Heterozygous genome assembly via binary classification of homologous sequence.

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). CONCLUSION: 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.

Rhizobial plasmids that cause impaired symbiotic nitrogen fixation and enhanced host invasion.

The genetic rules that dictate legume-rhizobium compatibility have been investigated for decades, but the causes of incompatibility occurring at late stages of the nodulation process are not well understood. An evaluation of naturally diverse legume (genus Medicago) and rhizobium (genus Sinorhizobium) isolates has revealed numerous instances in which Sinorhizobium strains induce and occupy nodules that are only minimally beneficial to certain Medicago hosts. Using these ineffective strain-host pairs, we identified gain-of-compatibility (GOC) rhizobial variants. We show that GOC variants arise by loss of specific large accessory plasmids, which we call HR plasmids due to their effect on symbiotic host range. Transfer of HR plasmids to a symbiotically effective rhizobium strain can convert it to incompatibility, indicating that HR plasmids can act autonomously in diverse strain backgrounds. We provide evidence that HR plasmids may encode machinery for their horizontal transfer. On hosts in which HR plasmids impair N fixation, the plasmids also enhance competitiveness for nodule occupancy, showing that naturally occurring, transferrable accessory genes can convert beneficial rhizobia to a more exploitative lifestyle. This observation raises important questions about agricultural management, the ecological stability of mutualisms, and the genetic factors that distinguish beneficial symbionts from parasites.

Transcriptome characterization and polymorphism detection between subspecies of big sagebrush (Artemisia tridentata).

BACKGROUND: Big sagebrush (Artemisia tridentata) is one of the most widely distributed and ecologically important shrub species in western North America. This species serves as a critical habitat and food resource for many animals and invertebrates. Habitat loss due to a combination of disturbances followed by establishment of invasive plant species is a serious threat to big sagebrush ecosystem sustainability. Lack of genomic data has limited our understanding of the evolutionary history and ecological adaptation in this species. Here, we report on the sequencing of expressed sequence tags (ESTs) and detection of single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) markers in subspecies of big sagebrush. RESULTS: cDNA of A. tridentata sspp. tridentata and vaseyana were normalized and sequenced using the 454 GS FLX Titanium pyrosequencing technology. Assembly of the reads resulted in 20,357 contig consensus sequences in ssp. tridentata and 20,250 contigs in ssp. vaseyana. A BLASTx search against the non-redundant (NR) protein database using 29,541 consensus sequences obtained from a combined assembly resulted in 21,436 sequences with significant blast alignments (≤ 1e⁻¹5). A total of 20,952 SNPs and 119 polymorphic SSRs were detected between the two subspecies. SNPs were validated through various methods including sequence capture. Validation of SNPs in different individuals uncovered a high level of nucleotide variation in EST sequences. EST sequences of a third, tetraploid subspecies (ssp. wyomingensis) obtained by Illumina sequencing were mapped to the consensus sequences of the combined 454 EST assembly. Approximately one-third of the SNPs between sspp. tridentata and vaseyana identified in the combined assembly were also polymorphic within the two geographically distant ssp. wyomingensis samples. CONCLUSION: We have produced a large EST dataset for Artemisia tridentata, which contains a large sample of the big sagebrush leaf transcriptome. SNP mapping among the three subspecies suggest the origin of ssp. wyomingensis via mixed ancestry. A large number of SNP and SSR markers provide the foundation for future research to address questions in big sagebrush evolution, ecological genetics, and conservation using genomic approaches.