Other tools in metflow2

metflow2 also provide some other useful tools.

Metabolite ID convert

Note: This function has been deprecated, try to new function in tinyTools package.

transID() function can be used to convert metabolites in more than 225 databases. It is based on the http://cts.fiehnlab.ucdavis.edu/ and https://www.chemspider.com/InChI.asmx.

Convert chemical ID based on http://cts.fiehnlab.ucdavis.edu/

You can use the databaseName() function to get the databases supported in transID().

library(metflow2)
#> ✓ xcms    3.12.0     ✓ MSnbase 2.16.0
#> ✓ mzR     2.24.1
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
#> ✓ tibble  3.1.0     ✓ dplyr   1.0.4
#> ✓ tidyr   1.1.3     ✓ stringr 1.4.0
#> ✓ readr   1.4.0     ✓ forcats 0.5.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::collect()    masks xcms::collect()
#> x dplyr::combine()    masks MSnbase::combine(), Biobase::combine(), BiocGenerics::combine()
#> x tidyr::expand()     masks S4Vectors::expand()
#> x dplyr::filter()     masks stats::filter()
#> x dplyr::first()      masks S4Vectors::first()
#> x dplyr::groups()     masks xcms::groups()
#> x dplyr::lag()        masks stats::lag()
#> x ggplot2::Position() masks BiocGenerics::Position(), base::Position()
#> x purrr::reduce()     masks MSnbase::reduce()
#> x dplyr::rename()     masks S4Vectors::rename()

database <- metflow2::databaseName(server = "http://cts.fiehnlab.ucdavis.edu/service/convert")
#> databaseName() is deprecated, please use the trans_id_database() in tinyTools.
#> 225 databases are supported in server http://cts.fiehnlab.ucdavis.edu/service/convert for 'from'.
#> 226 databases are supported in server http://cts.fiehnlab.ucdavis.edu/service/convert for 'to'.
database$From
#> # A tibble: 225 x 1
#>    From             
#>    <chr>            
#>  1 AAA Chemistry    
#>  2 ABBLIS Chemicals 
#>  3 Abbott Labs      
#>  4 ABI Chem         
#>  5 AbMole Bioscience
#>  6 Acesobio         
#>  7 Achemica         
#>  8 Acorn PharmaTech 
#>  9 Active Biopharma 
#> 10 Adooq BioScience 
#> # … with 215 more rows
database$To
#> # A tibble: 226 x 1
#>    From             
#>    <chr>            
#>  1 AAA Chemistry    
#>  2 ABBLIS Chemicals 
#>  3 Abbott Labs      
#>  4 ABI Chem         
#>  5 AbMole Bioscience
#>  6 Acesobio         
#>  7 Achemica         
#>  8 Acorn PharmaTech 
#>  9 Active Biopharma 
#> 10 Adooq BioScience 
#> # … with 216 more rows

Then you can convert ID using transID() function.

transID(query = "C00001", from = "KEGG", 
        to = "PubChem SID", top = 2,
        server = "http://cts.fiehnlab.ucdavis.edu/service/convert")
#> transID() is deprecated, please use the trans_ID() in tinyTools.
#> # A tibble: 2 x 2
#>   KEGG   `PubChem SID`
#>   <chr>  <chr>        
#> 1 C00001 103059594    
#> 2 C00001 10322299

The from and to argument must be from databaseName() function.

transID(query = "C00001", from = "KEGG", 
        to = "Human Metabolome Database", top = 2,
        server = "http://cts.fiehnlab.ucdavis.edu/service/convert")
#> # A tibble: 1 x 2
#>   KEGG   `Human Metabolome Database`
#>   <chr>  <chr>                      
#> 1 C00001 HMDB0002111

Convert chemical ID based on https://www.chemspider.com/InChI.asmx

Another databsae https://www.chemspider.com/InChI.asmx also support ID conveter. First please use databaseName() to know which databases are supported in this website.

database <- databaseName(server = "https://www.chemspider.com/InChI.asmx")
#> 9 are supported in server https://www.chemspider.com/InChI.asmx.
database
#> # A tibble: 9 x 2
#>   From     To      
#>   <chr>    <chr>   
#> 1 csid     mol     
#> 2 inchikey csid    
#> 3 inchikey inchi   
#> 4 inchikey mol     
#> 5 inchi    csid    
#> 6 inchi    inchikey
#> 7 inchi    mol     
#> 8 inchi    smiles  
#> 9 smiles   inchi

Here we first use the http://cts.fiehnlab.ucdavis.edu/service/convert convert C00001 to get it’s InChIKey ID.

transID(query = "C00010", 
        from = "KEGG", 
        to = "InChIKey", 
        top = 1,
        server = "http://cts.fiehnlab.ucdavis.edu/service/convert")
#> # A tibble: 1 x 2
#>   KEGG   InChIKey                   
#>   <chr>  <chr>                      
#> 1 C00010 RGJOEKWQDUBAIZ-IBOSZNHHSA-N

Then we use InChIKey ID to get its smiles.

inchi <- 
transID(query = "RGJOEKWQDUBAIZ-IBOSZNHHSA-N", 
        from = "inchikey", 
        to = "inchi",
        top = 2,
        server = "https://www.chemspider.com/InChI.asmx")
inchi
#> [1] "InChI=1S/C21H36N7O16P3S/c1-21(2,16(31)19(32)24-4-3-12(29)23-5-6-48)8-41-47(38,39)44-46(36,37)40-7-11-15(43-45(33,34)35)14(30)20(42-11)28-10-27-13-17(22)25-9-26-18(13)28/h9-11,14-16,20,30-31,48H,3-8H2,1-2H3,(H,23,29)(H,24,32)(H,36,37)(H,38,39)(H2,22,25,26)(H2,33,34,35)/t11-,14-,15-,16+,20-/m1/s1"

smiles <- 
transID(query = inchi,
        from = "inchi", 
        to = "smiles",
        top = 2,
        server = "https://www.chemspider.com/InChI.asmx")
smiles
#> [1] "CC(C)(COP(=O)(O)OP(=O)(O)OC[C@@H]1[C@H]([C@H]([C@H](n2cnc3c(N)ncnc23)O1)O)OP(=O)(O)O)[C@H](C(=NCCC(=NCCS)O)O)O"

Get metabolite class

Note: This function has been deprecated, try to new function in tinyTools package.

