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Quaid Morris

Publications and source records attributed to Quaid Morris.

13 recordsLinked to original sources
PubMed2026

An expanded codebook of human transcription factor DNA-binding specificity.

Gene expression is regulated by transcription factors (TFs), which recognize specific DNA sequence motifs. Several hundred putative human TFs, identified mainly by an apparent DNA-binding domain, lack known binding motifs1. Furthermore, even for well-characterized TFs, it remains controversial the degree to which motifs accurately reflect binding sites in living cells2. Here we describe a systematic effort ('Codebook') to determine the sequence specificity of 332 putative and poorly characterized human TFs. More than 4,000 independent experiments, encompassing multiple in vitro and in vivo assays, produced motifs for just over half (177; 53%) of the TFs, of which most are associated with only a single protein. These results extend the vocabulary of sequence recognition encoded by human TFs by around 130 distinct motifs. Moreover, binding motifs identified in vitro are strongly enriched in cellular binding sites. Collectively, the data reveal tens of thousands of previously unknown, conserved and direct TF-binding sites across the human genome. These sites are concentrated in promoter regions and are predictive of gene expression. In summary, this new codebook provides an important step forward in decoding the human genome.

Humans↗
PubMed2025

Orthrus: Towards Evolutionary and Functional RNA Foundation Models.

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature RNA properties that underlie transcript function and regulation remains limited. Pre-trained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks. However, existing genomic foundation models are trained using strategies borrowed from textual domains that do not leverage biological domain knowledge. Here, we introduce Orthrus, a Mamba-based mature RNA foundation model pre-trained using a novel self-supervised contrastive learning objective with biological augmentations. Orthrus is trained by maximizing embedding similarity between curated pairs of RNA transcripts, where pairs are formed from splice isoforms of 10 model organisms and transcripts from orthologous genes in 400+ mammalian species from the Zoonomia Project. This training objective results in a latent representation that clusters RNA sequences with functional and evolutionary similarities. We find that the generalized mature RNA isoform representations learned by Orthrus significantly outperform genomic foundation models on mRNA property prediction tasks, and requires only a fraction of fine-tuning data to do so. Finally, we show that Orthrus is capable of capturing divergent biological function of individual transcript isoforms.

Journal Article↗
PubMed2025

Dynamic clustering of genomics cohorts beyond race, ethnicity-and ancestry.

BACKGROUND: Recent decades have witnessed a steady decrease in the use of race categories in genomic studies. While studies that still include race categories vary in goal and type, these categories already build on a history during which racial color lines have been enforced and adjusted in the service of social and political systems of power and disenfranchisement. For early modern classification systems, data collection was also considerably arbitrary and limited. Fixed, discrete classifications have limited the study of human genomic variation and disrupted widely spread genetic and phenotypic continuums across geographic scales. Relatedly, the use of broad and predefined classification schemes-e.g. continent-based-across traits can risk missing important trait-specific genomic signals. METHODS: To address these issues, we introduce a dynamic approach to clustering human genomics cohorts based on genomic variation in trait-specific loci and without using a set of predefined categories. We tested the approach on whole-exome sequencing datasets in ten cancer types and partitioned them based on germline variants in cancer-relevant genes that could confer cancer type-specific disease predisposition. RESULTS: Results demonstrate clustering patterns that transcend discrete continent-based categories across cancer types. Functional analysis based on cancer type-specific clusterings also captures the fundamental biological processes underlying cancer, differentiates between dynamic clusters on a functional level, and identifies novel potential drivers overlooked by a predefined continent-based clustering. CONCLUSIONS: Through a trait-based lens, the dynamic clustering approach reveals genomic patterns that transcend predefined classification categories. We propose that coupled with diverse data collection, new clustering approaches have the potential to draw a more complete portrait of genomic variation and to address, in parallel, technical and social aspects of its study.

Humans↗
PubMed2024

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Humans↗
PubMed2007

A genome-wide assessment of adrenocorticotropin action in the Y1 mouse adrenal tumor cell line.

This report summarizes the genome-wide effects of ACTH on transcript accumulation in mouse adrenal Y1 cells and the relative contributions of the cAMP-, protein kinase C- and Ca(2+)-dependent signaling pathways to these actions of the hormone. ACTH affected the accumulation of 1386 transcripts, a much larger number than previously appreciated. The cAMP signaling pathway accounted for approximately 56% of the ACTH effects whereas the protein kinase C- and Ca(2+)-dependent pathways made smaller contributions to ACTH action. Approximately 38% of the ACTH-affected transcripts could not be assigned to these signaling pathways and thus represent candidates for regulation via other mechanisms. The set of ACTH-regulated transcripts included clusters with functions in steroid metabolism, cell proliferation and alternative splicing. Collectively, our results suggest that Y1 adrenal cells undergo extensive remodeling upon prolonged stimulation with ACTH. The functional implications of ACTH on alternative splicing are explored.

