GitHub - elsayed-lab/lmajor_alternate_acceptor_site_usage: Analysis comparing the use of alternative trans-splicing and alternative-polyadenylation sites across developmental stages in Leishmania major

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Analysis comparing the use of alternative trans-splicing and alternative-polyadenylation sites across developmental stages in Leishmania major
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  <title>L. major UTR Analysis</title>
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      $('#highlight[rel=stylesheet]').attr('href', css.attr('href'));
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  <style>
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    overflow: hidden;
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  .toggle{
    text-transform: capitalize;
  }
  
  .toggle-global{
    text-transform: capitalize;
  }
  
  /* Sticky footer styles */
  * {
    margin:0;
  }
  html,
  body {
      height: 100%;
      padding:0 !important;
      /* The html and body elements cannot have any padding or margin. */
      /*overflow-x: hidden;*/
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  /* Wrapper for page content to push down footer */
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      min-height: 100%;
      height: auto !important;
      height: 100%;
      /* Negative indent footer by it's height */
      margin: 0 auto -120px;
  }
  
  /* Set the fixed height of the footer here */
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  #footer {
      height: 120px;
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  #footer {
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  /* Makes the font smaller for all subheader elements. */
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  button.thumbnails {
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  /*
   * Side navigation
   *
   * Scrollspy and affixed enhanced navigation to highlight sections and secondary
   * sections of docs content.
   */
  
  /* By default it's not affixed in mobile views, so undo that */
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    .sidebar.affix,
    .sidebar.affix-bottom {
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    .sidebar.affix-top,
    .sidebar.affix {
      position: fixed; /* Undo the static from mobile first approach */
      top: 30px;
    }
    .sidebar.affix-bottom {
      position: absolute; /* Undo the static from mobile first approach */
    }
    .sidebar.affix-bottom .sidenav,
    .sidebar.affix .sidenav {
      margin-top: 0;
      margin-bottom: 0;
    }
  }
  @media screen and (min-width: 1200px) {
    /* Widen the fixed sidebar again */
    .sidebar.affix-bottom,
    .sidebar.affix {
      width: 263px;
    }
  }
  
  #toc {
    padding: 10px 0px;
    margin:0;
    border:0;
  }
  
  
  .panel pre {
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    padding: 0;
    border: 0;
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  button + pre {
    margin: 0;
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  pre code {
    border-radius: 0;
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    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    z-index: 1042;
    overflow: hidden;
    position: fixed;
    background: #0b0b0b;
    opacity: 0.8;
    filter: alpha(opacity=80); }
  
  .mfp-wrap {
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    left: 0;
    width: 100%;
    height: 100%;
    z-index: 1043;
    position: fixed;
    outline: none !important;
    -webkit-backface-visibility: hidden; }
  
  .mfp-container {
    text-align: center;
    position: absolute;
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    height: 100%;
    left: 0;
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    padding: 0 8px;
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    -moz-box-sizing: border-box;
    box-sizing: border-box; }
  
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    height: 100%;
    vertical-align: middle; }
  
  .mfp-align-top .mfp-container:before {
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  .mfp-content {
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    display: inline-block;
    vertical-align: middle;
    margin: 0 auto;
    text-align: left;
    z-index: 1045; }
  
  .mfp-inline-holder .mfp-content,
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  .mfp-ajax-cur {
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  .mfp-zoom-out-cur,
  .mfp-zoom-out-cur .mfp-image-holder .mfp-close {
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    cursor: -webkit-zoom-out;
    cursor: zoom-out; }
  
  .mfp-zoom {
    cursor: pointer;
    cursor: -webkit-zoom-in;
    cursor: -moz-zoom-in;
    cursor: zoom-in; }
  
  .mfp-auto-cursor .mfp-content {
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  .mfp-close,
  .mfp-arrow,
  .mfp-preloader,
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    -webkit-user-select: none;
    -moz-user-select: none;
    user-select: none; }
  
  .mfp-loading.mfp-figure {
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  .mfp-hide {
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  .mfp-preloader {
    color: #cccccc;
    position: absolute;
    top: 50%;
    width: auto;
    text-align: center;
    margin-top: -0.8em;
    left: 8px;
    right: 8px;
    z-index: 1044; }
  
  .mfp-preloader a {
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  .mfp-preloader a:hover {
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  .mfp-s-ready .mfp-preloader {
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  .mfp-s-error .mfp-content {
    display: none; }
  
  button.mfp-close,
  button.mfp-arrow {
    overflow: visible;
    cursor: pointer;
    background: transparent;
    border: 0;
    -webkit-appearance: none;
    display: block;
    padding: 0;
    z-index: 1046;
    -webkit-box-shadow: none;
    box-shadow: none; }
  
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    border: 0; }
  
  .mfp-close {
    width: 44px;
    height: 44px;
    line-height: 44px;
    position: absolute;
    right: 0;
    top: 0;
    text-decoration: none;
    text-align: center;
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  .mfp-close-btn-in .mfp-close {
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  .mfp-image-holder .mfp-close,
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    right: -6px;
    text-align: right;
    padding-right: 6px;
    width: 100%; }
  
  .mfp-counter {
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    top: 0;
    right: 0;
    color: #cccccc;
    font-size: 12px;
    line-height: 18px; }
  
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    position: absolute;
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    margin: 0;
    top: 50%;
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  .mfp-arrow:hover,
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  .mfp-arrow:before, .mfp-arrow:after,
  .mfp-arrow .mfp-b,
  .mfp-arrow .mfp-a {
    content: '';
    display: block;
    width: 0;
    height: 0;
    position: absolute;
    left: 0;
    top: 0;
    margin-top: 35px;
    margin-left: 35px;
    border: medium inset transparent; }
  .mfp-arrow:after,
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    border-bottom-width: 13px;
    top: 8px; }
  .mfp-arrow:before,
  .mfp-arrow .mfp-b {
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    border-bottom-width: 21px; }
  
  .mfp-arrow-left {
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    .mfp-arrow-left:after,
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      border-right: 17px solid white;
      margin-left: 31px; }
    .mfp-arrow-left:before,
    .mfp-arrow-left .mfp-b {
      margin-left: 25px;
      border-right: 27px solid #3f3f3f; }
  
  .mfp-arrow-right {
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    .mfp-arrow-right:after,
    .mfp-arrow-right .mfp-a {
      border-left: 17px solid white;
      margin-left: 39px; }
    .mfp-arrow-right:before,
    .mfp-arrow-right .mfp-b {
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  .mfp-iframe-holder {
    padding-top: 40px;
    padding-bottom: 40px; }
  
  .mfp-iframe-holder .mfp-content {
    line-height: 0;
    width: 100%;
    max-width: 900px; }
  
  .mfp-iframe-scaler {
    width: 100%;
    height: 0;
    overflow: hidden;
    padding-top: 56.25%; }
  
  .mfp-iframe-scaler iframe {
    position: absolute;
    display: block;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    box-shadow: 0 0 8px rgba(0, 0, 0, 0.6);
    background: black; }
  
  .mfp-iframe-holder .mfp-close {
    top: -40px; }
  
  /* Main image in popup */
  img.mfp-img {
    width: auto;
    max-width: 100%;
    height: auto;
    display: block;
    line-height: 0;
    -webkit-box-sizing: border-box;
    -moz-box-sizing: border-box;
    box-sizing: border-box;
    padding: 40px 0 40px;
    margin: 0 auto; }
  
  /* The shadow behind the image */
  .mfp-figure:after {
    content: '';
    position: absolute;
    left: 0;
    top: 40px;
    bottom: 40px;
    display: block;
    right: 0;
    width: auto;
    height: auto;
    z-index: -1;
    box-shadow: 0 0 8px rgba(0, 0, 0, 0.6);
    background: #444444; }
  
  .mfp-figure {
    line-height: 0; }
  
  .mfp-bottom-bar {
    margin-top: -36px;
    position: absolute;
    top: 100%;
    left: 0;
    width: 100%;
    cursor: auto; }
  
  .mfp-title {
    text-align: left;
    line-height: 18px;
    color: #f3f3f3;
    word-wrap: break-word;
    padding-right: 36px; }
  
  .mfp-figure small {
    color: #bdbdbd;
    display: block;
    font-size: 12px;
    line-height: 14px; }
  
  .mfp-image-holder .mfp-content {
    max-width: 100%; }
  
  .mfp-gallery .mfp-image-holder .mfp-figure {
    cursor: pointer; }
  
  @media screen and (max-width: 800px) and (orientation: landscape), screen and (max-height: 300px) {
    /**
     * Remove all paddings around the image on small screen
     */
    .mfp-img-mobile .mfp-image-holder {
      padding-left: 0;
      padding-right: 0; }
  
