The goal of mspcompiler is to offer ways to compile either EI or tandem mass spectral libraries from various sources, such as NIST (if you have it installed), MoNA, and GPNS, and organize them into a neat and up-to-date msp file that can be used in MS-DIAL.

Install by trying the following code.

install.packages("devtools")
devtools::install_github("QizhiSu/mspcompiler", build_vignettes = TRUE)

If ChemineR and ChemineOB fail, please try:

if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    BiocManager::install("ChemmineR")

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    BiocManager::install("ChemmineOB")

NIST is the most commonly used commercial EI library. Once you have the NIST library installed, you can transformed it into a msp file by Lib2NIST. Normally Lib2NIST will be installed along with the NIST library installation. If not, you can download it from https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nist17. Please use the following settings in Lib2NIST:

1. Add Input Libraries/Files. For Agilent users, the input file can be found in, for example, “C:/database/NIST14.L”.

2. Tick Use Subset and click Define Subset to set detail parameters:

   • Enter which spectra should be exported in List of Spectra IDs.
   inside Define Subset in Lib2NIST Programe
   • The total number of spectra that your NIST library have can be checked in the MS Search program (Options -> Libraries -> Add both mainlib and relib). Then you can see the number of spectra your NIST library have.
   of checking total number of spectra in the MS Search Program

3. Select “Text File(.MSP) + MOLfiles linked by BOTH” in Output Format;

4. Select the library in Input Libraries or Text Files and Convert.

Once you have the *.MSP file (normally hundreds megabytes) and the correspondent *.MOL folder (hundreds thousands .MOL files inside the folder) exported, you can use the following code to add SMILES and Retention Index (RI).

The whole process is time-consuming (several hours, depending on the capability of your PC), so we suggests to use parallel computing.

library(mspcompiler)
library(future)
library(future.apply)
library(parallel)
# Set up parallel computing. Just remember to set it back once you have the 
# library compiled by "plan(sequential)". We will include it later.
plan(multisession(workers = detectCores() - 1))

Below are the code to process NIST EI library assuming that we have all files stored in the MS_libraries folder in the D disk. You may need to change the path accordingly. Note: Since the *.MOL folder contains a large number of mol files, it will be time-consuming to move, copy, or delete this folder. As such, we recommend pick up a good location to put this file when you are exporting in Lib2NIST, avoiding moving it.

# Read the msp file into R.
nist_ei <- read_lib("D:/MS_libraries/NIST.MSP", type = "EI")
# Combine all mol files into a single sdf file for subsequent structure retrieval.
combine_mol2sdf("D:/MS_libraries/NIST.MOL", "D:/MS_libraries/nist.sdf")
# Extract structure based on the sdf file exported before.
nist_ei_structure <- extract_structure("D:/MS_libraries/nist.sdf", "D:/MS_libraries/nist_structure.txt")
# Assign SMILES to the library. If you are working with Linux-based or Mac OS,
# please use "match = "inchikey". 
nist_ei <- assign_smiles(nist_ei, nist_ei_structure, match = "name")

In case you want to expand your library by adding publicly available libraries, continue with the instructions for each library. Otherwise, jump to the “combine all libraries” section to add RI values accordingly.

The MS-DIAL developers have compiled an EI library with Kovat RI included. This library can be downloaded from http://prime.psc.riken.jp/compms/msdial/main.html#MSP. Please download “All records with Kovats RI…EI-MS…” As it contains Kovats RI, we can set remove_ri to FALSE to keep original RI in this file. This file already have SMILES and InChIKey well-organized. Hence, no further treatment is needed.

riken_ei <- read_lib("D:/MS_libraries/GCMS DB-Public-KovatsRI-VS3.msp",
                     type = "EI", remove_ri = FALSE)

The MassBank of North America (MoNA) has an EI library available for download as well, https://mona.fiehnlab.ucdavis.edu/downloads. Please download “GC-MS Spectra” in “MSP” form. This file has SMILES information though, it is in the Comment field. Therefore, the SMILES has to be extracted from the Comment and put into the SMILES field by the reorganize_mona function.

