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.
To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO’s capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes.
Our software tool on investigating COI amplicon data just got published on MBMG journal. We hope that darn will benefit researchers as a quality control tool for their sequenced samples in terms of distinguishing eukaryotic from non-eukaryotic OTUs/ASVs, but also in terms of understanding the known unknowns.
The mitochondrial cytochrome C oxidase subunit I gene (COI) is commonly used in environmental DNA (eDNA) metabarcoding studies, especially for assessing metazoan diversity. Yet, a great number of COI operational taxonomic units (OTUs) or/and amplicon sequence variants (ASVs) retrieved from such studies do not get a taxonomic assignment with a reference sequence. To assess and investigate such sequences, we have developed the Dark mAtteR iNvestigator (DARN) software tool. For this purpose, a reference COI-oriented phylogenetic tree was built from 1,593 consensus sequences covering all the three domains of life. DARN makes use of this phylogenetic tree to investigate COI pre-processed sequences of amplicon samples to provide both a tabular and a graphical overview of their phylogenetic assignments.
We demonstrate that a large proportion of non-target prokaryotic organisms, such as bacteria and archaea, are also amplified in eDNA samples and we suggest prokaryotic COI sequences to be included in the reference databases used for the taxonomy assignment to allow for further analyses of dark matter
Beyond learning the basics of the various methods used when studying fluxes, I was so glad that I was proved wrong about online events and whether the attendees can actually interact one another.
A big, big “thank you” to all members of the organizing committee and a promise for an in-person meeting sometime soon! 🦠 🧬
We are still in the start, but GSoC many thanks for supporting our efforts for random sampling over the flux space of microbial communities metabolic networks!
A revised version of the darn software tool is now available. Dark mAtteR iNvestigator (DARN) uses a COI reference tree covering all domains of life (eukaryotes, bacteria, archaea) to assign your sequences to the 3 domains of life.
Its purpose is not to provide you with certain taxonomic assignment but to give an overview of the species present.
PFam oriented bacterial sequences have been added in the initial sequences dataset and allowing us to cover 371 families plus 60 taxonomic groups of higher level not assigned in family.
To get this latest version, you just need to install docker and run
docker pull hariszaf/darn:latest
Have fun discovering more and more bacteria on your COI amplicon data! 🥳