    .mfp-img-mobile img.mfp-img {
      padding: 0; }
  
    /* The shadow behind the image */
    .mfp-img-mobile .mfp-figure:after {
      top: 0;
      bottom: 0; }
  
    .mfp-img-mobile .mfp-bottom-bar {
      background: rgba(0, 0, 0, 0.6);
      bottom: 0;
      margin: 0;
      top: auto;
      padding: 3px 5px;
      position: fixed;
      -webkit-box-sizing: border-box;
      -moz-box-sizing: border-box;
      box-sizing: border-box; }
  
    .mfp-img-mobile .mfp-bottom-bar:empty {
      padding: 0; }
  
    .mfp-img-mobile .mfp-counter {
      right: 5px;
      top: 3px; }
  
    .mfp-img-mobile .mfp-close {
      top: 0;
      right: 0;
      width: 35px;
      height: 35px;
      line-height: 35px;
      background: rgba(0, 0, 0, 0.6);
      position: fixed;
      text-align: center;
      padding: 0; }
  
    .mfp-img-mobile .mfp-figure small {
      display: inline;
      margin-left: 5px; } }
  @media all and (max-width: 900px) {
    .mfp-arrow {
      -webkit-transform: scale(0.75);
      transform: scale(0.75); }
  
    .mfp-arrow-left {
      -webkit-transform-origin: 0;
      transform-origin: 0; }
  
    .mfp-arrow-right {
      -webkit-transform-origin: 100%;
      transform-origin: 100%; }
  
    .mfp-container {
      padding-left: 6px;
      padding-right: 6px; } }
  .mfp-ie7 .mfp-img {
    padding: 0; }
  .mfp-ie7 .mfp-bottom-bar {
    width: 600px;
    left: 50%;
    margin-left: -300px;
    margin-top: 5px;
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<body>
<div id="header">
<h1 class="title">L. major UTR Analysis</h1>
</div>
<div id="wrap">
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<div class="contents col-xs-12 col-md-10">
<h1>L. major UTR Length and Alternative Trans-splicing / Poly-adenylation Analysis</h1>
<h2>Overview</h2>
<p>The goal of this analysis is to parse the output from our <a href="https://github.com/khughitt/utr_analysis">UTR analysis pipeline</a>, perform some basic smoothing of the spliced leader and polya acceptor site counts and determine the most likely primary UTR boundaries for each gene where information is available. 5'- and 3'-UTR boundaries, along with some other useful information such as UTR GC- and CT-richness will be outputted in a format that is convenient for downstream analysis.</p>
<p>Finally, we will look for evidence of either alternative trans-splicing or poly-adenylation and attempt to visualize the prevelance and magnitude of these events across the different developmental stages.</p>
<h2>TODO</h2>
<ul>
<li>Include Trey's uORFs</li>
<li>Removal RNAs (SLRNA, etc)</li>
</ul>
<div class="row">
<a href='mailto:khughitt@umd.edu'>Keith Hughitt</a> (<time>2015-02-02</time>)
</div>
<h2>Settings</h2>
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<pre style=""><code class="source r"># Number of individual UTR plots to display for each developmental stage and
# UTR side. The purpose of these plots are to provide a sense of the data
# distribution and to visualize the effect of primary # site selection with and
# without smoothing.
max_plots = 10</code></pre>
</div>
<p><a href="README.rmd">view source</a></p>
<h2>Methods</h2>
<h3>Load data</h3>
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<pre style=""><code class="source r">library(GenomicRanges)
library(Biostrings)
library(rtracklayer)
library(ggplot2)
library(ggvis)
library(reshape2)
library(dplyr)

# Genome sequence and annotations from TriTrypDB (8.0)
gff = import.gff(file.path('/cbcb/lab/nelsayed/ref_data/lmajor_friedlin',
                           '/annotation/TriTrypDB-8.0_LmajorFriedlin.gff'),
                 version='3')

chromosomes = gff[gff$type == 'chromosome']
genes       = gff[gff$type == 'gene']

# Load unannotated ORFs detected from ribosome profiling data
uorfs = import.gff(gzfile('input/lmajor_orfs_manual_2014-09-08_clean.gff.gz'), version='3')
uorfs$description = 'Unannotated ORF'

# Drop GFF columns not shared betwene TriTrypDB GFF and uORF GFF
keep_cols = intersect(colnames(mcols(genes)), colnames(mcols(uorfs)))
genes = genes[,keep_cols]
uorfs = uorfs[,keep_cols]

genes = append(genes, uorfs)

input_fasta = file.path(Sys.getenv("REF"), 
                        "lmajor_friedlin/genome/TriTrypDB-8.0_LmajorFriedlin_Genome.fasta")
fasta = readDNAStringSet(input_fasta)

# Fix names (L. major chromosome identifiers)
names(fasta) = substring(names(fasta), 0, 7)

# Number of bases flanking CDS to scan for motifs when actual UTR length is not
# known; numbers are based on median 5' and 3' UTR lengths for L. major
default_5utr_width = 250
default_3utr_width = 575

# Taken from L. major UTR analysis output from 2014/11/14
procyclic_polya  = import.gff(gzfile('input/lmajor_procyclic_ncrnas_removed_polya.gff.gz'), version='3')
metacyclic_polya = import.gff(gzfile('input/lmajor_metacyclic_ncrnas_removed_polya.gff.gz'), version='3')
amastigote_polya = import.gff(gzfile('input/lmajor_amastigote_ncrnas_removed_polya.gff.gz'), version='3')
procyclic_sl  = import.gff(gzfile('input/lmajor_procyclic_ncrnas_removed_sl.gff.gz'), version='3')
metacyclic_sl = import.gff(gzfile('input/lmajor_metacyclic_ncrnas_removed_sl.gff.gz'), version='3')
amastigote_sl = import.gff(gzfile('input/lmajor_amastigote_ncrnas_removed_sl.gff.gz'), version='3')

# Filter noncoding RNAs
id_filter_string = 'rRNA|snRNA|snoRNA|SLRNA|TRNA|SRP'
noncoding_ids = genes$ID[grepl(id_filter_string, genes$ID)]

genes            = genes[!genes$ID %in% noncoding_ids]
procyclic_polya  = procyclic_polya[!procyclic_polya$Name %in% noncoding_ids,]
metacyclic_polya = metacyclic_polya[!metacyclic_polya$Name %in% noncoding_ids,]
amastigote_polya = amastigote_polya[!amastigote_polya$Name %in% noncoding_ids,]
procyclic_sl     = procyclic_sl[!procyclic_sl$Name %in% noncoding_ids,]
metacyclic_sl    = metacyclic_sl[!metacyclic_sl$Name %in% noncoding_ids,]
amastigote_sl    = amastigote_sl[!amastigote_sl$Name %in% noncoding_ids,]

# Create output and build directories if needed
for (x in c('build', 'output')) {
    if (!file.exists(x)) {
        dir.create(x)
    }
}</code></pre>
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<pre style=""><code class="source r">#
# find_peak
#
find_peak = function(gene, acceptor_sites, gene_strand, feature_type,
                     smoothed=FALSE, include_plot=FALSE, secondary=FALSE) {
    # Determine orientation of feature relative to CDS
    if ((feature_type == 'sl'    && gene_strand == '+') ||
        (feature_type == 'polya' && gene_strand == '-')) {
        feature_side = 'left'
    } else {
        feature_side = 'right'
    }

    # Create a vector from the furthest SL site to the CDS boundary
    if (feature_side == 'left') {
        cds_boundary = start(gene)
        raw_scores = rep(0, cds_boundary - min(start(acceptor_sites)))
        rel_start =  cds_boundary - start(acceptor_sites) + 1
    } else {
        cds_boundary = end(gene)
        raw_scores = rep(0, max(start(acceptor_sites)) - cds_boundary)
        rel_start = start(acceptor_sites) - cds_boundary + 1
    }

    # add scores at indices where acceptor sites were detected
    raw_scores[rel_start] = score(acceptor_sites)
    x = 1:length(raw_scores)

    # create smooted version of scores (optional)
    if (smoothed) {
        input_scores = ksmooth(x, raw_scores, kernel="normal", bandwidth=6,
                               n.points=length(x))$y
    } else {
        input_scores = raw_scores
    }

    # find acceptor site peak
    if (secondary && (length(acceptor_sites) >= 2)) {
        # secondary site
        j = 2
    } else {
        # primary site
        j = 1
    }
    sorted_scores = sort(input_scores, decreasing=TRUE)
    raw_idx       = which(input_scores == sorted_scores[j])

    if (length(raw_idx) > 1) {
        if (feature_side == 'left') {
            raw_idx = head(raw_idx, 1)
        } else {
            raw_idx = tail(raw_idx, 1)
        }
    }

