\section*{Conclusions}


We introduced new tools to facilitate single molecule map assembly optimization and genome finishing steps using the resultant consensus genome maps. These tools were validated using the medium-sized (200 Mb) \textit{T. castaneum} beetle genome. The Tcas3.0 genome was  assembled using the gold-standard Sanger assembly strategy \cite{Beetle2008}. The Tcas5.0 assembly benefitted from the use of LongDistance Illumina Jump libraries to anchor additional scaffolds and fill gaps. Despite this, we were able to more than triple the scaffold N50 by leveraging the optimal consensus genome maps and Stitch. We demonstrated that the AssembleIrysCluster method of optimization and Stitch can be used together to improve the contiguity of a draft genome.


As the variety of genome assembly projects increases, we are discovering that tools appropriate for all projects (e.g. genomes of varying size and complexity, assemblies of varying quality, various taxonomic groups, etc.) do not exist. Indeed, the results of Assemblation2 indicate that no one suite of datatypes or assembly workflow may be sufficient to best assemble even the subcategory of vertebrate genomes \cite{Assem22013}. Here we described two software tools and many shorter scripts to summarize and work with these new data formats. However, we anticipate the development of a variety of bioinformatics tools for extremely long, single molecule map data as more applications for these maps are explored.

Some draft assemblies may currently be too fragmented to align to genome maps assembled from single molecule maps. However as NGS genome assemblies improve from longer read advancements existing genome maps may become useful for scaffolding new or updated sequence assemblies.

Regions where consensus genome maps disagree with sequence assemblies (e.g. negative gap lengths or partial alignments) are flagged by Stitch for investigation at a sequence level. Bioinformatics tools that could automate assembly editing based on such discrepancies are needed to fully support genome improvement with consensus genome maps. 

