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Table 1 List of EC tools evaluated in this paper

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

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
  1. 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