Avoiding taxonomic inference - or providing your own about metabolic HOT 8 OPEN

Hi,
You will need to provide the gtdb-tk result for the downstream visualization steps. Maybe you can suppress gtdb-tk and just make a folder named "gtdbtk_Genome_files" with self-made mocked gtdb-tk taxonomy files.

The gtdb-tk tax files that METABOLIC will use:
../../intermediate_files/gtdbtk_Genome_files/gtdbtk.bac120.summary.tsv
../../intermediate_files/gtdbtk_Genome_files/gtdbtk.ar122.summary.tsv

from metabolic.

Hi, You will need to provide the gtdb-tk result for the downstream visualization steps. Maybe you can suppress gtdb-tk and just make a folder named "gtdbtk_Genome_files" with self-made mocked gtdb-tk taxonomy files.

The gtdb-tk tax files that METABOLIC will use: ../../intermediate_files/gtdbtk_Genome_files/gtdbtk.bac120.summary.tsv ../../intermediate_files/gtdbtk_Genome_files/gtdbtk.ar122.summary.tsv

Thank you for your quick response! So, I could do the following: 1) run METABOLIC-C with the genomes I need; 2) add the taxonomic inferences to the intermediate files; 3) re-run METABOLIC-C. Would it be correct?

from metabolic.

Hey, thanks for the information. I fixed a couple of issues with the dependencies (which weren't fully installed) and managed to reproduce the summary files: in this way, METABOLIC-C manages to produce much of its outputs. However, since the first run didn't produce one specific PDF file (a "network.plot.pdf") as an output on energy analysis, and the second run specifically appears to look for it, I'll try to do a new first run (which now would likely end still better) and then add the taxonomic output prior to the second run. Fingers crossed!

from metabolic.

Just an update: the first step in the pipeline was completed but, as expected, it complained about the lack of genome taxonomy information. Again, as the final metabolic network appears to need the taxonomic information, it was not produced. Thus, I'm experimenting with its code (I have no coding knowledge, though): I'm commenting out the GTDBtk step and substituting the related input files with the ones I already have generated. If it works, I'm done. I guess it can be turned into some kind of flag to be added, something like
-taxinfo - how to carry out taxonomic inference: "gtdb" (default) to run the main GTDBtk annotation, "own" to provide your own -taxinfo_path - when using -taxinfo "own" , declare the /path/to/taxonomic/input - GTDB-like format

from metabolic.

Yes, when you do the re-run (step 3 as you listed), you can use this script: "https://github.com/AnantharamanLab/METABOLIC/blob/master/METABOLIC-C.2nd_run.pl".
See the instruction here: https://github.com/AnantharamanLab/METABOLIC/wiki/METABOLIC-Usage#a-2nd-metabolic-c-run

from metabolic.

Hi, you can use conda to install all the dependencies in a much easier way (will be fully installed):
https://github.com/AnantharamanLab/METABOLIC/wiki/Installation#-quick-installation

from metabolic.

I know, thank you, that's the way I installed it. However, a couple of Perl dependencies were not installed and this was leading to errors. Now I fixed it following the thread from which the quick Conda installation came.

from metabolic.

Hi @tenguzame ,
Are you trying to draw the network diagram with the nodes and colors? (Just making sure I understand your issue) (Functional Network slide here):

If so, you can run the following Rscript on its own in the terminal without having to run any METABOLIC-C steps.
Rscript draw_functional_network_diagram.R [path to energy flow input] [name of output folder]

Make sure you have the full path to draw_functional_network_diagram.R if needed. and that these Rpackages are installed:
library(ggraph)
library(igraph)
library(tidyverse)
library(tidygraph)

The energy flow input looks something like this. It's a 5 column text file, tab separated. Would changing the column "Taxonomic" column to your own annotations help solve your issue?

Energy_flow_input.txt

Edit: If you want to use your own genome coverage values (let's say METABOLIC-C doesn't work or you prefer to use another mapping method than bowtie2, you'd need to change the values in the column "Coverage value(average)" accordingly as well.

from metabolic.