Biology subjects

Geller-McGrath, D.

Publications and source records attributed to Geller-McGrath, D..

5 recordsLinked to original sources

bioRxiv2026

Improving splice site usage prediction with SPLAIRE

BackgroundAlternative splicing, the mechanism by which intronic sequences are excised from pre-mRNAs to produce mature mRNA, affects >95% of human protein-coding genes and is a major driver of human disease states. The spliceosome, a protein-RNA complex responsible for splicing pre-mRNA, identifies candidate splice sites partly through the recognition of characteristic sequence motifs at exon-intron junctions. Deep learning models that predict the presence of splice sites from pre-mRNA sequence have achieved breakthrough performance relative to previous machine-learning techniques, and these models have improved our ability to identify pathogenic genetic variants that alter splicing. ResultsWe show that, while overall performance measures from these models suggest near-perfect performance, substantial gaps in prediction remain, including the identification of splice sites with low usage rates and tissue-specific splice sites. We leverage one of the largest paired RNA and genotyping datasets used to date to train a novel splicing model optimized for a specific cell type, human airway epithelial cells. We trained a dilated convolutional neural network on data from cultured airway epithelial cells from 100 donors, and showed that this model outperforms current state-of-the-art models on splice site identification and splice site usage quantification, including on multiple tissues not included in the model training data. ConclusionsWe present the most comprehensive evaluation of state-of-the-art splicing models published to date, revealing reasonable performance across models for genetic variant effect prediction along with important performance gaps and insights into directions for future model development.

genomics↗

bioRxiv2023

Metagenomic Profiles of Archaea and Bacteria within Thermal and Geochemical Gradients of the Guaymas Basin Deep Subsurface

While the temperature gradients of Earths crust and sediments are thought to delineate the downward extent and ultimate limits of the subsurface biosphere, the actual course of consecutively changing microbial communities and activities, from cool surficial sediments towards the deep, hot biosphere, remains to be charted. We used metagenomic and metatranscriptomic analyses of the hydrothermally heated, massive sediment layers of Guaymas Basin (Gulf of California, Mexico) to examine the environmental distribution and activity patterns of bacteria and archaea along thermal, geochemical and cell count gradients. Composition and distribution of MAGs, dominated by Chloroflexota and Thermoproteota, were shaped by biogeochemical parameters as long as temperatures remained moderate, but downcore increasing temperatures overrode other factors beyond ca. 45{degrees}C. Consistently, MAG genome size and diversity decreased with increasing temperature, indicating a conspicuous downcore winnowing of the subsurface biosphere. In contrast, specific archaeal MAGs within the Thermoproteota and Hadarchaeota increased in relative abundance and in recruitment of transcriptome reads towards deeper, hotter sediments, and mark the transition towards a distinct deep, hot biosphere.

bioinformatics↗

bioRxiv2022

MetaPredict: A machine learning-based tool for predicting metabolic modules in incomplete bacterial genomes

The reconstruction of complete microbial metabolic pathways using omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from KEGG module databases, MetaPathPredict employs neural network and XGBoost stacked ensemble models to reconstruct and predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as an R package, and both options are designed to be run locally or on a compute cluster. In our benchmarks, MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

bioinformatics↗

bioRxiv2022

Diverse secondary metabolites are expressed in particle-associated and free-living microorganisms of the permanently anoxic Cariaco Basin

Secondary metabolites play essential roles in ecological interactions and nutrient acquisition, and are of interest for their potential uses in medicine and biotechnology. Under laboratory conditions the discovery of new compounds is hindered by low production, high rediscovery rates, and detection evasion. Genome mining for biosynthetic gene clusters (BGCs) is a popular approach to address these limitations, thereby uncovering often cryptic secondary metabolic potential. Marine systems are often underrepresented in bulk genome mining efforts; and habitats with unique physicochemical characteristics such as oxygen-depleted and anoxic water columns remain virtually unexplored regarding BGCs. Here, we use genome mining and differential gene expression analyses to show that redox potential and particle-associated vs. free-living lifestyles both influence the composition and production of secondary metabolites through the stratified water column of the Cariaco Basin, Venezuela. Microbiota, including understudied clades, such as Planctomycetota, encode and express a wide range of secondary metabolites in these anoxic/euxinic waters.

bioinformatics↗

bioRxiv2018

The developing premature infant gut microbiome is a major factor shaping the microbiome of neonatal intensive care unit rooms

1.1BackgroundThe neonatal intensive care unit (NICU) contains a unique cohort of patients with underdeveloped immune systems and nascent microbiome communities. Patients often spend several months in the same room and it has been previously shown that the gut microbiomes of these infants often resemble the microbes found in the NICU. Little is known, however, about the identity, persistence and absolute abundance of NICU room-associated bacteria over long stretches of time. Here we couple droplet digital PCR (ddPCR), 16S rRNA gene surveys, and recently published metagenomics data from infant gut samples to infer the extent to which the NICU microbiome is shaped by its room occupants.\n\nResultsOver 2,832 swabs, wipes, and air samples were collected from sixteen private-style NICU rooms housing very low birthweight (<1,500 g), premature (<31 weeks gestation) infants. For each infant, room samples were collected daily, Monday through Friday, for one month. The first samples from the first infant and last samples from the last infant were collected 383 days apart. Twenty-two NICU locations spanning room surfaces, hands, electronics, sink basins, and air were collected. Results point to an incredibly simple room community where 5-10 taxa, mostly skin associated, account for over 50% of 16S reads. Biomass estimates reveal 4-5 orders of magnitude difference between the least to the most dense microbial communities, air and sink basins, respectively. Biomass trends from bioaerosol samples and petri dish dust collectors suggest occupancy to be a main driver of suspended biological particles within the NICU. Using a machine learning algorithm to classify the origin of room samples, we show that each room has a unique microbial fingerprint. Several important taxa driving this model were dominant gut colonizers of infants housed within each room.\n\nConclusionsDespite regular cleaning of hospital surfaces, bacterial biomass was detectable at varying densities. A room specific microbiome signature was detected, suggesting microbes seeding NICU surfaces are sourced from reservoirs within the room and that these reservoirs contain actively dividing cells. Collectively, the data suggests that hospitalized infants, in combination with their caregivers, shape the microbiome of NICU rooms.

microbiology↗