Biomedical subjects

Mark D Robinson

Publications and source records attributed to Mark D Robinson.

5 recordsLinked to original sources

PubMed2026

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects function. However, the reliability of current benchmarks of spatially aware clustering (SAC) methods is undermined by their narrow focus on Visium and brain tissue datasets and the incorrect interpretation of manual annotation as ground truth. Here we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration and metric evaluation, enabling rapid inclusion of new methods and datasets. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods and shows that anatomical labels commonly used as ground truths are often biased, error prone and unsuitable for benchmarking. Rather than ranking methods, we propose a consensus-guided workflow where descriptive spatial metrics highlight high-entropy regions of method disagreement, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual SAC methods and manual annotations, highlighting the need for iterative, expert-in-the-loop evaluation.

Benchmarking↗

PubMed2003

A panoramic view of yeast noncoding RNA processing.

Predictive analysis using publicly available yeast functional genomics and proteomics data suggests that many more proteins may be involved in biogenesis of ribonucleoproteins than are currently known. Using a microarray that monitors abundance and processing of noncoding RNAs, we analyzed 468 yeast strains carrying mutations in protein-coding genes, most of which have not previously been associated with RNA or RNP synthesis. Many strains mutated in uncharacterized genes displayed aberrant noncoding RNA profiles. Ten factors involved in noncoding RNA biogenesis were verified by further experimentation, including a protein required for 20S pre-rRNA processing (Tsr2p), a protein associated with the nuclear exosome (Lrp1p), and a factor required for box C/D snoRNA accumulation (Bcd1p). These data present a global view of yeast noncoding RNA processing and confirm that many currently uncharacterized yeast proteins are involved in biogenesis of noncoding RNA.

Cells, Cultured↗

PubMed2002

FunSpec: a web-based cluster interpreter for yeast.

BACKGROUND: For effective exposition of biological information, especially with regard to analysis of large-scale data types, researchers need immediate access to multiple categorical knowledge bases and need summary information presented to them on collections of genes, as opposed to the typical one gene at a time. RESULTS: We present here a web-based tool (FunSpec) for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations (e.g. functional roles, biochemical properties, localization). FunSpec is available online at http://funspec.med.utoronto.ca CONCLUSION: FunSpec is helpful for interpretation of any data type that generates groups of related genes and proteins, such as gene expression clustering and protein complexes, and is useful for predictive methods employing "guilt-by-association."

Cluster Analysis↗

PubMed2002

Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters.

Genome sequencing has led to the discovery of tens of thousands of potential new genes. Six years after the sequencing of the well-studied yeast Saccharomyces cerevisiae and the discovery that its genome encodes approximately 6,000 predicted proteins, more than 2,000 have not yet been characterized experimentally, and determining their functions seems far from a trivial task. One crucial constraint is the generation of useful hypotheses about protein function. Using a new approach to interpret microarray data, we assign likely cellular functions with confidence values to these new yeast proteins. We perform extensive genome-wide validations of our predictions and offer visualization methods for exploration of the large numbers of functional predictions. We identify potential new members of many existing functional categories including 285 candidate proteins involved in transcription, processing and transport of non-coding RNA molecules. We present experimental validation confirming the involvement of several of these proteins in ribosomal RNA processing. Our methodology can be applied to a variety of genomics data types and organisms.

Algorithms↗

PubMed2002

High-resolution genetic mapping with ordered arrays of Saccharomyces cerevisiae deletion mutants.

We present a method for high-resolution genetic mapping that takes advantage of the ordered set of viable gene deletion mutants, which form a set of colinear markers covering almost every centimorgan of the Saccharomyces cerevisiae genome, and of the synthetic genetic array (SGA) system, which automates the construction of double mutants formed by mating and meiotic recombination. The Cbk1 kinase signaling pathway, which consists minimally of CBK1, MOB2, KIC1, HYM1, and TAO3 (PAG1), controls polarized morphogenesis and activation of the Ace2 transcription factor. Deletion mutations in the Cbk1 pathway genes are tolerated differently by common laboratory strains of S. cerevisiae, being viable in the W303 background but dead in the S288C background. Genetic analysis indicated that the lethality of Cbk1 pathway deletions in the S288C background was suppressed by a single allele specific to the W303 background. SGA mapping (SGAM) was used to locate this W303-specific suppressor to the SSD1 locus, which contains a known polymorphism that appears to compromise SSD1 function. This procedure should map any mutation, dominant or recessive, whose phenotype is epistatic to wild type, that is, a phenotype that can be scored from a mixed population of cells obtained by germination of both mutant and wild-type spores. In principle, SGAM should be applicable to the analysis of multigenic traits. Large-scale construction of ordered mutations in other model organisms would broaden the application of this approach.

Adaptor Proteins, Signal Transducing↗