    # find raw score (number of reads) for the desired primary or secondary
    # site peak
    raw_score = raw_scores[raw_idx]

    # If smoothing was used, the actual location of the smoothed peak may not
    # have any support. In this case we will find the nearest site to the peak
    # with a non-zero score in the raw scores vector
    while(raw_score == 0) {
        # find next highest peak
        j = j + 1 
        raw_idx = which(input_scores == sorted_scores[j])

        # if more than one peak, choose the furthest one from CDS
        if (length(raw_idx) > 1) {
            if (feature_side == 'left') {
                raw_idx = head(raw_idx, 1)
            } else {
                raw_idx = tail(raw_idx, 1)
            }
        }
        raw_score = raw_scores[raw_idx]
    }

    # plot raw and smoothed peaks
    if (include_plot) {
        if (smoothed) {
            df = melt(data.frame(x, raw=raw_scores, smoothed=input_scores),
                      id=c("x"), variable.name='type')
            print(qplot(x, value, data=df, color=type, geom='line') 
                  + ggtitle(gene$ID))
        } else {
            df = melt(data.frame(x, raw=raw_scores), id=c("x"),
                    variable.name='type')
            print(qplot(x, value, data=df, geom='line')
                  + ggtitle(gene$ID))
        }
    }

    # find index of the site with the highest score (or second highest score,
    # in the case of secondary site selection)
    idx = which(score(acceptor_sites) == raw_score)

    # if there is a tie, use the furthest site from CDS
    if (length(idx) > 1) {
        if (feature_side == 'left') {
            idx = head(idx, 1)
        } else {
            idx = tail(idx, 1)
        }
    }
    return(idx)
}

#
# find_primary_site
#
# Determines the primary site among a list of acceptor sites and their
# associated score (number of reads mapped).
#
# Returns a list containing the site and score for the primary site.
#
find_primary_site = function(acceptor_sites, feature, gene, gene_strand, smoothed=FALSE) {
    if (length(acceptor_sites) == 0) {
        return(list(location=NA, num_reads=NA))
    } else if (length(acceptor_sites) == 1) {
        # if only one site found, use it
        return(list(location=start(acceptor_sites)[1],
                    num_reads=score(acceptor_sites)[1]))
    } else {
        # two or more sites

        # find highest smoothed peak which has non-zero coverage in the raw
        # data as well
        max_index = find_peak(gene, acceptor_sites, gene_strand, feature,
                              smoothed=smoothed)
        max_index_smoothed = find_peak(gene, acceptor_sites, gene_strand,
                                       feature, smoothed=TRUE)

        # if the smoothing would result in a different primary UTR choice,
        # plot the raw and smoothed versions of the data
        if (abs(max_index - max_index_smoothed) > 15) {
            num_diff = num_diff + 1
            if (plot_num <= max_plots) {
                sprintf("PLOTTING %d/%d", plot_num, max_plots)
                #tmp = find_peak(gene, acceptor_sites, gene_strand, feature,
                #                smoothed=TRUE, include_plot=TRUE)
                plot_num = plot_num + 1
            }
        }

        return(list(location=start(acceptor_sites)[max_index], 
                    num_reads=score(acceptor_sites)[max_index]))
    }
}

#
# find_secondary_site
#
# Determines the secondary site among a list of acceptor sites and their
# associated score (number of reads mapped).
#
# Returns a list containing the site and score for the secondary site.
#
find_secondary_site = function(acceptor_sites, feature, gene, gene_strand,
                               smoothed=FALSE) {
    if (length(acceptor_sites) == 0) {
        return(list(location=NA, num_reads=NA))
    } else if (length(acceptor_sites) == 1) {
        # if only one site found, use it
        return(list(location=start(acceptor_sites)[1],
                    num_reads=score(acceptor_sites)[1]))
    } else {
        # two or more sites

        # find highest smoothed peak which has non-zero coverage in the raw
        # data as well
        secondary_site_index = find_peak(gene, acceptor_sites, gene_strand,
                                         feature, smoothed=smoothed,
                                         secondary=TRUE)
        secondary_site_index_smoothed = find_peak(gene, acceptor_sites,
                                                  gene_strand, feature,
                                                  smoothed=TRUE,
                                                  secondary=TRUE)

        # if the smoothing would result in a different secondary UTR choice,
        # plot the raw and smoothed versions of the data
        #if (abs(secondary_site_index - secondary_site_index_smoothed) > 15) {
        #    num_diff = num_diff + 1
        #    if (plot_num <= max_plots) {
        #        tmp = find_peak(gene, acceptor_sites, gene_strand, feature,
        #                        smoothed=TRUE, include_plot=TRUE,
        #                        secondary=TRUE)
        #        plot_num = plot_num + 1
        #    }
        #}

        return(list(location=start(acceptor_sites)[secondary_site_index], 
                    num_reads=score(acceptor_sites)[secondary_site_index]))
    }
}

#
# get_utr_sequences
#
# Returns a vector of Biostrings instances containing the UTR sequence for each
# input gene.
#
get_utr_sequences = function(genes, fasta, utr_lengths, default_width,
                             utr5=TRUE) {
    # retrieve utr length if known
    widths = data.frame(
        id=genes$ID,
        width=NA)

    widths$width = utr_lengths[match(genes$ID, utr_lengths$name),]$length
    widths$width[is.na(widths$width)] = default_width

    # get positive and negative strand genes
    if (utr5) { 
        utr = flank(genes, widths$width)
    } else {
        utr = flank(genes, widths$width, start=FALSE)
    }
    pos_strand = utr[as.character(strand(utr)) == "+"]
    neg_strand = utr[as.character(strand(utr)) == "-"]

    # for genes that were assigned the default UTR size, make sure that the                                                                                                               
    # assigned boundaries fall within the chromosome                                                                                                                                      
    start(pos_strand) = pmax(1, start(pos_strand))                                                                                                                                        
    start(neg_strand) = pmax(1, start(neg_strand))                                                                                                                                        

    end(pos_strand) = pmin(end(pos_strand), width(fasta[seqnames(pos_strand)]))                                                                                                           
    end(neg_strand) = pmin(end(neg_strand), width(fasta[seqnames(neg_strand)]))  

    seqs = fasta[pos_strand]
    seqs = append(seqs, reverseComplement(fasta[neg_strand]))
    names(seqs) = c(pos_strand$ID, neg_strand$ID)

    return(seqs)
}

#
# get_num_reads
#
# Returns the number of reads mapped to a given position for the specified
# stage, or 0 if none are found
#
get_num_reads = function(utr_reads, site) {
    if (is.na(site)) {
        return(NA)
    }
    num_reads = score(utr_reads[start(utr_reads) == site])
    return(ifelse(length(num_reads) == 0, 0, num_reads))
}</code></pre>
</div>
<h3>Compute UTR coordinates and features</h3>
<h4>5'UTR</h4>
<h5>5'UTR Length</h5>
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<pre style=""><code class="source r"># Output columns
procyclic_lengths             = c()
procyclic_num_reads           = c()
procyclic_num_reads_primary   = c()
procyclic_num_reads_secondary = c()

metacyclic_lengths             = c()
metacyclic_num_reads           = c()
metacyclic_num_reads_primary   = c()
metacyclic_num_reads_secondary = c()

amastigote_lengths             = c()
amastigote_num_reads           = c()
amastigote_num_reads_primary   = c()
amastigote_num_reads_secondary = c()

combined_lengths             = c()
combined_num_reads           = c()
combined_num_reads_primary   = c()
combined_num_reads_secondary = c()

# Vectors to keep track of stage-specific primary site coverage across
# different stages (e.g. amastigote primary site reads in procyclic samples)
# This will be useful later on when looking for evidence of alternative trans-
# splicing and polyadenylation.
#
# For example, "pro_meta_primary_sl_reads" will contain the number of
# metacyclic reads mapped to the metacyclic primary site.
#
# Note 2015/01/15 -- for now, we will use the simpler "primary to secondary"
# ratios *within* each condition to give a sense of the degree of "dominance"
# for a given primary site.
#
pro_meta_primary_sl_reads = c()
pro_amast_primary_sl_reads = c()
meta_pro_primary_sl_reads = c()
meta_amast_primary_sl_reads = c()
amast_pro_primary_sl_reads = c()
amast_meta_primary_sl_reads = c()

# Start GFF output for combined set of acceptor sites
gff_lines = c("##gff-version\t3",
              "##feature-ontology\tsofa.obo",
              "##attribute-ontology\tgff3_attributes.obo")

# Add chromosome entries
for (i in 1:length(chromosomes)) {
    ch = chromosomes[i]
    gff_lines = append(gff_lines, paste("##sequence-region", ch$Name, 1,
                                        ch$size, sep='\t'))
}