mona_ei <- read_lib("D:/MS_libraries/MoNA-export-GC-MS_Spectra.msp", type = "EI")
mona_ei <- reorganize_mona(mona_ei)

The Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) has complied an EI library with drug or drug-related compounds. The library is available in https://swgdrug.org/ms.html. The library is available in multiple formats. To correctly parse this library by mspcompiler, please download both NIST Format and Agilent Format. Then use Lib2NIST to convert the NIST Format into a msp file and to transform the Agilent Format into the mol files. Finally, we can read it in a way similar to that used for the NIST library.

swgdrug_ei <- read_lib("D:/MS_libraries/SWGDRUG.MSP", type = "EI")
combine_mol2sdf("D:/MS_libraries/SWGDRUG.MOL", "D:/MS_libraries/swgdrug.sdf")
swgdrug_ei_structure <- extract_structure("D:/MS_libraries/swgdrug.sdf", "D:/MS_libraries/swgdrug_structure.txt")
# As the SWGDRUG file does not contain InChIKey information, even though you
# are working with Linux-based or Mac OS, you should not use "match = inchikey".
# "match = "name" is more than enough in this case.
swgdrug_ei <- assign_smiles(swgdrug_ei, swgdrug_ei_structure, match = "name")

After read in and organize all these libraries, we can now combine them into a single file, assign experimental RI retrieved from the “ri.dat” and “USER.DBU” files (if you have NIST library installed).

# Combine them
combine_ei <- c(nist_ei, riken_ei, mona_ei, swgdrug_ei)

# Extract experimental RI from the "ri.dat" and "USER.DBU" files. Once you have
# NIST library installed, these files can be found in, for example, 
# "~/Programs/nist14/mssearch/nist_ri". Assuming you have copied these two files
# in the D:/MS_libraries folder, then you will have:
nist_ri <- extract_ri("D:/MS_libraries/ri.dat", "D:/MS_libraries/USER.DBU")
# Assign experimental RI to the combined library depending on the column 
# polarity. The polarity can be "semi-polar", "non-polar", or "polar".
# Providing that "capillary" GC columns are commonly used. This
# function will only keep RI records from "capillary" columns and "Lee RI" 
# will be removed. When there are multiple records for a single compound, 
# the median RI will be used and if the standard deviation is higher than 30, 
# this value will be discarded. 
combine_ei <- assign_ri(combine_ei, nist_ri, polarity = "semi-polar")
# At the end, disable parallel computing
plan(sequential)
# Then you can write it out
write_EI_msp(combine_ei, "D:MS_libraries/combine_ei.msp")

library(mspcompiler)
library(future)
library(future.apply)
library(parallel)
plan(multisession(workers = detectCores() - 1))

MS2 libraries can be processed in a similar way, but positive and negative modes are normally separated into 2 msp files. #### NIST MS2 library The NIST MS2 library can be treated as the same as the NIST EI library detailed above with only one exception. That is the input file for Lib2NIST can be found in “C:/Programs/nist14/mssearch/nist_msms”. The exported msp file has both positive and negative modes mixed in a singled file, so we have to separated them by the separate_polarity function.

nist_ms2 <- read_lib("D:/MS_libraries/NIST_msms.MSP", type = "MS2")
combine_mol2sdf("D:/MS_libraries/NIST_msms.MOL", "D:/MS_libraries/nist_msms.sdf")
nist_ms2_structure <- extract_structure("D:/MS_libraries/nist_msms.sdf", "D:/MS_libraries/nist_msms_structure.txt")
nist_ms2 <- assign_smiles(nist_ms2, nist_ms2_structure, match = "name")
nist_ms2_pos <- separate_polarity(nist_ms2, polarity = "pos")
nist_ms2_neg <- separate_polarity(nist_ms2, polarity = "neg")

