# HG changeset patch
# User jdv
# Date 1512410576 18000
# Node ID 192df6f6a41e181e31b722e84fbd92b177a63b7b

planemo upload for repository https://github.com/jvolkening/galaxy-tools/tree/master/tools/nanopore_qc commit 59d1272f707bdd73a3f7d085f96789e649d54240-dirty

diff -r 000000000000 -r 192df6f6a41e README.html
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.html	Mon Dec 04 13:02:56 2017 -0500
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+<h1 id="quality-control-for-nanopore-sequencing-data">Quality control for nanopore sequencing data</h1>
+<p><code>NanoporeQC</code> is a Galaxy tool for quality control of data from Oxford Nanopore sequencers. It utilizes the summary table produced by Albacore to generate QC statistics and plots.</p>
+<h2 id="attributions">Attributions</h2>
+<p><code>NanoporeQC</code> was originally forked from the <code>minion_qc</code> codebase (<a href="https://github.com/roblanf/minion_qc" class="uri">https://github.com/roblanf/minion_qc</a>)</p>
diff -r 000000000000 -r 192df6f6a41e README.md
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.md	Mon Dec 04 13:02:56 2017 -0500
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+# Quality control for nanopore sequencing data
+
+`NanoporeQC` is a Galaxy tool for quality control of data from Oxford Nanopore
+sequencers. It utilizes the summary table produced by Albacore to generate QC
+statistics and plots.
+
+## Attributions
+
+`NanoporeQC` was originally forked from the `minion_qc` codebase
+([https://github.com/roblanf/minion_qc](https://github.com/roblanf/minion_qc))
+
diff -r 000000000000 -r 192df6f6a41e nanopore_qc.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/nanopore_qc.R	Mon Dec 04 13:02:56 2017 -0500
@@ -0,0 +1,508 @@
+#!/usr/bin/Rscript
+
+# MinionQC version 1.0
+# Copyright (C) 2017 Robert Lanfear
+#
+# 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/>.
+
+
+# supress warnings
+options(warn=-1)
+
+library(ggplot2)
+library(plyr)
+library(reshape2)
+library(readr)
+library(yaml)
+suppressMessages(library(scales))
+#library(parallel)
+library(futile.logger)
+suppressPackageStartupMessages(library(data.table))
+suppressPackageStartupMessages(library(optparse))
+
+
+# option parsing #
+
+parser <- OptionParser()
+
+parser <- add_option(parser, 
+                     opt_str = c("-i", "--input"), 
+                     type = "character",
+                     dest = 'input.file',
+                     help="Input file or directory (required). Either a full path to a sequence_summary.txt file, or a full path to a directory containing one or more such files. In the latter case the directory is searched recursively."
+                     )
+
+parser <- add_option(parser, 
+                     opt_str = c("-o", "--outputdirectory"), 
+                     type = "character",
+                     dest = 'output.dir',
+                     help="Output directory (required). If a single sequencing_summary.txt file is passed as input, then the output directory will contain just the plots associated with that file. If a directory containing more than one sequencing_summary.txt files is passed as input, then the plots will be put into sub-directories that have the same names as the parent directories of each sequencing_summary.txt file"
+                     )
+
+parser <- add_option(parser, 
+                     opt_str = c("-q", "--qscore_cutoff"), 
+                     type="double", 
+                     default=7.0,
+                     dest = 'q',
+                     help="The cutoff value for the mean Q score of a read (default 7). Used to create separate plots for reads above and below this threshold"
+                     )
+
+opt = parse_args(parser)
+
+input.file = opt$input.file
+output.dir = opt$output.dir
+q = opt$q
+
+# this is how we label the reads at least as good as q
+q_title = paste("Q>=", q, sep="")
+
+
+# build the map for R9.5
