In mirnavazquez/RbiMs: R Tools for Reconstruncting Bin Metabolisms

knitr::opts_chunk$set(
  collapse = TRUE,
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The MEROPS database classifies peptidases (proteases) and their inhibitors using a hierarchical, structure-based system. Peptidases are grouped into Families based on significant sequence similarities, and related families are further grouped into Clans, indicating evolutionary relationships. This classification helps researchers understand enzyme function, structure, and evolution. The database provides sequence identifiers, structural data (if available), and literature references for deeper exploration.

First, load the rbims package.

library(rbims)

The function that allow us to load merops data is read_merops.

merops_profile <- read_merops("../inst/extdata/peptidase_2/", profile = T)

library(readr)
merops_profile <- read.delim("../inst/extdata/merops_profile.tsv")

• The database argument will parse the database. In this example, I will explore the KO output.

• The output format is chosen with the profile argument. When profile = T, a wide output is obtained.

• The write argument saves the formatted table generated in .tsv extension. When write = F gives you the output but not saves the table in your current directory.

head(merops_profile)

Or print a long table profile = F.

merops_profile_long <- read_merops("../inst/extdata/peptidase_2/", profile = F)

merops_profile_long <- read.delim("../inst/extdata/merops_profile_long.tsv")

head(merops_profile_long)

You can export this to a table like this:

write.table(merops_profile_long, "KO_picrust2.tsv", quote = F, sep = "\t", row.names = F, col.names = T)

Or setting write write = T.

mirnavazquez/RbiMs documentation built on April 17, 2025, 7:37 p.m.