anchor.diff:              </li>
               <li>
                 A newly developed hybrid strategy,
  -              which combines LCA* and MRTL based on a given scaling factor.
  +              which combines LCA* and MRTL based on a given scaling factor (default 0.25).
                 The hybrid method aims to ignore some outliers without compromising generality.
  +              This strategy is the default and will therefore be used below.
               </li>
             </ul>
           </p>
           <p>
  -          Before this aggregation, we combine the results of each reading frame
  -          and paired-end with <code>uniq</code>.
  -        </p>
  -        <pre>
  -<!--   --><b>$</b> umgap uniq < found-kmers.fa | umgap taxa2agg -m tree -a hybrid -f 0.75 taxons.tsv > classification.fa
  +          Before this aggregation, we remove the markers from the headers with <code>sed</code>
  +          and combine the results of each reading frame and paired-end with <code>uniq</code>.
  +        </p>
  +        <pre>
  +<!--   --><b>$</b> sed '/^>/s_/.*__' < found-kmers.fa | umgap uniq | umgap taxa2agg taxons.tsv | tee classification.fa
  +<!--   -->>read1
  +<!--   -->940615
  +<!--   -->>read2
  +<!--   -->392734
  +<!--   -->>read3
  +<!--   -->332163
           </pre>
           <p>
             Finally, the whole sample is summarized using e.g. a frequency table,
  
positive.code: <!--   --><b>$</b> rm predicted-genes.fnn predicted-genes.out # we don't use these files
          </pre>
          <pre>
  <!--   --><b>$</b> FGSpp -s stdin -o stdout -w 0 -r train -t illumina_10 -p 16 -m 3000 < preprocessed.fa > predicted-genes.faa
          </pre>
          <p>
            With our alternative six-frame translation option,
            we choose to translate all (<code>-a</code>) frames using the standard translation table.
          </p>
          <pre>
  <!--   --><b>$</b> umgap translate -a < preprocessed.fa > predicted-genes.faa
          </pre>
        </div>
      </div>
  
negative.code:           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
  <!--   -->>read1/1
  <!--   -->ATCGCGCACGCGGCCGATGCCCCAGAAGAGATTGACAGCGGTGGGGCGGGCGGCGGCGAGGTGGTCGCAGATCTCGGCGACCTCTGCGTTGAGGGTCGGG
  <!--   -->>read1/2
  <!--   -->AAGATGGCGACTGGATGATGATCGCGCGGCAGGCCACCATCTACGATCCCGCAGTGAAGCATTACTAACCATGATGCGCAACGACTCGCTGTCAGAGCTA
  <!--   -->>read2/1
  <!--   -->AGATTGCTGGTGCGGGTGCTCTGCCGGGCTTCTTCATCTCGGACCGCGCATCCGATGCGCACACGGCACAGTAGGTATGCGGTGAGAGCACCTCGCTTTT
  <!--   -->>read2/2
  <!--   -->CGCAATCTTGCGGCGCACCGCGATCAAATCGGGATAGTCCGGTGTGTAACGCGTGCTCAGGTCATCTGCCCGTACCCGCAATACATTCAACTGTGTTTTA
  <!--   -->>read3/1
  <!--   -->CCCTCAAGCCCGATCGAACTTTCCTACTGCCCGACCTCAGCGCGGAGCATTACCGAATCGTCGTCAACAATCTCCCCGATGGCTTCTATGTGAACTCCAT
  <!--   -->>read3/2
  <!--   -->CAAAATGGAGTGGGCGCTACTGCTCCGTGAGCAGGGTCAGTTGGAGGGATTGGGGCGTGCGCTCGGGGATGCTAATGGCGGTGCCCCTGGATTCCTGAGA
          </pre>
          <p>
            This command will interleave the given FASTQ files and output a FASTA stream,
            in which the paired-ends alternate each other.
          </p>
        </div>
      </div>
  