anchor.diff:  <div class="apidocs casestudies row">
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       <h1 class="api-title">Case Study: Unipept Metagenomics Analysis Pipeline</h1>
  -    <p class="lead">This case study shows how to use UMGAP for the taxonomic analysis of a metagenomics dataset.</p>
  +    <p class="lead">This case study shows how to use <span class="initialism">UMGAP</span> for the taxonomic analysis of a metagenomics dataset.</p>
       <div class="card">
         <div class="card-title card-title-colored"><h2 class="card-title-text">Introduction</h2></div>
         <div class="card-supporting-text">
           <p>
  -          The Unipept Metagenomics Analysis Pipeline (UMGAP) is a tool for mapping (short) metagenomics reads to taxa,
  +          The Unipept Metagenomics Analysis Pipeline (<span class="initialism">UMGAP</span>) is a tool for mapping (short) metagenomics reads to taxa,
             rivalling state of the art alternatives such as Kraken (2014)
             with the additional benefit of species level identification.
  -          UMGAP achieves this by transforming the mapping task into a metaproteomics problem
  +          <span class="initialism">UMGAP</span> achieves this by transforming the mapping task into a metaproteomics problem
             by using various alternative gene predictors on the metagenomics reads
             and processing the resulting data with the existing Unipept Metaproteomics Analysis Pipeline.
  -          Each step in UMGAP is highly configurable.
  +          Each step in <span class="initialism">UMGAP</span> is highly configurable.
           </p>
         </div>
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positive.code:       <div class="card-title card-title-colored"><h2 class="card-title-text">Preprocessing</h2></div>
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          <p>
            Throughout this overview of UMGAP, all steps will have examples to demonstrate the exact usage.
            For this, we have sampled 100 paired-end DNA reads from a dataset
            generated by Lindgreen <i>et al.</i> (2016) for their metabenchmark.
            As this dataset contains paired-end reads in FASTQ format, and UMGAP operates on FASTA data,
            we perform a first preprocessing step to convert the FASTQ into FASTA files.
          </p>
          <pre style="max-height: initial;">
  <!--   --><b>$</b> umgap fastq2fasta A1.fq A2.fq | tee preprocessed.fa
  
negative.code:         <p>
            To convert the metagenomics reads into a metaproteomics problem,
            we have to perform some kind of gene prediction.
            While any gene prediction tool can be used, <span class="initialism">UMGAP</span> was tested with FragGeneScan (2010) and FragGeneScan++,
            our in-house improved version of FragGeneScanPlus (2015), a predictor using Hidden Markov Models.
            Alternatively, you can also try all possible translations and use the built-in six-frame translation tool.
          </p>
  