+p1 = data.frame(channel=33:64, row=rep(1:4, each=8), col=rep(1:8, 4))
+p2 = data.frame(channel=481:512, row=rep(5:8, each=8), col=rep(1:8, 4))
+p3 = data.frame(channel=417:448, row=rep(9:12, each=8), col=rep(1:8, 4))
+p4 = data.frame(channel=353:384, row=rep(13:16, each=8), col=rep(1:8, 4))
+p5 = data.frame(channel=289:320, row=rep(17:20, each=8), col=rep(1:8, 4))
+p6 = data.frame(channel=225:256, row=rep(21:24, each=8), col=rep(1:8, 4))
+p7 = data.frame(channel=161:192, row=rep(25:28, each=8), col=rep(1:8, 4))
+p8 = data.frame(channel=97:128, row=rep(29:32, each=8), col=rep(1:8, 4))
+
+q1 = data.frame(channel=1:32, row=rep(1:4, each=8), col=rep(16:9, 4))
+q2 = data.frame(channel=449:480, row=rep(5:8, each=8), col=rep(16:9, 4))
+q3 = data.frame(channel=385:416, row=rep(9:12, each=8), col=rep(16:9, 4))
+q4 = data.frame(channel=321:352, row=rep(13:16, each=8), col=rep(16:9, 4))
+q5 = data.frame(channel=257:288, row=rep(17:20, each=8), col=rep(16:9, 4))
+q6 = data.frame(channel=193:224, row=rep(21:24, each=8), col=rep(16:9, 4))
+q7 = data.frame(channel=129:160, row=rep(25:28, each=8), col=rep(16:9, 4))
+q8 = data.frame(channel=65:96, row=rep(29:32, each=8), col=rep(16:9, 4))
+
+map = rbind(p1, p2, p3, p4, p5, p6, p7, p8, q1, q2, q3, q4, q5, q6, q7, q8)
+
+add_cols <- function(d, min.q){
+    # take a sequencing sumamry file (d), and a minimum Q value you are interested in (min.q)
+    # return the same data frame with the following columns added 
+        # cumulative.bases
+        # hour of run
+        # reads.per.hour
+    
+    d = subset(d, mean_qscore_template >= min.q)
+    
+    if(nrow(d)==0){ 
+        flog.error(paste("There are no reads with a mean Q score higher than your cutoff of ", min.q, ". Please choose a lower cutoff and try again.", sep = ""))
+        quit()
+    }
+    
+    d = merge(d, map, by="channel")
+    d = d[with(d, order(start_time)), ] # sort by read length
+    d$cumulative.bases.time = cumsum(as.numeric(d$start_time))
+    d = d[with(d, order(-sequence_length_template)), ] # sort by read length
+    d$cumulative.bases = cumsum(as.numeric(d$sequence_length_template))
+    d$hour = d$start_time %/% 3600
+    
+    # add the reads generated for each hour
+    reads.per.hour = as.data.frame(table(d$hour))
+    names(reads.per.hour) = c("hour", "reads_per_hour")
+    reads.per.hour$hour = as.numeric(as.character(reads.per.hour$hour))
+    d = merge(d, reads.per.hour, by = c("hour"))    
+    return(d)
+}
+
+
+load_summary <- function(filepath, min.q){
+    # load a sequencing summary and add some info
+    # min.q is a vector of length 2 defining 2 levels of min.q to have
+    # by default the lowest value is -Inf, i.e. includes all reads. The 
+    # other value in min.q is set by the user at the command line
+    d = read_tsv(filepath, col_types = cols_only(channel = 'i', 
+                                                num_events_template = 'i', 
+                                                sequence_length_template = 'i', 
+                                                mean_qscore_template = 'n',
+                                                sequence_length_2d = 'i',
+                                                mean_qscore_2d = 'n',
+                                                start_time = 'n'))
+    
+    if("sequence_length_2d" %in% names(d)){
+        # it's a 1D2 or 2D run
+        d$sequence_length_template = as.numeric(as.character(d$sequence_length_2d))
+        d$mean_qscore_template = as.numeric(as.character(d$mean_qscore_2d))
+        d$num_events_template = NA
+        d$start_time = as.numeric(as.character(d$start_time))
+        
+    }else{
+        d$sequence_length_template = as.numeric(as.character(d$sequence_length_template))
+        d$mean_qscore_template = as.numeric(as.character(d$mean_qscore_template))
+        d$num_events_template = as.numeric(as.character(d$num_events_template))
