Biology subjects

Segarra, S.

Publications and source records attributed to Segarra, S..

6 recordsLinked to original sources

bioRxiv2025

GRNITE: Gene Regulatory Network Inference with Text Embeddings

Gene regulatory networks (GRNs) capture complex regulatory relationships that govern gene expression in cells. Inference of GRNs from single-cell RNA-seq (scRNA-seq) data has been an active topic of research in the past several years. However, despite the improvements in the data quality, the GRN inference problem remains a challenging task with many approaches showing variable performance dependent on the organism and cell type. To improve the quality of GRN inference and enable more comprehensive exploratory analyses of GRNs across various phenotypes of interest, we developed a two-stage meta-method called GRNITE. In the first step, GRNITE leverages LLM-based embeddings of plain text gene descriptions to create a prior gene interaction graph, which is then optimized with a graph neural network (GNN) to achieve a "universal" biological prior for GRN inference. In the second step, GRNITE uses a GNN to incorporate information from a GRN inferred from scRNA-seq data with any baseline inference method into our prior. The result of this two-step approach is a near-universal improvement in AUROC and recall of all evaluated methods, with minor trade-offs in precision. Furthermore, GRNITE is a lightweight meta-method, which adds a minimal amount of extra compute time on top of the original GRN inference performed. GRNITE and our pre-trained universal prior GRN are available on GitHub: https://github.com/aliaaz99/GRNITE.

bioinformatics↗

bioRxiv2024

Reference-free Structural Variant Detection inMicrobiomes via Long-read Coassembly Graphs

Bacterial genome dynamics are vital for understanding the mechanisms underlying microbial adaptation, growth, and their broader impact on host phenotype. Structural variants (SVs), genomic alterations of 10 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to absence of clear reference genomes and presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing a single metagenome coassembly graph constructed from all samples in a series. The log fold change in graph coverage between subsequent samples is then calculated to call SVs that are thriving or declining throughout the series. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, which is particularly noticeable as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between subsequent time and temperature samples, suggesting host advantage. Our innovative approach leverages raw read patterns rather than references or MAGs to include all sequencing reads in analysis, and thus provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial genome dynamics.

bioinformatics↗

bioRxiv2022

Joint embedding of biological networks for cross-species functional alignment

Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions (PPIs) to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem. Here, we propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). More specifically, ETNA generates individual network embeddings based on network topological structures and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNAs embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.

bioinformatics↗

bioRxiv2021

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

The COVID-19 pandemic has emphasized the importance of detecting known and emerging pathogens from clinical and environmental samples. However, robust characterization of pathogenic sequences remains an open challenge. To this end, we developed SeqScreen, which can accurately characterize short nucleotide sequences using taxonomic and functional labels, and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed pathogen characterization and is available for download at: www.gitlab.com/treangenlab/seqscreen

bioinformatics↗

bioRxiv2021

Clinical Efficacy of Prebiotics and Glycosaminoglycans versus Placebo In Dogs with Food Responsive Enteropathy Receiving a Hydrolyzed Diet: A Pilot Study

Induction of remission is easily achieved with dietary treatment in dogs diagnosed with Food Responsive Chronic Enteropathy (FRD). Administration of prebiotics and glycosaminoglycans (GAGs) may improve epithelial cell integrity and therefore be useful as adjunct treatment. This study evaluated whether the relapse rate of FRD dogs that are switched back to a normal diet can be influenced using supplemental treatment with prebiotics and GAGs. A randomized, controlled clinical trial (RCCT) was performed in dogs diagnosed with FRD. Dogs were diagnosed based on clinical exclusion diagnosis, endoscopic biopsies showing predominantly lymphoplasmacytic infiltration, and response to dietary treatment. Dogs were randomized to be fed a combination of prebiotics and GAGs (group 1) or placebo (group 2) in addition to a hydrolyzed diet. At week 10, a second endoscopy was performed and dogs were switched back to normal diet. Relapse rate was monitored every 2 weeks after that until week 18. Statistical analysis was performed for each outcome (Canine Chronic Enteropathy Clinical Activity Index (CCECAI), clinicopathological data, endoscopic scoring, mWSAVA histological scoring index (mWSAVA), and number of relapses following switch to normal diet) using a linear mixed effects model for group comparison. Time, group, and their interactions were included as a fixed effect, whereas each dog was treated as a random effect. Of the 35 dogs enrolled into the clinical trial, 10 in each group reached the point of second endoscopy. A total of 13 dogs (n=8 in group 1 and n=5 in group 2) reached the trial endpoint of 18 weeks. After switching back to normal diet, none of the dogs in either group relapsed. No significant differences were found over time or between groups for CCECAI, endoscopy scoring and histological scoring. Although there was a clinical worsening in the placebo group after switching back to the original diet, this was not statistically significant (CCECAI p=0.58). Post-hoc power calculation revealed that 63 dogs per group would have been needed to detect statistically significant differences in CIBDAI between treatment groups. Standard dietary treatment induced rapid clinical response in all cases, however, additional supplementation with prebiotics and GAGs did not significantly improve clinical outcome within 4 months after switching back to normal diet. Since there are very few RCCT published in CE in dogs, this pilot study provides important power analyses for planning of further studies.

immunology↗

bioRxiv2020

KOMB: Taxonomy-oblivious Characterization of Metagenome Dynamics via K-core Decomposition

Characterizing metagenomic samples via kmer-based, database-dependent taxonomic classification methods has provided crucial insight into underlying host-associated microbiome dynamics. However, novel approaches are needed that are able to track microbial community dynamics within metagenomes to elucidate genome flux in response to perturbations and disease states. Here we describe KOMB, a novel approach for tracking homologous regions within microbiomes. KOMB utilizes K-core graph decomposition on metagenome assembly graphs to identify repetitive and homologous regions to varying degrees of resolution. K-core performs a hierarchical decomposition which partitions the graph into shells containing nodes having degree at least K, called K-shells, yielding O(V + E) complexity compared to exact betweenness centrality complexity of O(V E) found in prior related approaches. We show through rigorous validation on simulated, synthetic, and real metagenomic datasets that KOMB accurately recovers and profiles repetitive and homologous genomic regions across organisms in the sample. KOMB can also identify functionally-rich regions in Human Microbiome Project (HMP) datasets, and can be used to analyze longitudinal data and identify pivotal taxa in fecal microbiota transplantation (FMT) samples. In summary, KOMB represents a novel approach to microbiome characterization that can efficiently identify sequences of interest in metagenomes.

bioinformatics↗