Biology subjects

Walsh, A. M.

Publications and source records attributed to Walsh, A. M..

3 recordsLinked to original sources

bioRxiv2025

Generative genomics accurately predicts future experimental results

Realizing AIs promise to accelerate biomedical research requires AI models that are both accurate and sufficiently flexible to capture the diversity of real-life experiments. Here, we describe a generative genomics framework for AI-based experimental prediction that mirrors the process of designing and conducting an experiment in the lab or clinic. We created GEM-1 (Generate Expression Model-1), an AI system that effectively models the enormous range of bulk and single-cell gene expression experiments performed by scientists and benchmarked its performance across multiple biological axes. GEM-1s prediction of future gene expression experiments-RNA-seq data deposited in public archives after our training data cutoff-yielded accuracy comparable to the best-possible performance estimated by comparing the results of matched lab experiments. Overall, our approach illustrates the transformative potential of generative genomics for applications ranging from predicting cellular perturbations in vitro to de novo generation of data from large clinical cohorts.

genomics↗

bioRxiv2020

Rapid Single Cell Evaluation of Human Disease and Disorder Targets Using REVEAL: SingleCell™

Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypothesis by querying extensive single-cell datasets. Large single-cell datasets like human cell atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics and vaccine targets; as the world struggles to cope with the on-going spread of COVID-19 infections. In this manuscript we describe, REVEAL: SingleCell which enables storage, retrieval and rapid query of single-cell datasets inclusive of millions of cells. The analytical database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypothesis. One such example includes identifying the most prevalent cell type annotation tag on returned cells. We used REVEAL: SingleCell to evaluate expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in <60 seconds. We highlighted cells expressing COVID-19 associated genes are expressed on multiple tissue types, thus in part explains the multi-organ involvement in infected patients observed worldwide during the on-going COVID-19 pandemic.

molecular biology↗

bioRxiv2020

Fermented food metagenomics reveals substrate-associated differences in taxonomy, health-associated- and antibiotic resistance-determinants

Fermented foods have been the focus of ever greater interest as a consequence of purported health benefits. Indeed, it has been suggested that the consumption of these foods that help to address the negative consequences of industrialization of the human gut microbiota in Western society. However, as the mechanisms via which the microbes in fermented foods improve health are not understood, it is necessary to develop an understanding of the composition and functionality of the fermented food microbiota to better harness desirable traits. Here we considerably expand the understanding of fermented food microbiomes by employing shotgun metagenomic sequencing to provide a comprehensive insight into the microbial composition, diversity and functional potential (including antimicrobial resistance, carbohydrate-degrading and health-associated gene content) of a diverse range of 58 fermented foods from artisanal producers from around the Globe. Food type, i.e., dairy-, sugar- or brine-type fermented foods, was to be the primary driver of microbial composition, with dairy foods found to have the lowest microbial diversity. From the combined dataset, 127 high quality metagenome-assembled genomes (MAGs), including 10 MAGs representing putatively novel species of Acetobacter, Acidisphaera, Gluconobacter, Lactobacillus, Leuconostoc and Rouxiella, were generated. Potential health promoting attributes were more common in fermented foods than non-fermented equivalents, with waterkefirs, sauerkrauts and kvasses containing the greatest numbers of potentially health-associated gene clusters (PHAGCs). Ultimately, this study provides the most comprehensive insight into the microbiomes of fermented foods to date, and yields novel information regarding their relative health-promoting potential. ImportanceFermented foods are regaining popularity in Western society due in part to an appreciation of the potential for fermented food microbiota to positively impact on health. Many previous studies have studied fermented microbiota using classical culture-based microbiological methods, older molecular techniques or, where deeper analyses have been performed, have involved a relatively small number of one specific food type. Here, we have used a state-of-the-art shotgun metagenomic approach to investigate 58 different fermented foods of different type and origin. Through this analysis, we were able to identify the differences in the microbiota across these foods, the factors that drove their microbial composition, and the relative potential functional benefits of these microbes. The information provided here will provide significant opportunities for the further optimisation of fermented food production and the harnessing of their health promoting potential.

microbiology↗