From: Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness
# | Category | Tool |
---|---|---|
A | Clustering based | i. LOOPbit [23] |
 |  | ii. LASCA [24] |
 |  | iii. cLoops [25] |
 |  | iv. cLoops2 [26] |
 |  | v. HiCCUPS [48] |
B | Probability-based | i. HiCExplorer [27] |
 |  | ii. HiC-ACT [28] |
 |  | iii. FitHiC [29] |
 |  | iv. FitHiC2 [30] |
 |  | v. FitHiChIP [31] |
 |  | vi. GOTHiC [32] |
 |  | vii. HiC-DC [33] |
 |  | viii. ZipHiC [34] |
 |  | ix. NeoLoopFinder [35] |
 |  | x. HMRF Bayesian caller [36] |
C | Classification based | i. FIREcaller [37] |
 |  | ii. Peakachu [38] |
D | Computer vision based | i. Mustache [40] |
 |  | ii. Chromosight [42] |
 |  | iii. SIP & SIPMeta [41] |
 |  | iv. DeepLoop [49] |
E | Pile-up procedure based | i. Coolpup.py [39] |