Adrenal Gland Neoplasms↗
PubMed2006

Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling.

Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.

Animals↗
PubMed2006

Global profiles of gene expression induced by adrenocorticotropin in Y1 mouse adrenal cells.

ACTH regulates the steroidogenic capacity, size, and structural integrity of the adrenal cortex through a series of actions involving changes in gene expression; however, only a limited number of ACTH-regulated genes have been identified, and these only partly account for the global effects of ACTH on the adrenal cortex. In this study, a National Institute on Aging 15K mouse cDNA microarray was used to identify genome-wide changes in gene expression after treatment of Y1 mouse adrenocortical cells with ACTH. ACTH affected the levels of 1275 annotated transcripts, of which 46% were up-regulated. The up-regulated transcripts were enriched for functions associated with steroid biosynthesis and metabolism; the down- regulated transcripts were enriched for functions associated with cell proliferation, nuclear transport and RNA processing, including alternative splicing. A total of 133 different transcripts, i.e. only 10% of the ACTH-affected transcripts, were represented in the categories above; most of these had not been described as ACTH-regulated previously. The contributions of protein kinase A and protein kinase C to these genome-wide effects of ACTH were evaluated in microarray experiments after treatment of Y1 cells and derivative protein kinase A-defective mutants with pharmacological probes of each pathway. Protein kinase A-dependent signaling accounted for 56% of the ACTH effect; protein kinase C-dependent signaling accounted for an additional 6%. These results indicate that ACTH affects the expression profile of Y1 adrenal cells principally through cAMP- and protein kinase A- dependent signaling. The large number of transcripts affected by ACTH anticipates a broader range of actions than previously appreciated.

Adrenal Cortex↗
PubMed2005

Alternative splicing of conserved exons is frequently species-specific in human and mouse.

In this article, we provide evidence that a frequent source of diversity between mammalian transcripts occurs as a consequence of species-specific alternative splicing (AS) of conserved exons. Using a highly predictive computational method, we estimate that >11% of human and mouse cassette alternative exons undergo skipping in one species but constitutively splicing in the other. These species-specific AS events are predicted to modify conserved domains in proteins more frequently than other classes of AS events. The results thus provide evidence that species-specific AS of conserved exons constitutes an additional potential source of complexity and species-specific differences between mammals.

Alternative Splicing↗
PubMed2004

Exploration of essential gene functions via titratable promoter alleles.

Nearly 20% of yeast genes are required for viability, hindering genetic analysis with knockouts. We created promoter-shutoff strains for over two-thirds of all essential yeast genes and subjected them to morphological analysis, size profiling, drug sensitivity screening, and microarray expression profiling. We then used this compendium of data to ask which phenotypic features characterized different functional classes and used these to infer potential functions for uncharacterized genes. We identified genes involved in ribosome biogenesis (HAS1, URB1, and URB2), protein secretion (SEC39), mitochondrial import (MIM1), and tRNA charging (GSN1). In addition, apparent negative feedback transcriptional regulation of both ribosome biogenesis and the proteasome was observed. We furthermore show that these strains are compatible with automated genetic analysis. This study underscores the importance of analyzing mutant phenotypes and provides a resource to complement the yeast knockout collection.

Alleles↗
PubMed2004

Transcriptional networks: reverse-engineering gene regulation on a global scale.

A major objective in post-genome research is to fully understand the transcriptional control of each gene and the targets of each transcription factor. In yeast, large-scale experimental and computational approaches have been applied to identify co-regulated genes, cis regulatory elements, and transcription factor DNA binding sites in vivo. Methods for modeling and predicting system behavior, and for reconciling discrepancies among data types, are being explored. The results indicate that a complete and comprehensive yeast transcriptional network will ultimately be achieved.

Computational Biology↗
PubMed2004

Probing microRNAs with microarrays: tissue specificity and functional inference.

MicroRNAs (miRNAs) are short, stable, noncoding RNAs involved in post-transcriptional gene silencing via hybridization to mRNA. Few have been thoroughly characterized in any species. Here, we describe a method to detect miRNAs using micro-arrays, in which the miRNAs are directly hybridized to the array. We used this method to analyze miRNA expression across 17 mouse organs and tissues. More than half of the 78 miRNAs detected were expressed in specific adult tissues, suggesting that miRNAs have widespread regulatory roles in adults. By comparing miRNA levels to mRNA levels determined in a parallel microarray analysis of the same tissues, we found that the expression of target mRNAs predicted on the basis of sequence complementarity is unrelated to the tissues in which the corresponding miRNA is expressed.

Animals↗
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↗