+        d$start_time = as.numeric(as.character(d$start_time))
+    }
+        
+    d$events_per_base = d$num_events_template/d$sequence_length_template
+
+    flowcell = basename(dirname(filepath))
+    
+    # add columns for all the reads
+    d1 = add_cols(d, min.q[1])
+    d1$Q_cutoff = "All reads"
+    
+    # add columns for just the reads that pass the user Q threshold
+    d2 = add_cols(d, min.q[2])
+    d2$Q_cutoff = q_title
+    
+    # bind those two together into one data frame
+    d = as.data.frame(rbindlist(list(d1, d2)))
+
+    # name the flowcell (useful for analyses with >1 flowcell)
+    d$flowcell = flowcell
+    
+    # make sure this is a factor
+    d$Q_cutoff = as.factor(d$Q_cutoff)
+
+    
+    keep = c("hour","start_time", "channel", "sequence_length_template", "mean_qscore_template", "row", "col", "cumulative.bases", "cumulative.bases.time", "reads_per_hour", "Q_cutoff", "flowcell", "events_per_base")
+    d = d[keep]
+
+    d$start_bin = cut(d$start_time, 9,labels=c(1:9))
+    write.table(d,"foo.tsv",sep="\t",quote=F)
+        
+    return(d)
+}
+
+reads.gt <- function(d, len){
+    # return the number of reads in data frame d
+    # that are at least as long as length len
+    return(length(which(d$sequence_length_template>=len)))
+}
+
+bases.gt <- function(d, len){
+    # return the number of bases contained in reads from 
+    # data frame d
+    # that are at least as long as length len
+    reads = subset(d, sequence_length_template >= len)
+    return(sum(as.numeric(reads$sequence_length_template)))
+}
+
+log10_minor_break = function (...){
+    # function to add minor breaks to a log10 graph
+    # hat-tip: https://stackoverflow.com/questions/30179442/plotting-minor-breaks-on-a-log-scale-with-ggplot
+    function(x) {
+        minx         = floor(min(log10(x), na.rm=T))-1;
+        maxx         = ceiling(max(log10(x), na.rm=T))+1;
+        n_major      = maxx-minx+1;
+        major_breaks = seq(minx, maxx, by=1)
+        minor_breaks = 
+            rep(log10(seq(1, 9, by=1)), times = n_major)+
+            rep(major_breaks, each = 9)
+        return(10^(minor_breaks))
+    }
+}
+
+binSearch <- function(min, max, df, t = 100000) {
+    # binary search algorithm, thanks to https://stackoverflow.com/questions/46292438/optimising-a-calculation-on-every-cumulative-subset-of-a-vector-in-r/46303384#46303384
+    # the aim is to return the number of reads in a dataset (df)
+    # that comprise the largest subset of reads with an N50 of t
+    # we use this to calculte the number of 'ultra long' reads
+    # which are defined as those with N50 > 100KB
+    mid = floor(mean(c(min, max)))
+    if (mid == min) {
+        if (df$sequence_length_template[min(which(df$cumulative.bases>df$cumulative.bases[min]/2))] < t) {
+            return(min - 1)
+        } else {
+            return(max - 1)
+        }
+    }
+    
+    n = df$sequence_length_template[min(which(df$cumulative.bases>df$cumulative.bases[mid]/2))]
+    if (n >= t) {
+        return(binSearch(mid, max, df))
+    } else {
+        return(binSearch(min, mid, df))
+    }
+}
+
+
+summary.stats <- function(d, Q_cutoff="All reads"){
+    # Calculate summary stats for a single value of min.q
+    
+    rows = which(as.character(d$Q_cutoff)==Q_cutoff)
+    d = d[rows,]
+    d = d[with(d, order(-sequence_length_template)), ] # sort by read length, just in case
+    
+    total.bases = sum(as.numeric(d$sequence_length_template))
+    total.reads = nrow(d)
+    N50.length = d$sequence_length_template[min(which(d$cumulative.bases > (total.bases/2)))]
+    mean.length = round(mean(as.numeric(d$sequence_length_template)), digits = 1)
+    median.length = round(median(as.numeric(d$sequence_length_template)), digits = 1)
+    max.length = max(as.numeric(d$sequence_length_template))
+    mean.q = round(mean(d$mean_qscore_template), digits = 1)
+    median.q = round(median(d$mean_qscore_template), digits = 1)
