Biology subjects

Mount, S. M.

Publications and source records attributed to Mount, S. M..

7 recordsLinked to original sources

bioRxiv2026

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer characterized by substantial intratumoral heterogeneity. Previous research demonstrates that GBM may have complex cell origins. To elucidate the interplay between brain development and GBM progression, we developed Qombucha (Quadratic prOgraMming Based tUmor deConvolution with cell HierArchy), a computational framework that uses DNA methylation data to infer tumor cell-type composition and profiles of unobserved progenitor cells. Unprecedentedly, Qombucha incorporates a developmental cell hierarchy that models mature brain cell types and their progenitors. Applied to a large TCGA GBM dataset spanning the RTK I, RTK II, and MES TYP subtypes, Qombucha identifies a distinct cell type composition profile for each subtype and recapitulates known biological patterns, including elevated microglia infiltration in MES TYP tumors. It also identifies subtype-specific developmental programs and shows that higher progenitor-cell abundance is associated with poorer survival. Qombucha-imputed cell fractions map methylation profiles of tumor samples to a compact, 11-dimensional latent space; in an independent NCI GBM cohort, this compact representation improves subtype clustering and enables accurate subtype classification, achieving performance comparable to state-of-the-art models based on full methylation profiles with much higher dimensionality. These results suggest that tumor cellular composition captures the core biological axes along which GBM subtypes diverge.

bioinformatics↗

bioRxiv2026

Detection of a sequence feature for recursive splicing

RNA splicing is directed by cis-acting sequence signals interacting with trans-acting factors to remove introns from newly transcribed pre-mRNA, joining exons to generate mature mRNA. Splicing happens far more often than observed exon-exon junctions in mRNA. As one contributing process, the spliceosome progressively removes large introns in small segments by recursive splicing, instead of splicing the whole intron at one time. However, the cis-acting sequences associated with recursive splicing have not been identified. Using probabilistic mixture models, we found that recursive splicing occurs more frequently in first introns, which are typically longer, and exhibit a distinct CG-rich sequence feature in the sequences flanking the upstream 5SS, and depletion of CGs in the downstream polypyrimidine tract. Remarkably, recursive splicing is also more frequent in downstream introns of genes containing first introns with these properties. Mechanistically, these data suggest that early events in RNA synthesis and processing influence the prevalence of recursive splicing for the rest of the transcript. Finally, we developed a sequence-dependent classifier for recursive splicing, which we tested with a novel medium-throughput primer extension assay. In summary, the usage of recursive splicing sites is established at the beginning of RNA synthesis through newly-identified sequence motifs flanking both ends of the first intron.

genomics↗

bioRxiv2025

FveOCRs - a database for open chromatin prediction in wild strawberry based on a large language model

BackgroundWoodland strawberry (Fragaria vesca) is a widely used model system for cultivated strawberries and Rosaceae for molecular genetic studies. Nevertheless, available databases for its cis-regulatory element identification are limited. With the emergence of large language models in plant research, strawberry research could benefit significantly from applying these models. One such model derived from Plant DNA Large Language Models (PDLLMs) effectively predicts open chromatin regions (OCRs), which are nucleosome-depleted areas typically associated with cis-regulatory elements. However, improvement of the models accessibility and utility will be needed to facilitate the models application in strawberry as well as other plant species. ResultsWe developed the FveOCRs database (http://liulab.online:33838/FveOCRLiuLabTestVer25), which predicts open chromatin regions located within 5,000 bp upstream sequences, the first and second introns and exons, and the longest introns in Fragaria vesca genes. Predictions were generated using a sliding-window approach based on one of the PDLLMs. In the database, users could predict open chromatin regions for input DNA sequences from any plant species and obtain visual graphs with precise predicted regions of open chromatins. ConclusionsThe FveOCRs database is the first database for open chromatin region prediction in Fragaria vesca based on a Plant DNA Large Language Model. It can also predict OCRs in user-provided DNA sequences from any plant species. The resource and its accessible visual graphs will facilitate discovery of cis-regulatory elements and engineering of gene expression in strawberry and other plant species.

bioinformatics↗

bioRxiv2024

REAL-TIME VISUALIZATION OF SPLICEOSOME ASSEMBLY REVEALS BASIC PRINCIPLES OF SPLICE SITE SELECTION

The spliceosome is a megadalton protein-RNA complex which removes introns from pre-mRNA, yet the dynamic early assembly steps have not been structurally resolved. Specifically, how the spliceosome selects the correct 3 splice site (3SS) amongst highly similar non-functional sites is not known. Here, we develop a kinetic model of splice site selection based on single-molecule U2AF heterodimer imaging in vitro and in vivo. The model successfully predicts alternative splicing patterns and indicates that 3SS selection occurs while U2AF is in complex with the spliceosome, not during initial binding. This finding indicates the spliceosome operates in a partial kinetic proofreading regime, catalyzed in part by the helicase DDX42, which increases selectivity to the underlying U2AF binding site while still allowing for efficient forward progression. ONE-SENTENCE SUMMARYWe apply a kinetic proofreading model to elucidate how transient U2AF binding leads to high fidelity splice site selection.

biophysics↗

bioRxiv2024

scCensus: Off-target scRNA-seq reads reveal meaningful biology

Single-cell RNA-sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity. Although scRNA-seq reads from most prevalent and popular tagged-end protocols are expected to arise from the 3' end of polyadenylated RNAs, recent studies have shown that "off-target" reads can constitute a substantial portion of the read population. in this work, we introduced scCensus, a comprehensive analysis workflow for systematically evaluating and categorizing off-target reads in scRNA-seq. We applied scCensus to seven scRNA-seq datasets. Our analysis of intergenic reads shows that these off-target reads contain information about chromatin structure and can be used to identify similar cells across modalities. Our analysis of antisense reads suggests that these reads can be used to improve gene detection and capture interesting transcriptional activities like antisense transcription. Furthermore, using splice-aware quantification, we find that spliced and unspliced reads provide distinct information about cell clusters and biomarkers, suggesting the utility of integrating signals from reads with different splicing statuses. Overall, our results suggest that off-target scRNA-seq reads contain underappreciated information about various transcriptional activities. These observations about yet-unexploited information in existing scRNA-seq data will help guide and motivate the community to improve current algorithms and analysis methods, and to develop novel approaches that utilize off-target reads to extend the reach and accuracy of single-cell data analysis pipelines. Data AvailabilityThe scripts for reproducing the results are available at https://github.com/COMBINE-lab/sc-census. Supplementary files can be found at https://doi.org/10.5281/zenodo.10520670.

bioinformatics↗

bioRxiv2023

Predicting the impact of rare variants on RNA splicing in CAGI6

BackgroundVariants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variants impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. ResultsThe performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum overall accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. ConclusionsSeveral challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

bioinformatics↗

bioRxiv2021

Sex biases in cancer and autoimmune disease incidence are correlated across human tissues

Cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. To explore whether these sex biases have a common basis, we collected 167 AID incidence studies from many countries for tissues that have both a cancer type and an AID that arise from that tissue. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that, indeed, the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. The common key factor whose levels across human tissues are most strongly associated with these incidence rate sex biases is the sex bias in the expression of the 37 genes encoded in the mitochondrial genome.

cancer biology↗