| A novel computational methodology which uses a Support Vector Machine (SVM) with k-mer sequence features (kmer-SVM) to identify predictive combinations of short TF binding sites which determine the tissue specificity of these genomic assays. |
hg clone https://toolshed.g2.bx.psu.edu/repos/cafletezbrant/kmersvm
| Name | Description | Version | Minimum Galaxy Version |
|---|---|---|---|
| using SVM weights | 1.0.0 | any | |
| using kmerSVM predictions | 1.0.0 | any | |
| using random sampling from genomic DNA | 1.0.0 | any | |
| provide length, gc content, and repeat fraction of each sequence | 1.0.0 | any | |
| Tomtom tool for motif searching | 1.0.0 | any | |
| using kmerSVM predictions | 1.0.0 | any | |
| Convert kmers to MEME format for motif finding by Tomtom | 1.0.0 | any | |
| split genome into overlapping segments for feature prediction | 1.0.0 | any | |
| on regulatory DNA sequences | 1.0.0 | any | |