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debug1.R
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# This source code file is a component of the larger INSPIIRED genomic analysis software package.
# Copyright (C) 2016 Frederic Bushman
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
libs <- c("plyr", "dplyr", "BiocParallel", "Biostrings", "GenomicAlignments" ,"hiAnnotator" ,"sonicLength", "GenomicRanges", "BiocGenerics", "ShortRead", "GenomicRanges", "igraph")
null <- suppressMessages(sapply(libs, library, character.only=TRUE))
source("~/intSiteCaller/hiReadsProcessor.R")
processPsl <- function(algns, from){
stopifnot(from == "R1" | from == "R2")
algns$from <- from
algns <- merge(algns, keys, by.x="qName", by.y=from)
algns <- subset(algns, strand=="+" | strand=="-")
needed <- c("tName", "tStart", "tEnd", "strand", "matches", "repMatches", "misMatches", "qStart", "qEnd", "qSize", "tBaseInsert", "from", "uPID")
algns <- algns[, needed]
algns <- dplyr::distinct(algns) %>%
dplyr::filter( 100*(matches+repMatches)/qSize >minPercentIdentity &
qStart<=maxAlignStart ) %>%
dplyr::select(uPID, tName, strand, tStart, tEnd, from)
return(algns)
}
load("keys.RData")
keys$uPID <- paste(keys$R2, keys$R1, sep=":")
psl.R1 <- read.psl(list.files(".", "R1.*.fa.psl.gz"),
bestScoring=F, removeFile=F)
psl.R1 <- processPsl(psl.R1, "R1")
strand <- c("+", "-")
psl.R1$Cstrand <- strand[3-match(psl.R1$strand, strand)]
psl.R1$strand <- psl.R1$Cstrand
psl.R2 <- read.psl(list.files(".", "R2.*.fa.psl.gz"),
bestScoring=F,
removeFile=F)
psl.R2 <- processPsl(psl.R2, "R2")
psl.Pair <- merge(psl.R2, psl.R1,
by=c("uPID", "tName", "strand"),
suffixes = c(".R2",".R1"))
psl.Pair <- dplyr::mutate(psl.Pair,
position=ifelse(strand=="+", tStart.R2, tEnd.R2),
breakpoint=ifelse(strand=="+", tEnd.R1, tStart.R1),
chr=tName) %>%
dplyr::filter( (strand=="+" & breakpoint-position>0 & breakpoint-position<2500) |
(strand=="-" & breakpoint-position<0 & breakpoint-position>-2500) ) %>%
dplyr::select(uPID, chr, strand, position, breakpoint) %>%
dplyr::arrange(uPID, chr, position, breakpoint) %>%
dplyr::distinct()
keysCount <- dplyr::group_by(keys, uPID) %>% dplyr::summarize(count=n())
psl.Pair <- merge(psl.Pair, keysCount, by="uPID")
trim_primerIDlinker_side_reads <- function(reads.l, linker, maxMisMatch=3) {
## note how maxMisMatch was set explained in trim_ltr_side_reads
stopifnot(class(reads.l) %in% "DNAStringSet")
stopifnot(!any(duplicated(names(reads.l))))
stopifnot(length(linker)==1)
pos.N <- unlist(gregexpr("N", linker))
len.N <- length(pos.N)
link1 <- substr(linker, 1, min(pos.N)-1)
link2 <- substr(linker, max(pos.N)+1, nchar(linker))
## allows gap, and del/ins count as 1 mismatch
submat1 <- nucleotideSubstitutionMatrix(match=1,
mismatch=0,
baseOnly=TRUE)
## search at the beginning for 1st part of linker
aln.1 <- pairwiseAlignment(pattern=subseq(reads.l, 1, 2+nchar(link1)),
subject=link1,
substitutionMatrix=submat1,
gapOpening = 0,
gapExtension = 1,
type="overlap")
aln.1.df <- PairwiseAlignmentsSingleSubject2DF(aln.1)
## search after 1st part of linker for the 2nd part of linker
aln.2 <- pairwiseAlignment(pattern=subseq(reads.l, max(pos.N)-1, nchar(linker)+1),
subject=link2,
substitutionMatrix=submat1,
gapOpening = 0,
gapExtension = 1,
type="overlap")
aln.2.df <- PairwiseAlignmentsSingleSubject2DF(aln.2, max(pos.N)-2)
goodIdx <- (aln.1.df$score >= nchar(link1)-maxMisMatch &
aln.2.df$score >= nchar(link2)-maxMisMatch)
primerID <- subseq(reads.l[goodIdx],
aln.1.df$end[goodIdx]+1,
aln.2.df$start[goodIdx]-1)
reads.l <- subseq(reads.l[goodIdx], aln.2.df$end[goodIdx]+1)
stopifnot(all(names(primerID)==names(reads.l)))
return(list("reads.l"=reads.l, "primerID"=primerID))
}
trim_Ltr_side_reads <- function(reads.p, primer, ltrbit, maxMisMatch=2) {
