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Oligonucleotide microarrays were used to map the detailed topography of chromosome replication in the budding yeast Saccharomyces cerevisiae. The times of replication of thousands of sites across the genome were determined by hybridizing replicated and unreplicated DNAs, isolated at different times in S phase, to the microarrays. Origin activations take place continuously throughout S phase but with most firings near mid-S phase. Rates of replication fork movement vary greatly from region to region in the genome. The two ends of each of the 16 chromosomes are highly correlated in their times of replication. This microarray approach is readily applicable to other organisms, including humans.

The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.

Genome sequence information in combination with new technologies has allowed researchers to approach genetic problems in new ways. High-density oligonucleotide arrays were used to probe the genome content of the yeast Saccharomyces cerevisiae. We show that these arrays, containing oligonucleotides complementary to the sequenced strain of S. cerevisiae, can be used to identify open reading frames that are missing or present in higher or lower copy number in related isolates of S. cerevisiae. We apply this method to the characterization of the genome of a strain derived from a clinical isolate of S. cerevisiae. Our results show that the telomeres are the regions with the most variability between the two strains.

As more genomes are sequenced, the identification and characterization of the causes of heritable variation within a species will be increasingly important. It is demonstrated that allelic variation in any two isolates of a species can be scanned, mapped, and scored directly and efficiently without allele-specific polymerase chain reaction, without creating new strains or constructs, and without knowing the specific nature of the variation. A total of 3714 biallelic markers, spaced about every 3.5 kilobases, were identified by analyzing the patterns obtained when total genomic DNA from two different strains of yeast was hybridized to high-density oligonucleotide arrays. The markers were then used to simultaneously map a multidrug-resistance locus and four other loci with high resolution (11 to 64 kilobases).

Progression through the eukaryotic cell cycle is known to be both regulated and accompanied by periodic fluctuation in the expression levels of numerous genes. We report here the genome-wide characterization of mRNA transcript levels during the cell cycle of the budding yeast S. cerevisiae. Cell cycle-dependent periodicity was found for 416 of the 6220 monitored transcripts. More than 25% of the 416 genes were found directly adjacent to other genes in the genome that displayed induction in the same cell cycle phase, suggesting a mechanism for local chromosomal organization in global mRNA regulation. More than 60% of the characterized genes that displayed mRNA fluctuation have already been implicated in cell cycle period-specific biological roles. Because more than 20% of human proteins display significant homology to yeast proteins, these results also link a range of human genes to cell cycle period-specific biological functions.

The release of the complete genome sequence of the yeast Saccharomyces cerevisiae has ushered in a new phase of genome research in which sequence function will be assigned. The goal is to determine the biological function of each of the > 6,000 open reading frames in the yeast genome. Innovative approaches have been developed that exploit the sequence data and yield information about gene expression levels, protein levels, subcellular localization and gene function for the entire genome.