title: Heterozygous genome assembly via binary classification of homologous sequence.
abstract: BACKGROUND
  Genome assemblers to date have predominantly targeted haploid reference reconstruction from homozygous data. When applied to diploid genome assembly, these assemblers perform poorly, owing to the violation of assumptions during both the contigging and scaffolding phases. Effective tools to overcome these problems are in growing demand. Increasing parameter stringency during contigging is an effective solution to obtaining haplotype-specific contigs; however, effective algorithms for scaffolding such contigs are lacking.
  
  
  METHODS
  We present a stand-alone scaffolding algorithm, ScaffoldScaffolder, designed specifically for scaffolding diploid genomes. The algorithm identifies homologous sequences as found in "bubble" structures in scaffold graphs. Machine learning classification is used to then classify sequences in partial bubbles as homologous or non-homologous sequences prior to reconstructing haplotype-specific scaffolds. We define four new metrics for assessing diploid scaffolding accuracy: contig sequencing depth, contig homogeneity, phase group homogeneity, and heterogeneity between phase groups.
  
  
  RESULTS
  We demonstrate the viability of using bubbles to identify heterozygous homologous contigs, which we term homolotigs. We show that machine learning classification trained on these homolotig pairs can be used effectively for identifying homologous sequences elsewhere in the data with high precision (assuming error-free reads).
  
  
  CONCLUSIONS
  More work is required to comparatively analyze this approach on real data with various parameters and classifiers against other diploid genome assembly methods. However, the initial results of ScaffoldScaffolder supply validity to the idea of employing machine learning in the difficult task of diploid genome assembly. Software is available at http://bioresearch.byu.edu/scaffoldscaffolder.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Bodily
authors.forename: M
authors.initials: M
authors.lastname: Fujimoto
authors.forename: Cameron
authors.initials: C
authors.lastname: Ortega
authors.forename: Nozomu
authors.initials: N
authors.lastname: Okuda
authors.forename: Jared C
authors.initials: JC
authors.lastname: Price
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Clement
authors.forename: Quinn
authors.initials: Q
authors.lastname: Snell
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020451:Contig Mapping / Q000379:methods*; D004171:Diploidy; D015894:Genome, Human; D006579:Heterozygote; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods*; D017385:Sequence Homology; D012984:Software