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| Dataset | Description | Keywords | Number of Conditions | Reference |
|---|---|---|---|---|
| A High Resolution Profile of NMD Substrates in Yeast | We report a high resolution catalouge of NMD substrates using RNA-Seq | genome variation, RNA catabolism | 21 | Celik A, et al. (2017) PMID:28209632 |
| A Method for High-throughput Production of Sequence-verified DNA Libraries and Strain Collections | The low costs of array-synthesized oligonucleotide libraries are empowering rapid advances in quantitative and synthetic biology. Unfortunately, high synthesis error rates, uneven representation, and lack of access to individual oligonucleotides limit the true potential of these libraries. We have developed a cost-effective method called Recombinase Directed Indexing (REDI) to address these problems. The method involves integration of a complex library into yeast, site-specific recombination to index (i.e. barcode) library DNA, and then next-generation sequencing to identify clones containing the DNA of interest. We used REDI to generate a molecular probe library (n = ~3,300) that exhibited >96% purity and remarkable uniformity (>95% of probes were within 2-fold relative abundance of the median). Moreover, each sequence-verified probe was readily accessible. We also used REDI to rapidly create an arrayed collection of ~9,000 strains for CRISPR interference in yeast and demonstrate the utility of this collection for highly sensitive phenotypic screening. Our approach will enable a variety of applications requiring accurate, high-quality DNA libraries. | bioinformatics and computational biology, genome variation | 102 | Smith JD, et al. (2017) PMID:28193641 |
| A species-specific nucleosomal signature defines a periodic distribution of amino acids in proteins | Sequencing of mononucleosomal DNA during asynchronous mitosis in Schizosaccharomyces pombe, Schizosaccharomyces octosporus, Schizosaccharomyces japonicus and Saccharomyces cerevisiae | genome variation, amino acid metabolism | 4 | Quintales L, et al. (2015) PMID:25854683 |
| AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae | The characterization and public release of genome sequences from thousands of organisms is expanding the scope for genetic variation studies | genome variation | 0 | Song G, et al. (2015) PMID:25781462 |
| An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts | Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage “the awesome power of yeast” by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time, and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity given its single-cell nature. For example, in the data presented here we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, etc. all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome. | transcriptome, genome variation | 7 | Brettner L, et al. (2024) PMID:38282330 |
| Array competitive genomic hybridization of three Saccharomyces cerevisiae strains vs. DBY8268 | A six array study using total gDNA recovered from two separate cultures of each of three different strains of Saccharomyces cerevisiae (YB-210 or CRB, Y389 or MUSH, and Y2209 or LEP) and two separate cultures of Saccharomyces cerevisiae DBY8268 | genome variation | 6 | Wohlbach DJ, et al. (2014) PMID:25364804 |
| CGH array of opportunistic strains | Genomic comparation of 3 opportunistic strains (60, 102 and D14) and one laboratory strain (W303) with the type strain S288C | nucleotide metabolism, genome variation | 10 | Pérez-Torrado R, et al. (2015) PMID:25816288 |
| Classifying pathogenic variants in amyloid beta using intramolecular genetic interaction profiling | Changes in the amino acid sequences of proteins cause thousands of human genetic diseases. However, only a subset of variants in any protein is typically pathogenic, with variants having a diversity of molecular consequences. Determining which of the thousands of possible variants in any protein have similar molecular effects is very challenging, but crucial for identifying pathogenic variants, determining disease mechanisms, understanding clinical phenotypic variation, and developing targeted therapeutics. Here we present a general method to classify variants by their molecular effects that we term intramolecular genetic interaction profiling. The approach relies on the principle that variants with similar molecular consequences have similar genetic interactions with other variants in the same protein. These intramolecular genetic interactions are straightforward to quantify for any protein with a selectable function. We apply intramolecular genetic interaction profiling to amyloid beta, the protein that aggregates in Alzheimer’s disease (AD) and is mutated in familial AD (fAD). Genetic interactions identify two classes of gain-of-function variants, with all known familial Alzheimer’s disease variants having very similar genetic interaction profiles, consistent with a common gain-of-function mechanism leading to pathology. We believe that intramolecular genetic interaction profiling is a powerful approach for classifying variants in disease genes that will empower rare variant association studies and the discovery of disease mechanisms. | genome variation, disease | 6 | |
| Comparative Genomic Hybridization in traditional fermentative Saccharomyces cerevisiae yeasts | We study the genetics, including microarray karyotyping using comparative genomic hybridization to explore global changes in the genomic DNA, of four S. bayanus var uvarum strains related to traditional fermentations of very different sources comparing to the sequenced S. cerevisiae laboratory strain (S288C) | genome variation | 4 | |
| Comparison of the complete protein sets of worm and yeast | Chervitz et al. conducted a comparative analysis of predicted protein sequences encoded by the genomes of Caenorhabditis elegans and Saccharomyces cerevisiae suggests that most of the core biological functions are carried out by orthologous proteins that occur in comparable numbers. | genome variation | 0 | Chervitz SA, et al. (1998) PMID:9851918 |