Nothing
R/zzz.R
In TmCalculator: Genome-Wide Nucleic Acid Melting Temperature Profiling and Multi-Omics Integration
Defines functions
.verify_nn_tables
.build_all_tm_tables
.complete_nn_rc
# =============================================================================
# .build_all_tm_tables() - updated with reverse complement completion (Bug 9 fix)
#
# WHAT CHANGED:
# Added .complete_nn_rc() helper that adds the 6 missing reverse complement
# rows to any standard 17-row DNA/RNA NN table.
#
# The 16 Watson-Crick NN pairs split into:
# 10 asymmetric pairs: each has a distinct RC partner → 6 unique new rows needed
# AA/TT ↔ TT/AA (TT/AA missing)
# CA/GT ↔ TG/AC (TG/AC missing)
# GA/CT ↔ TC/AG (TC/AG missing)
# CT/GA ↔ AG/TC (AG/TC missing)
# GT/CA ↔ AC/TG (AC/TG missing)
# GG/CC ↔ CC/GG (CC/GG missing)
# 6 self-complementary palindromes: RC maps to themselves → no new rows needed
# AT/TA, TA/AT, CG/GC, GC/CG, CA/GT→already, GC/CG→already
# (Actually: AT/TA, TA/AT, CG/GC, GC/CG are true palindromes)
#
# RNA_DNA_NN_Sugimoto_1995 already contains all 16 + 6 RC rows → skip.
# RNA_NN_Chen_2012 has extra wobble rows → complete only the standard 16.
# =============================================================================
# -- Helper: add the 6 missing reverse complement rows to a standard NN table --
#
# There are exactly 16 valid Watson-Crick dinucleotide keys (XY/X'Y' where
# X' = complement of X, Y' = complement of Y). Published NN tables list only
# 10 of these - the 6 missing ones are thermodynamically equivalent to an
# existing row when read from the other strand direction.
#
# The complete mapping (verified by enumeration):
# TT/AA <- same ΔH/ΔS as AA/TT (homodimer palindrome)
# AC/TG <- same ΔH/ΔS as CA/GT (RC pair)
# AG/TC <- same ΔH/ΔS as GA/CT (RC pair)
# TC/AG <- same ΔH/ΔS as CT/GA (RC pair)
# TG/AC <- same ΔH/ΔS as GT/CA (RC pair)
# CC/GG <- same ΔH/ΔS as GG/CC (homodimer palindrome)
#
# @param tbl Matrix with dimnames - a standard NN table (17+ rows).
# @param skip_rows Row names to exclude from completion (init/sym rows and any
# extra wobble rows like in RNA_NN_Chen_2012).
# @return The input matrix with up to 6 new rows appended.
#
.complete_nn_rc <- function(tbl,
skip_rows = c("init", "init_A/T", "init_G/C",
"init_oneG/C", "init_allA/T",
"init_5T/A", "sym")) {
# Fixed mapping: new key -> source key to copy values from
# This is the complete, verified set - do not derive algorithmically
# (TT/AA and CC/GG are self-palindromes under the RC formula and would
# be missed by a naive "RC != self" filter).
missing_map <- c(
"TT/AA" = "AA/TT",
"AC/TG" = "CA/GT",
"AG/TC" = "GA/CT",
"TC/AG" = "CT/GA",
"TG/AC" = "GT/CA",
"CC/GG" = "GG/CC"
)
# Only add rows not already present (handles RNA_DNA_NN_Sugimoto_1995
# which was published with the full 16-pair set)
to_add <- missing_map[!names(missing_map) %in% rownames(tbl)]
if (length(to_add) == 0L) return(tbl) # already complete
# Verify source rows exist before copying
missing_sources <- to_add[!to_add %in% rownames(tbl)]
if (length(missing_sources) > 0L)
stop(sprintf("Source rows missing from table: %s",
paste(missing_sources, collapse=", ")))
new_rows <- tbl[to_add, , drop = FALSE]
rownames(new_rows) <- names(to_add)
rbind(tbl, new_rows)
}
# -- Main table builder --------------------------------------------------------
.build_all_tm_tables <- function() {