+    
+    #calculate ultra-long reads and bases (max amount of data with N50>100KB)
+    ultra.reads = binSearch(1, nrow(d), d, t = 100000)    
+    if(ultra.reads>=1){
+        ultra.gigabases = sum(as.numeric(d$sequence_length_template[1:ultra.reads]))/1000000000
+    }else{
+        ultra.gigabases = 0
+    }
+        
+    reads = list(
+                reads.gt(d, 10000), 
+                reads.gt(d, 20000), 
+                reads.gt(d, 50000),
+                reads.gt(d, 100000),
+                reads.gt(d, 200000),
+                reads.gt(d, 500000),
+                reads.gt(d, 1000000),
+                ultra.reads)
+    names(reads) = c(">10kb", ">20kb", ">50kb", ">100kb", ">200kb", ">500kb", ">1m", "ultralong")
+
+    bases = list(
+                bases.gt(d, 10000)/1000000000, 
+                bases.gt(d, 20000)/1000000000, 
+                bases.gt(d, 50000)/1000000000,
+                bases.gt(d, 100000)/1000000000,
+                bases.gt(d, 200000)/1000000000,
+                bases.gt(d, 500000)/1000000000,
+                bases.gt(d, 1000000)/1000000000,
+                ultra.gigabases)
+    names(bases) = c(">10kb", ">20kb", ">50kb", ">100kb", ">200kb", ">500kb", ">1m", "ultralong")
+    
+    return(list('total.gigabases' = total.bases/1000000000,
+                'total.reads' = total.reads,
+                'N50.length' = N50.length, 
+                'mean.length' = mean.length, 
+                'median.length' = median.length,
+                'max.length' = max.length,
+                'mean.q' = mean.q,
+                'median.q' = median.q,
+                'reads' = reads,
+                'gigabases' = bases
+                ))
+}
+
+channel.summary <- function(d){
+    # calculate summaries of what happened in each of the channels 
+    # of a flowcell
+    
+    a = ddply(d, .(channel), 
+              summarize, 
+              total.bases = sum(sequence_length_template), 
+              total.reads = sum(which(sequence_length_template>=0)), 
+              mean.read.length = mean(sequence_length_template), 
+              median.read.length = median(sequence_length_template))
+    b = melt(a, id.vars = c("channel"))
+    return(b)    
+}
+
+
+single.flowcell <- function(input.file, output.dir, q=8){
+    # wrapper function to analyse data from a single flowcell
+    # input.file is a sequencing_summary.txt file from a 1D run
+    # output.dir is the output directory into which to write results
+    # q is the cutoff used for Q values, set by the user
+    flog.info(paste("Loading input file:", input.file))
+    d = load_summary(input.file, min.q=c(-Inf, q))
+
+    flowcell = unique(d$flowcell)
+
+    flog.info(paste(sep = "", flowcell, ": creating output directory:", output.dir))
+    dir.create(output.dir)
+    out.txt = file.path(output.dir, "summary.yaml")
+    
+    flog.info(paste(sep = "", flowcell, ": summarising input file for flowcell"))
+    all.reads.summary = summary.stats(d, Q_cutoff = "All reads")
+    q10.reads.summary = summary.stats(d, Q_cutoff = q_title)
+    
+    summary = list("input file" = input.file,
+                   "All reads" = all.reads.summary,
+                   cutoff = q10.reads.summary,
+                   "notes" = 'ultralong reads refers to the largest set of reads with N50>100KB')
+    
+    names(summary)[3] = q_title
+    
+    write(as.yaml(summary), out.txt)
+    
+    muxes = seq(from = 0, to = max(d$hour), by = 8)
+    
+    # make plots
+    flog.info(paste(sep = "", flowcell, ": plotting length histogram"))
+    len.lims <- quantile(d$sequence_length_template,c(0.01,0.99))
+    
+    p1 = ggplot(d, aes(x = sequence_length_template)) + 
+        geom_histogram(bins = 200, fill="steelblue") + 
+        scale_x_log10(breaks=round(10^pretty(log10(len.lims),n=5),0),limits=len.lims) + 
+        facet_wrap(~Q_cutoff, ncol = 1, scales = "free_y") + 
