Perfect Match Genomic Landscape strategy: Refinement and customization of reference genomes.

When addressing a genomic question, having a reliable and adequate reference genome is of utmost importance. This drives the necessity to refine and customize reference genomes (RGs). Our laboratory has recently developed a strategy, the Perfect Match Genomic Landscape (PMGL), to detect variation between genomes [K. Palacios-Flores et al.Genetics 208, 1631-1641 (2018)]. The PMGL is precise and sensitive and, in contrast to most currently used algorithms, is nonstatistical in nature. Here we demonstrate the power of PMGL to refine and customize RGs. As a proof-of-concept, we refined different versions of the Saccharomyces cerevisiae RG. We applied the automatic PMGL pipeline to refine the genomes of microorganisms belonging to the three domains of life: the archaea Methanococcus maripaludis and Pyrococcus furiosus; the bacteria Escherichia coli, Staphylococcus aureus, and Bacillus subtilis; and the eukarya Schizosaccharomyces pombe, Aspergillus oryzae, and several strains of Saccharomyces paradoxus. We analyzed the reference genome of the virus SARS-CoV-2 and previously published viral genomes from patients' samples with COVID-19. We performed a mutation-accumulation experiment in E. coli and show that the PMGL strategy can detect specific mutations generated at any desired step of the whole procedure. We propose that PMGL can be used as a final step for the refinement and customization of any haploid genome, independently of the strategies and algorithms used in its assembly.

Prediction and identification of recurrent genomic rearrangements that generate chimeric chromosomes in Saccharomyces cerevisiae.

Genomes are dynamic structures. Different mechanisms participate in the generation of genomic rearrangements. One of them is nonallelic homologous recombination (NAHR). This rearrangement is generated by recombination between pairs of repeated sequences with high identity. We analyzed rearrangements mediated by repeated sequences located in different chromosomes. Such rearrangements generate chimeric chromosomes. Potential rearrangements were predicted by localizing interchromosomal identical repeated sequences along the nuclear genome of the Saccharomyces cerevisiae S288C strain. Rearrangements were identified by a PCR-based experimental strategy. PCR primers are located in the unique regions bordering each repeated region of interest. When the PCR is performed using forward primers from one chromosome and reverse primers from another chromosome, the break point of the chimeric chromosome structure is revealed. In all cases analyzed, the corresponding chimeric structures were found. Furthermore, the nucleotide sequence of chimeric structures was obtained, and the origin of the unique regions bordering the repeated sequence was located in the expected chromosomes, using the perfect-match genomic landscape strategy (PMGL). Several chimeric structures were searched in colonies derived from single cells. All of the structures were found in DNA isolated from each of the colonies. Our findings indicate that interchromosomal rearrangements that generate chimeric chromosomes are recurrent and occur, at a relatively high frequency, in cell populations of S. cerevisiae.

A Perfect Match Genomic Landscape Provides a Unified Framework for the Precise Detection of Variation in Natural and Synthetic Haploid Genomes.

We present a conceptually simple, sensitive, precise, and essentially nonstatistical solution for the analysis of genome variation in haploid organisms. The generation of a Perfect Match Genomic Landscape (PMGL), which computes intergenome identity with single nucleotide resolution, reveals signatures of variation wherever a query genome differs from a reference genome. Such signatures encode the precise location of different types of variants, including single nucleotide variants, deletions, insertions, and amplifications, effectively introducing the concept of a general signature of variation. The precise nature of variants is then resolved through the generation of targeted alignments between specific sets of sequence reads and known regions of the reference genome. Thus, the perfect match logic decouples the identification of the location of variants from the characterization of their nature, providing a unified framework for the detection of genome variation. We assessed the performance of the PMGL strategy via simulation experiments. We determined the variation profiles of natural genomes and of a synthetic chromosome, both in the context of haploid yeast strains. Our approach uncovered variants that have previously escaped detection. Moreover, our strategy is ideally suited for further refining high-quality reference genomes. The source codes for the automated PMGL pipeline have been deposited in a public repository.