Biology subjects

Dickinson, Q.

Publications and source records attributed to Dickinson, Q..

7 recordsLinked to original sources

bioRxiv2026

REPLAY: A reproducible and user-friendly application for DNA replication timing analysis from Repli-seq data

BackgroundDNA replication timing (RT) is a fundamental feature of genome organization that is regulated in a cell-type-specific manner and frequently altered in disease. Repli-seq is the standard approach for genome-wide RT profiling; however, its analysis typically requires multiple independent tools and custom scripts, limiting reproducibility, portability, and accessibility, particularly for users without computational expertise. In addition, existing workflows often lack standardization and require substantial user intervention. ResultsWe developed REPLAY, a fully automated, reproducible, and user-friendly application for replication timing analysis. REPLAY is distributed as a standalone executable that enables end-to-end processing from compressed FASTQ files to genome-wide RT profiles without requiring software installation or programming experience. Through an intuitive graphical interface, users can configure analysis parameters, including input and output directories, reference genome, normalization strategy (quantile, median, or interquartile range), and smoothing. The application integrates all processing steps--quality control, trimming, alignment, binning, RT log2 calculation, normalization, smoothing, and visualization-- within a single automated workflow. Application of REPLAY to publicly available datasets demonstrate accurate reconstruction of RT profiles and high reproducibility across samples. ConclusionsREPLAY offers a portable, reproducible, and accessible solution for the analysis of RT data. By eliminating the need for command-line tools and complex installations, it lowers the entry barrier enabling standardized analysis across diverse research settings.

genomics↗

bioRxiv2025

Cryptic multicellularity in wild yeast brings selective advantages under stress

Multicellularity has evolved dozens of times in many branches across the Tree of Life, in each case producing a new kind of individual that has the potential for division of labor among its constituent cells. In many of these instances, nascent multicellularity has remained facultative, manifesting only under conditions where it provides a decisive fitness advantage to the genome in which it has arisen. Here we investigate the mechanistic basis for, and the selective advantages of, cryptic multicellularity in wild strains of the yeast Saccharomyces cerevisiae. Meiotic purification of this trait, followed by genome sequencing, indicates that the genome of diploid champagne yeast DB146 is homozygous at the AMN1 locus (Antagonist of Mitotic exit Network) for an allele that dysregulates post-mitotic cell separation. Expression of this trait in haploid derivatives of DB146 results in clonal multicellular clusters in which daughters remain attached to mother cell walls. Systematic analysis of viability in haploid and diploid DB146 derivatives subjected to benign conditions as well as to starvation, desiccation, and low temperature at different cell titers demonstrates that haploid multicellular variants exhibit higher survivorship under conditions that wild yeast likely experience when they overwinter. Examining a collection of other wild strains, we find that [~]20% exhibit ploidy-dependent expression of clonal multicellularity. Altogether, our data suggest that in yeast that sporulate when starved, cryptic multicellularity may come under balancing selection because haploid multicellular progeny enjoy a transient survival advantage that fades once favorable growth conditions resume.

microbiology↗

bioRxiv2025

PARTAGE: Parallel analysis of replication timing and gene expression

The human genome is partitioned into functional compartments that replicate at specific times during the S-phase. This temporal program, referred to as replication timing (RT), is co-regulated with the 3D genome organization, is cell type-specific, and changes during development in coordination with gene expression. Moreover, RT alterations are linked to abnormal gene expression, genome instability, and structural variation in multiple diseases, including cancer. However, mechanistic links between RT, large-scale 3D genome architecture, and transcriptional regulation remain poorly understood. A major limitation is that current approaches require the separate profiling of RT and transcriptomes from independent batches of samples, obscuring the complex co-regulation between the epigenome and transcriptome. Here, we developed PARTAGE, a multiomics approach that enables joint profiling of copy number variation (CNV), RT, and gene expression from the same sample, providing a more accurate integrative view of the complex relationships between RT and gene regulation.

genomics↗

bioRxiv2022

Multi-Omic Integration by Machine Learning (MIMaL) Reveals Protein-Metabolite Connections and New Gene Functions

Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow the ability to gather information of cellular states of its components, measuring abundances of transcripts, their translation, the accumulation of proteins, lipids and metabolites. These highly complex datasets reflect the state of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through integration of these data remains challenging. Here we show that connections between omic layers can be discovered through a combination of machine learning and model interpretation. We find that model interpretation values connecting proteins to metabolites are valid experimentally and reveal also largely new connections. Further, clustering the magnitudes of protein control over all metabolites enabled prediction of gene five gene functions, each of which was validated experimentally. We accurately predicted that two uncharacterized genes in yeast modulate mitochondrial translation, YJR120W and YLD157C.We also predict and validate functions for several incompletely characterized genes, including SDH9, ISC1, and FMP52. Our work demonstrates that multi-omic analysis with machine learning (MIMaL) views multi-omic data through a new lens to reveal new insight that was not possible using existing methods.

