R/mslibrary-json.R
loadMSLibraryMoNAJSON.Rd
This function loads, verifies and curates MS library data from MoNA
.json
files.
loadMSLibraryMoNAJSON(
file,
prefCalcChemProps = TRUE,
neutralChemProps = FALSE,
potAdducts = TRUE,
potAdductsLib = TRUE,
absMzDev = 0.002,
calcSPLASH = TRUE
)
Guessing adducts from neutral/ionic mass differences was inspired from MetFrag.
A character
string that specifies the file path to the JSON library.
If TRUE
then calculated chemical properties such as the formula and
InChIKey are preferred over what is already present in the MS library. For efficiency reasons it is
recommended to set this to TRUE
. See the Validating and calculating chemical properties
section for
more details.
If TRUE
then the neutral form of the molecule is considered to calculate
SMILES, formulae etc. Enabling this may improve feature matching when considering common adducts
(e.g. [M+H]+
, [M-H]-
). See the Validating and calculating chemical properties
section
for more details.
If and how missing adducts (Precursor_type
data) are guessed,
potAdducts
should be either:
FALSE
: do not perform adduct guessing.
TRUE
: guesses adducts based on a common set of known adducts (currently based on
GenFormAdducts
and MetFragAdducts
). If potAdductsLib
is TRUE
then also
any adducts specified in the library are used.
A list
with adduct
objects or character
vector that can be converted with
as.adduct
. Only the specified adducts will be used for guessing missing values.
The maximum absolute m/z deviation when guessing missing adducts.
If set to TRUE
then missing SPLASH values will be calculated (see below).
The loaded data is returned in an MSLibrary
object.
This function uses an efficient C++
JSON loader to load MS library data. This function is called when calling loadMSLibrary
with
algorithm="json"
.
This function uses C++
with Rcpp and rapidjsonr to efficiently load and parse
JSON files from MoNA. An advantage compared to
loadMSLibraryMSP
is that this function supports loading spectral annotations.
The record field names are converted to those used in .msp
files.
Several strategies are applied to automatically verify and improve
library data. This is important, since library records may have inconsistent or erroneous data, which makes them
unsuitable in automated workflows such as compounds annotation with generateCompoundsLibrary
.
The loaded library data is post-treated as follows:
The DB#
field is renamed to DB_ID
to improve compatibility with R column names.
Synonyms (Synon
fields) are merged together, mainly to save memory usage.
Inconsistently formatted NA
data (e.g. "n/a"
, "N/A"
or empty strings) are set to
regular R NA
values.
The case of record field names are made consistent.
The Formula
and ExactMass
fields are renamed to formula
and neutralMass
,
respectively. This is for consistency with other data generated with patRoon.
character
field data is trimmed from leading/trailing whitespace.
Mass data is verified to be properly numeric, and set to NA
otherwise.
The format of formulae data is made consistent: ionic species (with or without square brackets) or converted to a regular formula format.
Chemical identifiers such as SMILES and formulae are verified and missing values are calculated if possible. See below for more details.
Shortened data in the Ion_mode
field (P/N) is converted to the long format
(POSITIVE
/NEGATIVE
).
Many different adduct flavors typically found as Precursor_type
data are converted and normalized to
the generic textual format used by patRoon (see as.adduct
).
If potAdducts!=FALSE
then missing or invalid adduct data in Precursor_type
is guessed based on
the difference between the neutral and ionic mass. If multiple adducts explain the mass difference the result is
NA
.
Missing ion m/z data (PrecursorMZ
field) is calculated from adduct data, if possible.
Missing SPLASH data is calculated with the splashR package
if calcSPLASH=TRUE
.
Chemical properties such as SMILES, InChIKey and formula in the MS library are automatically validated and calculated if missing/invalid.
The internal validation/calculation process performs the following steps:
Validation of SMILES, InChI, InChIKey and formula data (if present). Invalid
entries will be set to NA
.
If neutralChemProps=TRUE
then chemical data (SMILES, formulae etc.) is neutralized by
(de-)protonation (using the –neutralized
option of OpenBabel
). An additional column
molNeutralized
is added to mark those molecules that were neutralized. Note that neutralization requires
either SMILES or InChI data to be available.
The SMILES and InChI data are used to calculate missing or invalid SMILES,
InChI, InChIKey and formula data. If prefCalcChemProps=TRUE
then existing
InChIKey and formula data is overwritten by calculated values whenever possible.
The chemical formulae which were not calculated are verified and normalized. This process may be time
consuming, and is potentially largely avoided by setting prefCalcChemProps=TRUE
.
Neutral masses are calculated for missing values (prefCalcChemProps=FALSE
) or whenever possible
(prefCalcChemProps=TRUE
).
Note that calculation of formulae for molecules that are isotopically labelled is currently only supported for deuterium (2H) elements.
This functionality relies heavily on OpenBabel, please make sure it is installed.
Wohlgemuth2016patRoon
Ruttkies2016patRoon
Rcpp1
Rcpp2
Rcpp3
OBoyle2011patRoon
loadMSLibrary
for more details and other algorithms.
The MSLibrary
documentation for various methods to post-process the data and
generateCompoundsLibrary
for annotation of features with the library data.