# GFF chromosome entries
#for (i in 1:length(chromosomes)) {
#    ch = chromosomes[i]
#    # ID=LmjF.01;Name=LmjF.01;description=LmjF.01;size=268988;web_id=LmjF.01;
#    # molecule_type=dsDNA;organism_name=Leishmania
#    #  major;translation_table=1;topology=linear;localization=nuclear;
#    # Dbxref=ApiDB:LmjF.01,taxon:347515

#    descr_template = paste0(
#        "ID=%s;Name=%s;description=%s;size=%s;web_id=%s;",
#        "molecule_type=%s;organism_name=%s;translation_table=%s",
#        "topology=%s;localization=%s;Dbxref=%s")
        
#    descr = sprintf(descr_template,
#                    ch$ID, ch$Name, ch$description, ch$size, ch$web_id,
#                    ch$molecule_type, ch$organism_name, ch$translation_table,
#                    ch$topology, ch$localization, 
    
#                    paste(ch$Dbxref[[1]][1], ch$Dbxref[[1]][2], sep=','))
#    gff_lines = append(gff_lines, paste(ch$Name, "TriTrypDB", "chromosome", 1,
#                                        ch$size, ".", "+", ".", descr,
#                                        sep='\t'))
#}

# keep track of the number of plots created
num_diff = 0
plot_num = 0

# Add gene entries
i = 1
for (gene_id in genes$ID) {
    message(sprintf("Processing SL sites for gene %d/%d", i, length(genes)))
    gene = genes[genes$ID == gene_id]

    gene_strand = as.character(strand(gene)) 
    # get all of the SL acceptor sites as a GRanges object
    metacyclic_utr5 = metacyclic_sl[metacyclic_sl$Name == gene_id]
    procyclic_utr5  = procyclic_sl[procyclic_sl$Name == gene_id]
    amastigote_utr5  = amastigote_sl[amastigote_sl$Name == gene_id]
    combined_utr5   = metacyclic_sl[metacyclic_sl$Name == gene_id]

    # total number of reads found which contain an acceptor site
    metacyclic_num_reads = append(metacyclic_num_reads, 
                                  sum(metacyclic_utr5$score))
    procyclic_num_reads  = append(procyclic_num_reads,
                                  sum(procyclic_utr5$score))
    amastigote_num_reads  = append(amastigote_num_reads,
                                  sum(amastigote_utr5$score))
    combined_num_reads   = append(combined_num_reads,
                                  sum(procyclic_utr5$score) +
                                  sum(metacyclic_utr5$score) +
                                  sum(amastigote_utr5$score))
    
    # Combined output

    # Add procyclic reads
    if (length(procyclic_utr5) > 0) {
        for (j in 1:length(procyclic_utr5)) {
            # if new site, add a new entry
            entry = procyclic_utr5[j]
            if(!start(entry) %in% start(combined_utr5)) {
                combined_utr5 = c(combined_utr5, entry)
            }
            # otherwise add procyclic score to existing metacyclic score
            else {
                score(combined_utr5[start(combined_utr5) == start(entry)]) = (
                    score(combined_utr5[start(combined_utr5) == start(entry)]) +
                    score(entry))
            }
        }
    }
    # Add amastigote reads
    if (length(amastigote_utr5) > 0) {
        for (j in 1:length(amastigote_utr5)) {
            # if new site, add a new entry
            entry = amastigote_utr5[j]
            if(!start(entry) %in% start(combined_utr5)) {
                combined_utr5 = c(combined_utr5, entry)
            }
            # otherwise add amastigote score to existing metacyclic score
            else {
                score(combined_utr5[start(combined_utr5) == start(entry)]) = (
                    score(combined_utr5[start(combined_utr5) == start(entry)]) +
                    score(entry))
            }
        }
    }
    i = i + 1

    # Determine length and scores for procyclic, metacyclic, amastigote, 
    # and combined outputs
    pro_primary_site      = find_primary_site(procyclic_utr5, 'sl', gene, gene_strand)
    meta_primary_site     = find_primary_site(metacyclic_utr5, 'sl', gene, gene_strand)
    amast_primary_site    = find_primary_site(amastigote_utr5, 'sl', gene, gene_strand)
    combined_primary_site = find_primary_site(combined_utr5, 'sl', gene, gene_strand)

    # secondary site
    pro_secondary_site      = find_secondary_site(procyclic_utr5, 'sl', gene, gene_strand)
    meta_secondary_site     = find_secondary_site(metacyclic_utr5, 'sl', gene, gene_strand)
    amast_secondary_site    = find_secondary_site(amastigote_utr5, 'sl', gene, gene_strand)
    combined_secondary_site = find_secondary_site(combined_utr5, 'sl', gene, gene_strand)

    # compute 5'utr length and coordinates
    if (gene_strand == '+') {
        # procylic + strand
        procyclic_utr5_length = start(gene) - pro_primary_site$location

        # metacyclic + strand
        metacyclic_utr5_length = start(gene) - meta_primary_site$location

        # amastigote + strand
        amastigote_utr5_length = start(gene) - amast_primary_site$location

        # combined + strand
        combined_utr5_start  = combined_primary_site$location + 1
        combined_utr5_end    = start(gene) - 1
        combined_utr5_length = start(gene) - combined_primary_site$location
    } else {
        # procyclic - strand
        procyclic_utr5_length = pro_primary_site$location - end(gene)

        # metacyclic - strand
        metacyclic_utr5_length = meta_primary_site$location - end(gene)

        # procyclic - strand
        amastigote_utr5_length = amast_primary_site$location - end(gene)
        
        # combined - strand
        combined_utr5_start  = end(gene) + 1
        combined_utr5_end    = combined_primary_site$location - 1
        combined_utr5_length = combined_primary_site$location - end(gene)
    }

    # Add primary site read count and UTR length
    procyclic_lengths  = append(procyclic_lengths,  procyclic_utr5_length)
    metacyclic_lengths = append(metacyclic_lengths, metacyclic_utr5_length)
    amastigote_lengths = append(amastigote_lengths, amastigote_utr5_length)
    combined_lengths   = append(combined_lengths,   combined_utr5_length)

    procyclic_num_reads_primary  = append(procyclic_num_reads_primary,
                                          pro_primary_site$num_reads)
    metacyclic_num_reads_primary = append(metacyclic_num_reads_primary,
                                          meta_primary_site$num_reads)
    amastigote_num_reads_primary = append(amastigote_num_reads_primary,
                                          amast_primary_site$num_reads)
    combined_num_reads_primary   = append(combined_num_reads_primary,
                                          combined_primary_site$num_reads)

    procyclic_num_reads_secondary  = append(procyclic_num_reads_secondary,
                                            pro_secondary_site$num_reads)
    metacyclic_num_reads_secondary = append(metacyclic_num_reads_secondary,
                                            meta_secondary_site$num_reads)
    amastigote_num_reads_secondary = append(amastigote_num_reads_secondary,
                                            amast_secondary_site$num_reads)
    combined_num_reads_secondary   = append(combined_num_reads_secondary,
                                            combined_secondary_site$num_reads)

    # Update counts for cross-stage primary sites
    pro_meta_primary_sl_reads = append(pro_meta_primary_sl_reads,
                                 get_num_reads(procyclic_utr5, meta_primary_site$location))
    pro_amast_primary_sl_reads = append(pro_amast_primary_sl_reads,
                                 get_num_reads(procyclic_utr5, amast_primary_site$location))
    meta_pro_primary_sl_reads = append(meta_pro_primary_sl_reads,
                                 get_num_reads(metacyclic_utr5, pro_primary_site$location))
    meta_amast_primary_sl_reads = append(meta_amast_primary_sl_reads,
                                 get_num_reads(metacyclic_utr5, amast_primary_site$location))
    amast_pro_primary_sl_reads = append(amast_pro_primary_sl_reads,
                                 get_num_reads(amastigote_utr5, pro_primary_site$location))
    amast_meta_primary_sl_reads = append(amast_meta_primary_sl_reads,
                                 get_num_reads(amastigote_utr5, meta_primary_site$location))

    # Add GFF entry
    descr = sprintf("ID=%s_5utr;Name=%s;description=%s", gene$ID, gene$ID,
                    gene$description)

    gff_entry = paste(
        seqnames(gene),
        "El-Sayed",
        "five_prime_UTR",
        combined_utr5_start,
        combined_utr5_end,
        combined_primary_site$num_reads,
        strand(gene),
        '.',
        descr, sep='\t')

    gff_lines = append(gff_lines, gff_entry)
}

# metacyclic
metacyclic_utr5_df = data.frame(
    name=genes$ID,
    length=metacyclic_lengths,
    num_reads=metacyclic_num_reads,
    num_reads_primary=metacyclic_num_reads_primary,
    num_reads_secondary=metacyclic_num_reads_secondary
)

