| Previous changeset 0:582b7bd4ae4c (2019-01-24) Next changeset 2:5ade5cf200da (2019-06-12) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/crosscontamination_barcode_filter commit f967afe562781e5c8ed4e24e9d1e0bc3ebb29401 |
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modified:
crosscontamination_barcode_filter.xml scripts/batch_plotting_functions.R scripts/config_assertions.R scripts/contamination_plot.R scripts/crosscontamination_filter.R scripts/reorder_matrix_headers.R |
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added:
scripts/generateTestData.R test-data/out.pdf test-data/out.table test-data/test.matrix |
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removed:
test-data/out3.subtable test-data/test.pdf test-data/test.table |
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| diff -r 582b7bd4ae4c -r 253c9448f524 crosscontamination_barcode_filter.xml --- a/crosscontamination_barcode_filter.xml Thu Jan 24 09:52:58 2019 -0500 +++ b/crosscontamination_barcode_filter.xml Mon Jun 03 14:55:24 2019 -0400 |
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| @@ -1,15 +1,7 @@ <tool id="crosscontamination_barcode_filter" name="Cross-contamination Barcode Filter" version="@VERSION@"> <description>for use in plate-based barcoded analyses</description> <macros> - <token name="@VERSION@">0.1</token> - <macro name="assert_conts" > - <assert_contents> - <has_text text="/CreationDate" /> - <has_text text="/Producer" /> - <has_line line="startxref" /> - <has_line line="%%EOF" /> - </assert_contents> - </macro> + <token name="@VERSION@">0.2</token> <macro name="sanitize_batch"> <sanitizer invalid_char=""> <valid initial="string.digits"> @@ -35,10 +27,11 @@ <add value=")"/> <!-- right parenthesis --> </valid> </sanitizer> - </macro> + </macro> </macros> <requirements> - <requirement type="package" version="2.2.1" >r-ggplot2</requirement> + <requirement type="package" version="3.1.1">r-ggplot2</requirement> + <requirement type="package" version="1.12.2">r-data.table </requirement> </requirements> <version_command><![CDATA[ Rscript '$__tool_directory__/scripts/crosscontamination_filter.R' | head -1 | cut -d' ' -f 2 @@ -61,12 +54,12 @@ spec = list( barcodes = '$input_barcodes', format = list( - "1-96" = c(1,3,5,7), - "97-192" = c(2,4,6,8) + "1-96" = c(1,3), + "97-192" = c(2,4) ), plates = list( - "1" = c(1,2,3,4), - "2" = c(5,6,7,8) + "1" = c(1,2), + "2" = c(3,4) ) ) #elif str($inbuilt_spec.select_use) == "custom": @@ -74,7 +67,7 @@ barcodes = '$input_barcodes', format = list( #for $i, $s in enumerate($inbuilt_spec.barcode_format) - "${s.range_start}-${s.range_end}" = c( ${s.batches} ) + "${s.range_start}-${s.range_end}" = c( ${s.batches} ) #if $i < len(list($inbuilt_spec.barcode_format)) - 1 , #end if @@ -123,7 +116,7 @@ </when> </conditional> <section name="advanced" expanded="false" title="RegEx Parameters" > - <param name="regex_extract" type="text" value=".*P(\\d)_(\\d)_([ACTG]+)" label="RegEx to extract Plate, Batch, and Barcodes from headers" > + <param name="regex_extract" type="text" value=".*P(\\d)_B(\\d)_([ACTG]+)" label="RegEx to extract Plate, Batch, and Barcodes from headers" > <expand macro="sanitize_regex" /> </param> <param name="regex_display" type="text" value="P\\1_B\\2_\\3" label="RegEx to replace Plate, Batch, and Barcodes from headers" > @@ -137,44 +130,40 @@ </outputs> <tests> <test><!-- Inbuilt MPI --> - <param name="input_table" value="out3.subtable" /> + <param name="input_table" value="test.matrix" /> <param name="input_barcodes" value="celseq_barcodes.192.raw" /> <conditional name="inbuilt_spec" > <param name="select_use" value="mpi_sagar" /> </conditional> - <output name="out_plots" > - <expand macro="assert_conts" /> - </output> - <output name="out_table" value="test.table" /> + <output name="out_plots" value="out.pdf" compare="sim_size" /> + <output name="out_table" value="out.table" /> </test> <test><!-- Plate and Lane test --> - <param name="input_table" value="out3.subtable" /> + <param name="input_table" value="test.matrix" /> <param name="input_barcodes" value="celseq_barcodes.192.raw" /> <conditional name="inbuilt_spec" > <param name="select_use" value="custom" /> <repeat name="barcode_format" > <param name="range_start" value="1"/> <param name="range_end" value="96" /> - <param name="batches" value="1,3,5,7" /> + <param name="batches" value="1,3" /> </repeat> <repeat name="barcode_format" > <param name="range_start" value="97"/> <param name="range_end" value="192" /> - <param name="batches" value="2,4,6,8" /> + <param name="batches" value="2,4" /> </repeat> <repeat name="plate_format" > <param name="plate" value="1" /> - <param name="batches" value="1,2,3,4" /> + <param name="batches" value="1,2" /> </repeat> <repeat name="plate_format" > <param name="plate" value="2" /> - <param name="batches" value="5,6,7,8" /> + <param name="batches" value="3,4" /> </repeat> </conditional> - <output name="out_plots" > - <expand macro="assert_conts" /> - </output> - <output name="out_table" value="test.table" /> + <output name="out_plots" value="out.pdf" compare="sim_size" /> + <output name="out_table" value="out.table" /> </test> </tests> <help><![CDATA[ @@ -194,10 +183,10 @@ Consider the following experimental setup, with a list of 100 possible barcodes, used over 3 sequencing plates, with each plate containing 4 unique batches, and each plate using a specific subset of the 100 barcodes. -:: +:: Barcodes - + 1 - 10 | AAA AAC AAT AAG ACA AGA ATA CAC GAG TAT 11 - 20 | CCC CCA CCT CCG CTC CGC TCT GCG TCT CGT . @@ -209,7 +198,7 @@ Plate 1 +-------+-------+-------+-------+ | B1 | B2 | B3 | B4 | +-------+-------+-------+-------+ - 1-50 51-100 51-100 1-50 + 1-50 51-100 51-100 1-50 Plate 2 +-------+-------+-------+-------+ | B5 | B6 | B7 | B8 | @@ -219,7 +208,7 @@ Plate 3 +-------+-------+-------+-------+ | B9 | B10 | B11 | B12 | +-------+-------+-------+-------+ - 1-40 41-80 1-40 41-80 + 1-40 41-80 1-40 41-80 **** @@ -244,4 +233,3 @@ <citation type="doi">10.1007/978-1-4939-7768-0_15</citation> </citations> </tool> - |
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| diff -r 582b7bd4ae4c -r 253c9448f524 scripts/batch_plotting_functions.R --- a/scripts/batch_plotting_functions.R Thu Jan 24 09:52:58 2019 -0500 +++ b/scripts/batch_plotting_functions.R Mon Jun 03 14:55:24 2019 -0400 |