The RIKEN MS2 libraries can be download from the MS-DIAL homepage http://prime.psc.riken.jp/compms/msdial/main.html#MSP. Please download all public MS/MS positive and negative, separately.

riken_ms2_pos <- read_lib("D:/MS_libraries/MSMS-Public-Pos-VS15.msp")
riken_ms2_neg <- read_lib("D:/MS_libraries/MSMS-Public-Neg-VS15.msp")

The MoNA MS2 libraries can be downloaded from https://mona.fiehnlab.ucdavis.edu/downloads. Please download “LC-MS/MS Positive Mode” and “LC-MS/MS Negative Mode”, separately.

mona_ms2_pos <- read_lib("D:/MS_libraries/MoNA-export-LC-MS-MS_Positive_Mode.msp")
# Reorganize the SMILES field.
mona_ms2_pos <- reorganize_mona(mona_ms2_pos)

mona_ms2_neg <- read_lib("D:/MS_libraries/MoNA-export-LC-MS-MS_Negitive_Mode.msp")
mona_ms2_neg <- reorganize_mona(mona_ms2_neg)

Unlike others, the GNPS library is organized in mgf format, so it has to be treated differently. Hence, we have to set format = “mgf” in the read_lib function. Besides, this library does not have the Molecular Formula (MF) field, so we can calculated the MF from the SMILES (if it exists) by the complete_mgf function. Finally, both positive and negative modes are in a single file as well. Therefore, we need to separated the polarity by the separate_polarity function as well. The GNPS library can be download from https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp. Please download “All GNPS Library Spectra”.

gnps <- read_lib("D:/MS_libraries/ALL_GNPS.mgf", format = "mgf")
# Compute MF
gnps <- complete_mgf(gnps)
gnps_pos <- separate_polarity(gnps, polarity = "pos")
gnps_nge <- separate_polarity(gnps, polarity = "neg")

Now we have all MS2 libraries well-organized, so we can combine them.

combine_ms2_pos <- c(nist_ms2_pos, riken_ms2_pos, mona_ms2_pos, gnps_pos)
combine_ms2_neg <- c(nist_ms2_neg, riken_ms2_neg, mona_ms2_neg, gnps_nge)
# Disable parallel computing
plan(sequential)
# Then you can write them out separately
write_MS2_msp(combine_ms2_pos, "D:MS_libraries/combine_ms2_pos.msp")
write_MS2_msp(combine_ms2_neg, "D:MS_libraries/combine_ms2_neg.msp")

When you have multiple libraries to be read in, for instance if you are building your in-house library and you have one msp file for each batch of standards, then you will have many msp files to combine. The read_multilibs function give you an easy way to read all of them at once. In this case, what you need to input is the folder that contain all these msp files, say the in_house folder.

in_house <- read_multilibs("D:/MS_libraries/in_house")

This package offers ways to remove RI and RT as well. You can use remove_ri and remove_rt functions, respectively. For in-house libraries, you might want to change meta data, e.g., comment (both EI and MS2 libraries, to add principle investigator and data collector for example), collision energy and instrument type (for MS2 libraries). You can use the change_meta function for this purpose.

Note:

1. The RIKEN, MoNA, and GNPS MS2 libraries might contains some identical spectra as they all compile some well-known libraries. However, it is tricky to separate them. This might increase the size of the final msp file, but should not affect its use.

2. It takes a long time to process the NIST libraries. For a given lab, it may have a particular version of NIST and may not change frequently. For this reason, we recommend that once you have NIST libraries organized, save them as .Rda file and reuse it the next time. For others, you can check if there is any update since your last compilation.

3. All the aforementioned tandem mass spectral libraries are relatively big and would consuming rather high amount of memory once they are read into R. If your PC does not have enough memory, you can process each library separately, write it out as single msp file, and then combine them in a text editor, e.g., Notepad++ as the msp file is basically a text file.

Many thanks to Miao YU, the author of the enviGCMS R package, for his help to the read_lib function. I would also like to thank Dmitriy D. Matyushin for his hints to extract RI information from the NIST library.