+        #theme(text = element_text(size = 15)) +
+        theme_linedraw() +
+        xlab("Read length") +
+        ylab("Number of reads")
+    ggsave(filename = file.path(output.dir, "length_histogram.png"), width = 7, height = 4, dpi=300, plot = p1)
+    ggsave(filename = file.path(output.dir, "length_histogram.screen.png"), width = 7, height = 4, dpi=90, plot = p1)
+
+    flog.info(paste(sep = "", flowcell, ": plotting mean Q score histogram"))
+    q.lims <- quantile(d$mean_qscore_template,c(0.005,0.995))
+    p2 = ggplot(subset(d, Q_cutoff=="All reads"), aes(x = mean_qscore_template)) + 
+        geom_histogram(bins = 200, fill="steelblue") + 
+        xlim(q.lims) +
+        geom_vline(xintercept=q) +
+        #theme(text = element_text(size = 15)) +
+        theme_linedraw() +
+        xlab("Mean Q score of read") +
+        ylab("Number of reads")
+    ggsave(filename = file.path(output.dir, "q_histogram.png"), width = 7, height = 4, dpi=300, plot = p2)
+    ggsave(filename = file.path(output.dir, "q_histogram.screen.png"), width = 7, height = 4, dpi=90, plot = p2)
+
+   
+    flog.info(paste(sep = "", flowcell, ": plotting flowcell overview"))
+
+    df <- data.frame(row=integer(),col=integer(),bin=integer(), quality=numeric())
+
+    # swap rows and columns
+    for (b in c(1,5,9)) {
+        m <- matrix(data=rep(0,512),nrow=16,ncol=32)
+        for (r in 1:32) {
+        for (c in 1:16) {
+            sub <- subset(d, Q_cutoff=="All reads" & col == c & row == r & start_bin == b)
+            Q <-  median(sub$mean_qscore_template)
+            df <- rbind(df, list(row=r, col=c, bin=b, quality=Q) )
+        }
+        }
+    }
+
+    df$bin <- factor(df$bin)
+    levels(df$bin) <- c("Run start", "Run middle", "Run end")
+    p5 = ggplot(df, aes(col, row)) + 
+        geom_tile(aes(fill = quality), color="white") +
+        scale_fill_gradient(low = "white", high="steelblue") +
+        xlab("Column") +
+        ylab("Row") +
+        facet_wrap(~bin, ncol=3) +
+        theme_linedraw() +
+        theme(panel.grid.minor = element_blank(), panel.grid.major=element_blank())
+        #theme(text = element_text(size = 40), axis.text.x = element_text(size=12), axis.text.y = element_text(size=12), legend.text=element_text(size=12))
+    ggsave(filename = file.path(output.dir, "flowcell_overview.png"), width = 7, height = 4, dpi=300, plot = p5)
+    ggsave(filename = file.path(output.dir, "flowcell_overview.screen.png"), width = 7, height = 4, dpi=90, plot = p5)
+
+    flog.info(paste(sep = "", flowcell, ": plotting cumulative yield summary"))
+    p5a = ggplot(d, aes(x=start_time/3600, y=cumulative.bases.time, colour = Q_cutoff)) + 
+        geom_line(size = 1) + 
+        geom_vline(xintercept = muxes, colour = 'red', linetype = 'dashed', alpha = 0.5) +
+        xlab("Elapsed time (hrs)") +
+        ylab("Total yield in bases") +
+        scale_colour_discrete(guide = guide_legend(title = "Reads")) +
+        scale_x_continuous() +
+        theme_linedraw()
+        #theme(text = element_text(size = 15))
+
+    ggsave(filename = file.path(output.dir, "cumulative_yield.png"), width = 7, height = 4, dpi=300, plot = p5a)
+    ggsave(filename = file.path(output.dir, "cumulative_yield.screen.png"), width = 7, height = 4, dpi=90, plot = p5a)
+
+    flog.info(paste(sep = "", flowcell, ": plotting flowcell yield summary"))
+    p6 = ggplot(d, aes(x=sequence_length_template, y=cumulative.bases, colour = Q_cutoff)) + 
+        geom_line(size = 1) + 
+        xlab("Minimum read length") +
+        ylab("Total yield in bases") +
+        scale_colour_discrete(guide = guide_legend(title = "Reads")) +
+        #theme(text = element_text(size = 15))
+        theme_linedraw()
+    xmax = max(d$sequence_length_template[which(d$cumulative.bases > 0.01 * max(d$cumulative.bases))])
+    p6 = p6 + scale_x_continuous(limits = c(0, xmax))
+
