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Register a new userProspects for Multi-omics in the Microbial Ecology of Water Engineering
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Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions-including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, whole community gene expression surveys and metabolite profiling have permitted direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.
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Genomics, especially multi-omics, has made precision medicine feasible. The completion and publicly accessible multi-omics resource with clinical outcome, such as The Cancer Genome Atlas (TCGA) is a great test bed for developing computational methods that integrate multi-omics data to predict patient cancer phenotypes. We have been utilizing TCGA multi-omics data to predict cancer patient survival, using a variety of approaches, including prior-biological knowledge (such as pathways), and more recently, deep-learning methods. Over time, we have developed methods such as Cox-nnet, DeepProg, and two-stage Cox-nnet, to address the challenges due to multi-omics and multi-modality. Despite the limited sample size (hundreds to thousands) in the training datasets as well as the heterogeneity nature of human populations, these methods have shown significance and robustness at predicting patient survival in independent population cohorts. In the following, we would describe in detail these methodologies, the modeling results, and important biological insights revealed by these methods.
Combining different modalities of data from human tissues has been critical in advancing biomedical research and personalised medical care. In this study, we leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Gene-Gene Interaction (GGI) networks. Through ablation experiments, we prove that the combination of multiple biological modalities (i.e multi-omics) leads to powerful embeddings and better link prediction performances. Our evaluation shows that the integration of gene methylation profiles and RNA-sequencing data significantly improves the link prediction performance. Overall, the combination of RNA-sequencing and gene methylation data leads to a link prediction accuracy of 71% on GGI networks. By harnessing graph representation learning on multi-omics data, our work brings novel insights to the current literature on multi-omics integration in bioinformatics.
An active microbiota, reaching up to 10 E+7 cells/g, was found to inhabit a naturally occurring asphalt lake characterized by low water activity and elevated temperature. Geochemical and molecular taxonomic approaches revealed novel and deeply branching microbial assemblages mediating anaerobic hydrocarbon degradation, metal respiration and C1 utilization pathways. These results open a window into the origin and adaptive evolution of microbial life within recalcitrant hydrocarbon matrices, and establish the site as a useful analog for the liquid hydrocarbon environments on Saturns moon Titan.
Motivation: Omics data, such as transcriptomics or phosphoproteomics, are broadly used to get a snap-shot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signaling networks. Results: We have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of several omics data, either from bulk or single-cell experiments. FUNKI also features different options to visualise the results and run post-analyses, and is mirrored as a scripted version in R. Availability: FUNKI is a free and open-source application built on R and Shiny, available in GitHub at https://github.com/saezlab/ShinyFUNKI under GNU v3.0 license and accessible also in https://saezlab.shinyapps.io/funki/ Contact: [email protected] Supplementary information: We provide data examples within the app, as well as extensive information about the different variables to select, the results, and the different plots in the help page.
We present two algorithms designed to learn a pattern of correspondence between two data sets in situations where it is desirable to match elements that exhibit an affine relationship. In the motivating case study, the challenge is to better understand micro-RNA (miRNA) regulation in the striatum of Huntingtons disease (HD) model mice. The two data sets contain miRNA and messenger-RNA (mRNA) data, respectively, each data point consisting in a multi-dimensional profile. The biological hypothesis is that if a miRNA induces the degradation of a target mRNA or blocks its translation into proteins, or both, then the profile of the former should be similar to minus the profile of the latter (a particular form of affine relationship). The algorithms unfold in two stages. During the first stage, an optimal transport plan P and an optimal affine transformation are learned, using the Sinkhorn-Knopp algorithm and a mini-batch gradient descent. During the second stage, P is exploited to derive either several co-clusters or several sets of matched elements. A simulation study illustrates how the algorithms work and perform. A brief summary of the real data application in the motivating case-study further illustrates the applicability and interest of the algorithms.
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