Table 4.
Data resources providing quantitative scores for the deleterious impact of non-coding variants on gene regulation
| Resource | Training Methods | INDEL | Stand-alone Tools | Web Service | Batch Data Download | Pubmed Reference |
|---|---|---|---|---|---|---|
| Basset | Deep Convolutional Neural Network | No | Python | No | No | 27197224 |
| CADD | Linear Kernel Support Vector Machine | Yes | Python | Yes | Yes | 24487276 |
| CScape | Sequential Learning | No | Python | Yes | Yes | 28912487 |
| DANN | Deep Neural Network | No | Python | No | Yes | 25338716 |
| DanQ | Deep Learning | No | Python | No | No | 27084946 |
| DeepSEA | Deep Convolutional Neural Network | Yes | – | Yes | Yes | 26301843 |
| deltaSVM | Gapped k-mer Support Vector Machine | No | R & C++ | No | No | 26075791 |
| EIGEN | Unsupervised Spectral Learning | No | R | No | Yes | 26727659 |
| FATHMM-indel | Gaussian Kernel Support Vector Machine | Yes | – | Yes | Yes | 28985712 |
| FATHMM-MKL | Multiple Kernel Learning | No | Python, Perl | Yes | Yes | 25583119 |
| FATHMM-XF | Extended Features | No | Python | Yes | Yes | 28968714 |
| FunSeq2 | Weighted Scoring | No | Perl | Yes | Yes | 25273974 |
| GAVIN | Gene-specific Calibration | Yes | Java, MOLGENIS | Yes | No | 28093075 |
| GenoCanyon | Unsupervised Learning | No | R | Yes | Yes | 26015273 |
| GWAVA | Random Forest Classifier | Yes | Python | Yes | Yes | 24487584 |
| LINSIGHT | Probabilistic Modeling/GLM | No | C++ | Yes | Yes | 28288115 |
| PredictSNP2 | Consensus Scoring | No | N/A | Yes | Yes (disease-specific) | 27224906 |
| PRVCS | Composite Likelihood | No | Java, Perl | No | Yes | 27273672 |
| RSVP | Ensemble of Decision Trees | No | C++ | No | Upon Request | 27406314 |
| SNVrap | Integration of 18 Resources | No | N/A | Yes | No | 25308971 |