Convert a tales object to a list of predicted DNA-binding-specificity PWMs

One universalmotif position weight matrix per array, built by looking up each repeat's RVD in rvd_dna_specificity and stacking the rows in repeat order. The conversion tales_compare_functal is built on, exposed on its own so any universalmotif function (compare_motifs(), motif_tree(), view_motifs(), scan_sequences(), merge_motifs(), ...) can be run directly on these TALE binding models.

Usage

tales_to_universalmotif(x)

Arguments

x

A tales object carrying an rvd column.

Value

A named list of universalmotif objects, one per array, named by array_id: a plain list, which is what compare_motifs()/motif_tree() accept.

Details

Only rvd, in repeat order, is used (via tales_rvd_strings, which drops the two termini by default): DNA-binding specificity is a property of the repeat region, and QueTAL's FuncTAL, the tool this table comes from, scored repeats only. An RVD absent from rvd_dna_specificity is given a flat, uninformative row, so an unusual RVD lowers the specificity of the comparison without causing an error.

See also

tales_compare_functal(), the one comparison built on this; tales_rvd_strings(), the sibling projection at the string layer.

Other tales projections: tales_coded_strings(), tales_domain_codes(), tales_get_dna_seq(), tales_get_protein_seq(), tales_rvd_strings()

Examples

x <- tales_from_telltales(system.file("extdata", "tellTaleExampleOutput",
                                      package = "tantale"))
motifs <- tales_to_universalmotif(x)
motifs[[1]]
#> 
#>        Motif name:   ROI_00001
#>          Alphabet:   ACGT
#>              Type:   PCM
#>          Total IC:   15.98
#>       Pseudocount:   0
#>      Target sites:   72
#> 
#>   [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> A   30    5   30   15   15   50    1    5   15    50     5    30    15    50
#> C   10   10   10   50   50   10    4   10   50    10    10    10    50    10
#> G   30    5   30    5    5    5    1    5    5     5     5    30     5     5
#> T    2   50    2    5    5    5    4   50    5     5    50     2     5     5
#>   [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> A     5    50     5    30     5    15    50    50     5    15    30     5
#> C    10    10    10    10    10    50    10    10    10    50    10    10
#> G     5     5     5    30     5     5     5     5     5     5    30     5
#> T    50     5    50     2    50     5     5     5    50     5     2    50