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Reevaluating Assembly Evaluations with Feature Response Curves: GAGE and Assemblathons

Overview of attention for article published in PLOS ONE, December 2012
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Title
Reevaluating Assembly Evaluations with Feature Response Curves: GAGE and Assemblathons
Published in
PLOS ONE, December 2012
DOI 10.1371/journal.pone.0052210
Pubmed ID
Authors

Francesco Vezzi, Giuseppe Narzisi, Bud Mishra

Abstract

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 sequences/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. This situation has prompted the scientific community to rely on crowd-sourcing through international competitions, such as Assemblathons or GAGE, with the intention of identifying the best assembler(s) and their features. Somewhat 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 [Formula: see text], 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.

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Geographical breakdown

Country Count As %
United States 11 5%
Sweden 5 2%
United Kingdom 5 2%
Germany 4 2%
Norway 3 1%
Netherlands 2 <1%
Brazil 1 <1%
Czechia 1 <1%
Canada 1 <1%
Other 5 2%
Unknown 178 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 25%
Researcher 54 25%
Student > Master 27 13%
Other 12 6%
Student > Doctoral Student 11 5%
Other 36 17%
Unknown 21 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 122 56%
Biochemistry, Genetics and Molecular Biology 34 16%
Computer Science 14 6%
Medicine and Dentistry 5 2%
Immunology and Microbiology 3 1%
Other 14 6%
Unknown 24 11%