# procyclic
procyclic_utr5_df = data.frame(
    name=genes$ID,
    length=procyclic_lengths,
    num_reads=procyclic_num_reads,
    num_reads_primary=procyclic_num_reads_primary,
    num_reads_secondary=procyclic_num_reads_secondary
)

# amastigote
amastigote_utr5_df = data.frame(
    name=genes$ID,
    length=amastigote_lengths,
    num_reads=amastigote_num_reads,
    num_reads_primary=amastigote_num_reads_primary,
    num_reads_secondary=amastigote_num_reads_secondary
)

# combined
combined_utr5_df = data.frame(
    name=genes$ID,
    length=combined_lengths,
    num_reads=combined_num_reads,
    num_reads_primary=combined_num_reads_primary,
    num_reads_secondary=combined_num_reads_secondary
)

# Create cross-stage site usage dataframes
# For example, "pro_other_sl_sites"  lists the number of procyclic reads 
# which mapped to the primary sites detected for other stages (amast and meta).
pro_other_sl_sites = tbl_df(data.frame(
   gene=genes$ID,
   metacyclic=pro_meta_primary_sl_reads,
   amastigote=pro_amast_primary_sl_reads
))

meta_other_sl_sites = tbl_df(data.frame(
   gene=genes$ID,
   procyclic=meta_pro_primary_sl_reads,
   amastigote=meta_amast_primary_sl_reads
))

amast_other_sl_sites = tbl_df(data.frame(
   gene=genes$ID,
   procyclic=amast_pro_primary_sl_reads,
   metacyclic=amast_meta_primary_sl_reads
))</code></pre>
</div>
<h5>5'UTR Composition</h5>
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<pre style=""><code class="source r"># Metacyclic
metacyclic_utr5_sequences = get_utr_sequences(genes, fasta, metacyclic_utr5_df, 
                                              default_5utr_width, utr5=TRUE)
freqs = alphabetFrequency(metacyclic_utr5_sequences)[,1:4] 
metacyclic_utr5_features_df = data.frame(
    name=names(metacyclic_utr5_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
metacyclic_utr5_df = merge(metacyclic_utr5_df, metacyclic_utr5_features_df,
                           by='name')

# Procyclic
procyclic_utr5_sequences = get_utr_sequences(genes, fasta, procyclic_utr5_df, 
                                              default_5utr_width, utr5=TRUE)
freqs = alphabetFrequency(procyclic_utr5_sequences)[,1:4] 
procyclic_utr5_features_df = data.frame(
    name=names(procyclic_utr5_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
procyclic_utr5_df = merge(procyclic_utr5_df, procyclic_utr5_features_df,
                          by='name')

# amastigote
amastigote_utr5_sequences = get_utr_sequences(genes, fasta, amastigote_utr5_df, 
                                              default_5utr_width, utr5=TRUE)
freqs = alphabetFrequency(amastigote_utr5_sequences)[,1:4] 
amastigote_utr5_features_df = data.frame(
    name=names(amastigote_utr5_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
amastigote_utr5_df = merge(amastigote_utr5_df, amastigote_utr5_features_df,
                           by='name')

# Combined
combined_utr5_sequences = get_utr_sequences(genes, fasta, combined_utr5_df, 
                                              default_5utr_width, utr5=TRUE)
freqs = alphabetFrequency(combined_utr5_sequences)[,1:4] 
combined_utr5_features_df = data.frame(
    name=names(combined_utr5_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
combined_utr5_df = merge(combined_utr5_df, combined_utr5_features_df,
                         by='name')</code></pre>
</div>
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<pre style=""><code class="source r"># Write results
write.csv(metacyclic_utr5_df, file='output/lmajor_metacyclic_5utr_lengths.csv',
          quote=FALSE, row.names=FALSE)
write.csv(procyclic_utr5_df, file='output/lmajor_procyclic_5utr_lengths.csv',
          quote=FALSE, row.names=FALSE)
write.csv(amastigote_utr5_df, file='output/lmajor_amastigote_5utr_lengths.csv',
          quote=FALSE, row.names=FALSE)
write.csv(combined_utr5_df, file='output/lmajor_combined_5utr_lengths.csv',
          quote=FALSE, row.names=FALSE)

# Write result to GFF
fp = file("output/lmajor_5utr.gff")
writeLines(gff_lines, fp)
close(fp)</code></pre>
</div>
<h4>3'UTR</h4>
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<pre style=""><code class="source r"># Output columns
procyclic_lengths           = c()
procyclic_num_reads         = c()
procyclic_num_reads_primary = c()
procyclic_num_reads_secondary = c()

metacyclic_lengths           = c()
metacyclic_num_reads         = c()
metacyclic_num_reads_primary = c()
metacyclic_num_reads_secondary = c()

amastigote_lengths           = c()
amastigote_num_reads         = c()
amastigote_num_reads_primary = c()
amastigote_num_reads_secondary = c()

combined_lengths           = c()
combined_num_reads         = c()
combined_num_reads_primary = c()
combined_num_reads_secondary = c()

# Vectors to keep track of stage-specific primary site coverage across
# different stages (e.g. amastigote primary site reads in procyclic samples)
pro_meta_primary_polya_reads = c()
pro_amast_primary_polya_reads = c()
meta_pro_primary_polya_reads = c()
meta_amast_primary_polya_reads = c()
amast_pro_primary_polya_reads = c()
amast_meta_primary_polya_reads = c()

# Start GFF output for combined set of acceptor sites
gff_lines = c("##gff-version\t3",
             "##feature-ontology\tsofa.obo",
             "##attribute-ontology\tgff3_attributes.obo")

# Add chromosome entries
for (i in 1:length(chromosomes)) {
    ch = chromosomes[i]
    gff_lines = append(gff_lines, paste("##sequence-region", ch$Name, 1,
                                        ch$size, sep='\t'))
}

# GFF chromosome entries
#for (i in 1:length(chromosomes)) {
#    ch = chromosomes[i]
#    # ID=LmjF.01;Name=LmjF.01;description=LmjF.01;size=268988;web_id=LmjF.01;
#    # molecule_type=dsDNA;organism_name=Leishmania
#    #  major;translation_table=1;topology=linear;localization=nuclear;
#    # Dbxref=ApiDB:LmjF.01,taxon:347515

#    descr_template = paste0(
#        "ID=%s;Name=%s;description=%s;size=%s;web_id=%s;",
#        "molecule_type=%s;organism_name=%s;translation_table=%s",
#        "topology=%s;localization=%s;Dbxref=%s")
        
#    descr = sprintf(descr_template,
#                    ch$ID, ch$Name, ch$description, ch$size, ch$web_id,
#                    ch$molecule_type, ch$organism_name, ch$translation_table,
#                    ch$topology, ch$localization, 
    
#                    paste(ch$Dbxref[[1]][1], ch$Dbxref[[1]][2], sep=','))
#    gff_lines = append(gff_lines, paste(ch$Name, "TriTrypDB", "chromosome", 1,
#                                        ch$size, ".", "+", ".", descr,
#                                        sep='\t'))
#}

# keep track of the number of plots created
num_diff = 0
plot_num = 0

# Add gene entries
i = 1
for (gene_id in genes$ID) {
    message(sprintf("Processing Poly(A) sites for gene %d/%d", i, length(genes)))
    gene = genes[genes$ID == gene_id]

    gene_strand = as.character(strand(gene)) 

    # get all of the Poly(A) acceptor sites as a GRanges object
    metacyclic_utr3 = metacyclic_polya[metacyclic_polya$Name == gene_id]
    procyclic_utr3  = procyclic_polya[procyclic_polya$Name == gene_id]
    amastigote_utr3 = amastigote_polya[amastigote_polya$Name == gene_id]
    combined_utr3   = metacyclic_polya[metacyclic_polya$Name == gene_id]

    # total number of reads found which contain an acceptor site
    metacyclic_num_reads = append(metacyclic_num_reads, 
                                  sum(metacyclic_utr3$score))
    procyclic_num_reads  = append(procyclic_num_reads,
                                  sum(procyclic_utr3$score))
    amastigote_num_reads  = append(amastigote_num_reads,
                                  sum(amastigote_utr3$score))
    combined_num_reads   = append(combined_num_reads,
                                  sum(procyclic_utr3$score) +
                                  sum(metacyclic_utr3$score) +
                                  sum(amastigote_utr3$score))
    
