## Contact person: Brittany Suttner bsuttner3@gatech.edu +1-414-403-4218 ## Format of data file: excel spreadsheet ## Location where data collected: Georgia and California (USA) ## Time period during where data were collected Fecal samples were collected in 2017 Sediment samples were collected 2013-2014 ## File information: 2 total datafiles (1 excel spread sheet containing processed/analysed data + 1 README file) The first 3 sheets are supplementary data files for chapter 3: Ch3_S1 contains information on the metagenome assembled genomes that were assembled from fecal metagenomes. Ch3_S2 contains information on the manual groupings of differentially abundant genes between cow, pig, and human gut metagenomes. Ch3_S3 contains infomation on the manual groupings of differentially abudnant genes between animal gut and day 7 mesocosm metagenomes. The last 2 sheets are supplementary data files for chapter 4: Ch4_S1 contains information on the manual groupings of differentially abundant functional genes between sediment metagenomes from different locations. Ch4_S2 contains information on the manual groupings of differentially abundant taxa between sediment metagenomes from different locations. ## Site abbreviations: Ch4_S1 and Ch4_S2 columns refer to sediment metagenomes whose names are indicative of the location and date of sediment sample collection for “T” and “G” columns only. T = TownCreek; G=Gabilan; GC= upstream Gabilan; TC= upstream TownCreek; WS = West Salinas ## Variable information: Ch3_S2, Ch3_S3 columns A-E are KEGG fucnional gene categories, the remaining columns are metagenones. The values in these columns is the abundance (read counts) of each KEGG funcitonal gene. Ch4_S1, Ch4_S2 column A is either the SEED functional gene or taxon. The remaining columns are metagenomes. The values in these columns are the gene or taxon abundance (read counts) ## Software: Microsoft excel ## Methods: Metagenomic datasets were processed and analyzed following methods described in the main text of the thesis. In short metagenomic short reads were trimmed and filtered for quality with the MiGA package. Short reads or genes were annotated against the UniProt or GreenGenes databases using BlastP and matches were filtered for quality. The number of reads matching to each reference gene was summed and reported in the tables. The sum of the read counts for each funcitonal gene or taxonmic grouping are reported in bold. ## Last Modified: 2020-11