From: A genome-wide view of mutation rate co-variation using multivariate analyses
Data pre-processing tools | Â |
   Make windows | To partition genome into windows of a user-specified size |
   Feature coverage | To apportion various genomic features in genomic windows |
   Filter nucleotides | To identify and mask low-quality nucleotides from alignments based on a quality score cutoff specified by the user |
   Mask CpG/non-CpG sites | To identify and mask CpG/non-CpG-containing sites from alignments |
Tools for identifying mutations and computing their rates | Â |
   Fetch Indels | To identify insertions and deletions from three-way alignments using a user-specified outgroup |
   Estimate indel rates | To estimate indel rates by aggregating insertions and deletions in genomic regions specified by the user |
   Fetch substitutions | To identify nucleotide substitutions from pair-wise alignments |
   Estimate substitution rates | To estimate substitution rate according to Jukes-Cantor JC69 model |
   Extract orthologous microsatellites | To fetch microsatellites using SPUTNIK, and detect orthologous repeats |
   Estimate microsatellite mutability | To estimate microsatellite mutability by grouping (and sub-grouping) repeats based on their size, unit and motif |
Multiple regression tools | Â |
   Perform linear regression | To construct a linear regression model using the user-selected predictors and response variables |
   Perform best-subsets regression | To examine all of the linear regression models that can be created from all possible combinations of the predictors variables |
   Compute RCVE | To compute RCVE (relative contribution to variance) for all possible variable subsets |
Multivariate analysis tools | Â |
   PCA | To perform PCA on a set of variables |
   CCA | To perform CCA on two sets of variables |
   Kernel PCA | To perform kernel PCA on a set of variables, using a user-specified kernel |
   Kernel CCA | To perform kernel CCA on two sets of variables, using a user-specified kernel |