nn_col <- c("left", "right")
nn_row_std <- c("init", "init_A/T", "init_G/C", "init_oneG/C", "init_allA/T",
"init_5T/A", "sym",
"AA/TT", "AT/TA", "TA/AT", "CA/GT", "GT/CA",
"CT/GA", "GA/CT", "CG/GC", "GC/CG", "GG/CC")
# -- DNA NN Tables -----------------------------------------------------------
DNA_NN_SantaLucia_2004_bug <- .complete_nn_rc(matrix(c(
0.2,-5.7, 2.2,6.9, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-7.6,-21.3, -7.2,-20.4, -7.2,-21.3, -8.5,-22.7, -8.4,-22.4,
-7.8,-21.0, -8.2,-22.2, -10.6,-27.2, -9.8,-24.4, -8.0,-19.9
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_SantaLucia_2004 <- .complete_nn_rc(matrix(c(
0.2,-5.7, 2.2,6.9, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-7.6,-21.3, -7.2,-20.4, -7.2,-21.3, -8.5,-22.7, -8.4,-22.4,
-7.8,-21.0, -8.2,-22.2, -10.6,-27.2, -9.8,-24.4, -8.0,-19.9
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_Breslauer_1986 <- .complete_nn_rc(matrix(c(
0,0, 0,0, 0,0, 0,-16.8, 0,-20.1, 0,0, 0,-1.3,
-9.1,-24.0, -8.6,-23.9, -6.0,-16.9, -5.8,-12.9, -6.5,-17.3,
-7.8,-20.8, -5.6,-13.5, -11.9,-27.8, -11.1,-26.7, -11.0,-26.6
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_Sugimoto_1996 <- .complete_nn_rc(matrix(c(
0.6,-9.0, 0,0, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-8.0,-21.9, -5.6,-15.2, -6.6,-18.4, -8.2,-21.0, -9.4,-25.5,
-6.6,-16.4, -8.8,-23.5, -11.8,-29.0, -10.5,-26.4, -10.9,-28.4
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_Allawi_1998 <- .complete_nn_rc(matrix(c(
0,0, 2.3,4.1, 0.1,-2.8, 0,0, 0,0, 0,0, 0,-1.4,
-7.9,-22.2, -7.2,-20.4, -7.2,-21.3, -8.5,-22.7, -8.4,-22.4,
-7.8,-21.0, -8.2,-22.2, -10.6,-27.2, -9.8,-24.4, -8.0,-19.9
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
# -- RNA NN Tables -----------------------------------------------------------
RNA_NN_Freier_1986 <- .complete_nn_rc(matrix(c(
0,-10.8, 0,0, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-6.6,-18.4, -5.7,-15.5, -8.1,-22.6, -10.5,-27.8, -10.2,-26.2,
-7.6,-19.2, -13.3,-35.5, -8.0,-19.4, -14.2,-34.9, -12.2,-29.7
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
RNA_NN_Xia_1998 <- .complete_nn_rc(matrix(c(
3.61,-1.5, 3.72,10.5, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-6.82,-19.0, -9.38,-26.7, -7.69,-20.5, -10.44,-26.9, -11.40,-29.5,
-10.48,-27.1, -12.44,-32.5, -10.64,-26.7, -14.88,-36.9, -13.39,-32.7
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
# Chen_2012 has extra wobble rows - complete_nn_rc skips non-standard rows
# automatically and only processes the 16 standard Watson-Crick rows.
chen_rows <- c(nn_row_std,
"GT/TG","GG/TT","AG/TT","TG/AT","TT/AG","TG/GT",
"AT/TG","CG/GT","CT/GG","GG/CT","GT/CG")
RNA_NN_Chen_2012 <- .complete_nn_rc(matrix(c(
6.40,6.99, 3.85,11.04, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-7.09,-19.8, -9.11,-25.8, -8.50,-22.9, -11.03,-28.8, -11.98,-31.3,
-10.90,-28.5, -13.21,-34.9, -10.88,-27.4, -16.04,-40.6, -14.18,-35.0,
-13.83,-46.9, -17.82,-56.7, -3.96,-11.6, -0.96,-1.8, -10.38,-31.8,
-12.64,-38.9, -7.39,-21.0, -5.56,-13.9, -9.44,-24.7, -7.03,-16.8,
-11.09,-28.8
), ncol=2, byrow=TRUE, dimnames=list(chen_rows, nn_col)),
# Pass the extra wobble rows as skip so they are not RC-completed
skip_rows = c("init","init_A/T","init_G/C","init_oneG/C","init_allA/T",
"init_5T/A","sym",
"GT/TG","GG/TT","AG/TT","TG/AT","TT/AG","TG/GT",
"AT/TG","CG/GT","CT/GG","GG/CT","GT/CG"))