    # Combined output
    if (length(procyclic_utr3) > 0) {
        for (j in 1:length(procyclic_utr3)) {
            # if new site, add a new entry
            entry = procyclic_utr3[j]
            if(!start(entry) %in% start(combined_utr3)) {
                combined_utr3 = c(combined_utr3, entry)
            }
            # otherwise add procyclic score to existing metacyclic score
            else {
                score(combined_utr3[start(combined_utr3) == start(entry)]) = (
                    score(combined_utr3[start(combined_utr3) == start(entry)]) +
                    score(entry))
            }
        }
    }
    if (length(amastigote_utr3) > 0) {
        for (j in 1:length(amastigote_utr3)) {
            # if new site, add a new entry
            entry = amastigote_utr3[j]
            if(!start(entry) %in% start(combined_utr3)) {
                combined_utr3 = c(combined_utr3, entry)
            }
            # otherwise add amastigote score to existing metacyclic score
            else {
                score(combined_utr3[start(combined_utr3) == start(entry)]) = (
                    score(combined_utr3[start(combined_utr3) == start(entry)]) +
                    score(entry))
            }
        }
    }
    i = i + 1

    # Determine length and scores for procyclic, metacyclic, and combined
    # outputs
    pro_primary_site      = find_primary_site(procyclic_utr3, 'polya', gene, gene_strand)
    meta_primary_site     = find_primary_site(metacyclic_utr3, 'polya', gene, gene_strand)
    amast_primary_site    = find_primary_site(amastigote_utr3, 'polya', gene, gene_strand)
    combined_primary_site = find_primary_site(combined_utr3, 'polya', gene, gene_strand)

    # secondary sites
    pro_secondary_site      = find_secondary_site(procyclic_utr3, 'polya', gene, gene_strand)
    meta_secondary_site     = find_secondary_site(metacyclic_utr3, 'polya', gene, gene_strand)
    amast_secondary_site    = find_secondary_site(amastigote_utr3, 'polya', gene, gene_strand)
    combined_secondary_site = find_secondary_site(combined_utr3, 'polya', gene, gene_strand)

    # compute 3'utr length and coordinates
    if (gene_strand == '+') {
        # procylic + strand
        procyclic_utr3_length = pro_primary_site$location - end(gene)

        # metacyclic + strand
        metacyclic_utr3_length = meta_primary_site$location - end(gene)
        
        # amastigote + strand
        amastigote_utr3_length = amast_primary_site$location - end(gene)

        # combined + strand
        combined_utr3_start  = end(gene) + 1
        combined_utr3_end    = combined_primary_site$location - 1
        combined_utr3_length = combined_primary_site$location - end(gene)
    } else {
        # procyclic - strand
        procyclic_utr3_length = start(gene) - pro_primary_site$location

        # metacyclic - strand
        metacyclic_utr3_length = start(gene) - meta_primary_site$location

        # amastigote - strand
        amastigote_utr3_length = start(gene) - amast_primary_site$location
        
        # combined - strand
        combined_utr3_start  = combined_primary_site$location + 1
        combined_utr3_end    = start(gene) - 1
        combined_utr3_length = start(gene) - combined_primary_site$location
    }

    # Add primary site read count and UTR length
    procyclic_lengths  = append(procyclic_lengths,  procyclic_utr3_length)
    metacyclic_lengths = append(metacyclic_lengths, metacyclic_utr3_length)
    amastigote_lengths = append(amastigote_lengths, amastigote_utr3_length)
    combined_lengths   = append(combined_lengths,   combined_utr3_length)

    procyclic_num_reads_primary  = append(procyclic_num_reads_primary,
                                          pro_primary_site$num_reads)
    metacyclic_num_reads_primary = append(metacyclic_num_reads_primary,
                                          meta_primary_site$num_reads)
    amastigote_num_reads_primary = append(amastigote_num_reads_primary,
                                          amast_primary_site$num_reads)
    combined_num_reads_primary   = append(combined_num_reads_primary,
                                          combined_primary_site$num_reads)

    procyclic_num_reads_secondary  = append(procyclic_num_reads_secondary,
                                          pro_secondary_site$num_reads)
    metacyclic_num_reads_secondary = append(metacyclic_num_reads_secondary,
                                          meta_secondary_site$num_reads)
    amastigote_num_reads_secondary = append(amastigote_num_reads_secondary,
                                          amast_secondary_site$num_reads)
    combined_num_reads_secondary   = append(combined_num_reads_secondary,
                                          combined_secondary_site$num_reads)

    # Update counts for cross-stage primary sites
    pro_meta_primary_polya_reads = append(pro_meta_primary_polya_reads,
                                 get_num_reads(procyclic_utr5, meta_primary_site$location))
    pro_amast_primary_polya_reads = append(pro_amast_primary_polya_reads,
                                 get_num_reads(procyclic_utr5, amast_primary_site$location))
    meta_pro_primary_polya_reads = append(meta_pro_primary_polya_reads,
                                 get_num_reads(metacyclic_utr5, pro_primary_site$location))
    meta_amast_primary_polya_reads = append(meta_amast_primary_polya_reads,
                                 get_num_reads(metacyclic_utr5, amast_primary_site$location))
    amast_pro_primary_polya_reads = append(amast_pro_primary_polya_reads,
                                 get_num_reads(amastigote_utr5, pro_primary_site$location))
    amast_meta_primary_polya_reads = append(amast_meta_primary_polya_reads,
                                 get_num_reads(amastigote_utr5, meta_primary_site$location))

    # Add GFF entry
    descr = sprintf("ID=%s_3utr;Name=%s;description=%s", gene$ID, gene$ID,
                    gene$description)

    gff_entry = paste(
        seqnames(gene),
        "El-Sayed",
        "three_prime_UTR",
        combined_utr3_start,
        combined_utr3_end,
        combined_primary_site$num_reads,
        strand(gene),
        '.',
        descr, sep='\t')

    gff_lines = append(gff_lines, gff_entry)
}

# metacyclic
metacyclic_utr3_df = data.frame(
    name=genes$ID,
    length=metacyclic_lengths,
    num_reads=metacyclic_num_reads,
    num_reads_primary=metacyclic_num_reads_primary,
    num_reads_secondary=metacyclic_num_reads_secondary
)

# procyclic
procyclic_utr3_df = data.frame(
    name=genes$ID,
    length=procyclic_lengths,
    num_reads=procyclic_num_reads,
    num_reads_primary=procyclic_num_reads_primary,
    num_reads_secondary=procyclic_num_reads_secondary
)

# amastigote
amastigote_utr3_df = data.frame(
    name=genes$ID,
    length=amastigote_lengths,
    num_reads=amastigote_num_reads,
    num_reads_primary=amastigote_num_reads_primary,
    num_reads_secondary=amastigote_num_reads_secondary
)

# combined
combined_utr3_df = data.frame(
    name=genes$ID,
    length=combined_lengths,
    num_reads=combined_num_reads,
    num_reads_primary=combined_num_reads_primary,
    num_reads_secondary=combined_num_reads_secondary
)

# Create cross-stage site usage dataframes
pro_other_polya_sites = tbl_df(data.frame(
   gene=genes$ID,
   metacyclic=pro_meta_primary_polya_reads,
   amastigote=pro_amast_primary_polya_reads
))

meta_other_polya_sites = tbl_df(data.frame(
   gene=genes$ID,
   procyclic=meta_pro_primary_polya_reads,
   amastigote=meta_amast_primary_polya_reads
))

amast_other_polya_sites = tbl_df(data.frame(
   gene=genes$ID,
   procyclic=amast_pro_primary_polya_reads,
   metacyclic=amast_meta_primary_polya_reads
))</code></pre>
</div>
<h5>3'UTR Composition</h5>
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<pre style=""><code class="source r"># Metacyclic
metacyclic_utr3_sequences = get_utr_sequences(genes, fasta, metacyclic_utr3_df, 
                                              default_3utr_width, utr5=FALSE)
freqs = alphabetFrequency(metacyclic_utr3_sequences)[,1:4] 
metacyclic_utr3_features_df = data.frame(
    name=names(metacyclic_utr3_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
metacyclic_utr3_df = merge(metacyclic_utr3_df, metacyclic_utr3_features_df,
                           by='name')

# Procyclic
procyclic_utr3_sequences = get_utr_sequences(genes, fasta, procyclic_utr3_df, 
                                              default_3utr_width, utr5=FALSE)
freqs = alphabetFrequency(procyclic_utr3_sequences)[,1:4] 
procyclic_utr3_features_df = data.frame(
    name=names(procyclic_utr3_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
procyclic_utr3_df = merge(procyclic_utr3_df, procyclic_utr3_features_df,
                          by='name')