+    ggsave(filename = file.path(output.dir, "yield_summary.png"), width = 7, height = 4, dpi=300, plot = p6)
+    ggsave(filename = file.path(output.dir, "yield_summary.screen.png"), width = 7, height = 4, dpi=90, plot = p6)
+    
+    flog.info(paste(sep = "", flowcell, ": plotting sequence length over time"))
+    e = subset(d, Q_cutoff=="All reads")
+    e$Q = paste(">=", q, sep="")
+    e$Q[which(e$mean_qscore_template<q)] = paste("<", q, sep="")
+    p7 = ggplot(e, aes(x=start_time/3600, y=sequence_length_template, colour = Q, group = Q)) + 
+        geom_vline(xintercept = muxes, colour = 'red', linetype = 'dashed', alpha = 0.5) +
+        geom_smooth() + 
+        xlab("Elapsed time (hrs)") + 
+        ylab("Mean read length") + 
+        ylim(0, NA) +
+        theme_linedraw()
+    ggsave(filename = file.path(output.dir, "length_by_hour.png"), width = 7, height = 4, dpi=300, plot = p7)
+    ggsave(filename = file.path(output.dir, "length_by_hour.screen.png"), width = 7, height = 4, dpi=90, plot = p7)
+    
+    flog.info(paste(sep = "", flowcell, ": plotting Q score over time"))
+    p8 = ggplot(e, aes(x=start_time/3600, y=mean_qscore_template, colour = Q, group = Q)) + 
+        geom_vline(xintercept = muxes, colour = 'red', linetype = 'dashed', alpha = 0.5) +
+        geom_smooth() + 
+        xlab("Elapsed time (hrs)") + 
+        ylab("Mean Q score") + 
+        ylim(0, NA) +
+        theme_linedraw()
+    ggsave(filename = file.path(output.dir, "q_by_hour.png"), width = 7, height = 4, dpi=300, plot = p8)
+    ggsave(filename = file.path(output.dir, "q_by_hour.screen.png"), width = 7, height = 4, dpi=90, plot = p8)
+    
+    flog.info(paste(sep = "", flowcell, ": plotting reads per hour"))
+    f = d[c("hour", "reads_per_hour", "Q_cutoff")]
+    f = f[!duplicated(f),]
+    g = subset(f, Q_cutoff=="All reads")
+    h = subset(f, Q_cutoff==q_title)
+    max = max(f$hour)
+    # all of this is just to fill in hours with no reads recorded
+    all = 0:max
+    add.g = all[which(all %in% g$hour == FALSE)]
+    if(length(add.g)>0){
+        add.g = data.frame(hour = add.g, reads_per_hour = 0, Q_cutoff = "All reads")
+        g = rbind(g, add.g)
+    }
+    add.h = all[which(all %in% h$hour == FALSE)]
+    if(length(add.h)>0){
+        add.h = data.frame(hour = add.h, reads_per_hour = 0, Q_cutoff = q_title)
+        h = rbind(h, add.h)
+    }
+    i = rbind(g, h)
+    i$Q_cutoff = as.character(i$Q_cutoff)
+    i$Q_cutoff[which(i$Q_cutoff==q_title)] = paste("Q>=", q, sep="")
+    p9 = ggplot(i, aes(x=hour, y=reads_per_hour, colour = Q_cutoff, group = Q_cutoff)) + 
+        geom_vline(xintercept = muxes, colour = 'red', linetype = 'dashed', alpha = 0.5) +
+        #geom_point() +
+        geom_line(size=1) +
+        xlab("Elapsed time (hrs)") + 
+        ylab("Number of reads per hour") + 
+        ylim(0, NA) + 
+        scale_color_discrete(guide = guide_legend(title = "Reads")) +
+        theme_linedraw()
+    ggsave(filename = file.path(output.dir, "reads_per_hour.png"), width = 7, height = 4, dpi=300, plot = p9)
+    ggsave(filename = file.path(output.dir, "reads_per_hour.screen.png"), width = 7, height = 4, dpi=90, plot = p9)
+    
+    flog.info(paste(sep = "", flowcell, ": plotting read length vs. q score scatterplot"))
+    p10 = ggplot(subset(d, Q_cutoff=="All reads"), aes(x = sequence_length_template, y = mean_qscore_template, colour = events_per_base)) + 
+        geom_point(alpha=0.05, size = 0.4) + 
+        scale_x_log10(minor_breaks=log10_minor_break()) + 
+        labs(colour='Events per base\n(log scale)\n')  + 
+        theme(text = element_text(size = 15)) +
+        xlab("Read length") +
+        ylab("Mean Q score of read") +
+        theme_linedraw()
+
+    if(max(d$events_per_base, na.rm=T)>0){
+        # a catch for 1D2 runs which don't have events per base
+        p10 = p10 + scale_colour_distiller(trans = "log", labels = scientific, palette = 'Spectral') 
+    }
+