# -- RNA/DNA Hybrid - already complete (22 rows including all RC pairs) ------
# RNA_DNA_NN_Sugimoto_1995 was published with the full symmetric set.
# .complete_nn_rc() will find nothing to add (returns tbl unchanged).
hybrid_rows <- c("init","init_A/T","init_G/C","init_oneG/C","init_allA/T",
"init_5T/A","sym",
"AA/TT","AC/TG","AG/TC","AT/TA",
"CA/GT","CC/GG","CG/GC","CT/GA",
"GA/CT","GC/CG","GG/CC","GT/CA",
"TA/AT","TC/AG","TG/AC","TT/AA")
RNA_DNA_NN_Sugimoto_1995 <- matrix(c(
1.9,-3.9, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0,
-11.5,-36.4, -7.8,-21.6, -7.0,-19.7, -8.3,-23.9,
-10.4,-28.4, -12.8,-31.9, -16.3,-47.1, -9.1,-23.5,
-8.6,-22.9, -8.0,-17.1, -9.3,-23.2, -5.9,-12.3,
-7.8,-23.2, -5.5,-13.5, -9.0,-26.1, -7.8,-21.9
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
# No .complete_nn_rc() needed - already has all 16 unique pairs
# -- IMM, TMM, DE tables (unchanged) -----------------------------------------
imm_rows <- c(
"AG/TT","AT/TG","CG/GT","CT/GG","GG/CT","GG/TT","GT/CG","GT/TG",
"TG/AT","TG/GT","TT/AG","AA/TG","AG/TA","CA/GG","CG/GA","GA/CG",
"GG/CA","TA/AG","TG/AA","AC/TT","AT/TC","CC/GT","CT/GC","GC/CT",
"GT/CC","TC/AT","TT/AC","AA/TC","AC/TA","CA/GC","CC/GA","GA/CC",
"GC/CA","TA/AC","TC/AA","AA/TA","CA/GA","GA/CA","TA/AA","AC/TC",
"CC/GC","GC/CC","TC/AC","AG/TG","CG/GG","GG/CG","TG/AG","AT/TT",
"CT/GT","GT/CT","TT/AT","AI/TC","TI/AC","AC/TI","TC/AI","CI/GC",
"GI/CC","CC/GI","GC/CI","AI/TA","TI/AA","AA/TI","TA/AI","CI/GA",
"GI/CA","CA/GI","GA/CI","AI/TT","TI/AT","AT/TI","TT/AI","CI/GT",
"GI/CT","CT/GI","GT/CI","AI/TG","TI/AG","AG/TI","TG/AI","CI/GG",
"GI/CG","CG/GI","GG/CI","AI/TI","TI/AI","CI/GI","GI/CI"
)
DNA_IMM_Peyret_1999 <- matrix(c(
1.0,0.9, -2.5,-8.3, -4.1,-11.7, -2.8,-8.0, 3.3,10.4,
5.8,16.3, -4.4,-12.3, 4.1,9.5, -0.1,-1.7, -1.4,-6.2,
-1.3,-5.3, -0.6,-2.3, -0.7,-2.3, -0.7,-2.3, -4.0,-13.2,
-0.6,-1.0, 0.5,3.2, 0.7,0.7, 3.0,7.4, 0.7,0.2,
-1.2,-6.2, -0.8,-4.5, -1.5,-6.1, 2.3,5.4, 5.2,13.5,
1.2,0.7, 1.0,0.7, 2.3,4.6, 5.3,14.6, 1.9,3.7,
0.6,-0.6, 5.2,14.2, -0.7,-3.8, 3.4,8.0, 7.6,20.2,
1.2,1.7, -0.9,-4.2, -2.9,-9.8, 4.7,12.9, 0.0,-4.4,
-1.5,-7.2, 3.6,8.9, 6.1,16.4, -3.1,-9.5, -4.9,-15.3,
-6.0,-15.8, 1.6,3.6, -2.7,-10.8, -5.0,-15.8, -2.2,-8.4,
0.2,-1.5, -8.9,-25.5, -5.9,-17.4, -8.8,-25.4, -4.9,-13.9,
-5.4,-13.7, -6.8,-19.1, -8.3,-23.8, -5.0,-12.6, -8.3,-25.0,
-3.4,-11.2, -0.7,-2.6, -1.3,-4.6, 2.6,8.9, -7.8,-21.1,
-7.0,-20.0, -7.6,-20.2, 0.49,-0.7, -6.5,-22.0, -5.6,-18.7,
-0.8,-4.3, -1.0,-2.4, -3.5,-10.6, 0.1,-1.0, -4.3,-12.1,
-4.9,-15.8, -1.9,-8.5, 0.1,-1.8, 1.0,1.0, 7.1,21.3,
-1.1,-3.2, 5.8,16.9, -7.6,-22.0, -3.3,-11.9, 0.1,-2.3,
1.3,3.0, -0.5,-1.3
), ncol=2, byrow=TRUE, dimnames=list(imm_rows, nn_col))
tmm_rows <- c(
"AA/TA","TA/AA","CA/GA","GA/CA","AC/TC","TC/AC","CC/GC","GC/CC",
