On 07/13/2012 11:17 AM, Murli wrote: > Hi Martin, > Thanks for your suggestions. I have tried for some time to parse the > novoAlign output using read.table, with no success yet. I have just > started using shortRead and have not worked with GenomicRanges yet. I > have created two files of the paired end data containing ~500 lines > of data that may be downloaded from > http://bioinformatics.iusb.edu/seqSubset.tar . Would it be possible > for your take a look at the format and guide me a little here? I > would greatly appreciate it. > Cheers../Murli Really I think the most straight-forward approach is to get novoAlign to generate SAM or BAM output; if SAM then use Rsamtools::asBam to convert to BAM. You can then read the data in with Rsamtools::scanBam, GenomicRanges::readGappedAlignments, and in devel GenomicRanges::readGappedAlignmentPairs. Otherwise you'll spend a lot of time working through the appropriate interpretation of the novoAlign fields. I'm attaching a script that might at least provide some inspiration if you really want to parse the current text format; there are some significant problems (see the FIXME's, for example) and some aspects of your files look weird, e.g., the second column contains 'S' indicating single-end alignments, but the intention seems to have been to align paired end data? Martin > > > > On Thu, Jul 12, 2012 at 2:58 PM, Martin Morgan <mtmorgan at="" fhcrc.org=""> wrote: >> On 07/12/2012 11:42 AM, Murli Nair [guest] wrote: >>> >>> >>> Hi, >>> >>> I am trying to read the alignments generated using NovoAlign. The format I >>> have the data is Paired End Native Report >>> Format(http://computing.bio.cam.ac.uk/local/doc/NovoCraftV2.06.pdf). >>> What is the most efficient way to read this data into ShortRead? Since it >>> is paired end data I have two files corresponding to the two sides. >>> I tried without success using the different formats using readAligned(). I >>> also read an earlier posting about it which suggests to convert it to SAM >>> format. >>> I would appreciate your suggestions. >> >> >> From the document you reference >> >> Three output formats are provided. >> >> 1. Native >> >> 2. Extended Native >> >> 3. Pairwise >> >> 4. SAM >> >> If Paired End Native Report Format is 1 or 2 with a single record per line >> then I believe the only support for input would be as tab-delimited files >> (read.table and friends; these are flexible and could easily be used to >> iterate through a large file in a memory efficient way); you would then use >> an appropriate constructor, e.g., GenomicRanges::GappedAlignmentPairs, to >> create an object that you could manipulate. Format 3 looks challenging to >> parse. >> >> Generally, for aligned reads aim for BAM files, which is output format 4 >> followed by using Rsamtools or other with asBam, sortBam, indexBam to create >> a sorted bam file and index. use GenomicRanges::readGappedAlignmentPairs for >> many paired-end tasks. >> >> It might help to think a little further ahead about what you want to do, >> e.g., GenomicRanges::summarizeOverlaps would be useful in RNAseq >> differential expression to count reads in regions of interest, and would >> need bam files but would manage data input for you. >> >> Martin >> >>> Cheers../Murli >>> >>> >>> -- output of sessionInfo(): >>> >>> R version 2.15.0 (2012-03-30) >>> Platform: x86_64-unknown-linux-gnu (64-bit) >>> >>> locale: >>> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C >>> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 >>> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 >>> [7] LC_PAPER=C LC_NAME=C >>> [9] LC_ADDRESS=C LC_TELEPHONE=C >>> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C >>> >>> attached base packages: >>> [1] stats graphics grDevices utils datasets methods base >>> >>> other attached packages: >>> [1] ShortRead_1.14.4 latticeExtra_0.6-19 RColorBrewer_1.0-5 >>> [4] Rsamtools_1.8.5 lattice_0.20-6 Biostrings_2.24.1 >>> [7] GenomicRanges_1.8.7 IRanges_1.14.4 BiocGenerics_0.2.0 >>> >>> loaded via a namespace (and not attached): >>> [1] Biobase_2.16.0 bitops_1.0-4.1 grid_2.15.0 hwriter_1.3 >>> stats4_2.15.0 >>> [6] tools_2.15.0 zlibbioc_1.2.0 >>> >>> -- >>> Sent via the guest posting facility at bioconductor.org. >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> >> -- >> Computational Biology / Fred Hutchinson Cancer Research Center >> 1100 Fairview Ave. N. >> PO Box 19024 Seattle, WA 98109 >> >> Location: Arnold Building M1 B861 >> Phone: (206) 667-2793 >> >> -- Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793 -------------- next part -------------- library(GenomicRanges) .novoAlign_input <- function(fl, filt, gal, ...) { ## input col.names <- c("qname", "type", "qseq", "qual", "status", "maps", "mapq", "rname", "pos", "strand", "mrnm", "mpos", "mstrand", "mismatches") df <- read.table(fl, sep="\t", fill=TRUE, col.names=col.names, stringsAsFactors=FALSE, ...) ## filter and coerce to GappedAlignments object gal(filt(df)) } .novoAlign_filter <- function(df, ...) { ## keep only uniquely aligning pairs on the forward or reverse strand keep <- (df$status == "U") & (df$strand %in% c("F", "R")) df[keep,] } .novoAlign_GappedAlignments <- function(df, ...) { ## coerce some information to GappedAlignments object with(df, { GappedAlignments(seqnames=rname, pos=pos, ## FIXME: not correct for indels cigar=paste0(nchar(qseq), "M"), strand=strand(c(F="+", R="-")[strand]), names=qname, qseq=DNAStringSet(qseq), qual=PhredQuality(qual)) }) } .novoAlign_GappedAlignmentPairs <- function(first, last, ...) { ## create GappedAlignmentPairs nms <- union(as.character(seqnames(first)), as.character(seqnames(last))) seqlevels(first) <- seqlevels(last) <- nms isProperPair <- rep(TRUE, length(first)) GappedAlignmentPairs(first, last, isProperPair) } fls <- dir(pattern="novoOutput$") pairs <- lapply(fls, .novoAlign_input, .novoAlign_filter, .novoAlign_GappedAlignments) ## FIXME: is this matching correct? o <- match(names(pairs[[1]]), names(pairs[[2]])) galp <- .novoAlign_GappedAlignmentPairs(pairs[[1]][!is.na(o)], pairs[[2]][o[!is.na(o)]])