Additional file 1 of Enhanced correlation-based linking of biosynthetic gene clusters to their metabolic products through chemical class matching
Creators: name: Louwen, Joris J. R.
name: Medema, Marnix H.
name: van der Hooft, Justin J. J.
Publisher: name: figshare
Year: 2023
Subjects: subject: Biochemistry
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Figure S1. MIBiG classes matched to ClassyFire superclasses. Figure S2. antiSMASH-predicted classes matched to NPClassifier superclasses, where matches with counts above five are shown. Figure S3. antiSMASH-predicted classes matched to ClassyFire classes, where matches with counts above five are shown. Figure S4. Number of links with MS/MS spectra per GCF for all the GCFs in the Streptomyces/Salinispora dataset after using standardised Metcalf scoring in combination with NPClassScore filtering at varying cut-offs for the NPClassScore. The standardised Metcalf score cut-off was 2.5. Figure S5. Number of links with MS/MS spectra per GCF for all the GCFs in the Cyanobacteria dataset after using standardised Metcalf scoring in combination with NPClassScore filtering at varying cut-offs for the NPClassScore. The standardised Metcalf score cut-off was 2.5. Figure S6. Number of links with MS/MS spectra per GCF for all the GCFs in the Nocardia dataset after using standardised Metcalf scoring in combination with NPClassScore filtering at varying cut-offs for the NPClassScore. The standardised Metcalf score cut-off was 2.5. Figure S7. Histograms showing the number of candidate MS/MS spectrum links per GCF in the (a) Cyanobacteria dataset and (b) Nocardia dataset after co-occurrence scoring (standardised Metcalf), and after NPClassScore filtering with a cut-off of 0.25. The bin sizes are 5 in (a) and 25 in (b). The results highlight how NPClassScore narrows down the number of candidate links for most GCFs. Figure S8. Number of retained validated MS/MS spectrum links versus the percentage of filtered out candidate links per GCFs with different NPClassScore cut-offs. The percentage of filtered out candidate links is an average over the three datasets. Table S1. Scoring table from NPClassScore showing the scores from MIBiG classes to NPClassifier pathways. Table S2. Translation of BiG-SCAPE to MIBiG classes. Table S3. Translation of antiSMASH classes from all other versions to match antiSMASH v5 classes. Table S4. Information about the contents of the three datasets. We included the number of spectra in our versions of the datasets separately as some spectra present in the molecular networks occurred in, for example, control samples, or samples we did not use. Table S5. The average number of MS/MS spectrum links per GCF for each dataset after NPClassScore filtering with different cut-offs, along with the percentual decrease in the number of links. In this case, the MS/MS spectra without structure-based predictions are automatically excluded. Table S6. Counts for the number of spectra with certain ClassyFire superclasses as predicted by MolNetEnhancer (MNE), MNE for spectra below 850 Da, and CANOPUS. Counts are coloured from white to red, white being the lowest count and red being the highest count for each column. Table S7. Counts of the ClassyFire superclass predictions for the 6,606 spectra that could be predicted by both MolNetEnhancer (MNE), and CANOPUS, showing the MNE predictions and CANOPUS predictions. Counts are coloured from white to red, white being the lowest count and red being the highest count for each column. Table S8. The average number of candidate links per GCF for each dataset after NPClassScore filtering with different cut-offs, along with the percentual decrease in the number of links. In this case, the MS/MS spectra without structure-based predictions are automatically included.