+    ggsave(filename = file.path(output.dir, "length_vs_q.png"), width = 7, height = 4, dpi=300, plot = p10)
+    ggsave(filename = file.path(output.dir, "length_vs_q.screen.png"), width = 7, height = 4, dpi=90, plot = p10)
+    
+    return(d)
+}
+
+if(file_test("-f", input.file)==TRUE){
+    # if it's an existing file (not a folder) just run one analysis
+    d = single.flowcell(input.file, output.dir, q)
+}else{
+    #WTF
+    flog.warn(paste("Could find a sequencing summary file in your input which was: ", 
+        input.file, 
+        "\nThe input must be a sequencing_summary.txt file produced by Albacore"))
+}
diff -r 000000000000 -r 192df6f6a41e nanopore_qc.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/nanopore_qc.xml	Mon Dec 04 13:02:56 2017 -0500
@@ -0,0 +1,67 @@
+<tool id="nanopore_qc" name="NanoporeQC" version="0.001">
+
+    <description>QC report for nanopore data</description>
+
+    <requirements>
+        <requirement type="package" version="2.2.1">r-ggplot2</requirement>
+        <requirement type="package" version="1.8.4">r-plyr</requirement>
+        <requirement type="package" version="1.4.2">r-reshape2</requirement>
+        <requirement type="package" version="0.2.2">r-readr</requirement>
+        <requirement type="package" version="2.1.14">r-yaml</requirement>
+        <requirement type="package" version="0.4.1">r-scales</requirement>
+        <requirement type="package" version="1.4.1">r-futile.logger</requirement>
+        <requirement type="package" version="1.10.4">r-data.table</requirement>
+        <requirement type="package" version="1.3.2">r-optparse</requirement>
+        <requirement type="package" version="1.8">r-mgcv</requirement>
+        <requirement type="package" version="0.66">perl-yaml-libyaml</requirement>
+    </requirements>
+
+    <stdio>
+        <exit_code range="1:" level="fatal" description="Unknown error" />
+        <exit_code range=":-1" level="fatal" description="Unknown error" />
+    </stdio>
+
+    <version_command>
+    <![CDATA[
+        echo '0.001'
+    ]]>
+    </version_command>
+
+    <command>
+    <![CDATA[
+
+        mkdir -p '${html_file.files_path}'
+
+        &&
+
+        Rscript '${__tool_directory__}/nanopore_qc.R'
+            -i '$input'
+            -o '${html_file.files_path}'
+            -q '$q_cutoff'
+
+        &&
+
+        perl '${__tool_directory__}/yaml_to_html.pl'
+
+            '${html_file.files_path}/summary.yaml'
+            '$html_file'
+
+    ]]>
+    </command>
+
+    <inputs>
+        <param name="input" type="data" format="tabular" label="Basecall summary file from Albacore"/>
+        <param name="q_cutoff" type="float" value="10" min="0" label="Quality cutoff for QC calculations" />
+    </inputs>
+    <outputs>
+        <data format="html" name="html_file" label="NanoporeQC on ${on_string}" />
+    </outputs>
+    <tests>
+    </tests>
+    <help>
+<![CDATA[
+]]>
+    </help>
+    <citations>
+    </citations>
+</tool>
diff -r 000000000000 -r 192df6f6a41e yaml_to_html.pl
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/yaml_to_html.pl	Mon Dec 04 13:02:56 2017 -0500
@@ -0,0 +1,209 @@
+#!/usr/bin/env perl
+
+use strict;
+use warnings;
+use 5.012;
+
+use YAML::XS qw/LoadFile/;
+use autodie;
+
+my ($fn_in, $fn_out) = @ARGV;
+
+die "Can't find or read input file: $!\n"
+    if (! -r $fn_in);
+
+# set output filehandle based on arguments
+my $fh = \*STDOUT;
+if (defined $fn_out) {
+    open $fh, '>', $fn_out;
+}
+
+my $yaml = LoadFile($ARGV[0]);
+
+convert($yaml);
+
+sub convert {
+
+    my ($yaml) = @_;
+
+    print {$fh} header();
+
+    say {$fh} "    <h3>Summary statistics</h3>";
+
+    for my $grp (sort keys %$yaml) {
+
+        my $ref = $yaml->{$grp};
+
+        next if (! ref $ref);
+        next if (! defined $ref->{'total.gigabases'});
+        
+        print {$fh} <<"CONTENT"
+    <table>
+        <caption>$grp</caption>
+        <tr>
+            <td>Total Yield (Gb)</td>
