Evaluation of the impact of Illumina error correction tools on de novo genome assembly

BMC Bioinformatics

Table 1 List of EC tools evaluated in this paper

EC tool	Algorithm	Data structure	Indel support	Accuracy analysis	Assembly analysis	Year
ACE	k-mer	k-mer trie		Read level	-	2015
BayesHammer	k-mer	Hamming graph		Read level	SPAdes	2013
BFC	k-mer	Bloom filter		Read level	Velvet, ABySS [34]	2015
BLESS 2	k-mer	Bloom filter		Read level	Gossamer [35]	2016
Blue	k-mer	Hash table	\(\checkmark \)	Read level	Velvet	2014
Fiona	MSA	Suffix tree	\(\checkmark \)	Base level	-	2014
Karect	MSA	Partially-ordered graph	\(\checkmark \)	Read, base level	Velvet, SGA, Celera [36]	2015
Lighter	k-mer	Bloom filter		Read level	Velvet	2013
Musket	k-mer	Bloom filter		Base level	SGA	2013
RACER	k-mer	Hash table		Read level	-	2013
SGA-EC	MSA	Suffix array		Read level	SGA	2012
Trowel	k-mer	Hash table		Read, base level	Velvet, SOAPdenovo [37]	2014

The algorithmic approach is either k-mer spectrum based (‘k-mer’) or multiple sequence alignment based (‘MSA’). Tools can be further classified according to data structure and heuristics used. Some tools are able to correct insertions or deletions. In their accompanying publication, all tools were assessed directly on their ability to reduce error rate, either on the read or base level. Most tools did not use assembly analyses with modern assemblers in their evaluation. SPAdes was used for the evaluation of BayesHammer, but no comparison was made with assembly results from uncorrected data

ISSN: 1471-2105