Biology subjects

Farage, G.

Publications and source records attributed to Farage, G..

4 recordsLinked to original sources

bioRxiv2026

Using Shared Features Improves Metabolite Effect Estimation

External biological knowledge provides valuable information about relationships among metabolites, yet this information is usually not incorporated directly into statistical estimation procedures. Most existing approaches estimate metabolite effects independently, ignoring known biochemical structure such as shared subclasses and pathway membership. We propose a Bayesian hierarchical framework that improves metabolite effect estimates by incorporating external biological information describing relationships among metabolites. The proposed method improves metabolite-specific estimates by allowing related metabolites to borrow information from one another while preserving metabolite-level inference. We evaluate the methodology using simulation studies across a range of sample sizes and heterogeneity regimes together with three metabolomics applications involving distinct biological annotation structures. Across both simulated and real datasets, incorporating external biological information consistently improves metabolite effect estimation. Gains are most pronounced when sample sizes are small and metabolite classes are informative, i.e. more homogenous within classes.

bioinformatics↗

bioRxiv2023

BulkLMM: Real-time genome scans for multiple quantitative traits using linear mixed models

Genetic studies often collect data using high-throughput phenotyping. That has led to the need for fast genomewide scans for large number of traits using linear mixed models (LMMs). Computing the scans one by one on each trait is time consuming. We have developed new algorithms for performing genome scans on a large number of quantitative traits using LMMs, BulkLMM, that speeds up the computation by orders of magnitude compared to one trait at a time scans. On a mouse BXD Liver Proteome data with more than 35,000 traits and 7,000 markers, BulkLMM completed in a few seconds. We use vectorized, multi-threaded operations and regularization to improve optimization, and numerical approximations to speed up the computations. Our soft-ware implementation in the Julia programming language also provides permutation testing for LMMs and is available at https://github.com/senresearch/BulkLMM.jl.

bioinformatics↗

bioRxiv2023

Matrix Linear Models for connecting metabolite composition to individual characteristics

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

bioinformatics↗

bioRxiv2020

Speeding up eQTL scans in the BXD population using GPUs

The BXD recombinant inbred strains of mice are an important reference population for systems biology and genetics that have been full sequenced and deeply phenotyped. To facilitate inter-active use of genotype-phenotype relations using many massive omics data sets for this and other segregating populations, we have developed new algorithms and code that enables near-real time whole genome QTL scans for up to 1 million traits. By using easily parallelizable operations including matrix multiplication, vectorized operations, and element-wise operations, we have decreased run-time to a few seconds for large transcriptome data sets. Our code is ideal for interactive web services, such as GeneNetwork.org. We used parallelization of different CPU threads as well as GPUs. We found that the speed advantage of GPUs is dependent on problem size and shape (number of cases, number of genotypes, number of traits). Our results provide a path for speeding up eQTL scans using linear mixed models (LMMs). Our implementation is in the Julia programming language.

genetics↗