Additional file 1: Table S1. of Spherical: an iterative workflow for assembling metagenomic datasets

Effect of kmer size on assembly. For each dataset (column 1) the alignment rate (%) (column 3) of raw data aligning back to assembly produced using different kmers (column 2) was assessed. Table S2. Effect of kmer size across iterations of assembly using the simulated dataset. The percentage of raw data aligning to each iterations assembly was identified and iterations stopped once the alignment rate was under 0.1%. Table S3. Analysis of the quality of contigs produced in each iteration of assembling the simulated dataset using contig scores. The contig score identifies the percentage accuracy of a contig compared to the genomes used to create the simulated metagenome. We present the percentage of contigs from each iterations assembly with contig scores gereater than 95 or less than 50. Table S4. Percentage of reads assigned to each of the 400 genomes within the simulated dataset, base assembly and Spherical assembly of the simulated dataset. Table S5. Assembly statistics comparing dataset assemblies for each method. The first column indicates the dataset utilized whilst the second column identified the assembly methodology. Due to Spherical having a sub-sampling option the size of the sample utilized by Spherical was stated for each assembly in column 5. The final 6 columns provide information on the computational needs for each assembly (RAM usage) as well as statistics about the produced assemblies e.g. number of contigs and alignment (%). Table S6. The number of reads aligning to genes within the Spherical iterations assembling each dataset. Figure S1. The taxonomic variations at the phylum level between each experimental assembly method for each dataset. Each bar represents the number of reads that could be assigned to a taxonomic Phyla within each assembly method for the datasets. The legend identifies which Phyla is represented by each colour. (ZIP 562 kb)