Mauve assembly metrics

Aaron E. Darling, Andrew Tritt, Jonathan A Eisen, Marc T. Facciotti

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


Summary: 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.

Original languageEnglish (US)
Article numberbtr451
Pages (from-to)2756-2757
Number of pages2
Issue number19
StatePublished - Oct 2011

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)


Dive into the research topics of 'Mauve assembly metrics'. Together they form a unique fingerprint.

Cite this