Constraint-based approaches have been widely used for the analysis of such models and led to intriguing geometry-oriented challenges. In this setting, sampling uniformly points from polytopes derived from metabolic models (flux sampling) provides a representation of the solution space of the model under various conditions. However, the polytopes that result from such models are of high dimension (in the order of thousands) and usually considerably skinny. Therefore, to sample uniformly at random from such polytopes shouts for a novel algorithmic and computational framework specially tailored for the properties of metabolic models. We present a Multiphase Monte Carlo Sampling (MMCS) algorithm that unifies rounding and sampling in one pass, yielding both upon termination. It exploits an optimized variant of the Billiard Walk that enjoys faster arithmetic complexity per step than the original. Sampling on the most complicated human metabolic network accessible today, Recon3D, corresponding to a polytope of dimension 5 335, took less than 30 hours.
The present study combines 16S rRNA amplicon sequencing and shotgun metagenomics on a hypersaline marsh in Tristomo bay (Karpathos, Greece). Samples were collected in July 2018 and November 2019 from microbial mats, deeper sediment, aggregates observed in the water overlying the sediment, as well as sediment samples with no apparent layering. Metagenomic samples’ coassembly and binning revealed 250 bacterial and 39 archaeal metagenome-assembled genomes, with completeness estimates higher than 70% and contamination less than 5%. All MAGs had KEGG Orthology terms related to osmoadaptation, with the ‘salt in’ strategy ones being prominent. Halobacteria and Bacteroidetes were the most abundant taxa in the mats. Photosynthesis was most likely performed by purple sulphur and nonsulphur bacteria. All samples had the capacity for sulphate reduction, dissimilatory arsenic reduction, and conversion of pyruvate to oxaloacetate.
This work focuses on information Extraction (IE) from the marine historical biodiversity data perspective. It orchestrates IE tools and provides the curators with a unified view of the methodology; as a result the documentation of the strengths, limitations and dependencies of several tools was drafted. Additionally, the classification of tools into Graphical User Interface (web and standalone) applications and Command Line Interface ones enables the data curators to select the most suitable tool for their needs, according to their specific features
The high volume of already digitised marine documents that await curation is amassed and a demonstration of the methodology, with a new scalable, extendable and containerised tool, DECO (bioDivErsity data Curation programming wOrkflow) is presented. DECO’s usage will provide a solid basis for future curation initiatives and an augmented degree of reliability towards high value data products that allow for the connection between the past and the present, in marine biodiversity research.
dingo is a Python package that supports a variety of methods to sample from the flux space of metabolic models, based on state-of-the-art random walks and rounding methods. It relies on high dimensional sampling with Markov Chain Monte Carlo (MCMC) methods and fast optimization methods to analyze the possible states of a metabolic network. To perform MCMC sampling, dingo relies on the C++ library volesti, which provides several algorithms for sampling convex polytopes. Among the different ways to sample, dingo also implements the Multiphase Monte Carlo Sampling algorithm (see post for relative publication).
Flux sampling provides insgith of strong statistical evidence. For example, pairwise fluxes correlated with one another in a positive or negative way, can be found.
dingo also supports Flux Balance Analysis and Flux Variability Analysis, two standard methods to analyze the flux space of a metabolic network,.
Co-occurrence networks have been widely used for inferring microbial associations or/and interactions from metagenomic data. However, spurious associations and tool-dependence confine the network inference. To address this challenge, we are about to develop microbetag, an annotator tool to enhance co-occurrence network analysis for metagenomics data from microbial communities.
