metaGEM
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franciscozorrilla
:gem: An easy-to-use workflow for generating context specific genome-scale metabolic models and predicting metabolic interactions within microbial communities directly from metagenomic data
Hi! It seems the 'abundance' rule maps each sample's reads against the bins generated from that same sample. Reads are not mapped against bins generated from other samples. Is this...
Hello, again Francisco! I think I figured out the problem with the _modelVis_ rule. Just small corrections are needed for users working with a carveme >1.5.0. Changes (In the _Snakefile_):...
Hello! I am trying to install metaGEM using the instructions provided in https://github.com/franciscozorrilla/metaGEM/blob/master/config/README.md I have managed to get everything installed up until section 3, metawrap. When running `conda create -n...
The binning landscape has changed since the initial development of metaGEM. It would be a good idea to get a shortlist of novel binning tools for testing, with the ultimate...
Would be great if users could conda install metagem locally on apple ARM architecture machines, initially suggested here #122 This is not meant for standard usage, but could be helpful...
# 🩻 Single batch submission of GTDBTk-based taxonomic classification 🚀 Current GTDBTk rule is great when dealing with many metagenomes, but when dealing with many isolate samples it makes more...
# 🥚 eggNOG-mapper for better functional annotation 🍸 ## 🐍 Snakefile tasks - [x] create eggnog-mapper rule for processing ORF-annotated protein fasta files of genomes - [ ] create helper/supplementary...
# 🦠 Isolate genomes subworkflow 🔦 Adding support for isolate genome assembly in the next version of metaGEM 💎 ## 🐍 Snakefile tasks - [x] create shovill rule for dealing...
For abundance calculation we need to get bp of genome, currently we do this based on the contig headers info, although this can become problematic for genomes assembled by different...
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:gem: An easy-to-use workflow for generating context specific genome-scale metabolic models and predicting metabolic interactions within microbial communities directly from metagenomic data