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==ADDA algorithm== | ==ADDA algorithm== | ||
===How to get residue correlation matrix=== | ===How to get residue correlation matrix=== | ||
- | *sample data: [[file:sample.tab.txt|sample.tab]] | + | *sample data: [[file:sample.tab.txt|sample.tab.txt]] |
<pre style col="blue"> | <pre style col="blue"> |
Revision as of 13:06, 14 February 2011
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2011
- Reference: PDF download
Error fetching PMID 12706730:
- Error fetching PMID 12706730:
Schedule
ADDA algorithm
How to get residue correlation matrix
- sample data: File:Sample.tab.txt
# set working directory setwd("/Users/igchoi/Downloads/") # read blast table tmp = read.table("sample.tab",sep="\t") # check dimension of table dim(tmp) # change simpler GI number (skip this if you don't understand) tmp[,1] = sapply(as.vector(tmp[,1]), function(x) { y = strsplit(x,"\\|"); return(unlist(y)[2]) }) tmp[,2] = sapply(as.vector(tmp[,2]), function(x) { y = strsplit(x,"\\|"); return(unlist(y)[2]) }) # check the result tmp[1:5,] # aligned region: V7 V8, V9 V10 # query length = 590 residues count.que = matrix(0,nrow(tmp),590) # <- build empty 'aligned' matrix for(i in 1:nrow(tmp)) { res = unlist(tmp[i,7:8]) print(res) count.que[i,c(res[1]:res[2])] = 1 } # check aligned region (blue colored regions are aligned with other proteins - central region) image(t(count.que),col=c("white","blue")) # count number of neighbors occurring both position i and j (using 'aligned' matrix) corr = matrix(0,590,590) # <- set empty correlation matrix (590 x 590) for (i in 1:ncol(count.que)) { for (j in i:ncol(count.que)) { print(paste(i,j)) chk = count.que[,i]+count.que[,j] cnt = length(which(chk==2)) corr[i,j] = cnt } } # range of number of neighbors (among 52 hits) range(corr) # check highly correlated region in the correlation matrix image(corr,col=topo.colors(10))