RVD-to-DNA-binding-specificity weights

One row per RVD, giving its relative binding weight for each of the four bases. This is the table tales_compare_functal stacks, one row per repeat, to build an array's position weight matrix.

Usage

rvd_dna_specificity

Format

A tibble with 404 rows and 5 columns:

rvd

The two-letter RVD code. "N*" and "H*" are RVDs whose residue 13 is missing; "OO" is position 0, the base just before the first repeat's target, where TALEs prefer a T; "XX" is the flat fallback row used for an unrecognised RVD.

A, C, G, T

Relative binding weight for that base. The weights do not sum to one: tales_compare_functal hands them to create_motif as raw counts (type = "PCM"), which normalises internally.

Details

Taken verbatim from QueTAL FuncTAL's own table (Info/2014mat18 in FuncTAL 1.1, by A. L. Pérez-Quintero, redistributed with permission): the values are unchanged, only a header and column names added. Conceptually related to but distinct from the internal rvdSimDf used by tales_align's RVD scoring: that one is a derived RVD-vs-RVD similarity (a correlation between two RVDs' base-preference profiles, itself built from TALVEZ's much smaller 17-RVD mat1), used to score repeat substitutions during sequence alignment. This one is the raw, per-RVD base preference itself, one level upstream, used to build a whole array's binding-specificity model for comparison against another array's. The two tables serve different consumers and cannot replace each other.

References

Pérez-Quintero A.L. et al. (2015). QueTAL: a suite of tools to classify and compare TAL effectors functionally and phylogenetically. Frontiers in Plant Science 6, 545. doi:10.3389/fpls.2015.00545

See also

tales_compare_functal, its only consumer.

Other pairwise distances: [.pairwise_distances(), as.matrix.pairwise_distances(), distances_assert_square(), distances_restrict(), is_pairwise_distances(), pairwise_distances(), tales_assign_domain_codes(), tales_compare_distal(), tales_compare_functal(), tales_domain_distances(), tales_group_hclust(), tales_group_kmedoids(), tales_tale_distances(), validate_pairwise_distances()

Examples

rvd_dna_specificity[rvd_dna_specificity$rvd %in% c("HD", "NI", "NG", "NN"), ]
#> # A tibble: 4 × 5
#>   rvd       A     C     G     T
#>   <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 HD       15    50     5     5
#> 2 NG        5    10     5    50
#> 3 NI       50    10     5     5
#> 4 NN       30    10    30     2