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

Synthetic yeast chromosome XI design provides a testbed for the study of extrachromosomal circular DNA dynamics

We describe construction of the synthetic yeast chromosome XI (synXI) and reveal the effects of redesign at non-coding DNA elements. The 660-kb synthetic yeast genome project (Sc2.0) chromosome was assembled from synthesized DNA fragments before CRISPR-based methods were used in a process of bug discovery, redesign, and chromosome repair, including precise compaction of 200 kb of repeat sequence. Repaired defects were related to poor centromere function and mitochondrial health and were associated with modifications to non-coding regions. As part of the Sc2.0 design, loxPsym sequences for Cre-mediated recombination are inserted between most genes. Using the GAP1 locus from chromosome XI, we show that these sites can facilitate induced extrachromosomal circular DNA (eccDNA) formation, allowing direct study of the effects and propagation of these important molecules. Construction and characterization of synXI contributes to our understanding of non-coding DNA elements, provides a useful tool for eccDNA study, and will inform future synthetic genome design.

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

The accuracy of the nucleotide sequence of genomes is of utmost importance. The Perfect Match Genomic Landscape (PMGL) is a precise, sensitive, and nonstatistical strategy to detect genome variation. We used this strategy to refine reference genomes from microorganisms belonging to the three domains of life. Our studies show as well that the PMGL can be useful to detect variants in pathogen agents during a pandemic, and to isolate mutations generated during any desired stage of experimental evolution studies. We propose that the PMGL strategy could be the final step in the refinement of any haploid genome, independently of the methodology and algorithms used for its assembly.

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.

Precise detection of de novo single nucleotide variants in human genomes

The precise location of variants in the human genome is of utmost importance. We present a unique approach, coverage-based single nucleotide variant (SNV) identification (COBASI), which uses only perfect matches between the reads of a sequence project and a reference genome to detect and accurately identify de novo SNVs. From the perfect matches, a representation of the read coverage per nucleotide along the genome, the variation landscape, is generated. SNVs are then pinpointed as significant changes in coverage and de novo SNVs can be identified with high precision. The performance of COBASI was analyzed using simulations and experimentally validated by sequencing de novo SNVs identified from a parent–offspring trio. We propose this pipeline as a useful tool for different genomic applications.

The precise determination of de novo genetic variants has enormous implications across different fields of biology and medicine, particularly personalized medicine. Currently, de novo variations are identified by mapping sample reads from a parent–offspring trio to a reference genome, allowing for a certain degree of differences. While widely used, this approach often introduces false-positive (FP) results due to misaligned reads and mischaracterized sequencing errors. In a previous study, we developed an alternative approach to accurately identify single nucleotide variants (SNVs) using only perfect matches. However, this approach could be applied only to haploid regions of the genome and was computationally intensive. In this study, we present a unique approach, coverage-based single nucleotide variant identification (COBASI), which allows the exploration of the entire genome using second-generation short sequence reads without extensive computing requirements. COBASI identifies SNVs using changes in coverage of exactly matching unique substrings, and is particularly suited for pinpointing de novo SNVs. Unlike other approaches that require population frequencies across hundreds of samples to filter out any methodological biases, COBASI can be applied to detect de novo SNVs within isolated families. We demonstrate this capability through extensive simulation studies and by studying a parent–offspring trio we sequenced using short reads. Experimental validation of all 58 candidate de novo SNVs and a selection of non-de novo SNVs found in the trio confirmed zero FP calls. COBASI is available as open source at https://github.com/Laura-Gomez/COBASI for any researcher to use.