Spherical: an iterative workflow for assembling metagenomic datasets

BMC Bioinformatics

Table 2 Assembly statistics comparing dataset assemblies for each method

Dataset	Method	RAM usage (Gb)	Alignment (%)	False bases (%)	Longest contig	Number of contigs
Cecum	Normalised	119	29.5	0.01	831	103,618
	Base assembly	1	29.5	0.01	831	103,618
	Metavelvet	2	29.1	0.07	831	103,618
	Spherical (1)	2	30.9	0.04	831	138,995
Oral	Normalised	14	8.1	0.01	3337	1,825,177
	Base assembly	25	13.0	0.02	4548	1,178,611
	Metavelvet	15	13.0	0.07	4548	1,178,611
	Spherical (1)	5	24.6	0.19	2380	1,053,802
Ground water	Normalised	361	52.8	3.86	117,274	5,721,819
	Base assembly	376	52.0	3.84	117,274	5,772,465
	Metavelvet	376	52.0	4.04	117,274	5,772,461
	Spherical (1)	377	59.7	2.89	117,274	13,312,643
	Spherical (0.25)	129	51.5	3.50	104,353	7,851,021
	Spherical (0.033)	107	49.8	3.78	53,836	7,145,998

The first column indicates the dataset utilized whilst the second column identified the assembly methodology. To identify the different subsampling amounts during each Spherical assembly the subsample size is stated in brackets in the method column. The final 5 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 (%)

ISSN: 1471-2105