Table 1 The repeat finders used to detect repeat sequences incorporate a variety of approaches, ranging from library-based masking (e.g. RepeatMasker) to machine-learning using the reference genome (e.g. RED)
Software | Type of repeat | Method | References |
|---|---|---|---|
RED | Tandem repeats, TEs | Machine-learning | [20] |
LTRharvest | Long terminal repeat retrotransposons | Signature-based | [21] |
Tallymer | Tandem repeats, TEs (plants) | De novo based on k-mers | [22] |
MiteFinderII | Miniature inverted repeat TEs | De novo based on k-mers | [23] |
TRF | Low-complexity regions, tandem repeats | De novo | [24] |
Tantan | Low-complexity regions, short tandem repeats | De novo | [25] |
MsDetector | Microsatellites | Learning-based | [26] |
RepeatModeler2 | Tandem repeats, TEs | Consensus | [27] |
sDust | Low-complexity regions, tandem repeats | De novo | [28] |
Dustmasker | Low-complexity regions, tandem repeats | De novo | [29] |
mReps | Low-complexity regions, tandem repeats | De novo | [30] |
RepeatMasker | Tandem repeats, TEs | Library-based | A.F.A. Smit, R. Hubley & P. Green RepeatMasker, http://repeatmasker.org |