We can use get_metclass() function to get the metabolite class based on classyfire.

We need the inchikey for get_metclass() function, so if we don’t have the inchikey, we can use the transID() function to get their inchikey.

library(metflow2)

inchikey <- 
transID(query = "C00001", 
        from = "KEGG", 
        to = "InChIKey", 
        top = 1,
        server = "http://cts.fiehnlab.ucdavis.edu/service/convert")
#> transID() is deprecated, please use the trans_ID() in tinyTools.
inchikey <- inchikey$InChIKey
inchikey
#> [1] "XLYOFNOQVPJJNP-UHFFFAOYSA-N"

class <- get_metclass(inchikey = inchikey)
#> get_metclass() is deprecated, please use the get_compound_class() in tinyTools.

class is a classfire class.

class
#> ── classyfire Object ──────────────────────────────────────── metflow2 v0.9.2 ── 
#> Object Size: 8.6 Kb 
#>  
#> Information: 
#> SMILES:  O 
#> InChIKey:  InChIKey=XLYOFNOQVPJJNP-UHFFFAOYSA-N 
#> Formula:  H2O 
#> Mass:  18.015 
#> Kingdom : Inorganic compounds
#> └─Superclass : Homogeneous non-metal compounds
#>   └─Class : Homogeneous other non-metal compounds

class@compound_info
#> # A tibble: 4 x 2
#>   name     value                               
#>   <chr>    <chr>                               
#> 1 SMILES   O                                   
#> 2 InChIKey InChIKey=XLYOFNOQVPJJNP-UHFFFAOYSA-N
#> 3 Formula  H2O                                 
#> 4 Mass     18.015

class@taxonomy_tree
#> # A tibble: 3 x 2
#>   name       value                                
#>   <chr>      <chr>                                
#> 1 Kingdom    Inorganic compounds                  
#> 2 Superclass Homogeneous non-metal compounds      
#> 3 Class      Homogeneous other non-metal compounds

class@classification_info
#> # A tibble: 9 x 2
#>   name                    value                                
#>   <chr>                   <chr>                                
#> 1 Kingdom                 Inorganic compounds                  
#> 2 Superclass              Homogeneous non-metal compounds      
#> 3 Class                   Homogeneous other non-metal compounds
#> 4 Subclass                Not available                        
#> 5 Intermediate Tree Nodes Not available                        
#> 6 Direct Parent           Homogeneous other non-metal compounds
#> 7 Alternative Parents     Not available                        
#> 8 Molecular Framework     Not available                        
#> 9 Substituents            Homogeneous other non metal

class@description
#> # A tibble: 1 x 2
#>   name        value                                                             
#>   <chr>       <chr>                                                             
#> 1 Description This compound belongs to the class of inorganic compounds known a…

class@external_descriptors
#> # A tibble: 1 x 2
#>   name                 value        
#>   <chr>                <chr>        
#> 1 External Descriptors CHEBI:15377 :

Generate running list

We can use the getWorklist() function in metflow2 to generate a working list.

First, we should creat a batch.xlsx and put it in a folder. We use the demo data in demoData package.

The batch.xlsx is like this:

library(metflow2)
library(demoData)

##creat a folder nameed as example
path <- file.path(".", "example")
dir.create(path = path, showWarnings = FALSE)

##get demo data
demo_data <- system.file("metflow2", package = "demoData")

file.copy(from = file.path(demo_data, "batch.xlsx"), 
          to = path, overwrite = TRUE, recursive = TRUE)
#> [1] TRUE

Next, run the getWorklist() function.

getWorklist(
  table.name = "batch.xlsx",
  instrument = c("Thermo", "Agilent", "AB"),
  each.mode.number = 32,
  randommethod = c("no", "position", "injection"),
  samplenumber = NULL,
  QCstep = 8,
  conditionQCnumber = 8,
  qc.index.from = 1,
  dir = "D:\\Liang\\data\\PS4U\\HILIC\\batch3\\",
  method.path = "D:\\Liang\\Method\\urine\\HILIC\\",
  ms1.method.pos = "ZIC-HILIC_MS_pos",
  ms1.method.neg = "ZIC-HILIC_MS_neg",
  ms2.method.pos = c(
    "ZIC-HILIC_MSMS_pos_NCE25",
    "ZIC-HILIC_MSMS_pos_NCE25",
    "ZIC-HILIC_MSMS_pos_NCE25",
    "ZIC-HILIC_MSMS_pos_NCE25"
  ),
  ms2.method.neg = c(
    "ZIC-HILIC_MSMS_neg_NCE25",
    "ZIC-HILIC_MSMS_neg_NCE25",
    "ZIC-HILIC_MSMS_neg_NCE25",
    "ZIC-HILIC_MSMS_neg_NCE25"
  ),
  path = path
)

Then, the worklists will generated in this folder.

Xiaotao Shen PhD (https://www.shenxt.info/)

true

Created on 2020-04-01 and updated on 2021-04-07

Metabolite ID convert

Convert chemical ID based on http://cts.fiehnlab.ucdavis.edu/

Convert chemical ID based on https://www.chemspider.com/InChI.asmx

Get metabolite class

Generate running list