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| @@ -1,95 +1,119 @@ #!/usr/bin/env R +suppressPackageStartupMessages(require(data.table)) + ## ## Batch Plotting Functions ## -calculatePlateIndexes <- function(plate.form, full.barcode.size, num.plates){ - #' Determine plotting positions of plate lines (under false model) - #' - #' Assumes all plates are the same size and span the full range of the - #' barcodes. - #' - #' @param plate.form list of vectors mapping plates to batches - #' @param full.barcode.size size of the complete barcodes list - #' @param num.plates, number of plates - #' @return sequence of discrete plate-boundary positions - batches.per.plate <- length(plate.form[[1]]) - plate.size <- batches.per.plate * full.barcode.size - - return(seq(0, num.plates * plate.size, plate.size)) -} - - -calculateFullBarcodeIndexes <- function(num.batches, full.barcode.size){ - #' Determines plotting position of batch lines (under false model) - #' - #' For N batches and a list of actually detected barcodes in the header, - #' generates where the blue lines should be - #' - #' @param num.batches number of batches in experiment - #' @param full.barcode.size size of all barcodes - #' @return sequence of discrete batch positions - bsize <- full.barcode.size - return(seq(0, num.batches * bsize, bsize)) -} - - - -calculateRealBarcodeIndexes <- function(barcode.form, full.barcode.size){ - #' Determine plotting position of the true batch lines (under true model) - #' - #' For N batches a list of actually USED barcodes as given by the spec, - #' generates where the green lines should be +calculateBarcodePositions <- function(barcode.form, full.barcode.size){ + #' Determine x-axis positions of all batches under the context of + #' unfiltered barcodes (full set), filtered (real set), dividing line + #' (the position of real set in the full set). #' #' @param barcode.form list of barcode formats and the batches they map to #' @param full.barcode.size size of all barcodes - #' @return list of useful vectors: true batch positions using whole matrix, - #' true batch positions using the filtered matrix which contains - #' only real barcodes, and a list of batches and their respective - #' sizes. - batches <- c() + #' @return dataframe of batch information: sizes, unfiltered, filtered, + #' and dividing line. + sizes <- list(B0=0) + res <- sapply(names(barcode.form), function(key){ rng <- as.integer(unlist(strsplit(key, '-'))) size.of.range <- length(seq(rng[1],rng[2])) sub.batches <- barcode.form[[key]] # 1,3,5,7 or 2,4,6,8 res2 <- lapply(sub.batches, function(bat){ - batches[[bat]] <<- size.of.range + sizes[[paste("B",bat, sep="")]] <<- size.of.range }) }) + ## We now have sizes per batch, in order of batch ## Need to place these at positions after each full barcode size - positions <- c() - real_positions <- c(0) + + ## Below we have "positions" which has the END positions of each batch under + ## the assumption of using full barcodes. The "real_positions" contains the + ## END positions of each batch under the assumption of using only the real + ## subsetted barcodes. + unfilter_positions <- list(B0=0) + filtered_positions <- list(B0=0) + filter_in_unfilter <- list(B0=0) ## dividing line between real and false barcodes in each batch + + res <- sapply(sort(names(sizes)), function(batch.name){ - res <- sapply(1:length(batches), function(b){ - batch.start <- (b-1) * full.barcode.size - batch.size <- batches[[b]] - positions <<- c(positions, batch.start + batch.size) - real_positions[[b+1]] <<- batch.size + real_positions[[b]] + batch.num <- as.integer(sub("B","", batch.name)) + if (batch.num > 0){ + batch.size <- sizes[[batch.name]] ## 96 + batch.name.previous = paste("B", batch.num-1, sep="") + batch.start <- unfilter_positions[[batch.name.previous]] + filt.batch.start <- filtered_positions[[batch.name.previous]] + + unfilter_positions[[batch.name]] <<- batch.start + full.barcode.size + filtered_positions[[batch.name]] <<- filt.batch.start + batch.size + filter_in_unfilter[[batch.name]] <<- batch.start + batch.size + } }) - real_positions <- real_positions[2:length(real_positions)] - - return(list(unfiltered=positions,filtered=real_positions, batches=batches)) + # Put into a dataframe, merging lists on their common names + dd <- data.frame(rbindlist(list( + unfilter_positions=unfilter_positions, + filter_in_unfilter=filter_in_unfilter, + filtered_positions=filtered_positions, + sizes=sizes), ## sizes go last to not mess up the column name ordering + use.names = TRUE, idcol = TRUE)) + + rownames(dd) <- dd$.id + dd <- dd[,!(colnames(dd) %in% ".id")] + + return(dd) } -calculateRealPlateIndexes <- function(plate.form, batches, num.plates){ - #' Determine true plate positions given variable batch sizes +calculatePlatePositions <- function(plate.form, full.barcode.size, all.batch.data){ + #' Determine the x-axis plate positions for each of the unfiltered and filtered sets #' #' Given the true size of each batch, and which batches exist in which plates #' calculate the size of each plate #' #' @param plate.form list of vectors mapping plates to batches - #' @param batches list of batches and their respective sizes - #' @param num.plates number of plates - #' @return sequence of plate positions - batches.per.plate <- length(plate.form[[1]]) + #' @param full.barcode.size size of the full set of barcodes + #' @param all.batch.data the output of 'calculateBarcodePositions' + #' @return dataframe of plate information pertaining to positions and sizes of plates + unfilter.plates = list(P0=0) + filtered.plates = list(P0=0) + unfilter.plates.sizes = list(P0=0) + filtered.plates.sizes = list(P0=0) + + res <- sapply(sort(names(plate.form)), function(plate.num){ + + unfilter.plate.size = 0 + filtered.plate.size = 0 + + batches <- plate.form[[plate.num]] - size.of.plate <- 0 - res <- sapply(plate.form[[1]], function(batch){ - batch.size <- batches[[batch]] - size.of.plate <<- size.of.plate + batch.size + res2 <- sapply(sort(batches), function(batch.num){ + batch.size <- all.batch.data["sizes",paste("B", batch.num, sep="")] + + unfilter.plate.size <<- unfilter.plate.size + full.barcode.size + filtered.plate.size <<- filtered.plate.size + batch.size + }) + + plate.name = paste("P", plate.num, sep="") + plate.name.previous = paste("P", as.integer(plate.num) - 1, sep="") + + unfilter.plates.sizes[[plate.name]] <<- unfilter.plate.size + filtered.plates.sizes[[plate.name]] <<- filtered.plate.size + + filtered.plates[[plate.name]] <<- filtered.plates[[plate.name.previous]] + filtered.plate.size + unfilter.plates[[plate.name]] <<- unfilter.plates[[plate.name.previous]] + unfilter.plate.size }) - return(seq(0, num.plates * size.of.plate, size.of.plate)) + # Put into a dataframe, merging lists on their common names + dd <- data.frame(rbindlist(list( + unfilter.plates=unfilter.plates, + unfilter.plates.sizes=unfilter.plates.sizes, + filtered.plates=filtered.plates, + filtered.plates.sizes=filtered.plates.sizes), + use.names = TRUE, idcol = TRUE)) + + rownames(dd) <- dd$.id + dd <- dd[,!(colnames(dd) %in% ".id")] + + return(dd) } |
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| diff -r 582b7bd4ae4c -r 253c9448f524 scripts/config_assertions.R --- a/scripts/config_assertions.R Thu Jan 24 09:52:58 2019 -0500 +++ b/scripts/config_assertions.R Mon Jun 03 14:55:24 2019 -0400 |
