Few months ago, thanks to a FEMS Meeting Attendance Grant (MAG) I was able to participate a Physalia course on Metagenomics, Metatranscriptomics and Multi-omics for microbial community studies.
Here you can find the FEMS alumni bulletin of September 2020 and a thanks FEMS for this chance.
Thanks the @hutlab for all the great software and knowledge shared!
Just found out about the great paper of Aleksej Zelezniak et al. “Metabolic dependencies drive species co-occurrence in diverse microbial communities”.
As they authors claim “interspecies metabolic exchanges are widespread in natural communities, and such exchanges can provide group advantage under nutrient-poor conditions.””
Their results “highlight metabolic dependencies as a major driver of species co-occurrence”.
You may find this here.
A great article explaining why it is not the pangolin we have to blame, but the environmental degradation and its causes.
Here it is.
Amazon rainforest wildfires (27.08.2019 Region of Vila Nova Samuel )*<\i> </p>
“We must not lose sight of the persisting climate and ecological crisis when working out how to spur the economy after the coronavirus pandemic.” It is 13 ministers that say so.
It is exactly the way that research is funded with the antagonistic projects to rule over it, that has made this essential, worldwide issue to be an “opportunity” for many researchers and groups. That is why groups whose field could not actually aid on the coronavirus treatment submit proposal to do so. Of course, many researchers have an honest agony to help. But would we observe what is happening today if we had another way of funding in the research field?
It would be hard for anyone to say so. In fact, the way the economy works the last 200 years played a huge part for both the evolution of viruses and how prepared the states were and the way they address it. Thus, we should take into account the climate and ecological crisis but also the global economical crisis that is still here, to figure out another way for organizing our communities.
PEMA is a containerized assembly of key metabarcoding analysis tools that requires low effort in setting up, running, and customizing to researchers’ needs. Based on third-party tools, PEMA performs read pre-processing, (molecular) operational taxonomic unit clustering, amplicon sequence variant inference, and taxonomy assignment for 16S and 18S ribosomal RNA, as well as ITS and COI marker gene data. Owing to its simplified parameterization and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against both mock communities and previously published datasets and achieved results of comparable quality.
Read more for the PREGO software
Investigator
PEMA is a BigDataScript-based workflwow for the analysis of amplicon data.
PEMA supports the metabarcoding analysis of four marker genes, 16S rRNA (Bacteria), ITS (Fungi) as well as COI and 18S rRNA (metazoa). As input, PEMA accepts .fastq.gz files as returned by Illumina sequencing platforms. Since the v.2.1.4 release, PEMA supports also the analysis of the 12S rRNA marker gene!
PEMA is also the main workflow performed in the framework of the Autonomous Reef Monitoring Structures (ARMS) programme: long-term monitoring of invasive marine species an Internal Joint Initiative of LifeWatch ERIC.
PEMA always needs your help to keep up-to-date and integrate new features. If you are interested in contributing to the project, feel free to open an Issue, or a PR on GitHub or contact me for more! 
Read more on PEMA publication
Read also a review on relative amplicon workflows
