title: GAML: genome assembly by maximum likelihood.
abstract: BACKGROUND
  Resolution of repeats and scaffolding of shorter contigs are critical parts of genome assembly. Modern assemblers usually perform such steps by heuristics, often tailored to a particular technology for producing paired or long reads.
  
  
  RESULTS
  We propose a new framework that allows systematic combination of diverse sequencing datasets into a single assembly. We achieve this by searching for an assembly with the maximum likelihood in a probabilistic model capturing error rate, insert lengths, and other characteristics of the sequencing technology used to produce each dataset. We have implemented a prototype genome assembler GAML that can use any combination of insert sizes with Illumina or 454 reads, as well as PacBio reads. Our experiments show that we can assemble short genomes with N50 sizes and error rates comparable to ALLPATHS-LG or Cerulean. While ALLPATHS-LG and Cerulean require each a specific combination of datasets, GAML works on any combination.
  
  
  CONCLUSIONS
  We have introduced a new probabilistic approach to genome assembly and demonstrated that this approach can lead to superior results when used to combine diverse set of datasets from different sequencing technologies. Data and software is available at http://compbio.fmph.uniba.sk/gaml.
authors.affiliation: Faculty of Mathematics, Physics, and Informatics, Comenius University, Mlynská dolina, 842 48 Bratislava, Slovakia.
authors.forename: Vladimír
authors.initials: V
authors.lastname: Boža
authors.affiliation: Faculty of Mathematics, Physics, and Informatics, Comenius University, Mlynská dolina, 842 48 Bratislava, Slovakia.
authors.forename: Broňa
authors.initials: B
authors.lastname: Brejová
authors.affiliation: Faculty of Mathematics, Physics, and Informatics, Comenius University, Mlynská dolina, 842 48 Bratislava, Slovakia.
authors.forename: Tomáš
authors.initials: T
authors.lastname: Vinař
keywords: De Bruijn graphs; Genome assembly; Maximum likelihood; Next generation sequencing; Simulated annealing