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| @@ -31,12 +31,13 @@ return(barcodes[!not.in]) } + checkNoMissingBarcodes <- function(headers, barcodes){ #' Extracts barcodes in the headers and compares them with those in barcodes #' #' @param headers matrix headers, must be of P1_B2_ACTG format #' @param barcodes full list of barcodes - barcs.in.matrix <- unique(sort(sub(".*_.*_([ACTG]+)", "\\1", headers))) + barcs.in.matrix <- unique(sort(sub("^.*_([ACTGN]+)$", "\\1", headers))) not.in <- !(barcs.in.matrix %in% barcodes) if (sum(not.in) > 0){ message("Warning: Barcodes in matrix not in barcodes file\n", barcs.in.matrix[not.in]) @@ -45,9 +46,26 @@ } } +checkBatchNamesAreValid <- function(headers){ + #' Checks that Plate and Batch names follow good conventions + #' i.e. Batch names are NOT reused across plates + #' + #' @param headers matrix headers in P1_B2_ACTG format + plate.and.batch <- unique(sort(sub("^(.*)_([ATCGN]+)$", "\\1", headers))) + message("Discovered ", length(plate.and.batch), " batches: ", paste(plate.and.batch, collapse=" ")) + + batch.only <- sub("^.*_(B\\d)$", "\\1", plate.and.batch) + dupes.batches <- batch.only[duplicated(batch.only)] + + if (length(dupes.batches) > 0){ + stop("Batches ", paste(dupes.batches, collapse=" "), " have duplicate names in other plates!") + } +} + + assertNoMissingBatches <- function(format, plates){ #' Checks the barcode and plate spec match - #' + #' #' These must specify the same batches. #' #' @param format barcode format, ranges to batches @@ -56,14 +74,17 @@ batches.form = c() batches.plate = c() for (form in format){batches.form = c(batches.form, form)} - for (plate in plates){batches.plate = c(batches.plate, plate)} + for (plate in plates){ + batches.plate = c(batches.plate, plate) + } if (length(batches.plate) != length(batches.form)){ stop("Error: The number of batches specified in the plate do not match those given in the barcode format") } - range.form <- seq(min(batches.form), max(batches.form)) - range.plate <- seq(min(batches.plate), max(batches.plate)) + #range.form <- seq(min(batches.form), max(batches.form)) + range.form <- unique(sort(batches.form)) + range.plate <- unique(sort(batches.plate)) if (sum(!(range.form %in% batches.form)) > 0){ stop("Error: Missing batch in barcode format") @@ -83,9 +104,11 @@ barcodes <- scan(spec$barcodes, what="", sep="\n") num.barcodes <- length(barcodes) num.plates <- length(names(spec$plate)) + used.barcodes <- checkNoMissingRanges(spec$format, barcodes) num.batches <- assertNoMissingBatches(spec$format, spec$plates) checkNoMissingBarcodes(matrix.headers, used.barcodes) + checkBatchNamesAreValid(matrix.headers) return(list(barc=barcodes, barc.n=num.barcodes, plates.n=num.plates, batch.n=num.batches)) -} +} \ No newline at end of file |
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| diff -r 582b7bd4ae4c -r 253c9448f524 scripts/contamination_plot.R --- a/scripts/contamination_plot.R Thu Jan 24 09:52:58 2019 -0500 +++ b/scripts/contamination_plot.R Mon Jun 03 14:55:24 2019 -0400 |
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| b'@@ -1,8 +1,8 @@\n #!/usr/bin/env R\n \n-require(ggplot2)\n+suppressPackageStartupMessages(require(ggplot2))\n \n-log10histoPlot <- function(columncounts, title=""){\n+log10histoPlot <- function(title="", columncounts){\n #\' Log10 histogram plot\n #\'\n #\' @param columncounts colSums(input_matrix)\n@@ -17,124 +17,152 @@\n return(p1)\n }\n \n-contaminationPlot <- function(columncounts, title = "",\n- indexes.plates, indexes.fullbc, indexes.truebc,\n- filtered=FALSE)\n-{\n- #\' Plots true and false barcodes\n- #\'\n- #\'\n- #\' @param columncounts colSums(input_matrix)\n- #\' @param title plot title\n- #\' @param indexes.plates plate line positions\n- #\' @param indexes.fullbc full batch line positions\n- #\' @param indexes.truebc true batch line positions\n- #\' @param filtered specifies whether the positions have been adjusted for true barcodes\n- #\' @return ggplot grob\n- dfer <- data.frame(colcounts=columncounts)\n+## This is calculated by the first call to contaminationPlot\n+## and then re-used by the second call.\n+ylim.max = NULL\n \n- ## Remove indexes where plates and full barcodes mix\n- indexes.fullbc = indexes.fullbc[!(indexes.fullbc %in% indexes.plates)]\n- \n- nit <- length(indexes.truebc)\n- nif <- length(indexes.fullbc)\n- nip <- length(indexes.plates)\n- mval <- max(dfer)\n- \n- ## Aesthetics\n- min.height <- -200\n- tf.spacing.left <- 12\n- tf.spacing.right <- 12 \n- tf.height <- mval - 10000\n- bn.height <- mval - 2000\n- plate.color <- "grey"\n- plate.text.color <- "black"\n- plate.text.alpha <- 0.5\n- plate.text.size <- 3\n- plate.height <- 2* mval / 5\n- plate.spacing <- if (filtered) 12 else 24\n- plate.height.text <- plate.height - 3000\n- \n- truebcs <- data.frame(\n- x=indexes.truebc, y=rep(min.height,nit),\n- xend=indexes.truebc, yend=rep(mval,nit)\n- )\n- fullbcs <- data.frame(\n- x=indexes.fullbc, y=rep(min.height,nif),\n- xend=indexes.fullbc, yend=rep(mval,nif)\n- )\n- platess <- data.frame(\n- x=indexes.plates, y=rep(min.height,nip),\n- xend=indexes.plates, yend=rep(plate.height,nip)\n- )\n- connecting.bar <- data.frame(\n- xsta = min(indexes.plates), ysta = min.height,\n- xfin = max(indexes.plates), yfin = min.height\n- )\n- \n- p1 <- ggplot()\n-\n- if (!filtered){\n- p1 <- p1 +\n- geom_segment(data=truebcs, aes(x=x,y=y,xend=xend,yend=yend - 4000), col=\'grey\', lty=2, size=0.2) + \n- geom_segment(data=fullbcs, aes(x=x,y=y,xend=xend,yend=yend), col=\'blue\', lty=1, size=0.4, alpha=0.2)\n+contaminationPlot <- function(title, columndata, barcode.data, plate.data, RAW)\n+{\n+ coldata = data.frame(colsums=columndata)\n+ maxval = max(coldata)\n+ # Set once and once only\n+ if (is.null(ylim.max)){\n+ ylim.max <<- maxval + 500\n }\n- else {\n- p1 <- p1 +\n- geom_segment(data=truebcs, aes(x=x,y=y,xend=xend,yend=yend), col=\'blue\', lty=1, size=0.4, alpha=0.2)\n- }\n- \n- p1 <- p1 +\n- geom_segment(data=platess, aes(x=x,y=y,xend=xend,yend=yend), col=plate.color, lty=1, size=1) +\n- geom_segment(data=connecting.bar, aes(x=xsta,y=ysta,xend=xfin,yend=yfin), col=plate.color, lty=1, size=1) +\n- geom_point(\n- data=dfer, aes(x=1:length(rownames(dfer)), y=dfer$colcounts),\n- pch = 16, cex = 1) +\n- theme(plot.title = element_text(hjust = 0.5),\n- axis.ticks.x=element_blank(), axis.ticks.y=element_blank(),\n- axis.text.x=element_blank()) +\n- labs(title=paste("Contamination Plot\\n", title), y="Library Size", x="Barcode Index") +\n- scale_y_continuous(breaks=seq(0,mval + 10000, 10000)) +\n- scale_x_continuous(breaks=NULL)\n \n- ## Add true/false and batch labels\n- res <- lapply(indexes.truebc, function(xval){\n- batch <- match(xval, indexes.truebc)\n+ drawPlates <- function(plate.dat'..b't.tf.height <- maxval * 2/5\n+ batch.text.tf.spacing <- 12\n+ batch.text.alpha <- 0.8\n+ batch.text.size <- 3\n+ batch.label.text.size <- 6\n+ batch.height <- maxval * 4/5\n+ divide.height <- maxval * 4/5\n+ batch.spacing <- 24\n+\n+ boundary.pos <- barcode.data["filtered_positions",]\n+ plate.pos <- unname(unlist(plate.data["filtered.plates",])) ## just want values\n+\n+ if (RAW){\n+ boundary.pos <- barcode.data["unfilter_positions",]\n+ divider.pos <- barcode.data["filter_in_unfilter",]\n+ plate.pos <- unname(unlist(plate.data["unfilter.plates",])) ## just want values\n+ }\n+\n+\n+ zzz <- lapply(names(boundary.pos), function(batch.name){\n+ batch.num = as.integer(sub("B","",batch.name))\n+ batch.xval = unname(unlist(boundary.pos[[batch.name]]))\n+\n+ # Plot boundary line only if not intersecting with a plate line\n+ if (!