anchor.diff:            by selecting only 9-mer hits which are spatially close to other hits.
           </p>
           <pre>
  -<!--   --><b>$</b> umgap seedextend -g2 -s4 taxons.tsv < found-kmers.fa | grep -v '^0$' > selected-seeds.fa
  +<!--   --><b>$</b> umgap seedextend taxons.tsv < found-kmers.fa | tee selected-seeds.fa
  +<!-- -->>read1/1|1
  +<!-- -->35725 2759 1 1 1 2 515393 201174
  +<!-- -->>read1/1|2
  +<!-- -->1185650 1185650 140110
  +<!-- -->>read1/1|3
  +<!-- -->1773 47855 4530 1 1 1 1 1
  +<!-- -->>read1/1|1R
  +<!-- -->40674 1 1 2759
  +<!-- -->>read1/1|2R
  +<!-- -->1 940557 1 940615 940615 940615 940615 940615 56 2 1 1 1 1 2 2 940615 940615 940615 940615 940615
  +<!-- -->>read1/1|3R
  +<!-- -->2 1 1 1 2759 1 1 3916 3916 1760
  +<!-- -->>read1/2|1
  +<!-- -->
  +<!-- -->>read1/2|2
  +<!-- -->
  +<!-- -->>read1/2|3
  +<!-- -->940615 940615 940615 940615 940615 204434 204434 204434 204434 2 204434 204434 204434
  +<!-- -->>read1/2|1R
  +<!-- -->
  +<!-- -->>read1/2|2R
  +<!-- -->
  +<!-- -->>read1/2|3R
  +<!-- -->
  +<!-- -->>read2/1|1
  +<!-- -->
  +<!-- -->>read2/1|2
  +<!-- -->
  +<!-- -->>read2/1|3
  +<!-- -->2 392734 392734 392734 392734
  +<!-- -->>read2/1|1R
  +<!-- -->379 379
  +<!-- -->>read2/1|2R
  +<!-- -->
  +<!-- -->>read2/1|3R
  +<!-- -->
  +<!-- -->>read2/2|1
  +<!-- -->
  +<!-- -->>read2/2|2
  +<!-- -->
  +<!-- -->>read2/2|3
  +<!-- -->
  +<!-- -->>read2/2|1R
  +<!-- -->
  +<!-- -->>read2/2|2R
  +<!-- -->33882 4930 4930 392734 1 392734 392734 392734 392734 392734 392734 392734 392734 392733 392733 392733 392733 392734
  +<!-- -->>read2/2|3R
  +<!-- -->
  +<!-- -->>read3/1|1
  +<!-- -->
  +<!-- -->>read3/1|2
  +<!-- -->
  +<!-- -->>read3/1|3
  +<!-- -->332163 332163 332163 2 1 332163 2 332163 332163 332163 332163 332163 1 332163 332163 332163 332163 332163 332163 332163 332163 332163
  +<!-- -->>read3/1|1R
  +<!-- -->
  +<!-- -->>read3/1|2R
  +<!-- -->
  +<!-- -->>read3/1|3R
  +<!-- -->
  +<!-- -->>read3/2|1
  +<!-- -->
  +<!-- -->>read3/2|2
  +<!-- -->
  +<!-- -->>read3/2|3
  +<!-- -->
  +<!-- -->>read3/2|1R
  +<!-- -->332163 2 2 332163 332163 332163 332163 332163 332163 332163 1 332163 332163 332163 2 2 1 332163 332163
  +<!-- -->>read3/2|2R
  +<!-- -->5855 1 1 2 519963 5206
  +<!-- -->>read3/2|3R
  +<!-- -->
  +        </pre>
  +        <p>
  +          Note: the real output would contain newlines between taxa,
  +          which have been replaced here by spaces for readability.
  +        </p>
  +        <pre>
  +<!--   --><b>$</b> mv selected-seeds.fa found-kmers.fa # optional step, same format
           </pre>
           <p>
             This algorithm requires spatial information which is provided
             by supplying the <code>-o</code> flag to the previous command.
             The <code>taxons.tsv</code> is a file describing the NCBI taxonomic tree.
  -          The <code>grep</code> command filters out remaining unknown identifications.
           </p>
         </div>
       </div>
  
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:             </li>
              <li>
                A newly developed hybrid strategy,
                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 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,
  