Exploiting predictive metabolomics of pearl millet phenotypic traits using untargeted profiling across a Brazilian germplasm panel  

Mariana Pinheiro Costa Pimentel 1, 2, 3, 4, Alexandre Martins Abdão dos Passos 5, Sylvain Prigent 3, 4, Cédric Cassan 3, 4, Flavio Dessaune Tardin 5, Mariana Simões Larraz Ferreira 1, 2*, Pierre Pétriacq 3, 4*, Millena C. Barros Santos 3, 4, 6*

1Laboratory of Bioactives, Food and Nutrition Graduate Program (PPGAN), Federal University of the State of Rio de Janeiro (UNIRIO), 22290-240, Rio de Janeiro, RJ, Brazil
2Laboratory of Protein Biochemistry - Center of Innovation in Mass Spectrometry (LBP-IMasS), UNIRIO, 22290-240 Rio de Janeiro, RJ, Brazil
3Univ. Bordeaux, INRAE UMR1332 Biologie du Fruit et Pathologie, 33882 Villenave d’Ornon, France
4Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140 Villenave d’Ornon, France
5Embrapa Maize & Sorghum, 35702-098, Sete Lagoas, MG, Brazil
6 Currently affiliation: INRAE, Avignon University, UMR408 SQPOV, F-84000 Avignon, France 

*authors for correspondence: millena.barros-santos@inrae.fr, Pierre.Petriacq@inrae.fr, mariana.ferreira@unirio.br								
								
								
MS-DIAL ver. 4.9.221218								
								
#Project								
MS1 Data type	Profile							
MS2 Data type	Profile							
Ion mode	Negative							
Target	Metablomics							
Mode	ddMSMS							
								
#Data collection parameters								
Retention time begin	0							
Retention time end	18							
Mass range begin	50							
Mass range end	1500							
MS2 mass range begin	50							
MS2 mass range end	1500							
								
#Centroid parameters								
MS1 tolerance	0.01							
MS2 tolerance	0.025							
								
#Isotope recognition								
Maximum charged number	2							
								
#Data processing								
Number of threads	10							
								
#Peak detection parameters								
Smoothing method	LinearWeightedMovingAverage							
Smoothing level	4							
Minimum peak width	5							
Minimum peak height	30000							
								
#Peak spotting parameters								
Mass slice width	0.05							
Exclusion mass list (mass & tolerance)								
								
#Deconvolution parameters								
Sigma window value	0.5							
MS2Dec amplitude cut off	0							
Exclude after precursor	TRUE							
Keep isotope until	0.5							
Keep original precursor isotopes	FALSE							
								
#MSP file and MS/MS identification setting								
MSP file	C:\Users\mpinheiroco\Documents\AllDB_051222_retip_neg.msp							
Retention time tolerance	100							
Accurate mass tolerance (MS1)	0.01							
Accurate mass tolerance (MS2)	0.025							
Identification score cut off	80							
Using retention time for scoring	FALSE							
Using retention time for filtering	FALSE							
								
#Text file and post identification (retention time and accurate mass based) setting								
Text file								
Retention time tolerance	0.1							
Accurate mass tolerance	0.01							
Identification score cut off	85							
								
#Advanced setting for identification								
Relative abundance cut off	0							
Top candidate report	FALSE							
								
#Adduct ion setting								
[M-H]-								
[M-H2O-H]-								
[M+Na-2H]-								
[M+FA-H]-								
[M+Hac-H]-								
[2M-H]-								
[2M+FA-H]-								
[2M+Hac-H]-								
[3M-H]-								
[M-2H]2-								
[M-3H]3-								
								
#Alignment parameters setting								
Reference file	E:\Computador METABOHUB - Mari\Aquisicoes BAG\MS DIAL TEST 2\MET_2023_M06_MP_neg_ddMS2-30k_399_QC_BAG.raw							
Retention time tolerance	0.05							
MS1 tolerance	0.01							
Retention time factor	0.5							
MS1 factor	0.5							
Peak count filter	0							
N% detected in at least one group	0							
Remove feature based on peak height fold-change	FALSE							
Sample max / blank average	5							
Sample average / blank average	5							
Keep identified and annotated metabolites	TRUE							
Keep removable features and assign the tag for checking	TRUE							
Gap filling by compulsion	TRUE							
								
#Tracking of isotope labels								
Tracking of isotopic labels	FALSE							
								
#Ion mobility								
Ion mobility data	FALSE