(batch.xval %in% plate.pos)){\n+ gplot <<- gplot + geom_segment(aes(x=batch.xval, xend=batch.xval, y=batch.minyval, yend=batch.height),\n+ color=boundary.color, lty=boundary.style, size=boundary.size)\n+ }\n+\n+ ## Plot labels (except P0)\n+ if (batch.name != "B0"){\n+ if (RAW){\n+ divide.xval = unname(unlist(divider.pos[[batch.name]]))\n+\n+ ## Plot the divider line\n+ gplot <<- gplot + geom_segment(aes(x=divide.xval, xend=divide.xval, y=batch.minyval, yend=divide.height),\n+ color=divide.color, lty=divide.style, size=divide.size)\n+\n+ ## Plot the True/False labels\n+ gplot <<- gplot + annotate("text", x=divide.xval - batch.text.tf.spacing, label="True positives",\n+ size=batch.text.size, y=batch.text.tf.height, angle=+90,\n+ color=divide.text.true.color, alpha=batch.text.alpha)\n+ gplot <<- gplot + annotate("text", x=divide.xval + batch.text.tf.spacing, label="False positives",\n+ size=batch.text.size, y=batch.text.tf.height, angle=-90,\n+ color=divide.text.false.color, alpha=batch.text.alpha)\n+ ## Plot the Batch names\n+ gplot <<- gplot + annotate("text", x=divide.xval, label=batch.name,\n+ size=batch.label.text.size, y=batch.text.height, angle=0,\n+ color = divide.text.name.color, alpha=batch.text.alpha)\n+ } else {\n+ ## Plot the Batch names between blue lines\n+ gplot <<- gplot + annotate("text", x=batch.xval, label=batch.name,\n+ size=batch.label.text.size, y=batch.text.height, angle=0,\n+ color = divide.text.name.color, alpha=batch.text.alpha)\n+ }\n+ }\n+ })\n+ }\n+\n+\n+ plotCells <- function(coldata){\n+ gplot <<- gplot + geom_point(data=coldata, aes(x=1:nrow(coldata), y=coldata$colsums), pch = 16, cex = 1)\n+ }\n+\n+ plotTheme <- function(){\n+ gplot <<- gplot + theme(plot.title = element_text(hjust = 0.5),\n+ axis.ticks.x=element_blank(), axis.ticks.y=element_blank(),\n+ axis.text.x=element_blank()) +\n+ labs(title=paste("Contamination Plot\\n", title), y="Library Size", x="Barcode Index") +\n+ coord_cartesian(ylim = c(-200, ylim.max)) +\n+ scale_x_continuous(breaks=NULL)\n+ }\n+\n+\n+ gplot <- ggplot()\n+ drawPlates(plate.data)\n+ drawBatches(barcode.data, plate.data)\n+ plotCells(coldata)\n+ plotTheme()\n+\n+ return(gplot)\n+}\n' |
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| diff -r 582b7bd4ae4c -r 253c9448f524 scripts/crosscontamination_filter.R --- a/scripts/crosscontamination_filter.R Thu Jan 24 09:52:58 2019 -0500 +++ b/scripts/crosscontamination_filter.R Mon Jun 03 14:55:24 2019 -0400 |
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| @@ -1,5 +1,5 @@ #!/usr/bin/env R -VERSION = "0.1" +VERSION = "0.2" args = commandArgs(trailingOnly = T) @@ -9,6 +9,8 @@ } source(args[1]) + +## debug in debug dir source(file.path(script.dir, "config_assertions.R")) source(file.path(script.dir, "batch_plotting_functions.R")) source(file.path(script.dir, "reorder_matrix_headers.R")) @@ -28,46 +30,30 @@ num.batches <- sc$batch.n num.plates <- sc$plates.n -real.indexes = calculateRealBarcodeIndexes(spec$format, num.barcodes) -plate.indexes = calculatePlateIndexes(spec$plates, num.barcodes, num.plates) -plate.indexes.real = calculateRealPlateIndexes(spec$plates, real.indexes$batches, num.plates) - -ordering <- reorderMatrixHeaders(barcodes, colnames(input_matrix), spec$format) -## Unfiltered -nmatrix <- input_matrix[,ordering$all] +barcode.data <- calculateBarcodePositions(spec$format, num.barcodes) +plate.data <- calculatePlatePositions(spec$plates, num.barcodes, barcode.data) -plot.prefilter <- contaminationPlot( - colSums(nmatrix), - title="Pre-Filter", - plate.indexes, - calculateFullBarcodeIndexes(num.batches, num.barcodes), - real.indexes$unfiltered -) +ordering <- reorderMatrixHeaders(barcodes, colnames(input_matrix), spec$format, spec$plates) - -## Filtered -cmatrix <- input_matrix[,ordering$correct] +## Unfiltered, but sorted matrix +nmatrix <- input_matrix[,ordering$all] +plot.prefilter <- contaminationPlot("Pre-Filter", colSums(nmatrix), + barcode.data, plate.data, RAW=TRUE) -plot.postfilter <- contaminationPlot( - colSums(cmatrix), - title="Post-Filter", - plate.indexes.real, - calculateFullBarcodeIndexes(num.batches, num.barcodes), - real.indexes$filtered, - filtered = T -) +## Filtered, but sorted matrix +cmatrix <- input_matrix[,ordering$filtered] +plot.postfilter <- contaminationPlot("Post-Filter", colSums(cmatrix), + barcode.data, plate.data, RAW=FALSE) -plot.histogram <- log10histoPlot( - colSums(cmatrix), - "Histogram of Post-Filter Matrix Counts" -) +plot.histogram.pre <- log10histoPlot("Histogram of Pre-Filter Matrix Counts", colSums(nmatrix)) +plot.histogram.post <- log10histoPlot("Histogram of Post-Filter Matrix Counts", colSums(cmatrix)) pdf(out.pdf) plot.prefilter plot.postfilter -plot.histogram +plot.histogram.pre +plot.histogram.post dev.off() - write.table(cmatrix, file=out.table, quote=FALSE, na="0", sep="\t") |
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| diff -r 582b7bd4ae4c -r 253c9448f524 scripts/generateTestData.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/generateTestData.R Mon Jun 03 14:55:24 2019 -0400 |
| [ |
