Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL (https://vdblab.github.io/FLORAL/), an open-source computational tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility of longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for extended false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches, and better sensitivity over popular differential abundance testing methods for datasets with smaller sample size. In a survival analysis in allogeneic hematopoietic-cell transplant, we further demonstrated considerable improvement by FLORAL in microbial feature selection by utilizing longitudinal microbiome data over only using baseline microbiome data.
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http://dx.doi.org/10.1101/2023.05.02.538599 | DOI Listing |
mSystems
December 2024
School of Biological Sciences, The University of Auckland, Auckland, New Zealand.
The genus () is most often associated with human clinical samples and livestock. However, are also prevalent in the hindgut of the marine herbivorous fish (Silver Drummer), and analysis of their carbohydrate-active enzyme (CAZyme) encoding gene repertoires suggests degrade macroalgal biomass to support fish nutrition. To further explore host-associated traits unique to -derived , we compared 445 high-quality genomes of available in public databases (e.
View Article and Find Full Text PDFmSystems
December 2024
Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida, USA.
, particularly uncultured representatives, are one of the most abundant microbial groups in coastal salt marshes, dominating the belowground rhizosphere, where over half of plant biomass production occurs. However, this class generally remains poorly understood, particularly in a salt marsh context. Here, novel metagenome-assembled genomes (MAGs) were generated from the salt marsh rhizosphere representing , , JAAYZQ01, B4-G1, JAFGEY01, UCB3, and orders.
View Article and Find Full Text PDFJ Inflamm Res
December 2024
School of Basic Medical Sciences, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.
Background: Hyperuricemia (HUA), a common metabolic disorder associated with gout, renal dysfunction, and systemic inflammation, necessitates safer and more comprehensive therapeutic approaches. Traditional Tibetan medicine has a rich history of treating HUA. This study aimed to identify novel anti-hyperuricemic herb derived from traditional Tibetan medicine.
View Article and Find Full Text PDFA key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.
View Article and Find Full Text PDFMetabolite production, consumption, and exchange are intimately involved with host health and disease, as well as being key drivers of host-microbiome interactions. Despite the increasing prevalence of datasets that jointly measure microbiome composition and metabolites, computational tools for linking these data to the status of the host remain limited. To address these limitations, we developed MMETHANE, an open-source software package that implements a purpose-built deep learning model for predicting host status from paired microbial sequencing and metabolomic data.
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