# amastigote
amastigote_utr3_sequences = get_utr_sequences(genes, fasta, amastigote_utr3_df, 
                                              default_3utr_width, utr5=FALSE)
freqs = alphabetFrequency(amastigote_utr3_sequences)[,1:4] 
amastigote_utr3_features_df = data.frame(
    name=names(amastigote_utr3_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
amastigote_utr3_df = merge(amastigote_utr3_df, amastigote_utr3_features_df,
                          by='name')
# Combined
combined_utr3_sequences = get_utr_sequences(genes, fasta, combined_utr3_df, 
                                              default_3utr_width, utr5=FALSE)
freqs = alphabetFrequency(combined_utr3_sequences)[,1:4] 
combined_utr3_features_df = data.frame(
    name=names(combined_utr3_sequences),
    gc=(freqs[,'G'] + freqs[,'C']) / rowSums(freqs),
    ct=(freqs[,'C'] + freqs[,'T']) / rowSums(freqs)
)
combined_utr3_df = merge(combined_utr3_df, combined_utr3_features_df,
                         by='name')</code></pre>
</div>
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<pre style=""><code class="source r"># Write results
write.csv(metacyclic_utr3_df, file='output/lmajor_metacyclic_3utr_lengths.csv',
          quote=FALSE, row.names=FALSE)
write.csv(procyclic_utr3_df, file='output/lmajor_procyclic_3utr_lengths.csv',
          quote=FALSE, row.names=FALSE)
write.csv(amastigote_utr3_df, file='output/lmajor_amastigote_3utr_lengths.csv',
          quote=FALSE, row.names=FALSE)
write.csv(combined_utr3_df, file='output/lmajor_combined_3utr_lengths.csv',
          quote=FALSE, row.names=FALSE)

# Write result to GFF
fp = file("output/lmajor_3utr.gff")
writeLines(gff_lines, fp)
close(fp)</code></pre>
</div>
<h2>Results</h2>
<h3>Length statistics</h3>
<h4>5'UTR length statistics</h4>
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<pre style=""><code class="source r">lengths = combined_utr5_df$length[!is.na(combined_utr5_df$length)]

coverage = sum(!is.na(combined_utr5_df$length)) / nrow(combined_utr5_df)
print(sprintf("%% 5'UTRs detected: %0.2f", coverage))</code></pre>
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<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] "% 5'UTRs detected: 0.95"
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">quantile(lengths)</code></pre>
<button class="output R toggle btn btn-xs btn-success">
<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">##   0%  25%  50%  75% 100% 
##    1  114  234  501 6018
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">mean(lengths)</code></pre>
<button class="output R toggle btn btn-xs btn-success">
<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] 447.6343
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">median(lengths)</code></pre>
<button class="output R toggle btn btn-xs btn-success">
<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] 234
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">mode(lengths)</code></pre>
<button class="output R toggle btn btn-xs btn-success">
<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] "numeric"
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">hist(lengths)</code></pre>
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<div class="col-md-offset-3 col-md-6">
<a href="#" class="thumbnail"><img 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" /> </a>
</div>
</div>
</div>
<h4>3'UTR length statistics</h4>
<div class="row">
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</button>
<pre style=""><code class="source r">lengths = combined_utr3_df$length[!is.na(combined_utr3_df$length)]

coverage = sum(!is.na(combined_utr3_df$length)) / nrow(combined_utr3_df)
print(sprintf("%% 3'UTRs detected: %0.2f", coverage))</code></pre>
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<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] "% 3'UTRs detected: 0.94"
</code></pre>
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<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">quantile(lengths)</code></pre>
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<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">##   0%  25%  50%  75% 100% 
##    1  268  529 1078 6722
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">mean(lengths)</code></pre>
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<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] 843.9933
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">median(lengths)</code></pre>
<button class="output R toggle btn btn-xs btn-success">
<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] 529
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">mode(lengths)</code></pre>
<button class="output R toggle btn btn-xs btn-success">
<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] "numeric"
</code></pre>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">hist(lengths)</code></pre>
<div class="row">
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<a href="#" class="thumbnail"><img 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" /> </a>
</div>
</div>
</div>
<h3>Alternative trans-splicing</h3>
<h4>Metacyclic vs. Procyclic</h4>
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</button>
<pre style=""><code class="source r"># rescale to range 0-1
# if trimmed=TRUE, outliers will be trimmed first to reduce their influence on
# the resulting colorscale
rescale = function (x, trimmed=TRUE, trim_quantile=0.99) {
    if (trimmed) {
        lower_bound = quantile(x, 1 - trim_quantile)
        upper_bound = quantile(x, trim_quantile)
   
        x = pmax(pmin(x, upper_bound), lower_bound)

        return(pmax(0, ((x-min(x)) / (max(x) - min(x)))))
    }
    # no trimming
    return(pmax(0, ((x-min(x)) / (max(x) - min(x)))))
}

# metacyclic vs. procyclic
# For the purposes of comparing usage ratios, we will treat non-covered sites
# as having one read mapped
meta_pro_5utr_comparison = tbl_df(data.frame(
    name=metacyclic_utr5_df$name,
    metacyclic_len=metacyclic_utr5_df$length,
    procyclic_len=procyclic_utr5_df$length,
    average_primary_site_num_reads=(metacyclic_utr5_df$num_reads_primary +
                                    procyclic_utr5_df$num_reads_primary) / 2,
    average_ptos_ratio=((metacyclic_utr5_df$num_reads_primary /
                         metacyclic_utr5_df$num_reads_secondary) + 
                        (procyclic_utr5_df$num_reads_primary /
                         procyclic_utr5_df$num_reads_secondary)) / 2,
    count_average=(metacyclic_utr5_df$num_reads_primary +
                   procyclic_utr5_df$num_reads_primary) / 2,
    meta_pro_ratio_meta_samples=(metacyclic_utr5_df$num_reads_primary /
                           pmax(meta_other_sl_sites$procyclic, 1)),
    pro_meta_ratio_pro_samples=(procyclic_utr5_df$num_reads_primary / 
                          pmax(pro_other_sl_sites$metacyclic, 1))
))

# version with reads mapped for stages of interest
meta_pro_5utr_comparison_complete = meta_pro_5utr_comparison %>%
    filter(!is.na(metacyclic_len) & !is.na(procyclic_len)) %>%
    mutate(
        log_average_ratio=log2(0.5 * (meta_pro_ratio_meta_samples + pro_meta_ratio_pro_samples)),
        length_diff=abs(metacyclic_len - procyclic_len), 
        log_average_reads=log2(average_primary_site_num_reads),
        log_average_ptos=log2(average_ptos_ratio),
        log_length_diff=log2(length_diff)
    )

ggplot(meta_pro_5utr_comparison_complete, 
       aes(metacyclic_len, procyclic_len, color=log_average_ptos,
           size=average_primary_site_num_reads)) + 
    geom_point() +
    scale_color_gradient2(low="black", mid="blue", high="red") +
    geom_abline(slope=1, intercept=300, color='#666666', lwd=0.5) +
    geom_abline(slope=1, intercept=-300, color='#666666', lwd=0.5) +
    scale_x_continuous(expand = c(0.01, 0.01)) +
    scale_y_continuous(expand = c(0.01, 0.01))</code></pre>
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" /> </a>
</div>
</div>
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<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r">    #geom_segment(aes(x=300, y=0, xend=6000, yend=5700, color='#666666'))
    #scale_colour_continuous(high='red') 
    #scale_size(trans='log')

# sites with switching only
ggplot(meta_pro_5utr_comparison_complete %>% filter(metacyclic_len != procyclic_len),
       aes(metacyclic_len, procyclic_len, color=log_average_ptos,
           size=log_average_reads)) + 
    geom_point() +
    scale_color_gradient2(low="black", mid="blue", high="red") +
    geom_abline(slope=1, intercept=300, color='#666666', lwd=0.5) +
    geom_abline(slope=1, intercept=-300, color='#666666', lwd=0.5) +
    scale_x_continuous(expand = c(0.01, 0.01)) +
    scale_y_continuous(expand = c(0.01, 0.01))</code></pre>
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/> </a>
</div>
</div>
<button class="source R toggle btn btn-xs btn-primary">
<span class="glyphicon glyphicon-chevron-down"></span> R source
</button>
<pre style=""><code class="source r"># print top 25 genes with strongest site switching behavior
meta_pro_5utr_diff = meta_pro_5utr_comparison_complete %>% 
    filter(count_average & log_average_ratio > 0.75) %>%
    select(-log_length_diff)
#kable(head(meta_pro_5utr_diff  %>% arrange(-score), 25))

print(sprintf("Number of alternatively trans-spliced genes (meta vs. pro): %d",
              nrow(meta_pro_5utr_diff)))</code></pre>
<button class="output R toggle btn btn-xs btn-success">
<span class="glyphicon glyphicon-chevron-down"></span> R output
</button>
<pre style=""><code class="output r">## [1] "Number of alternatively trans-spliced genes (meta vs. pro): 3035"
</code></pre>
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<h4>Metacyclic vs. Amastigote</h4>
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<pre style=""><code class="source r"># metacyclic vs. amastigote
meta_amast_5utr_comparison = tbl_df(data.frame(
    name=metacyclic_utr5_df$name,
    metacyclic_len=metacyclic_utr5_df$length,
    amastigote_len=amastigote_utr5_df$length,
    average_primary_site_num_reads=abs(metacyclic_utr5_df$num_reads_primary -
                   amastigote_utr5_df$num_reads_primary),
    average_ptos_ratio=((metacyclic_utr5_df$num_reads_primary /
                         metacyclic_utr5_df$num_reads_secondary) + 
                        (amastigote_utr5_df$num_reads_primary /
                         amastigote_utr5_df$num_reads_secondary)) / 2,
    meta_amast_ratio_meta_samples=(metacyclic_utr5_df$num_reads_primary /
                           pmax(meta_other_sl_sites$amastigote, 1)),
    amast_meta_ratio_amast_samples=(amastigote_utr5_df$num_reads_primary / 
                          pmax(amast_other_sl_sites$metacyclic, 1))
)) %>% mutate(
    length_diff=abs(metacyclic_len - amastigote_len), 
    log_average_reads=log1p(average_primary_site_num_reads),
    log_average_ptos=log1p(average_ptos_ratio),
    log_length_diff=log1p(length_diff)
)

