Many binning methods have been developed
Overview of binning methods in this paragraph. argument for using deep learning is not very persuasive to me.. - Using composition (k-mer) or abundance (coverage) / combined - Hybrid models are "superior" - Integrating such different features efficiently is not easy, so enter the black box/ deep learning stuff.
Todd says this is no comparison to Sylph. Read later to figure out if interested (this one doesn't cite Sylph, maybe peers?)
(pbk1 private) Read annotations in public group
Interesting note to read on pseudocont value and how it should be set to be consistent across tools
We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations.
From reading the paper it seems like this was done on samples where the SARS-COV-2 in the sample is likely to be genetically uniform. Have you done this at all on samples such as those from wastewater or sewage or environmental swabs where there are likely to be many different variants within a single sample?
Hiatt, J., Patwardhan, R., Turner, E. et al. Parallel, tag-directed assembly of locally derived short sequence reads. Nat Methods 7, 119–122 (2010). https://doi.org/10.1038/nmeth.1416
pig gut
Pig gut dataset in GigaDB http://dx.doi.org/10.5524/100187
availability of gene reference catalogs from gut microbiomes
Like this data many hosted in our GigaDB repository, and this has been covered in more detail in GigaBlog http://gigasciencejournal.com/blog/monkey-microbiome/
Ocean Sampling Day
One of the authors has presented a talk on the Citizen Science offshoot of this project here https://youtu.be/t6WpLbd0Nf8
Workflow, Virtual-Machine
The dockerised workflows are discussed in more detail in this blog posting here: http://blogs.biomedcentral.com/gigablog/2015/07/30/fermenting-reproducible-research-revolution/