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Working with MetaCyc

MetaCyc (https://metacyc.org/) (Caspi et al., 2020) is a metabolic pathway database that has been widely used in genomic, metagenomic and metabolomic studies. It provides a hierarchical classification system, including genes, proteins, reactions, compounds, pathways and more.

Contents

Mapping files

We mapped all ORFs from the WoL reference genome database to the reference protein sequences in MetaCyc release 23.0. We provide this mapping file, as well as Woltka-compatible mapping and annotation files representing the higher levels in the MetaCyc classification system corresponding to the mapped WoL ORFs. These files are publicly available under the function/metacyc/ directory of the WoL data release (see details).

We also included a UniRef-to-MetaCyc mapping file, extracted from the UniProt data release and subsetted to WoL. It contains less entries though.

The original, full MetaCyc database is available for download from the official website. It includes the reference protein sequences with which one can perform custom alignments. We provide a Python script: metacyc_build.py to reformat a local copy of the MetaCyc database into simple mapping files which can be parsed by Woltka.

Protein profiling

The following command utilizes Woltka's coord matching function to classify sequences to proteins -- the entry point of the MetaCyc hierarchies.

woltka classify \
  --input  input_dir \
  --coords coords.txt.xz \
  --map    metacyc/protein.map.xz \
  --names  metacyc/protein_name.txt \
  --rank   protein \
  --output protein.biom
  • Note: The --names parameter is optional.
  • Note: Replacing .biom with .tsv can generate TSV output.

Alternatively, one can split this command into two:

woltka classify -i input_dir -c coords.txt.xz -o orf.biom
woltka collapse -i orf.biom -m metacyc/protein.map.xz -n metacyc/protein_name.txt -o protein.biom

Genes, reactions, compounds and pathways

The MetaCyc hierarcies are as follows:

               v
       go < protein > gene > pathway
               v
regulation < enzrxn
               v
       ec < reaction > compound (left / right) > type
               v
     type < pathway > taxonomic range
               v
         super pathway
               v
              type

All transitions are enabled using Woltka's collapse command with individual mapping files. For example, one can generate profiles along the following cascade:

protein - enzrxn - reaction - pathway - super pathway - type

Using the following commands:

# protein to enzrxn (enzymatic reaction):
woltka collapse -i protein.biom -m metacyc/protein-to-enzrxn.txt -n metacyc/enzrxn_name.txt -o enzrxn.biom
# enzrxn to reaction:
woltka collapse -i enzrxn.biom -m metacyc/enzrxn-to-reaction.txt -n metacyc/reaction_name.txt -o reaction.biom
# reaction to pathway:
woltka collapse -i reaction.biom -m metacyc/reaction-to-pathway.txt -n metacyc/pathway_name.txt -o pathway.biom
# pathway to super pathway:
woltka collapse -i pathway.biom -m metacyc/pathway-to-super_pathway.txt -n metacyc/pathway_name.txt -o super_pathway.biom
# super pathway (or pathway) to pathway type:
woltka collapse -i super_pathway.biom -m metacyc/pathway_type.txt -n metacyc/all_class_name.txt -o pathway_type.biom

The collapse command supports many-to-many mapping. For example, if one reaction is found in three pathways, each pathway will be counted once. In some instances (e.g., to retain compositionality of the profile), one may consider adding the --divide flag, which will instruct the program to count each pathway 1 / 3 times (see details).

Pathway coverage

It is usually important to assess how completed, in addition to how abundant a metabolic pathway is in a sample. This is because a pathway can only function when all components are present. Woltka's coverage command provides a solution (see details).

Among the mapping files there are two files recording the composition of metabolic pathways in terms of genes and reactions. We recommend using the reaction mapping because there are duplicated gene IDs.

woltka coverage -i reaction.biom -m pathway-to-reaction_list.txt -o pathway_coverage.biom

The output file is a coverage table in which every cell value represents the percentage of member reactions of a particular pathway present in a particular sample.

Stratifying by taxonomy

Woltka allows overlaying two or more classification schemes on the same profile using the stratification function. With this function, one can identify functional genes and metabolic pathways that belong to individual taxonomic groups.

First, perform taxonomic classification at the desired rank (e.g., genus) and generate read-to-genus maps (using parameter --outmap or -u).

woltka classify -i input_dir --lineage lineages.txt -r genus -o genus.biom -u map_dir

Second, perform functional classification. This command is identical to the first command in this document, except for the addition of --stratify or -t parameter pointing to the genus maps, which will be incorporated into the functional classes (see details).

woltka classify -i input_dir -c coords.txt.xz -m metacyc/protein.map.xz -r protein -t map_dir -o protein.biom