bioinformatics↗

bioRxiv2021

Positional SHAP for Interpretation of Deep Learning Models Trained from Biological Sequences

Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neural network architectures. Here, we introduce a framework called "Positional SHAP" (PoSHAP) to interpret models trained from biological sequences by utilizing SHapely Additive exPlanations (SHAP) to generate positional model interpretations. We demonstrate this using three long short-term memory (LSTM) regression models that predict peptide properties, including binding affinity to major histocompatibility complexes (MHC), and collisional cross section (CCS) measured by ion mobility spectrometry. Interpretation of these models with PoSHAP reproduced MHC class I (rhesus macaque Mamu-A1*001 and human A*11:01) peptide binding motifs, reflected known properties of peptide CCS, and provided new insights into interpositional dependencies of amino acid interactions. PoSHAP should have widespread utility for interpreting a variety of models trained from biological sequences.

bioinformatics↗

bioRxiv2017

Chemical genomic guided engineering of gamma-valerolactone tolerant yeast

BackgroundGamma valerolactone (GVL) is a promising technology for degradation of biomass for biofuel production; however, GVL has adverse toxicity effects on fermentative microbes. Using a combination of chemical genomics and chemical proteomics we sought to understand the mechanism toxicity and resistance to GVL with the goal of engineering a GVL-tolerant, xylose-fermenting yeast.\n\nResultsChemical genomic profiling of GVL predicted that this chemical affects membranes and membrane-bound processes. We show that GVL causes rapid, dose-dependent cell permeability, and is synergistic with ethanol. Chemical genomic profiling of GVL revealed that deletion of the functionally related enzymes Pad1p and Fdc1p, which act together to decarboxylate phenolic acids to vinyl derivatives, increases yeast tolerance to GVL. Further, overexpression of Pad1p sensitizes cells to GVL toxicity. To improve GVL tolerance, we deleted PAD1 and FDC1 in a xylose-fermenting yeast strain. The modified strain exhibited increased anaerobic growth, sugar utilization, and ethanol production in synthetic hydrolysate with 1.5% GVL, and under other conditions. Chemical proteomic profiling of the engineered strain revealed that enzymes involved in ergosterol biosynthesis were more abundant in the presence of GVL compared to the background strain. The engineered GVL strain contained greater amounts of ergosterol than the background strain.\n\nConclusionsWe found that GVL exerts toxicity to yeast by compromising cellular membranes, and that this toxicity is synergistic with ethanol. Deletion of PAD1 and FDC1 conferred GVL resistance to a xylose-fermenting yeast strain by increasing ergosterol content in cells. The GVL-tolerant strain fermented sugars in the presence of GVL levels that were inhibitory to the unmodified strain. This strain represents a xylose fermenting yeast specifically tailored to GVL produced hydrolysates

bioengineering↗

bioRxiv2016

Inhibition of microbial biofuel production in drought stressed switchgrass hydrolysate

BackgroundInterannual variability in precipitation, particularly drought, can affect lignocellulosic crop biomass yields and composition, and is expected to increase biofuel yield variability. However, the effect of precipitation on downstream fermentation processes has never been directly characterized. In order to investigate the impact of interannual climate variability on biofuel production, corn stover and switchgrass were collected during three years with significantly different precipitation profiles, representing a major drought year (2012) and two years with average precipitation for the entire season (2010 and 2013). All feedstocks were AFEX (ammonia fiber expansion)-pretreated, enzymatically hydrolyzed, and the hydrolysates separately fermented using xylose-utilizing strains of Saccharomyces cerevisiae and Zymomonas mobilis. A chemical genomics approach was also used to evaluate the growth of yeast mutants in the hydrolysates.\n\nResultsWhile most corn stover and switchgrass hydrolysates were readily fermented, growth of S. cerevisiae was completely inhibited in hydrolysate generated from drought stressed switchgrass. Based on chemical genomics analysis, yeast strains deficient in genes related to protein trafficking within the cell were significantly more resistant to the drought year switchgrass hydrolysate. Detailed biomass and hydrolysate characterization revealed that switchgrass accumulated greater concentrations of soluble sugars in response to the drought and these sugars were subsequently degraded to pyrazines and imidazoles during ammonia-based pretreatment. When added ex situ to normal switchgrass hydrolysate, imidazoles and pyrazines caused anaerobic growth inhibition of S. cerevisiae.\n\nConclusionsIn response to the osmotic pressures experienced during drought stress, plants accumulate soluble sugars that are susceptible to degradation during chemical pretreatments. For ammonia-based pretreatment these sugars degrade to imidazoles and pyrazines. These compounds contribute to S. cerevisiae growth inhibition in drought year switchgrass hydrolysate. This work discovered that variation in environmental conditions during the growth of bioenergy crops could have significant detrimental effects on fermentation organisms during biofuel production. These findings are relevant to regions where climate change is predicted to cause an increased incidence of drought and to marginal lands with poor water holding capacity, where fluctuations in soil moisture may trigger frequent drought stress response in lignocellulosic feedstocks.

microbiology↗