anchor.diff:        <div class="card-supporting-text">
           <p>
             Especially when running the pipeline with all six frames,
  -          a lot of the translations are invalid.
  +          a lot of the translations are invalid since we expect only one of the reading frames, rarely more, to yield a valid protein.
             The Seed-Extend step in the pipeline filters out most random hits
             by selecting only 9-mer hits which are spatially close to other hits.
           </p>
  
positive.code:       <div class="card-supporting-text">
          <p>
            UMGAP uses exact substring matching to identify reads.
            The next step of the pipeline fragments each amino acid sequence into 9-mers,
            all of its substrings of length 9, and looks them up in our index.
            This index maps each 9-mer encountered in the UniProt's protein knowledgebase (2014)
            to the lowest common ancestor (LCA) of all the organisms it occured in.
            The LCA of a set of taxa is equal to the taxon of the most specific rank
  
negative.code:           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, UMGAP 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>
          <p>
            Using gene prediction limits our tool to the coding regions of the given DNA reads.
            This limit caps the sensitivity of our predictions,
            as we can offer no prediction for a read from a non-coding region.
            On the other hand, coding regions are more conserved
            and the mutations that do occur often do not change the resulting amino acid.
          </p>
          <p>
            For more information about the available options for the gene predictors, the FragGeneScan and FragGeneScan++ commands,
            we refer to the <a href="http://omics.informatics.indiana.edu/FragGeneScan/">FragGeneScan</a>
            and <a href="https://github.com/unipept/FragGeneScanPlusPlus">FragGeneScan++</a> documentation.
            Here, it suffices to say that the commands below will write the predicted genes
            in FASTA format to a file called `predicted-genes.faa`.
          </p>
  