"AG/TG","TG/AG","CG/GG","GG/CG","AT/TT","TT/AT","CT/GT","GT/CT",
"AA/TC","AC/TA","CA/GC","CC/GA","GA/CC","GC/CA","TA/AC","TC/AA",
"AC/TT","AT/TC","CC/GT","CT/GC","GC/CT","GT/CC","TC/AT","TT/AC",
"AA/TG","AG/TA","CA/GG","CG/GA","GA/CG","GG/CA","TA/AG","TG/AA",
"AG/TT","AT/TG","CG/GT","CT/GG","GG/CT","GT/CG","TG/AT","TT/AG"
)
DNA_TMM_Bommarito_2000 <- matrix(c(
-3.1,-7.8, -2.5,-6.3, -4.3,-10.7, -8.0,-22.5, -0.1,0.5,
-0.7,-1.3, -2.1,-5.1, -3.9,-10.6, -1.1,-2.1, -1.1,-2.7,
-3.8,-9.5, -0.7,-19.2, -2.4,-6.5, -3.2,-8.9, -6.1,-16.9,
-7.4,-21.2, -1.6,-4.0, -1.8,-3.8, -2.6,-5.9, -2.7,-6.0,
-5.0,-13.8, -3.2,-7.1, -2.3,-5.9, -2.7,-7.0, -0.9,-1.7,
-2.3,-6.3, -3.2,-8.0, -3.9,-10.6, -4.9,-13.5, -3.0,-7.8,
-2.5,-6.3, -0.7,-1.2, -1.9,-4.4, -2.5,-5.9, -3.9,-9.6,
-6.0,-15.5, -4.3,-11.1, -4.6,-11.4, -2.0,-4.7, -2.4,-5.8,
-3.2,-8.7, -3.5,-9.4, -3.8,-9.0, -6.6,-18.7, -5.7,-15.9,
-5.9,-16.1, -3.9,-10.5, -3.6,-9.8
), ncol=2, byrow=TRUE, dimnames=list(tmm_rows, nn_col))
de_dna_rows <- c(
"AA/.T","AC/.G","AG/.C","AT/.A","CA/.T","CC/.G","CG/.C","CT/.A",
"GA/.T","GC/.G","GG/.C","GT/.A","TA/.T","TC/.G","TG/.C","TT/.A",
".A/AT",".C/AG",".G/AC",".T/AA",".A/CT",".C/CG",".G/CC",".T/CA",
".A/GT",".C/GG",".G/GC",".T/GA",".A/TT",".C/TG",".G/TC",".T/TA"
)
DNA_DE_Bommarito_2000 <- matrix(c(
0.2,2.3, -6.3,-17.1, -3.7,-10.0, -2.9,-7.6, 0.6,3.3,
-4.4,-12.6, -4.0,-11.9, -4.1,-13.0, -1.1,-1.6, -5.1,-14.0,
-3.9,-10.9, -4.2,-15.0, -6.9,-20.0, -4.0,-10.9, -4.9,-13.8,
-0.2,-0.5, -0.7,-0.8, -2.1,-3.9, -5.9,-16.5, -0.5,-1.1,
4.4,14.9, -0.2,-0.1, -2.6,-7.4, 4.7,14.2, -1.6,-3.6,
-3.9,-11.2, -3.2,-10.4, -4.1,-13.1, 2.9,10.4, -4.4,-13.1,
-5.2,-15.0, -3.8,-12.6
), ncol=2, byrow=TRUE, dimnames=list(de_dna_rows, nn_col))
de_rna_rows <- c(
".T/AA",".T/CA",".T/GA",".T/TA",".G/AC",".G/CC",".G/GC",".G/TC",
".C/AG",".C/CG",".C/GG",".C/TG",".T/AG",".T/CG",".T/GG",".T/TG",
".A/AT",".A/CT",".A/GT",".A/TT",".G/AT",".G/CT",".G/GT",".G/TT",
"AT/.A","CT/.A","GT/.A","TT/.A","AG/.C","CG/.C","GG/.C","TG/.C",
"AC/.G","CC/.G","GC/.G","TC/.G","AT/.G","CT/.G","GT/.G","TT/.G",
"AA/.T","CA/.T","GA/.T","TA/.T","AG/.T","CG/.T","GG/.T","TG/.T"
)
RNA_DE_Turner_2010 <- matrix(c(
-4.9,-13.20, -0.9,-1.30, -5.5,-15.10, -2.3,-5.50,
-9.0,-23.50, -4.1,-10.60, -8.6,-22.20, -7.5,-20.31,
-7.4,-20.30, -2.8,-7.70, -6.4,-16.40, -3.6,-9.70,
-4.9,-13.20, -0.9,-1.30, -5.5,-15.10, -2.3,-5.50,
-5.7,-16.10, -0.7,-1.90, -5.8,-16.40, -2.2,-6.80,
-5.7,-16.10, -0.7,-1.90, -5.8,-16.40, -2.2,-6.80,
-0.5,-0.60, 6.9,22.60, 0.6,2.60, 0.6,2.60,
-1.6,-4.50, 0.7,3.20, -4.6,-14.80, -0.4,-1.30,
-2.4,-6.10, 3.3,11.60, 0.8,3.20, -1.4,-4.20,
-0.5,-0.60, 6.9,22.60, 0.6,2.60, 0.6,2.60,
1.6,6.10, 2.2,8.10, 0.7,3.50, 3.1,10.60,
1.6,6.10, 2.2,8.10, 0.7,3.50, 3.1,10.60
), ncol=2, byrow=TRUE, dimnames=list(de_rna_rows, nn_col))
