--- title: "brendaDb" author: - name: Yi Zhou affiliation: Institute of Bioinformatics, University of Georgia email: Yi.Zhou@uga.edu date: "`r Sys.Date()`" output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{brendaDb} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` # Overview `r Biocpkg("brendaDb")` aims to make importing and analyzing data from the [BRENDA database](https://www.brenda-enzymes.org) easier. The main functions include: - Read [text file downloaded from BRENDA](https://www.brenda-enzymes.org/download_brenda_without_registration.php) into an R `tibble` - Retrieve information for specific enzymes - Query enzymes using their synonyms, gene symbols, etc. - Query enzyme information for specific [BioCyc](https://biocyc.org) pathways For bug reports or feature requests, please go to the [GitHub repository](https://github.com/y1zhou/brendaDb/issues). # Installation `r Biocpkg("brendaDb")` is a *Bioconductor* package and can be installed through `BiocManager::install()`. ```{r, eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("brendaDb", dependencies=TRUE) ``` Alternatively, install the development version from GitHub. ```{r, setup, message=FALSE} if(!requireNamespace("brendaDb")) { devtools::install_github("y1zhou/brendaDb") } ``` After the package is installed, it can be loaded into the *R* workspace by ```{r} library(brendaDb) ``` # Getting Started ## Downloading the BRENDA Text File Download the BRENDA database as [a text file](https://www.brenda-enzymes.org/download_brenda_without_registration.php) here. Alternatively, download the file in R (file updated 2019-04-24): ```{r, eval=FALSE} brenda.filepath <- DownloadBrenda() #> Please read the license agreement in the link below. #> #> https://www.brenda-enzymes.org/download_brenda_without_registration.php #> #> Found zip file in cache. #> Extracting zip file... ``` The function downloads the file to a local cache directory. Now the text file can be loaded into R as a `tibble`: ```{r, eval=FALSE} df <- ReadBrenda(brenda.filepath) #> Reading BRENDA text file... #> Converting text into a list. This might take a while... #> Converting list to tibble and removing duplicated entries... #> If you're going to use this data again, consider saving this table using data.table::fwrite(). ``` As suggested in the function output, you may save the `df` object to a text file using `data.table::fwrite()` or to an R object using `save(df)`, and load the table using `data.table::fread()` or `load()`^[This requires the R package `r CRANpkg("data.table")` to be installed.]. Both methods should be much faster than reading the raw text file again using `ReadBrenda()`. # Making Queries Since BRENDA is a database for enzymes, all final queries are based on EC numbers. ## Query for Multiple Enzymes If you already have a list of EC numbers in mind, you may call `QueryBrenda` directly: ```{r} brenda_txt <- system.file("extdata", "brenda_download_test.txt", package = "brendaDb") df <- ReadBrenda(brenda_txt) res <- QueryBrenda(df, EC = c("1.1.1.1", "6.3.5.8"), n.core = 2) res res[["1.1.1.1"]] ``` ## Query Specific Fields You can also query for certain fields to reduce the size of the returned object. ```{r} ShowFields(df) res <- QueryBrenda(df, EC = "1.1.1.1", fields = c("PROTEIN", "SUBSTRATE_PRODUCT")) res[["1.1.1.1"]][["interactions"]][["substrate.product"]] ``` It should be noted that most fields contain a `fieldInfo` column and a `commentary` column. The `fieldInfo` column is what's extracted by BRENDA from the literature, and the `commentary` column is usually some context from the original paper. `#` symbols in the commentary correspond to the `proteinID`s, and `<>` enclose the corresponding `refID`s. For further information, please see [the README file from BRENDA](https://www.brenda-enzymes.org/download_brenda_without_registration.php). ## Query Specific Organisms Note the difference in row numbers in the following example and in the one where we queried for [all organisms](#query-for-multiple-enzymes). ```{r} res <- QueryBrenda(df, EC = "1.1.1.1", organisms = "Homo sapiens") res$`1.1.1.1` ``` ## Extract Information in Query Results To transform the `brenda.entries` structure into a table, use the helper function `ExtractField()`. ```{r} res <- QueryBrenda(df, EC = c("1.1.1.1", "6.3.5.8"), n.core = 2) ExtractField(res, field = "parameters$ph.optimum") ``` As shown above, the returned table consists of three parts: the EC number, organism-related information (organism, protein ID, uniprot ID, and commentary on the organism), and extracted field information (description, commentary, etc.). # Foreign ID Retrieval ## Querying Synonyms A lot of the times we have a list of gene symbols or enzyme names instead of EC numbers. In this case, a helper function can be used to find the corresponding EC numbers: ```{r} ID2Enzyme(brenda = df, ids = c("ADH4", "CD38", "pyruvate dehydrogenase")) ``` The `EC` column can be then handpicked and used in `QueryBrenda()`. ## BioCyc Pathways Often we are interested in the enzymes involved in a specific [BioCyc](https://biocyc.org) pathway. As BioCyc now requires login credentials for using their web service, users are recommended to use the [metabolike](https://github.com/y1zhou/metabolike) package for more advanced queries. # Additional Information {.unnumbered} By default `QueryBrenda` uses all available cores, but often limiting `n.core` could give better performance as it reduces the overhead. The following are results produced on a machine with 40 cores (2 Intel Xeon CPU E5-2640 v4 @ 3.4GHz), and 256G of RAM: ```{r, eval=FALSE} EC.numbers <- head(unique(df$ID), 100) system.time(QueryBrenda(df, EC = EC.numbers, n.core = 0)) # default # user system elapsed # 4.528 7.856 34.567 system.time(QueryBrenda(df, EC = EC.numbers, n.core = 1)) # user system elapsed # 22.080 0.360 22.438 system.time(QueryBrenda(df, EC = EC.numbers, n.core = 2)) # user system elapsed # 0.552 0.400 13.597 system.time(QueryBrenda(df, EC = EC.numbers, n.core = 4)) # user system elapsed # 0.688 0.832 9.517 system.time(QueryBrenda(df, EC = EC.numbers, n.core = 8)) # user system elapsed # 1.112 1.476 10.000 ``` ```{r} sessionInfo() ```