title: De novo likelihood-based measures for comparing genome assemblies.
abstract: BACKGROUND
  The current revolution in genomics has been made possible by software tools called genome assemblers, which stitch together DNA fragments "read" by sequencing machines into complete or nearly complete genome sequences. Despite decades of research in this field and the development of dozens of genome assemblers, assessing and comparing the quality of assembled genome sequences still relies on the availability of independently determined standards, such as manually curated genome sequences, or independently produced mapping data. These "gold standards" can be expensive to produce and may only cover a small fraction of the genome, which limits their applicability to newly generated genome sequences. Here we introduce a de novo  probabilistic measure of assembly quality which allows for an objective comparison of multiple assemblies generated from the same set of reads. We define the quality of a sequence produced by an assembler as the conditional probability of observing the sequenced reads from the assembled sequence. A key property of our metric is that the true genome sequence maximizes the score, unlike other commonly used metrics.
  
  
  RESULTS
  We demonstrate that our de novo  score can be computed quickly and accurately in a practical setting even for large datasets, by estimating the score from a relatively small sample of the reads. To demonstrate the benefits of our score, we measure the quality of the assemblies generated in the GAGE and Assemblathon 1 assembly "bake-offs" with our metric. Even without knowledge of the true reference sequence, our de novo  metric closely matches the reference-based evaluation metrics used in the studies and outperforms other de novo  metrics traditionally used to measure assembly quality (such as N50). Finally, we highlight the application of our score to optimize assembly parameters used in genome assemblers, which enables better assemblies to be produced, even without prior knowledge of the genome being assembled.
  
  
  CONCLUSIONS
  Likelihood-based measures, such as ours proposed here, will become the new standard for de novo  assembly evaluation.
authors: Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA. mpop@umd.edu.
authors: Mohammadreza
authors: M
authors: Ghodsi
authors: Christopher M
authors: CM
authors: Hill
authors: Irina
authors: I
authors: Astrovskaya
authors: Henry
authors: H
authors: Lin
authors: Dan D
authors: DD
authors: Sommer
authors: Sergey
authors: S
authors: Koren
authors: Mihai
authors: M
authors: Pop
mesh_terms: D000465:Algorithms; D020451:Contig Mapping / Q000706:statistics & numerical data*; D016680:Genome, Bacterial; D023281:Genomics / Q000379:methods; D016013:Likelihood Functions; D012242:Rhodobacter sphaeroides / Q000235:genetics*; D017422:Sequence Analysis, DNA; D012984:Software; D013211:Staphylococcus aureus / Q000235:genetics*; D013212:Staphylococcus epidermidis / Q000235:genetics*
references: BMC Genomics. 2010 Apr 16;11:242
references: 20398345
references: BMC Bioinformatics. 2010 Jan 12;11:21
references: 20064276
references: Genome Biol. 2013 Jan 29;14(1):R8
references: 23360652
references: BMC Bioinformatics. 2011 Apr 26;12:119
references: 21521499
references: Genome Res. 1997 May;7(5):401-9
references: 9149936
references: Bioinformatics. 2013 Feb 15;29(4):435-43
references: 23303509
references: Science. 2009 Oct 9;326(5950):236-7
references: 19815760
references: Genome Res. 2009 Jun;19(6):1117-23
references: 19251739
references: Science. 2000 Mar 24;287(5461):2196-204
references: 10731133
references: BMC Bioinformatics. 2011;12 Suppl 6:S1
references: 21989211
references: Nat Methods. 2010 Aug;7(8):576-7
references: 20676076
references: J Bacteriol. 2002 Dec;184(23):6403-5; discusion 6405
references: 12426324
references: PLoS One. 2011 Apr 29;6(4):e19175
references: 21559467
references: Bioinformatics. 2005 Dec 15;21(24):4320-1
references: 16332717
references: BMC Genomics. 2007 Aug 15;8:278
references: 17697381
references: Genome Res. 1997 May;7(5):410-7
references: 9149937
references: Genome Res. 2008 May;18(5):821-9
references: 18349386
references: Genome Biol. 2008;9(3):R55
references: 18341692
references: J Comput Biol. 1995 Summer;2(2):275-90
references: 7497129
references: Genome Res. 2011 Jul;21(7):1193-200
references: 21536722
references: Genome Res. 2010 Feb;20(2):265-72
references: 20019144
references: Genome Res. 2011 Dec;21(12):2224-41
references: 21926179
references: J Comput Biol. 2009 Aug;16(8):1101-16
references: 19645596
references: Nat Methods. 2012 Mar 04;9(4):357-9
references: 22388286
references: J Bacteriol. 2002 Dec;184(23):6406-9; discussion 6409
references: 12426325
references: PLoS One. 2012;7(2):e31002
references: 22319599
references: J Comput Biol. 2011 Mar;18(3):429-43
references: 21385045
references: Genome Res. 2012 Mar;22(3):557-67
references: 22147368
references: Proc Natl Acad Sci U S A. 2011 Mar 22;108(12):5027-32
references: 21383169
references: J Comput Biol. 2009 Jul;16(7):897-908
references: 19580519
references: Bioinformatics. 2009 Aug 15;25(16):2078-9
references: 19505943