stopifnot(class(reads.p) %in% "DNAStringSet")
stopifnot(!any(duplicated(names(reads.p))))
stopifnot(length(primer)==1)
stopifnot(length(ltrbit)==1)
## allows gap, and del/ins count as 1 mismatch
submat1 <- nucleotideSubstitutionMatrix(match=1,
mismatch=0,
baseOnly=TRUE)
## p for primer
## search for primer from the beginning
aln.p <- pairwiseAlignment(pattern=subseq(reads.p, 1, 1+nchar(primer)),
subject=primer,
substitutionMatrix=submat1,
gapOpening = 0,
gapExtension = 1,
type="overlap")
aln.p.df <- PairwiseAlignmentsSingleSubject2DF(aln.p)
## l for ltrbit
## search for ltrbit fellowing primer
aln.l <- pairwiseAlignment(pattern=subseq(reads.p, nchar(primer), nchar(primer)+nchar(ltrbit)+1),
subject=ltrbit,
substitutionMatrix=submat1,
gapOpening = 0,
gapExtension = 1,
type="overlap")
aln.l.df <- PairwiseAlignmentsSingleSubject2DF(aln.l, shift=nchar(primer)-1)
##runSeq <- sapply(1:10000, function(i)
## paste(sample(c("A","C","G","T"), 8, replace=TRUE),
## collapse=""))
##runSeq.p <- pairwiseAlignment(pattern=runSeq,
## subject=primer,
## substitutionMatrix=submat1,
## gapOpening = 0,
## gapExtension = 1,
## type="overlap")
##runSeq.p.df <- PairwiseAlignmentsSingleSubject2DF(runSeq.p)
##table(runSeq.p.df$score)
## 1 2 3 4 5 6 7
##211 3338 4330 1751 344 25 1
##false positive rate 0.0025 and thus maxMisMatch=2, (1/4)^maxMisMatch*choose(7-maxMisMatch)
##false positive rate combining both primer and ltr is 0.0025*0.0025=6.25E-6
goodIdx <- (aln.p.df$score >= nchar(primer)-maxMisMatch &
aln.l.df$score >= nchar(ltrbit)-maxMisMatch)
reads.p <- subseq(reads.p[goodIdx], aln.l.df$end[goodIdx]+1)
return(reads.p)
}
PairwiseAlignmentsSingleSubject2DF <- function(PA, shift=0) {
stopifnot("PairwiseAlignmentsSingleSubject" %in% class(PA))
return(data.frame(
width=width(pattern(PA)),
score=score(PA),
mismatch=width(pattern(PA))-score(PA),
start=start(pattern(PA))+shift,
end=end(pattern(PA))+shift
))
}
trim_overreading <- function(reads, marker, maxMisMatch=3) {
reads=reads.p
marker=linker_common
maxMisMatch=3
reads=reads.l
marker=substr(largeLTRFrag,1,20)
maxMisMatch=3
stopifnot(class(reads) %in% "DNAStringSet")
stopifnot(!any(duplicated(names(reads))))
stopifnot(length(marker)==1)
submat1 <- nucleotideSubstitutionMatrix(match=1,
mismatch=0,
baseOnly=TRUE)
## allows gap, and del/ins count as 1 mismatch
tmp <- pairwiseAlignment(pattern=reads,
subject=marker,
substitutionMatrix=submat1,
gapOpening = 0,
gapExtension = 1,
type="overlap")
odf <- PairwiseAlignmentsSingleSubject2DF(tmp)
odf$isgood <- FALSE
## overlap in the middle or at right
odf$isgood <- with(odf, ifelse(mismatch<=maxMisMatch &
start>1,
TRUE, isgood))
## overlap at left
odf$isgood <- with(odf, ifelse(mismatch<=maxMisMatch &
start==1 &
width>=nchar(marker)-1,
TRUE, isgood))
## note with ovelrap alignmment, it only align with a minimum of 1/2 of the shorter one
odf$cut <- with(odf, ifelse(isgood, odf$start-1, nchar(reads)))
if( any(odf$cut < nchar(reads)) ) {
odf$cut <- nchar(reads)-nchar(marker)/2
odf$cut <- with(odf, ifelse(isgood, odf$start-1, cut))
}
reads <- subseq(reads, 1, odf$cut)
## debugging and machine learning code
## for linker_common, 16 bases, maxMisMatch=3 is the best
## mwidth <- as.integer(stringr::str_match(names(reads), "(\\d+):(\\d+)$")[,2])
## truecut <- ifelse( mwidth<nchar(reads), mwidth+1, nchar(reads) )
## ovldf <- data.frame(size=nchar(linker_common),
## width=width(pattern(tmp)),
## score=score(tmp),
## mismatch=width(pattern(tmp))-score(tmp),
## start=start(pattern(tmp)),
## end=end(pattern(tmp)),
## rlen=nchar(reads),
## mwidth=mwidth,
## cut=ifelse( mwidth<nchar(reads), mwidth+1, nchar(reads) ))
## ovldf$good <- ( abs(ovldf$cut-ovldf$start)<=2 )
## library(rpart)
## fit <- rpart(good ~ size + width + score + mismatch + start + end + rlen,
## data=ovldf,
## method="class")
}