Table 1.
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 | Read level | Velvet | 2014 | |
Fiona | MSA | Suffix tree | Base level | - | 2014 | |
Karect | MSA | Partially-ordered graph | 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