| @@ -0,0 +1,67 @@ +num.genes = 500 +num.cells.per.batch.good = 96 +num.cells.per.batch.bad = 11 +num.cells.per.batch.empty = 85 + +barcodes = read.table('../test-data/celseq_barcodes.192.raw', stringsAsFactors=F)[,1] + +barcodes.1_96 <- barcodes[1:96] +barcodes.97_192 <- barcodes[97:192] + + +goodCell <- function(){ + return(rnbinom(n=num.genes, size=0.1, mu=6)) +} + +badCell <- function(){ + return(rnbinom(n=num.genes, size=0.1, mu=0.5)) +} + +emptyCell <- function(){ + return(rnbinom(n=num.genes, size=0.1, mu=0.1)) +} + +generateGoodBatch <- function(batch.prefix="P1_B1", bar.codes){ + tmp.cell <- goodCell() + for (c in 2:num.cells.per.batch.good){ + tmp.cell <- cbind(tmp.cell, goodCell()) + } + bar.good <- paste(batch.prefix, bar.codes, sep="_") + colnames(tmp.cell) <- bar.good + return(tmp.cell) +} + + +generateBadBatch <- function(batch.prefix="P1_B1", bar.codes){ + tmp.cell <- emptyCell() + for (c in 2:num.cells.per.batch.empty){ + tmp.cell <- cbind(tmp.cell, emptyCell()) + } + for (c in 1:num.cells.per.batch.bad){ + tmp.cell <- cbind(tmp.cell, badCell()) + } + bar.bad <- paste(batch.prefix, bar.codes, sep="_") + colnames(tmp.cell) <- bar.bad + return(tmp.cell) +} + +makeBatch <- function(batch.prefix, odd=TRUE){ + batch.good <- generateGoodBatch(batch.prefix, bar.codes=if(odd) barcodes.1_96 else barcodes.97_192) + batch.bad <- generateBadBatch(batch.prefix, bar.codes=if(odd) barcodes.97_192 else barcodes.1_96) + batch <- cbind(batch.good, batch.bad) + + ## Shuffle columns + batch.shuff <- batch[,sample(ncol(batch))] + return(batch.shuff) +} + +batch.1 <- makeBatch("P1_B1", odd=TRUE) +batch.2 <- makeBatch("P1_B2", odd=FALSE) +batch.3 <- makeBatch("P2_B3", odd=TRUE) +batch.4 <- makeBatch("P2_B4", odd=FALSE) + +total.matrix <- cbind(batch.1, batch.2, batch.3, batch.4) +rownames(total.matrix) <- paste("GENE", 1:nrow(total.matrix), sep="") + +write.table(as.matrix(total.matrix), file="test.matrix", + col.names=NA, sep='\t', quote=FALSE) \ No newline at end of file |
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| diff -r 582b7bd4ae4c -r 253c9448f524 scripts/reorder_matrix_headers.R --- a/scripts/reorder_matrix_headers.R Thu Jan 24 09:52:58 2019 -0500 +++ b/scripts/reorder_matrix_headers.R Mon Jun 03 14:55:24 2019 -0400 |
| [ |
| @@ -12,7 +12,7 @@ return(sub(regex.from, regex.to, col.names)) } -reorderMatrixHeaders <- function(barcodes, headers, barcode.format){ +reorderMatrixHeaders <- function(barcodes, fixed.headers, barcode.format, plate.format){ #' Reorder headers to segment wanted and unwanted barcodes on opposite sides #' of each batch #' @@ -20,42 +20,61 @@ #' @param headers input matrix headers #' @param barcode.format batch list specifying valid barcodes for each batch #' @return list of all barcodes sorted bilaterally by batch, and true barcodes - form <- barcode.format + batch.ordering <- list() - batch.ordering.correct <- list() + batch.ordering.filtered <- list() - res <- sapply(names(form), function(key){ + res <- sapply(names(barcode.format), function(key){ rng <- as.integer(unlist(strsplit(key, '-'))) ranges <- seq(rng[1],rng[2]) + # Barcodes wanted and unwanted for this range of batches barc.wanted <- barcodes[ranges] barc.unwant <- barcodes[!(barcodes %in% barc.wanted)] - sub.batches <- form[[key]] # 1,3,5,7 or 2,4,6,8 + sub.batches <- barcode.format[[key]] # 1,3,5,7 or 2,4,6,8 + res2 <- lapply(sub.batches, function(bat){ - batch_bar <- headers[grepl(paste("P\\d_B",bat,"_([ACGT]+)", sep=""), headers)] - barcs.in.batch <- sub("P._B._([ACGT]+)", "\\1", batch_bar) - b.wanted <- batch_bar[barcs.in.batch %in% barc.wanted] - b.unwant <- batch_bar[barcs.in.batch %in% barc.unwant] + batch.match <- paste("_B",bat,"_",sep="") + headers.in.batch <- fixed.headers[grepl(batch.match, fixed.headers)] + barcodes.in.batch <- sub(".*_([ATCGN]+)$", "\\1", headers.in.batch) + + headers.in.batch.wanted <- headers.in.batch[barcodes.in.batch %in% barc.wanted] + headers.in.batch.unwant <- headers.in.batch[barcodes.in.batch %in% barc.unwant] + + if (sum(headers.in.batch.wanted %in% headers.in.batch.unwant) > 0){ + stop("Barcode given twice!", headers.in.batch.wanted[headers.in.batch.wanted %in% headers.in.batch.unwant]) + } + + headers.in.batch.neworder <- c(headers.in.batch.wanted, headers.in.batch.unwant) + batch.name <- paste("B", bat, sep="") - if (sum(b.wanted %in% b.unwant) > 0){ - stop("Barcode given twice!", b.wanted[b.wanted %in% b.unwant]) - } - barc_order <- c(b.wanted, b.unwant) - batch.ordering[[bat]] <<- barc_order - batch.ordering.correct[[bat]] <<- b.wanted + batch.ordering[[batch.name]] <<- headers.in.batch.neworder + batch.ordering.filtered[[batch.name]] <<- headers.in.batch.wanted + }) + }) + ## Now we have sorted all our barcodes in each batch to the correct order + ## we just have to sort the batches into the correct order according to plating setup + barcode.ordering <- c() + barcode.ordering.filtered <- c() + + res <- sapply(sort(names(plate.format)), function(plate.num){ + batches <- plate.format[[plate.num]] + + ## Preserve batch order on plates + res2 <- sapply(batches, function(batch.num){ + batch.name <- paste("B", batch.num, sep="") + barcs <- batch.ordering[[batch.name]] + barcs.filtered <- batch.ordering.filtered[[batch.name]] + + barcode.ordering <<- c(barcode.ordering, barcs) + barcode.ordering.filtered <<- c(barcode.ordering.filtered, barcs.filtered) }) }) - barcode.ordering <- c() - barcode.ordering.correct <- c() - - res <- lapply(1:length(batch.ordering), function(bat){ - barc_order <- batch.ordering[[bat]] - barc_order.correct <- batch.ordering.correct[[bat]] - barcode.ordering <<- c(barcode.ordering, barc_order) - barcode.ordering.correct <<- c(barcode.ordering.correct, barc_order.correct) - }) - - return(list(all=barcode.ordering,correct=barcode.ordering.correct)) -} + return(list( + all=barcode.ordering, + filtered=barcode.ordering.filtered, + debug.barcodes = batch.ordering + )) +} \ No newline at end of file |
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| diff -r 582b7bd4ae4c -r 253c9448f524 test-data/out.pdf |
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| Binary file test-data/out.pdf has changed |
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| diff -r 582b7bd4ae4c -r 253c9448f524 test-data/out.table --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out.table Mon Jun 03 14:55:24 2019 -0400 |
| b |
| b'@@ -0,0 +1,501 @@\n+P1_B1_CCACAA\tP1_B1_AGGACA\tP1_B1_ACTCTG\tP1_B1_TGTCTG\tP1_B1_AGGATC\tP1_B1_AGGAAG\tP1_B1_GTTGAG\tP1_B1_CTCAGA\tP1_B1_ACGTGA\tP1_B1_GAAGGA\tP1_B1_GTGAAG\tP1_B1_TGGTTG\tP1_B1_TGAACC\tP1_B1_AACGAG\tP1_B1_AGCTAG\tP1_B1_TCACAG\tP1_B1_ACCAAC\tP1_B1_ATGTCG\tP1_B1_GTCTCA\tP1_B1_CTAGGA\tP1_B1_GGTAAC\tP1_B1_TAGGAC\tP1_B1_TAACGG\tP1_B1_TTCCAG\tP1_B1_GATACG\tP1_B1_CACCAA\tP1_B1_CGATGA\tP1_B1_TAGTGG\tP1_B1_GAAGAC\tP1_B1_GTGACA\tP1_B1_CTGTTG\tP1_B1_CACTTC\tP1_B1_CGTCTA\tP1_B1_TGAAGG\tP1_B1_GCAACA\tP1_B1_GTGATC\tP1_B1_AGCTCA\tP1_B1_GATCTG\tP1_B1_GAATGG\tP1_B1_TGTACG\tP1_B1_ACAAGC\tP1_B1_ACTTCG\tP1_B1_CGATTG\tP1_B1_ATCACG\tP1_B1_GAGTGA\tP1_B1_ACCAGA\tP1_B1_TGCAAC\tP1_B1_TTCTCG\tP1_B1_ACCATG\tP1_B1_TCATCC\tP1_B1_ACAGTG\tP1_B1_GTGGAA\tP1_B1_GACGAA\tP1_B1_AGTGCA\tP1_B1_AGCTTC\tP1_B1_GGACAA\tP1_B1_TGGTGA\tP1_B1_ACTCAC\tP1_B1_ACAGGA\tP1_B1_AGACTC\tP1_B1_AACCTC\tP1_B1_CCATAG\tP1_B1_AAGCCA\tP1_B1_GAATCC\tP1_B1_TTGTGC\tP1_B1_CAGAAG\tP1_B1_CTATCC\tP1_B1_TGCAGA\tP1_B1_GTACTC\tP1_B1_ATTGCG\tP1_B1_AGTGTC\tP1_B1_GAGTTG\tP1_B1_TGTCGA\tP1_B1_TCTTGC\tP1_B1_CATGCA\tP1_B1_ACGTAC\tP1_B1_CTAGTG\tP1_B1_GTACCA\tP1_B1_AGACAG\tP1_B1_CTAGAC\tP1_B1_CTTCGA\tP1_B1_ACAGAC\tP1_B1_CATGTC\tP1_B1_TTCACC\tP1_B1_GATCGA\tP1_B1_TGAGGA\tP1_B1_CTAAGC\tP1_B1_ACGTTG\tP1_B1_AGACCA\tP1_B1_TCCGAA\tP1_B1_ACTCGA\tP1_B1_CTCATG\tP1_B1_GTACAG\tP1_B1_GACAGA\tP1_B1_CACTCA\tP1_B1_GACAAC\tP1_B2