Source Data: Intrahost dynamics, together with genetic and phenotypic effects predict the success of viral mutations

Source data for manuscript titled 'Intrahost dynamics, together with genetic and phenotypic effects predict the success of viral mutations' (BioRXiv: https://doi.org/10.1101/2024.10.18.619070). File descriptions mutation_stats.zipTraining data for machine learning models. *.dprime_stats.csv --> Includes all predictor types (physiochemical, intrahost, phenotypic, linkage)*.spike.stats.csv --> Data for spike only modelsX_to_Y.*.stats.csv --> Data for cross-dataset models. Intrahost data for timeframe X was used and fitness was estimated from after timeframe X to before timeframe Y. intrahost_linkage_statistics.zipIntrahost linkage statistics calculated for each intrahost dataset. observed_Dprime.all_time.gt1000.csv.gzInterhost linkage statistics calculated across the entire GISAID dataset. all_sra_metadata.csvBiosample metadata for all sequencing libraries considered in this study. missense_freq.filt.csv.gzIntrahost SAV frequencies for all datasets. tonkin.missense_freq.filt.dedup.csv.gzIntrahost SAV frequencies for the Tonkin-Hill dataset. aa_properties.blosum62.csvPhysiochemical properties for all amino acid substitutions estimated in silico with BioPython. parsed_dms_phenotypes.csvParsed Bloom lab deep mutational scanning estimates. 6vxx.filt.SASA.csvRelative solvent accessibility of residues in trimeric SARS-CoV-2 spike (PDB:6vxx), as estimated using the Shrake–Rupley algorithm in BioPython. cov-constellations.parsed_all.csvMutation constellations associated with the different lineages (obtained from: https://github.com/cov-lineages/constellations; https://cov-lineages.org/constellations.html).