\textbf{Steps of the stitch.pl algorithm.}
      Consensus genome maps (blue) are shown aligned to \textit{in silico} maps (green). Alignments are indicated with grey lines. CMAP orientation for \textit{in silico} maps is indicated with a "+" or "-" for positive or negative orientation respectively. (A) The \textit{in silico} maps are used as the reference. (B) The alignment is inverted and used as input for stitch.pl. (C) The alignments are filtered based on alignment length (purple) relative to total possible alignment length (black) and confidence. Here assuming all alignments have high confidence scores and the minimum percent aligned is 30\% two alignments fail for aligning over less than 30\% of the potential alignment length for that alignment. (D) Filtering produces an XMAP of high quality alignments with short (local) alignments removed. (E) High quality scaffolding alignments are filtered for longest and highest confidence alignment for each \textit{in silico} map. The third alignment (unshaded) is filtered because the second alignment is the longest alignment for \textit{in silico} map 2. (F) Passing alignments are used to super scaffold (captured gaps indicated in dark green). (G) Stitch is iterated and additional super scaffolding alignments are found using second best scaffolding alignments. (H) Iteration takes advantage of cases where \textit{in silico} maps scaffold consensus genome maps as \textit{in silico} map 2 does. Stitch is run iteratively until all super scaffolding alignments are found.