# version with reads mapped for stages of interest
meta_amast_5utr_comparison_complete = meta_amast_5utr_comparison %>%
    filter(!is.na(metacyclic_len) & !is.na(amastigote_len)) %>%
    mutate(log_average_ratio=log1p(0.5 * (meta_amast_ratio_meta_samples +
                                          amast_meta_ratio_amast_samples)))

#meta_amast_5utr_comparison_complete %>% 
#    ggvis(~metacyclic_len, ~amastigote_len, fill=~log_average_reads) %>% 
#    layer_points() %>% 
#    add_tooltip(function(df) df$name)
ggplot(meta_amast_5utr_comparison_complete, 
       aes(metacyclic_len, amastigote_len, color=log_average_ptos,
           size=log_average_reads)) + 
    geom_point() +
    scale_color_gradient2(low="black", mid="blue", high="red") + 
    geom_abline(slope=1, intercept=300, color='#666666') +
    geom_abline(slope=1, intercept=-300, color='#666666') +
    scale_x_continuous(expand = c(0.01, 0.01)) +
    scale_y_continuous(expand = c(0.01, 0.01))</code></pre>
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" /> </a>
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</div>
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<pre style=""><code class="source r">    #geom_segment(aes(x=300, y=0, xend=6000, yend=5700, color='#666666'))
    #scale_colour_continuous(high='red') 

# print top 25 genes with strongest site switching behavior
meta_amast_5utr_diff = meta_amast_5utr_comparison_complete %>% 
    filter(length_diff > 300 & log_average_ratio > 0.75) %>%
    select(-log_length_diff)
#kable(head(meta_amast_5utr_diff  %>% arrange(-score), 25))
print(sprintf("Number of alternatively trans-spliced genes (meta vs. amast): %d",
              nrow(meta_amast_5utr_diff)))</code></pre>
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<pre style=""><code class="output r">## [1] "Number of alternatively trans-spliced genes (meta vs. amast): 105"
</code></pre>
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<h4>Procyclic vs. Amastigote</h4>
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<pre style=""><code class="source r"># procyclic vs. amastigote
pro_amast_5utr_comparison = tbl_df(data.frame(
    name=procyclic_utr5_df$name,
    procyclic_len=procyclic_utr5_df$length,
    amastigote_len=amastigote_utr5_df$length,
    average_primary_site_num_reads=abs(procyclic_utr5_df$num_reads_primary -
                   amastigote_utr5_df$num_reads_primary),
    average_ptos_ratio=((procyclic_utr5_df$num_reads_primary /
                         procyclic_utr5_df$num_reads_secondary) + 
                        (amastigote_utr5_df$num_reads_primary /
                         amastigote_utr5_df$num_reads_secondary)) / 2,
    pro_amast_ratio_pro_samples=(procyclic_utr5_df$num_reads_primary /
                          pmax(pro_other_sl_sites$amastigote, 1)),
    amast_pro_ratio_amast_samples=(amastigote_utr5_df$num_reads_primary / 
                            pmax(amast_other_sl_sites$procyclic, 1))
)) %>% mutate(
    length_diff=abs(procyclic_len - amastigote_len), 
    log_average_reads=log1p(average_primary_site_num_reads),
    log_average_ptos=log1p(average_ptos_ratio),
    log_length_diff=log1p(length_diff)
)

# version with reads mapped for stages of interest
pro_amast_5utr_comparison_complete = pro_amast_5utr_comparison %>%
    filter(!is.na(procyclic_len) & !is.na(amastigote_len)) %>%
    mutate(log_average_ratio=log1p(0.5 * (pro_amast_ratio_pro_samples +
                                          amast_pro_ratio_amast_samples)))

ggplot(pro_amast_5utr_comparison_complete, 
       aes(procyclic_len, amastigote_len, color=log_average_ptos,
           size=log_average_reads)) + 
    geom_point() +
    scale_color_gradient2(low="black", mid="blue", high="red") + 
    geom_abline(slope=1, intercept=300, color='#666666') +
    geom_abline(slope=1, intercept=-300, color='#666666') +
    scale_x_continuous(expand = c(0.01, 0.01)) +
    scale_y_continuous(expand = c(0.01, 0.01))</code></pre>
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" /> </a>
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</div>
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<pre style=""><code class="source r">    #geom_segment(aes(x=300, y=0, xend=6000, yend=5700, color='#666666'))
    #scale_colour_continuous(high='red') 

# print top 25 genes with strongest site switching behavior
pro_amast_5utr_diff = pro_amast_5utr_comparison_complete %>% 
    filter(length_diff > 300 & log_average_ratio > 0.75) %>%
    select(-log_length_diff)
#kable(head(pro_amast_5utr_diff  %>% arrange(-score), 25))
print(sprintf("Number of alternatively trans-spliced genes (pro vs. amast): %d",
              nrow(pro_amast_5utr_diff)))</code></pre>
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<pre style=""><code class="output r">## [1] "Number of alternatively trans-spliced genes (pro vs. amast): 90"
</code></pre>
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<h3>Alternative poly-adenylation</h3>
<h4>Metacyclic vs. Procyclic</h4>
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<pre style=""><code class="source r"># metacyclic vs. procyclic
# For the purposes of comparing usage ratios, we will treat non-covered sites
# as having one read mapped
meta_pro_3utr_comparison = tbl_df(data.frame(
    name=metacyclic_utr3_df$name,
    metacyclic_len=metacyclic_utr3_df$length,
    procyclic_len=procyclic_utr3_df$length,
    count_average=(metacyclic_utr3_df$num_reads_primary +
                   procyclic_utr3_df$num_reads_primary) / 2,
    average_ptos_ratio=((metacyclic_utr3_df$num_reads_primary /
                         metacyclic_utr3_df$num_reads_secondary) + 
                        (procyclic_utr3_df$num_reads_primary /
                         procyclic_utr3_df$num_reads_secondary)) / 2,
    average_primary_site_num_reads=abs(metacyclic_utr3_df$num_reads_primary -
                   procyclic_utr3_df$num_reads_primary),
    meta_pro_ratio_meta_samples=(metacyclic_utr3_df$num_reads_primary /
                           pmax(meta_other_polya_sites$procyclic, 1)),
    pro_meta_ratio_pro_samples=(procyclic_utr3_df$num_reads_primary / 
                          pmax(pro_other_polya_sites$metacyclic, 1))
))

# version with reads mapped for stages of interest
meta_pro_3utr_comparison_complete = meta_pro_3utr_comparison %>%
    filter(!is.na(metacyclic_len) & !is.na(procyclic_len)) %>%
    mutate(
        log_average_ratio=log2(0.5 * (meta_pro_ratio_meta_samples + pro_meta_ratio_pro_samples)),
        length_diff=abs(metacyclic_len - procyclic_len), 
        log_average_reads=log2(average_primary_site_num_reads),
        log_average_ptos=log2(average_ptos_ratio),
        log_length_diff=log2(length_diff)
    )

ggplot(meta_pro_3utr_comparison_complete, 
       aes(metacyclic_len, procyclic_len, color=log_average_ptos,
           size=average_primary_site_num_reads)) + 
    geom_point() +
    scale_color_gradient2(low="black", mid="blue", high="red") +
    geom_abline(slope=1, intercept=300, color='#666666') +
    geom_abline(slope=1, intercept=-300, color='#666666') +
    scale_x_continuous(expand = c(0.01, 0.01)) +
    scale_y_continuous(expand = c(0.01, 0.01))</code></pre>
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