+            <td>$ref->{'total.gigabases'}</td>
+        </tr>
+        <tr>
+            <td>Total Reads</td>
+            <td>$ref->{'total.reads'}</td>
+        </tr>
+        <tr>
+            <td>Mean Length</td>
+            <td>$ref->{'mean.length'}</td>
+        </tr>
+        <tr>
+            <td>Median Length</td>
+            <td>$ref->{'median.length'}</td>
+        </tr>
+        <tr>
+            <td>Max Length</td>
+            <td>$ref->{'max.length'}</td>
+        </tr>
+        <tr>
+            <td>Mean Q</td>
+            <td>$ref->{'mean.q'}</td>
+        </tr>
+        <tr>
+            <td>Median Q</td>
+            <td>$ref->{'median.q'}</td>
+        </tr>
+    </table>
+CONTENT
+
+    }
+
+    my %figs = (
+        'length_histogram'  => "Read length distribution",
+        'q_histogram'       => "Mean quality score distribution",
+        'reads_per_hour'    => "Yield over time",
+        'cumulative_yield'  => "Cumulative yield over time",
+        'yield_summary'     => "Yield by read length cutoff",
+        'flowcell_overview' => "Median read quality per channel",
+        'length_by_hour'    => "Read length over time",
+        'q_by_hour'         => "Read quality over time",
+        'length_vs_q'       => "Read length vs. quality",
+    );
+
+    my @order = qw/
+        length_histogram
+        q_histogram
+        reads_per_hour
+        cumulative_yield
+        yield_summary
+        flowcell_overview
+        length_by_hour
+        q_by_hour
+        length_vs_q
+    /;
+
+
+    say {$fh} "    <h3>QC plots</h3>";
+    say {$fh} "    <p>(Click on plot for hi-resolution version)</p>";
+
+    for my $base (@order) {
+
+        my $caption = $figs{$base} // die "No caption found for $base";
+
+        print {$fh} <<"CONTENT"
+    <a href="$base.png">
+        <figure>
+            <img src="$base.screen.png" alt="$base" />
+            <figcaption>$caption</figcaption>
+        </figure>
+    </a>
+CONTENT
+
+    }
+
+    print {$fh} footer();
+
+}
+
+
+
+sub header {
+
+    return <<'HEADER';
+<?xml version="1.0" encoding="utf-8"?>
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+    <title></title>
+    <meta http-equiv="content-type" content="application/xhtml+xml; charset=utf-8" />
+    <style>
+        h2 {
+            padding: 0.3em;
+            background-color: #000000;
+            color: #ffffff;
+            margin: 1em 0 2em 0;
+        }
+        h3 {
+            padding: 0em 0.2em 0em 0.2em;
+            color: #555555;
+            border: solid 1px black;
+            border-width: 0px 0px 1px 0px;
+            margin: 2em 0 0.4em 0;
+        }
+        tr {
+            margin: 0;
+        }
+        tr:nth-child(even) {
+            background-color: #bbbbbb;
+        }
+        tr:nth-child(odd) {
+            background-color: #eeeeee;
+        }
+        caption {
+            text-align: left;
+            font-weight: bold;
+            background-color: #550000;
+            color: #ffffff;
+            padding: 0.1em 0.2em;
+        }
+        table {
+            margin: 1em;
+            padding: 0.3em;
+        }
+        td {
+            margin: 0;
+            padding: 0 0.4em;
+        }
+        tr td:nth-child(1) {
+            color: #550000;
+        }
+        figure {
+            display: table;
+            margin: 2em 0;
+        }
+        figcaption {
+            display: table-caption;
+            caption-side: top;
+            font-size: 1.1em;
+            text-decoration: none;
+            text-align: center;
+            font-weight: bold;
+            background-color: #550000;
+            color: #ffffff;
+            padding: 0.1em 0.2em;
+            margin: 2em 0 0.7em 0;
+        }
+            
+    </style>
+</head>
+<body>
+
+    <h2>NanoporeQC Report</h2>
+HEADER
+
+}
+
+sub footer {
+
+    return <<'FOOTER';
+
+</body>
+</html>
+FOOTER
+
+}