & codonJ %in% codons) { transitionCount[codonI, codonJ] <- transitionCount[codonI, codonJ] +1 } else { warning("codon ", codonI, " or ", codonJ, " which does not defined was detected...") } } postTransition <- log(transition) * transitionCount posterior <- initProb + sum(postTransition) NTseq <- unlist(strsplit(ORF[j], split="")) orf.len <- nchar(ORF[j]) - nchar(ORF[j]) %% codonLength NTseq <- NTseq[1:orf.len] randomModel <- sum(log(background[NTseq,2])) likelihood <- posterior - randomModel result[j, 2] <- likelihood } return(result) } |