BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly

BMC Bioinformatics

Table 2 Comparison of performance of BIGMAC with other post-processing tools for long read assemblies

Data	Method	# Mis	N50	N75	N50/# Mis	N75/# Mis
1	HGAP	18	818655	274801	45481	15267
	BIGMAC	7	4352719	274801	*621817*	*39257*
	FinisherSC	32	2531294	415024	79103	12970
	PBJelly	19	4642330	418480	244333	22025
	SSPACE_LR	32	4657611	493683	145550	15428
2	HGAP	187	397611	38471	2126	206
	BIGMAC	28	397611	75666	*14200*	*2702*
	FinisherSC	192	654163	43018	3407	224
	PBJelly	271	1585584	61775	5851	228
	SSPACE_LR	255	1568442	95133	6151	373
3	HGAP	26	257044	82370	9886	3168
	BIGMAC	14	359704	99878	25693	*7134*
	FinisherSC	25	996532	97964	39861	3919
	PBJelly	27	1103847	128718	*40883*	4767
	SSPACE_LR	43	1266912	290104	29463	6747

BIGMAC shows the largest N75/# Mis on all three datasets and largest N50/# Mis on two out of three datasets. We also remark that BIGMAC is the only tool that can improve N50 and N75 while reducing the number of mis-assemblies. Note that # Mis is an abbreviation for the number of mis-assemblies

ISSN: 1471-2105