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Table 3 'Regional variation', 'multiple regression' and 'multivariate analysis' toolsets in Galaxy

From: A genome-wide view of mutation rate co-variation using multivariate analyses

Data pre-processing tools

 

   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

 

   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

 

   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

 

   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

  1. RCVE, relative contribution to variability explained.