_CCGTAA\tP1_B2_AGTAGG\tP1_B2_TATCGC\tP1_B2_ATTCGC\tP1_B2_GTATCG\tP1_B2_CCTATG\tP1_B2_CCAATC\tP1_B2_AAGCAC\tP1_B2_CGTTAC\tP1_B2_TAGCTC\tP1_B2_GCATTC\tP1_B2_GCAGAA\tP1_B2_AGTACC\tP1_B2_TTGCTG\tP1_B2_TTGCAC\tP1_B2_GGTTAG\tP1_B2_GCTTAC\tP1_B2_GCCATA\tP1_B2_TCAACG\tP1_B2_ATGCTC\tP1_B2_TTGGCA\tP1_B2_AACACC\tP1_B2_CGCATA\tP1_B2_TTGGAG\tP1_B2_AAGGTG\tP1_B2_GTTAGG\tP1_B2_GTATGC\tP1_B2_GTAACC\tP1_B2_ATGACC\tP1_B2_CCTGAA\tP1_B2_GGTATG\tP1_B2_ATGGAC\tP1_B2_CGGTTA\tP1_B2_CAGGAA\tP1_B2_ATCCAC\tP1_B2_GGCTTA\tP1_B2_GCAATG\tP1_B2_GAGCAA\tP1_B2_ACATGG\tP1_B2_AGCGAA\tP1_B2_TTGGTC\tP1_B2_GGATCA\tP1_B2_GAGGTA\tP1_B2_ATCGCA\tP1_B2_TACCAC\tP1_B2_TACCGA\tP1_B2_AGAACG\tP1_B2_ATCGTG\tP1_B2_ATCCGA\tP1_B2_CCGATA\tP1_B2_TGTTCC\tP1_B2_TACGCA\tP1_B2_CGCTAA\tP1_B2_CTAACG\tP1_B2_GATTGC\tP1_B2_GCTTCA\tP1_B2_CCTTGA\tP1_B2_TGTAGC\tP1_B2_GGAATC\tP1_B2_AAGTGC\tP1_B2_GGATAC\tP1_B2_CACGTA\tP1_B2_GCATGA\tP1_B2_TACTCC\tP1_B2_GCTAAG\tP1_B2_ATCAGC\tP1_B2_ATACGG\tP1_B2_GGTAGA\tP1_B2_CCTACA\tP1_B2_TCTAGG\tP1_B2_CGAACA\tP1_B2_ATGAGG\tP1_B2_TTCGAC\tP1_B2_AGATGC\tP1_B2_GCGTTA\tP1_B2_AGGCTA\tP1_B2_GCTCTA\tP1_B2_TGTTGG\tP1_B2_CTCGAA\tP1_B2_CTGCTA\tP1_B2_CGTAAG\tP1_B2_ATGCAG\tP1_B2_CCAGTA\tP1_B2_GGTGTA\tP1_B2_TGATCG\tP1_B2_ATCTCC\tP1_B2_AACTGG\tP1_B2_TTACGC\tP1_B2_TTGCGA\tP1_B2_CAATGC\tP1_B2_ACGGTA\tP1_B2_CGTTCA\tP1_B2_TACAGG\tP1_B2_CATTGG\tP1_B2_TATCCG\tP1_B2_CCATCA\tP2_B3_AGCTAG\tP2_B3_CGATTG\tP2_B3_CTAGTG\tP2_B3_AACGAG\tP2_B3_AGACCA\tP2_B3_ATTGCG\tP2_B3_TGAACC\tP2_B3_ACTCGA\tP2_B3_CATGCA\tP2_B3_AGGATC\tP2_B3_CTATCC\tP2_B3_GTCTCA\tP2_B3_GAGTTG\tP2_B3_TGCAAC\tP2_B3_TGCAGA\tP2_B3_CAGAAG\tP2_B3_CTCAGA\tP2_B3_GATCGA\tP2_B3_ACGTAC\tP2_B3_GAATCC\tP2_B3_GAAGAC\tP2_B3_GTGAAG\tP2_B3_ACAGAC\tP2_B3_CTAAGC\tP2_B3_TGAGGA\tP2_B3_ACGTTG\tP2_B3_GAGTGA\tP2_B3_ACCAGA\tP2_B3_CCACAA\tP2_B3_ACGTGA\tP2_B3_CGATGA\tP2_B3_CGTCTA\tP2_B3_TGTACG\tP2_B3_TGTCGA\tP2_B3_ACAGGA\tP2_B3_CACCAA\tP2_B3_GTTGAG\tP2_B3_AGACAG\tP2_B3_AGGACA\tP2_B3_CACTTC\tP2_B3_TCATCC\tP2_B3_TTCCAG\tP2_B3_GGTAAC\tP2_B3_CTGTTG\tP2_B3_TAACGG\tP2_B3_AGACTC\tP2_B3_TCACAG\tP2_B3_GACAGA\tP2_B3_GGACAA\tP2_B3_TGGTTG\tP2_B3_ACCATG\tP2_B3_TGGTGA\tP2_B3_AGCTCA\tP2_B3_ACTCTG\tP2_B3_GTGACA\tP2_B3_GTGGAA\tP2_B3_GCAACA\tP2_B3_TTGTGC\tP2_B3_CCATAG\tP2_B3_GAATGG\tP2_B3_GTACCA\tP2_B3_CTAGAC\tP2_B3_AGCTTC\tP2_B3_TGAAGG\tP2_B3_AGTGTC\tP2_B3_TTCACC\tP2_B3_GATACG\tP2_B3_GTACTC\tP2_B3_ACTTCG\tP2_B3_ATGTCG\tP2_B3_ACAAGC\tP2_B3_GATCTG\tP2_B3_TAGTGG\tP2_B3_CTCATG\tP2_B3_CACTCA\tP2_B3_ACTCAC\tP2_B3_TTCTCG\tP2_B3_GAAGGA\tP2_B3_AGTGCA\tP2_B3_CTAGGA\tP2_B3_GACGAA\tP2_B3_CATGTC\tP2_B3_TAGGAC\tP2_B3_TCCGAA\tP2_B3_TCTTGC\tP2_B3_AACCTC\tP2_B3_ACCAAC\tP2_B3_AGGAAG\tP2_B3_ATCACG\tP2_B3_GACAAC\tP2_B3_GTACAG\tP2_B3_ACAGTG\tP2_B3_TGTCTG\tP2_B3_AAGCCA\tP2_B3_CTTCGA\tP2_B3_GTGATC\tP2_B4_TGTAGC\tP2_B4_ATGCAG\tP2_B4_GCATGA\tP2_B4_AACTGG\tP2_B4_ATCTCC\tP2_B4_GCTTCA\tP2_B4_GATTGC\tP2_B4_ATGCTC\tP2_B4_GGATCA\tP2_B4_GCAGAA\tP2_B4_GTAACC\tP2_B4_TAGCTC\tP2_B4_GGTGTA\tP2_B4_GTATCG\tP2_B4_AAGCAC\tP2_B4_ATCGTG\tP2_B4_GTTAGG\tP2_B4_TCAACG\tP2'..b'\t0\t0\t3\t0\t6\t0\t0\t4\t0\t0\t1\t0\t0\t8\t0\t39\t0\t5\t0\t11\t35\t0\t0\t1\t10\t0\t0\t0\t0\t0\t0\t0\t6\t7\t0\t41\t0\t5\t0\t0\t2\t6\t0\t0\t0\t8\t1\t0\t1\t0\t0\t4\t0\t0\t2\t0\t0\t0\t3\t0\t0\t1\t0\t0\t0\t0\t5\t0\t0\t0\t0\t0\t0\t23\t1\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t16\t3\t0\t8\t0\t5\t0\t0\t0\t2\t1\t0\t0\t0\t0\t0\t0\t52\t1\t0\t0\t0\t110\t1\t63\t0\t64\t0\t0\t0\t5\t0\t0\t1\t0\t0\t0\t68\t132\t0\t0\t0\t0\t25\t0\t0\t10\t0\t7\t0\t0\t16\t0\t0\t0\t2\t0\t15\t0\t11\t3\t2\t0\t0\t3\t0\t12\t1\t0\t26\t0\t0\t0\t0\t0\t7\t0\t0\t0\t0\t0\t2\t0\t0\t0\t0\t2\t0\t2\t0\t0\t0\t3\t0\t5\t0\t0\t0\t0\t0\t0\t0\t2\t15\t0\t2\t117\t0\t0\t3\t0\t0\t5\t0\t0\t0\t0\t0\t20\t0\t23\t0\t0\t0\t0\t0\t0\t4\t0\t10\t0\t0\t14\t2\t0\t2\t0\t2\t0\t0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2\t0\t0\t0\t0\t0\t0\t140\t31\t0\t0\t0\t0\t2\t25\t3\t0\t0\t0\t20\t11\t5\t0\t114\t2\t0\t0\t17\t0\t0\t8\t0\t0\t0\t0\t50\t0\t0\t0\t10\t0\t0\n' |
| b |
| diff -r 582b7bd4ae4c -r 253c9448f524 test-data/out3.subtable --- a/test-data/out3.subtable Thu Jan 24 09:52:58 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,176 +0,0 @@\n-WD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AACACC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AACCTC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AACGAG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AACTGG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AAGCAC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AAGCCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AAGGTG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AAGTGC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACAAGC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACAGAC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACAGGA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACAGTG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACATGG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACCAAC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACCAGA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACCATG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACGGTA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACGTAC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACGTGA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACGTTG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACTCAC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACTCGA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACTCTG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ACTTCG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGAACG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGACAG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGACCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGACTC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGATGC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGCGAA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGCTAG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGCTCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGCTTC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGGAAG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGGACA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGGATC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGGCTA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGTACC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGTAGG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGTGCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_AGTGTC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATACGG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATCACG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATCAGC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATCCAC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATCCGA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATCGCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATCGTG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATCTCC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATGACC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATGAGG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATGCAG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATGCTC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATGGAC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATGTCG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATTCGC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_ATTGCG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CAATGC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CACCAA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CACGTA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CACTCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CACTTC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CAGAAG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CAGGAA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CATGCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CATGTC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CATTGG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCAATC\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCACAA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCAGTA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCATAG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCATCA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCGATA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCGTAA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCTACA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCTATG\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCTGAA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CCTTGA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CGAACA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CGATGA\tWD_DP_120218_P1_1.fastq_WD_DP_120218_P1_1_CGATTG\tWD_DP_12021'..b'\t0\t0\t0\t7\t0\t0\t0\t0\t0\t1\t1\t0\t5\t1\t0\t0\t0\t3\t0\t1\t0\t2\t7\t1\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t2\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t5\t0\t0\t2\t1\t0\t0\t5\t0\t0\t0\t0\t0\t1\t8\t6\t0\t0\t0\t0\t3\t2\t0\t0\t0\t8\t0\t0\t0\t1\t0\t0\t0\t1\t0\t1\t2\t0\t0\t0\t10\t0\t0\t0\t0\t0\t3\t0\t2\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t2\t1\t9\t0\t0\t0\t0\t1\t0\t0\t4\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t1\t0\t0\t1\t0\t2\t0\t1\t8\t1\t5\t0\t0\t0\t4\t2\t0\t4\t4\t5\t0\t2\t0\t0\t0\t0\t0\t1\t2\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t2\t0\t0\t1\t0\t1\t0\t0\t1\t0\t0\t0\t1\t6\t0\t0\t0\t2\t4\t4\t0\t0\t0\t1\t0\t0\t0\t0\t2\t0\t5\t0\t0\t1\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\t0\t0\t0\t2\t0\t0\t0\t3\t1\t0\t2\t0\t1\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t1\t0\t0\t3\t1\t0\t0\t2\t1\t4\t0\t2\t2\t0\t0\t1\t0\t0\t1\t0\t0\t0\t5\t0\t0\t0\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t0\t0\t0\t0\t0\t0\t1\t0\t0\t1\t0\t2\t2\t0\t0\n' |
| b |
| diff -r 582b7bd4ae4c -r 253c9448f524 test-data/test.matrix --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test.matrix Mon Jun 03 14:55:24 2019 -0400 |
| b |
| b'@@ -0,0 +1,501 @@\n+\tP1_B1_CCATCA\tP1_B1_CCACAA\tP1_B1_GTATGC\tP1_B1_GGTGTA\tP1_B1_AGGACA\tP1_B1_ACTCTG\tP1_B1_TGTCTG\tP1_B1_CGTTCA\tP1_B1_AGGATC\tP1_B1_GCTTCA\tP1_B1_AGGAAG\tP1_B1_GTTGAG\tP1_B1_CTCAGA\tP1_B1_AGCGAA\tP1_B1_AGAACG\tP1_B1_ACGTGA\tP1_B1_TTCGAC\tP1_B1_GTTAGG\tP1_B1_GAAGGA\tP1_B1_GTGAAG\tP1_B1_ATCGTG\tP1_B1_AGGCTA\tP1_B1_TCTAGG\tP1_B1_GTATCG\tP1_B1_TTGCAC\tP1_B1_CGCTAA\tP1_B1_GGTATG\tP1_B1_GCTAAG\tP1_B1_TGGTTG\tP1_B1_CGGTTA\tP1_B1_CTAACG\tP1_B1_CACGTA\tP1_B1_TCAACG\tP1_B1_TGAACC\tP1_B1_GGTTAG\tP1_B1_ATCAGC\tP1_B1_TGTTCC\tP1_B1_AACGAG\tP1_B1_AGCTAG\tP1_B1_TCACAG\tP1_B1_GTAACC\tP1_B1_ACCAAC\tP1_B1_ATGTCG\tP1_B1_AAGTGC\tP1_B1_GCATGA\tP1_B1_GTCTCA\tP1_B1_TTGCTG\tP1_B1_CTAGGA\tP1_B1_GCAGAA\tP1_B1_TACGCA\tP1_B1_TTGCGA\tP1_B1_GCTTAC\tP1_B1_GGTAAC\tP1_B1_TGTTGG\tP1_B1_TAGGAC\tP1_B1_CATTGG\tP1_B1_TAGCTC\tP1_B1_TAACGG\tP1_B1_CTGCTA\tP1_B1_TACAGG\tP1_B1_TTCCAG\tP1_B1_GATACG\tP1_B1_TTGGAG\tP1_B1_CACCAA\tP1_B1_CGATGA\tP1_B1_ACGGTA\tP1_B1_CAATGC\tP1_B1_GAGGTA\tP1_B1_TAGTGG\tP1_B1_CCAATC\tP1_B1_ATCGCA\tP1_B1_GAAGAC\tP1_B1_GTGACA\tP1_B1_CTGTTG\tP1_B1_CACTTC\tP1_B1_CGTCTA\tP1_B1_TGAAGG\tP1_B1_CAGGAA\tP1_B1_GCAACA\tP1_B1_GTGATC\tP1_B1_CCGTAA\tP1_B1_AACACC\tP1_B1_AGCTCA\tP1_B1_GGCTTA\tP1_B1_GAGCAA\tP1_B1_GCCATA\tP1_B1_GATCTG\tP1_B1_GAATGG\tP1_B1_TGTACG\tP1_B1_GCATTC\tP1_B1_ACAAGC\tP1_B1_GGAATC\tP1_B1_ACTTCG\tP1_B1_GCTCTA\tP1_B1_CGATTG\tP1_B1_ATCACG\tP1_B1_GGTAGA\tP1_B1_ATGAGG\tP1_B1_GAGTGA\tP1_B1_ACCAGA\tP1_B1_TGCAAC\tP1_B1_CGTTAC\tP1_B1_TTCTCG\tP1_B1_ACCATG\tP1_B1_ATGCAG\tP1_B1_TCATCC\tP1_B1_CCTGAA\tP1_B1_ACAGTG\tP1_B1_GTGGAA\tP1_B1_GACGAA\tP1_B1_AGTGCA\tP1_B1_AGCTTC\tP1_B1_ATCCGA\tP1_B1_ATGCTC\tP1_B1_CCTATG\tP1_B1_GGACAA\tP1_B1_GATTGC\tP1_B1_ATGGAC\tP1_B1_TGGTGA\tP1_B1_ACTCAC\tP1_B1_ACAGGA\tP1_B1_AGACTC\tP1_B1_AACCTC\tP1_B1_GCAATG\tP1_B1_ATACGG\tP1_B1_CCATAG\tP1_B1_AAGCCA\tP1_B1_AGTACC\tP1_B1_GAATCC\tP1_B1_TATCCG\tP1_B1_AGTAGG\tP1_B1_CGTAAG\tP1_B1_TTGTGC\tP1_B1_TGTAGC\tP1_B1_CAGAAG\tP1_B1_CTATCC\tP1_B1_TTACGC\tP1_B1_TGCAGA\tP1_B1_GTACTC\tP1_B1_ATTGCG\tP1_B1_ATGACC\tP1_B1_TACCGA\tP1_B1_AGTGTC\tP1_B1_GAGTTG\tP1_B1_TGTCGA\tP1_B1_GCGTTA\tP1_B1_ATTCGC\tP1_B1_TCTTGC\tP1_B1_AAGGTG\tP1_B1_CATGCA\tP1_B1_ACGTAC\tP1_B1_CTAGTG\tP1_B1_ACATGG\tP1_B1_GTACCA\tP1_B1_CGAACA\tP1_B1_AGACAG\tP1_B1_GGATAC\tP1_B1_GGATCA\tP1_B1_CGCATA\tP1_B1_AACTGG\tP1_B1_CTAGAC\tP1_B1_CTTCGA\tP1_B1_ACAGAC\tP1_B1_CCAGTA\tP1_B1_CATGTC\tP1_B1_TTCACC\tP1_B1_TACTCC\tP1_B1_TTGGCA\tP1_B1_AAGCAC\tP1_B1_AGATGC\tP1_B1_TACCAC\tP1_B1_CCTACA\tP1_B1_CTCGAA\tP1_B1_GATCGA\tP1_B1_TGAGGA\tP1_B1_TTGGTC\tP1_B1_CCTTGA\tP1_B1_CCGATA\tP1_B1_ATCCAC\tP1_B1_TGATCG\tP1_B1_CTAAGC\tP1_B1_ACGTTG\tP1_B1_AGACCA\tP1_B1_TCCGAA\tP1_B1_TATCGC\tP1_B1_ACTCGA\tP1_B1_CTCATG\tP1_B1_GTACAG\tP1_B1_GACAGA\tP1_B1_CACTCA\tP1_B1_GACAAC\tP1_B1_ATCTCC\tP1_B2_TGAGGA\tP1_B2_CCGTAA\tP1_B2_GTGAAG\tP1_B2_GACAGA\tP1_B2_AGTAGG\tP1_B2_TATCGC\tP1_B2_ATTCGC\tP1_B2_CTAGAC\tP1_B2_GTATCG\tP1_B2_TCATCC\tP1_B2_CCTATG\tP1_B2_GGACAA\tP1_B2_CCAATC\tP1_B2_AAGCAC\tP1_B2_CGTTAC\tP1_B2_TAGCTC\tP1_B2_TGCAAC\tP1_B2_GCATTC\tP1_B2_GCAGAA\tP1_B2_AGACCA\tP1_B2_AGTACC\tP1_B2_TTGCTG\tP1_B2_TTGCAC\tP1_B2_TAACGG\tP1_B2_ACGTTG\tP1_B2_GTGGAA\tP1_B2_GGTTAG\tP1_B2_GCTTAC\tP1_B2_GCCATA\tP1_B2_TCAACG\tP1_B2_CCATAG\tP1_B2_ATGCTC\tP1_B2_TTGTGC\tP1_B2_TTGGCA\tP1_B2_ATCACG\tP1_B2_AACACC\tP1_B2_CATGCA\tP1_B2_TTCACC\tP1_B2_GAAGGA\tP1_B2_GAAGAC\tP1_B2_CGCATA\tP1_B2_TGAACC\tP1_B2_TTGGAG\tP1_B2_TTCCAG\tP1_B2_AAGGTG\tP1_B2_GTTAGG\tP1_B2_AGCTTC\tP1_B2_GTTGAG\tP1_B2_TGTCGA\tP1_B2_TCACAG\tP1_B2_CTCAGA\tP1_B2_GTATGC\tP1_B2_GTAACC\tP1_B2_CTAGGA\tP1_B2_ATGACC\tP1_B2_AAGCCA\tP1_B2_ACCAGA\tP1_B2_GTCTCA\tP1_B2_GAGTGA\tP1_B2_TCTTGC\tP1_B2_CCTGAA\tP1_B2_GGTATG\tP1_B2_ACCAAC\tP1_B2_ATGGAC\tP1_B2_TAGTGG\tP1_B2_CACTCA\tP1_B2_CTAGTG\tP1_B2_CGGTTA\tP1_B2_CAGGAA\tP1_B2_AGGATC\tP1_B2_TTCTCG\tP1_B2_AGCTCA\tP1_B2_GTGATC\tP1_B2_ATCCAC\tP1_B2_GGCTTA\tP1_B2_ACGTAC\tP1_B2_ACTCGA\tP1_B2_GCAATG\tP1_B2_TGTACG\tP1_B2_ACAGAC\tP1_B2_GAGCAA\tP1_B2_ACATGG\tP1_B2_AGCGAA\tP1_B2_TTGGTC\tP1_B2_CTGTTG\tP1_B2_GACGAA\tP1_B2_TGAAGG\tP1_B2_ATTGCG\tP1_B2_GGATCA\tP1_B2_GTACTC\tP1_B2_GAGGTA\tP1_B2_ATCGCA\tP1_B2_CTATCC\tP1_B2_TACCAC\tP1_B2_TGTCTG\tP1_B2_CCACAA\tP1_B2_TACCGA\tP1_B2_AGAACG\tP1_B2_CGTCTA\tP1_B2_ATCGTG\tP1_B2_GAGTTG\tP1_B2_ATCCGA\tP1_B2_AGACAG\tP1_B2_CCGATA\tP1_B2_TGTTCC\tP1_B2_ACTTCG\tP1_B2_TACGCA\tP1_B2_GATACG\tP1_B2_CGATTG\tP1_B2_GGTAAC\tP1_B2_CGCTAA\tP1_B2_ACCATG\tP1_B2_TAGGAC\tP1_B2_CTAACG\tP'..b'\t0\t1\t0\t0\t0\t0\t0\t0\t0\t1\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t4\t0\t0\t0\t0\t0\t10\t0\t166\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t63\t95\t0\t0\t0\t0\t0\t0\t0\t0\t0\t40\t0\t1\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t1\t0\t0\t10\t0\t17\t0\t0\t55\t0\t0\t0\t0\t0\t0\t0\t72\t0\t0\t0\t0\t0\t0\t1\t0\t0\t0\t0\t0\t2\t0\t3\t0\t0\t2\t0\t0\t0\t0\t0\t0\t0\t0\t9\t1\t0\t3\t0\t23\t1\t0\t0\t1\t0\t0\t0\t0\t0\t51\t0\t0\t0\t0\t0\t0\t9\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t171\t0\t0\t0\t0\t1\t0\t0\t41\t0\t0\t0\t0\t0\t0\t8\t18\t0\t1\t54\t0\t0\t0\t2\t0\t1\t1\t0\t2\t0\t0\t0\t0\t0\t0\t2\t2\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t18\t17\t8\t0\t0\t1\t0\t0\t0\t2\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t0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| diff -r 582b7bd4ae4c -r 253c9448f524 test-data/test.pdf |
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| diff -r 582b7bd4ae4c -r 253c9448f524 test-data/test.table --- a/test-data/test.table Thu Jan 24 09:52:58 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,176 +0,0 @@\n-P1_B1_AACCTC\tP1_B1_AACGAG\tP1_B1_AAGCCA\tP1_B1_ACAAGC\tP1_B1_ACAGAC\tP1_B1_ACAGGA\tP1_B1_ACAGTG\tP1_B1_ACCAAC\tP1_B1_ACCAGA\tP1_B1_ACCATG\tP1_B1_ACGTAC\tP1_B1_ACGTGA\tP1_B1_ACGTTG\tP1_B1_ACTCAC\tP1_B1_ACTCGA\tP1_B1_ACTCTG\tP1_B1_ACTTCG\tP1_B1_AGACAG\tP1_B1_AGACCA\tP1_B1_AGACTC\tP1_B1_AGCTAG\tP1_B1_AGCTCA\tP1_B1_AGCTTC\tP1_B1_AGGAAG\tP1_B1_AGGACA\tP1_B1_AGGATC\tP1_B1_AGTGCA\tP1_B1_AGTGTC\tP1_B1_ATCACG\tP1_B1_ATGTCG\tP1_B1_ATTGCG\tP1_B1_CACCAA\tP1_B1_CACTCA\tP1_B1_CACTTC\tP1_B1_CAGAAG\tP1_B1_CATGCA\tP1_B1_CATGTC\tP1_B1_CCACAA\tP1_B1_CCATAG\tP1_B1_CGATGA\tP1_B1_CGATTG\tP1_B1_CGTCTA\tP1_B1_CTAAGC\tP1_B1_CTAGAC\tP1_B1_CTAGGA\tP1_B1_CTAGTG\tP1_B1_CTATCC\tP1_B1_CTCAGA\tP1_B1_CTCATG\tP1_B1_CTGTTG\tP1_B1_CTTCGA\tP1_B1_GAAGAC\tP1_B1_GAAGGA\tP1_B1_GAATCC\tP1_B1_GAATGG\tP1_B1_GACAAC\tP1_B1_GACAGA\tP1_B1_GACGAA\tP1_B1_GAGTGA\tP1_B1_GAGTTG\tP1_B1_GATACG\tP1_B1_GATCGA\tP1_B1_GATCTG\tP1_B1_GCAACA\tP1_B1_GGACAA\tP1_B1_GGTAAC\tP1_B1_GTACAG\tP1_B1_GTACCA\tP1_B1_GTACTC\tP1_B1_GTCTCA\tP1_B1_GTGAAG\tP1_B1_GTGACA\tP1_B1_GTGATC\tP1_B1_GTGGAA\tP1_B1_GTTGAG\tP1_B1_TAACGG\tP1_B1_TAGGAC\tP1_B1_TAGTGG\tP1_B1_TCACAG\tP1_B1_TCATCC\tP1_B1_TCCGAA\tP1_B1_TCTTGC\tP1_B1_TGAACC\tP1_B1_TGAAGG\tP1_B1_TGAGGA\tP1_B1_TGCAAC\tP1_B1_TGCAGA\tP1_B1_TGGTGA\tP1_B1_TGGTTG\tP1_B1_TGTACG\tP1_B1_TGTCGA\tP1_B1_TGTCTG\tP1_B1_TTCACC\tP1_B1_TTCCAG\tP1_B1_TTCTCG\tP1_B1_TTGTGC\tP1_B2_AACACC\tP1_B2_AACTGG\tP1_B2_AAGCAC\tP1_B2_AAGGTG\tP1_B2_AAGTGC\tP1_B2_ACATGG\tP1_B2_ACGGTA\tP1_B2_AGAACG\tP1_B2_AGATGC\tP1_B2_AGCGAA\tP1_B2_AGGCTA\tP1_B2_AGTACC\tP1_B2_AGTAGG\tP1_B2_ATACGG\tP1_B2_ATCAGC\tP1_B2_ATCCAC\tP1_B2_ATCCGA\tP1_B2_ATCGCA\tP1_B2_ATCGTG\tP1_B2_ATCTCC\tP1_B2_ATGACC\tP1_B2_ATGAGG\tP1_B2_ATGCAG\tP1_B2_ATGCTC\tP1_B2_ATGGAC\tP1_B2_ATTCGC\tP1_B2_CAATGC\tP1_B2_CACGTA\tP1_B2_CAGGAA\tP1_B2_CATTGG\tP1_B2_CCAATC\tP1_B2_CCAGTA\tP1_B2_CCATCA\tP1_B2_CCGATA\tP1_B2_CCGTAA\tP1_B2_CCTACA\tP1_B2_CCTATG\tP1_B2_CCTGAA\tP1_B2_CCTTGA\tP1_B2_CGAACA\tP1_B2_CGCATA\tP1_B2_CGCTAA\tP1_B2_CGGTTA\tP1_B2_CGTAAG\tP1_B2_CGTTAC\tP1_B2_CGTTCA\tP1_B2_CTAACG\tP1_B2_CTCGAA\tP1_B2_CTGCTA\tP1_B2_GAGCAA\tP1_B2_GAGGTA\tP1_B2_GATTGC\tP1_B2_GCAATG\tP1_B2_GCAGAA\tP1_B2_GCATGA\tP1_B2_GCATTC\tP1_B2_GCCATA\tP1_B2_GCGTTA\tP1_B2_GCTAAG\tP1_B2_GCTCTA\tP1_B2_GCTTAC\tP1_B2_GCTTCA\tP1_B2_GGAATC\tP1_B2_GGATAC\tP1_B2_GGATCA\tP1_B2_GGCTTA\tP1_B2_GGTAGA\tP1_B2_GGTATG\tP1_B2_GGTGTA\tP1_B2_GGTTAG\tP1_B2_GTAACC\tP1_B2_GTATCG\tP1_B2_GTATGC\tP1_B2_GTTAGG\tP1_B2_TACAGG\tP1_B2_TACCAC\tP1_B2_TACCGA\tP1_B2_TACGCA\tP1_B2_TACTCC\tP1_B2_TAGCTC\tP1_B2_TATCCG\tP1_B2_TATCGC\tP1_B2_TCAACG\tP1_B2_TCTAGG\tP1_B2_TGATCG\tP1_B2_TGTAGC\tP1_B2_TGTTCC\tP1_B2_TGTTGG\tP1_B2_TTACGC\tP1_B2_TTCGAC\tP1_B2_TTGCAC\tP1_B2_TTGCGA\tP1_B2_TTGCTG\tP1_B2_TTGGAG\tP1_B2_TTGGCA\tP1_B2_TTGGTC\tP1_B3_AACCTC\tP1_B3_AACGAG\tP1_B3_AAGCCA\tP1_B3_ACAAGC\tP1_B3_ACAGAC\tP1_B3_ACAGGA\tP1_B3_ACAGTG\tP1_B3_ACCAAC\tP1_B3_ACCAGA\tP1_B3_ACCATG\tP1_B3_ACGTAC\tP1_B3_ACGTGA\tP1_B3_ACGTTG\tP1_B3_ACTCAC\tP1_B3_ACTCGA\tP1_B3_ACTCTG\tP1_B3_ACTTCG\tP1_B3_AGACAG\tP1_B3_AGACCA\tP1_B3_AGACTC\tP1_B3_AGCTAG\tP1_B3_AGCTCA\tP1_B3_AGCTTC\tP1_B3_AGGAAG\tP1_B3_AGGACA\tP1_B3_AGGATC\tP1_B3_AGTGCA\tP1_B3_AGTGTC\tP1_B3_ATCACG\tP1_B3_ATGTCG\tP1_B3_ATTGCG\tP1_B3_CACCAA\tP1_B3_CACTCA\tP1_B3_CACTTC\tP1_B3_CAGAAG\tP1_B3_CATGCA\tP1_B3_CATGTC\tP1_B3_CCACAA\tP1_B3_CCATAG\tP1_B3_CGATGA\tP1_B3_CGATTG\tP1_B3_CGTCTA\tP1_B3_CTAAGC\tP1_B3_CTAGAC\tP1_B3_CTAGGA\tP1_B3_CTAGTG\tP1_B3_CTATCC\tP1_B3_CTCAGA\tP1_B3_CTCATG\tP1_B3_CTGTTG\tP1_B3_CTTCGA\tP1_B3_GAAGAC\tP1_B3_GAAGGA\tP1_B3_GAATCC\tP1_B3_GAATGG\tP1_B3_GACAAC\tP1_B3_GACAGA\tP1_B3_GACGAA\tP1_B3_GAGTGA\tP1_B3_GAGTTG\tP1_B3_GATACG\tP1_B3_GATCGA\tP1_B3_GATCTG\tP1_B3_GCAACA\tP1_B3_GGACAA\tP1_B3_GGTAAC\tP1_B3_GTACAG\tP1_B3_GTACCA\tP1_B3_GTACTC\tP1_B3_GTCTCA\tP1_B3_GTGAAG\tP1_B3_GTGACA\tP1_B3_GTGATC\tP1_B3_GTGGAA\tP1_B3_GTTGAG\tP1_B3_TAACGG\tP1_B3_TAGGAC\tP1_B3_TAGTGG\tP1_B3_TCACAG\tP1_B3_TCATCC\tP1_B3_TCCGAA\tP1_B3_TCTTGC\tP1_B3_TGAACC\tP1_B3_TGAAGG\tP1_B3_TGAGGA\tP1_B3_TGCAAC\tP1_B3_TGCAGA\tP1_B3_TGGTGA\tP1_B3_TGGTTG\tP1_B3_TGTACG\tP1_B3_TGTCGA\tP1_B3_TGTCTG\tP1_B3_TTCACC\tP1_B3_TTCCAG\tP1_B3_TTCTCG\tP1_B3_TTGTGC\tP1_B4_AACACC\tP1_B4_AACTGG\tP1_B4_AAGCAC\tP1_B4_AAGGTG\tP1_B4_AAGTGC\tP1_B4_ACATGG\tP1_B4_ACGGTA\tP1_B4_AGAACG\tP1_B4_AGATGC\tP1_B4_AGCGAA\tP1_B4_AGGCTA\tP1_B4_AGTACC\tP1_B4_AGTAGG\tP1_B4_ATACGG\tP1_B4_ATCAGC\tP1_B4_ATCCAC\tP1_B4_ATCCGA\tP1_B4_ATCGCA\tP1'..b'\t0\t0\t0\t0\t0\t1\t7\t0\t0\t0\t3\t0\t1\t0\t55\t0\t9\t0\t0\t0\t0\t3\t5\t5\t4\t0\t3\t0\t1\t0\t0\t0\t0\t0\t0\t14\t0\t0\t47\t0\t0\t8\t1\t0\t26\t0\t6\t24\t0\t6\t6\t0\t0\t4\t19\t0\t1\t4\t0\t0\t9\t0\t11\t1\t19\t15\t0\t10\t39\t0\t0\t4\t0\t14\t1\t1\t0\t3\t3\t0\t0\t8\t1\t0\t9\t12\t0\t1\t15\t0\t30\t1\t2\t1\t5\t21\t2\t7\t0\t6\t0\t0\t0\t0\t0\t0\t1\t0\t1\t0\t10\t0\t1\t0\t23\t0\t9\t2\t1\t4\t10\t0\t8\t1\t3\t0\t0\t0\t2\t0\t0\t0\t0\t2\t1\t1\t0\t6\t4\t0\t0\t0\t12\t1\t0\t2\t7\t34\t2\t0\t11\t4\t1\t0\t0\t1\t1\t4\t3\t0\t2\t7\t0\t0\t0\t0\t0\t12\t0\t0\t0\t0\t22\t14\t10\t0\t6\t2\t0\t9\t1\t0\t1\t0\t0\t7\t5\t2\t0\t0\t1\t0\t0\t0\t2\t14\t4\t7\t6\t2\t0\t0\t1\t0\t9\t0\t2\t9\t0\t6\t1\t0\t7\t10\t2\t0\t1\t0\t6\t2\t2\t16\t0\t0\t0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|