  In just the last decade, a multitude of bio-technologies and software
pipelines have emerged to revolutionize genomics. To further their central
goal, they aim to accelerate and improve the quality of de novo whole-genome
assembly starting from short DNA reads. However, the performance of each of
these tools is contingent on the length and quality of the sequencing data, the
structure and complexity of the genome sequence, and the resolution and quality
of long-range information. Furthermore, in the absence of any metric that
captures the most fundamental "features" of a high-quality assembly, there is
no obvious recipe for users to select the most desirable assembler/assembly.
International competitions such as Assemblathons or GAGE tried to identify the
best assembler(s) and their features. Some what circuitously, the only
available approach to gauge de novo assemblies and assemblers relies solely on
the availability of a high-quality fully assembled reference genome sequence.
Still worse, reference-guided evaluations are often both difficult to analyze,
leading to conclusions that are difficult to interpret. In this paper, we
circumvent many of these issues by relying upon a tool, dubbed FRCbam, which is
capable of evaluating de novo assemblies from the read-layouts even when no
reference exists. We extend the FRCurve approach to cases where lay-out
information may have been obscured, as is true in many deBruijn-graph-based
algorithms. As a by-product, FRCurve now expands its applicability to a much
wider class of assemblers -- thus, identifying higher-quality members of this
group, their inter-relations as well as sensitivity to carefully selected
features, with or without the support of a reference sequence or layout for the
reads. The paper concludes by reevaluating several recently conducted assembly
competitions and the datasets that have resulted from them.
