title: Mauve assembly metrics.
abstract: CONCLUSIONS
  High-throughput DNA sequencing technologies have spurred the development of numerous novel methods for genome assembly. With few exceptions, these algorithms are heuristic and require one or more parameters to be manually set by the user. One approach to parameter tuning involves assembling data from an organism with an available high-quality reference genome, and measuring assembly accuracy using some metrics. We developed a system to measure assembly quality under several scoring metrics, and to compare assembly quality across a variety of assemblers, sequence data types, and parameter choices. When used in conjunction with training data such as a high-quality reference genome and sequence reads from the same organism, our program can be used to manually identify an optimal sequencing and assembly strategy for de novo sequencing of related organisms.
  
  
  BACKGROUND
  GPL source code and a usage tutorial is at http://ngopt.googlecode.com
  
  
  BACKGROUND
  aarondarling@ucdavis.edu
  
  
  BACKGROUND
  Supplementary data is available at Bioinformatics online.
authors.affiliation: Genome Center, University of California-Davis, Davis, CA 95616, USA. aarondarling@ucdavis.edu
authors.forename: Aaron E
authors.initials: AE
authors.lastname: Darling
authors.forename: Andrew
authors.initials: A
authors.lastname: Tritt
authors.forename: Jonathan A
authors.initials: JA
authors.lastname: Eisen
authors.forename: Marc T
authors.initials: MT
authors.lastname: Facciotti
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D016678:Genome; D019622:Haloferax volcanii / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000592:standards*; D016415:Sequence Alignment / Q000592:standards*; D017422:Sequence Analysis, DNA / Q000592:standards