scEnhancer: a single-cell enhancer resource with gene annotation across hundreds of tissue/cell types in three species

Other enhancer resources

scEnhancer listed most databases related with enhancers and the computational predictors for enhancers or enhancer-gene interactions. Comparing with these resources, scEnhancer gave a comprehensive definition for enhancers in single-cell level.Following are the classifications for these resources:

Public Databases

Computational Tools

Public Databases

1. EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species. (Tianshun G, et al., 2020).

2. SEA version 3.0: a comprehensive extension and update of the Super-Enhancer archive. (Chuangeng C, et al., 2020).

3. ENdb: a manually curated database of experimentally supported enhancers for human and mouse. (Xuefeng B, et al., 2020).

4. dbInDel: a database of enhancer-associated insertion and deletion variants by analysis of H3K27ac ChIP-Seq. (Moli H, et al., 2020).

5. CancerEnD: A database of cancer associated enhancers. (Rajesh K, et al., 2020).

6. SEdb: a comprehensive human super-enhancer database. (Yong J, et al., 2019).

7. HACER: an atlas of human active enhancers to interpret regulatory variants. (Wang J, et al., 2019).

8. RAEdb: a database of enhancers identified by high-throughput reporter assays. (Zena C, et al., 2019).

9. HEDD: Human Enhancer Disease Database. (Zhen W, et al., 2018).

10. DiseaseEnhancer: a resource of human disease-associated enhancer catalog. (Zhang G, et al., 2018).

11. TiED: a free user-friendly tissue-specific enhancer database. (Xiong L, et al., 2018).

12. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. (Simon F, et al., 2017).

13. SEA: a super-enhancer archive. (Yanjun W, et al., 2016).

14. DENdb: database of integrated human enhancers. (Haitham A, et al., 2015).

15. dbSUPER: an integrated database of super-enhancers in mouse and human genome (Aziz Khan, et al., 2015).

16. EMAGE: a freely available database of in situ gene expression patterns that allows users to perform online queries of mouse developmental gene expression (Richardson L, et al., 2014).

17. cis-Decoder: a Drosophila genome-wide conserved sequence database to identify functionally related cis-regulatory enhancers (Thomas Brody, et al., 2012).

18. ZETRAP 2.0: an updated online database of novel Zebrafish Enhancer TRAP transgenic lines (Kondrychyn I, et al., 2011).

19. zTrap: a database of zebrafish gene trap and enhancer traps (Kawakami K, et al., 2010).

20. PEDB: a mammalian promoter/enhancer database (Kumaki Y, et al., 2008).

21. VISTA Enhancer Browser: a central resource for experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice (Visel A, et al., 2007).

22. EI cENHs: a resource of candidate tissue-speicific enhancers in human and mouse (Pennacchio LA, et al., 2007).

23. ZETRAP: a database of Zebrafish transgenic Enhancer TRAP lines (Choo BG, et al., 2006).

24. GETDB: a database compiling expression patterns and molecular locations of a collection of gal4 enhancer traps (Hayashi S, et al., 2002).

Computational Tools

1. pCRMeval: a pipeline for in silico evaluation of any enhancer prediction tools that are flexible enough to be applied to the Drosophila melanogaster genome. (Hasiba A, et al., 2019).

2. Enhancer-CRNN: Enhancer prediction with histone modification marks using a hybrid neural network model. (Kleftogiannis D, et al., 2019).

3. eHMM: a supervised hidden Markov model designed to learn the molecular structure of promoters and enhancers. (Zehnder T, et al., 2019).

4. EnhancerDBN: A new method for enhancer prediction based on deep belief network. (AP Singh, et al., 2018).

5. Sequence based predictor: prediction of enhancer regions from DNA random walk. (AP Singh, et al., 2018).

6. JEME: a new method for determining the target genes of transcriptional enhancers in specific cells and tissues. (Qin C, et al., 2017).

7. TargetFinder: a computational method that reconstructs regulatory landscapes from genomic features along the genome. (Sean W, et al., 2016).

8. RIPPLE: a general computational framework for predicting enhancers. (Roy S, et al., 2015).

9. DEEP: a general computational framework for predicting enhancers. (Kleftogiannis D, et al., 2015).

10. DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications (Lu Y, et al., 2015).

11. IM-PET: A useful tool using integrated methods for predicting enhancer targets (He B, et al., 2014).

12. EnhancerFinder: a tool with a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity (Erwin GD, et al., 2014). The tool is not available.

13. RFECS: Predicting protein sumoylation sites from sequence features (Rajagopal N, et al., 2013).

14. EMdeCODE: a novel predictor capable of reading words of epigenetic code to predict enhancers. (Santoni FA, 2013).

15. WashU Epigenome Browser: a next-generation genomic data visualization system for human and model organisms to support multiple types of long-range genome interaction data (Xin Z, et al., 2013).

16. ChromaGenSVM: a tool with optimum combinations of specific histone epigenetic marks to predict enhancers (Fernandez M, et al., 2012).

17. ReLA: a local alignment search tool for the identification of distal and proximal gene regulatory regions and their conserved transcription factor binding sites (Gonzalez S, et al., 2012).

18. p300enhancer: A predictor of EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features (Lee D, et al., 2011).

19. CSI-ANN: A tool to identify regulatory DNA elements using chromatin signatures and artificial neural network (Firpi HA, et al., 2010).