Identify metabolites based on MS1 or MS/MS database.
identify_metabolites( ms1.data, ms2.data = NULL, ms1.ms2.match.mz.tol = 25, ms1.ms2.match.rt.tol = 10, ms1.match.ppm = 25, ms2.match.ppm = 30, mz.ppm.thr = 400, ms2.match.tol = 0.5, fraction.weight = 0.3, dp.forward.weight = 0.6, dp.reverse.weight = 0.1, rt.match.tol = 30, polarity = c("positive", "negative"), ce = "all", column = c("rp", "hilic"), ms1.match.weight = 0.25, rt.match.weight = 0.25, ms2.match.weight = 0.5, path = ".", total.score.tol = 0.5, candidate.num = 3, database, threads = 3 )
The name of ms1 peak table (csv format). Column 1 is "name", Column 2 is "mz" and column is "rt" (second).
MS2 data, must be mgf, msp or mzXML format. For example, ms2.data = c("test.mgf", "test2.msp").
MS1 peak and MS2 spectrum matching m/z tolerance. Default is 25 pm.
MS1 peak and MS2 spectrum matching RT tolerance. Default is 10 s.
Precursor match ppm tolerance.
Fragment ion match ppm tolerance.
Accurate mass tolerance for m/z error calculation.
MS2 match (MS2 similarity) tolerance.
The weight for matched fragments.
Forward dot product weight.
Reverse dot product weight.
RT match tolerance.
The polarity of data, "positive"or "negative".
Collision energy. Please confirm the CE values in your database. Default is "all".
"hilic" (HILIC column) or "rp" (reverse phase).
The weight of MS1 match for total score calculation.
The weight of RT match for total score calculation.
The weight of MS2 match for total score calculation.
Total score tolerance. The total score are refering to MS-DIAL.
The number of candidate.
MS2 database name or MS database.
Number of threads
A metIdentifyClass object.
The example and demo data of this function can be found https://tidymass.github.io/metid/articles/metid.html