Reference-assisted chromosome assembly

Abstract

One of the most difficult problems in modern genomics is the assembly of full-length chromosomes using next generation sequencing (NGS) data. To address this problem, we developed "reference-assisted chromosome assembly" (RACA), an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads. Evaluation of results using simulated and real genome assemblies indicates that our approach can substantially improve genomes generated by a wide variety of de novo assemblers if a good reference assembly of a closely related species and outgroup genomes are available. We used RACA to reconstruct 60 Tibetan antelope (Pantholops hodgsonii) chromosome fragments from 1,434 SOAPdenovo sequence scaffolds, of which 16 chromosome fragments were homologous to complete cattle chromosomes. Experimental validation by PCR showed that predictions made by RACA are highly accurate. Our results indicate that RACA will significantly facilitate the study of chromosome evolution and genome rearrangements for the large number of genomes being sequenced by NGS that do not have a genetic or physical map.computational genomics | comparative genomics | bioinformatics | chromosome breakpoints | mammals

Upgrading short-read animal genome assemblies to chromosome level using comparative genomics and a universal probe set

“…Bioinformatic approaches, e.g., the Reference-Assisted Chromosome Assembly (RACA) algorithm (Kim et al 2013), were developed to approximate near chromosome-sized fragments for a de novo assembled NGS genome. RACA use requires a genome from the same clade (e.g., Order for mammals) of the target species being assembled to chromosomes (Kim et al 2013) and sequencing of long-insert libraries.…”

“…RACA use requires a genome from the same clade (e.g., Order for mammals) of the target species being assembled to chromosomes (Kim et al 2013) and sequencing of long-insert libraries. RACA produces, at best, subchromosomesized predicted chromosome fragments (PCFs) that require further verification and subsequent chromosome assembly.…”

“…It is worth mentioning that, unlike RACA, other reference-assisted assembly algorithms, e.g., Ragout (Kolmogorov et al 2014) or Chromosomer (Tamazian et al 2016), do not use the target genome short-and long-range paired-read data to verify synteny breaks in/between scaffolds, meaning that the target species-specific rearrangements could be missed from the reconstructed PCFs/pseudochromosomes, making the reconstructed target chromosome structures more heavily biased to the reference genome(s) than when using RACA. RACA algorithm applied to the Tibetan antelope and blind mole rat genomes significantly improved continuities of these assemblies, but they still contain more than one large PCF for most chromosomes (Kim et al 2013;Fang et al 2014). Therefore, a novel, integrative approach that would allow de novo assembled genomes to retain structures of the target species karyotypes is a necessity.…”

“…PCFs were generated for fragmented falcon and pigeon whole-genome sequences using RACA (Kim et al 2013). For falcon, the zebra finch chromosome assembly was used as reference (divergence 60 MYA) and the chicken genome as outgroup (divergence 89 MYA).…”

“…Only 3.50% of the original scaffolds used by RACA were split in the pigeon and 3.14% in falcon final PCFs (Table 1). The accuracy for the PCF assembly was estimated as ∼85% for falcon and ∼89% for pigeon based on the ratio of the number of SFs to the number of scaffolds (Kim et al 2013). …”