Diploid genome assembly and comprehensive haplotype sequence reconstruction
Outside of the simplest cases (haploid, bacteria, or inbreds), genomic information is not carried in a single reference per individual, but rather has higher ploidy (n=>2) for almost all organisms. The existence of two or more highly related sequences within an individual makes it extremely difficult to build high quality, highly contiguous genome assemblies from short DNA fragments. Based on the earlier work on a polyploidy aware assembler, FALCON ( https://github.com/PacificBiosciences/FALCON) , we developed new algorithms and software (“FALCON-unzip”) for de novo haplotype reconstructions from SMRT Sequencing data. We generate two datasets for developing the algorithms and the prototype software: (1) whole genome sequencing data from a highly repetitive diploid fungal (Clavicorona pyxidata) and (2) whole genome sequencing data from an F1 hybrid from two inbred Arabidopsis strains: Cvi-0 and Col-0. For the fungal genome, we achieved an N50 of 1.53 Mb (of the 1n assembly contigs) of the ~42 Mb 1n genome and an N50 of the haplotigs (haplotype specific contigs) of 872 kb from a 95X read length N50 ~16 kb dataset. We found that ~ 45% of the genome was highly heterozygous and ~55% of the genome was highly homozygous. We developed methods to assess the base-level accuracy and local haplotype phasing accuracy of the assembly with short-read data from the Illumina® platform. For the ArabidopsisF1 hybrid genome, we found that 80% of the genome could be separated into haplotigs. The long range accuracy of phasing haplotigs was evaluated by comparing them to the assemblies from the two inbred parental lines. We show that a more complete view of all haplotypes could provide useful biological insights through improved annotation, characterization of heterozygous variants of all sizes, and resolution of differential allele expression. The current Falcon-Unzip method will lead to understand how to solve more difficult polyploid genome assembly problems and improve the computational efficiency for large genome assemblies. Based on this work, we can develop a pipeline enabling routinely assemble diploid or polyploid genomes as haplotigs, representing a comprehensive view of the genomes that can be studied with the information at hand.