# -- GC Content Coefficient Table (unchanged) -----------------------------
GC_VARTAB <- data.frame(
A = c(69.3, 81.5, 81.5, 81.5, 78.0, 67.0, 81.5, 77.1),
B = c(0.41, 0.41, 0.41, 0.41, 0.70, 0.80, 0.41, 0.41),
C = c(650, 675, 675, 500, 500, 500, 600, 528),
D = rep(1, 8),
salt_correct = c(NA, NA, "Schildkraut2010",
rep("Wetmur1991", 3),
"Schildkraut2010", "SantaLucia1998-1"),
row.names = c("Chester1993","QuikChange","Schildkraut1965",
"Wetmur1991_MELTING","Wetmur1991_RNA","Wetmur1991_RNA/DNA",
"Primer3Plus","vonAhsen2001"),
stringsAsFactors = FALSE
)
# -- Weber group (UFMG) parameter sets, from VarGibbs 5.0 -----------------
# Generated from the shipped .par files, full precision. Mapping verified:
# P-SL98/P-SG96/P-XIA98/P-FR86 reproduce the tables above exactly, and
# P-PY99+P-AL97+P-AL98{,B,C}+P-WT05 reproduce all 87 IMM rows exactly.
# Each table carries attr 'salt_mM'; do not salt-correct a set twice.
# ---- DNA, salt-resolved (Weber 2015; Fig. 3 data, never tabulated) ----
# AOP-OW04 at 69 mM [Na+]
# source: AOP-OW04-69.par
DNA_NN_Weber_OW04_69 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 4.8549, 15.0479, 2.7622, 8.5772, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -6.8315, -20.4105,
-3.0941, -8.7078, -10.4021, -32.1233, -14.7626, -43.1062, -0.0815, 1.5161,
-7.4408, -20.5458, -6.1987, -17.9548, -14.9050, -41.4616, -1.8135, -2.2130,
-9.0220, -24.7968
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_69, "salt_mM") <- 69
# AOP-OW04 at 119 mM [Na+]
# source: AOP-OW04-119.par
DNA_NN_Weber_OW04_119 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 4.0569, 11.1000, 2.2427, 5.4788, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -7.5348, -22.2100,
-0.4697, -0.6289, -13.8995, -42.2140, -16.2807, -46.9669, 0.2914, 2.7872,
-6.2247, -16.5012, -8.2538, -23.7549, -15.3115, -41.9659, -3.1981, -6.0332,
-10.4625, -28.5610
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_119, "salt_mM") <- 119
# AOP-OW04 at 220 mM [Na+]
# source: AOP-OW04-220.par
DNA_NN_Weber_OW04_220 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 4.3309, 12.6063, 2.4710, 6.7606, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -7.4688, -21.6832,
-0.8398, -1.8294, -12.9102, -38.6391, -15.5770, -44.4811, 0.7896, 4.1875,
-6.7601, -18.0953, -7.5420, -21.2968, -15.6929, -43.0770, -2.5826, -4.1353,
-9.8508, -26.5768
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_220, "salt_mM") <- 220
# AOP-OW04 at 621 mM [Na+]
# source: AOP-OW04-621.par
DNA_NN_Weber_OW04_621 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 2.8755, 10.9634, 1.4790, 6.6027, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -5.7538, -16.2941,
-0.4605, -0.5974, -10.7988, -31.9130, -10.7744, -30.3081, -0.8610, -0.9626,
-4.1898, -10.7677, -7.2483, -20.2750, -10.5953, -28.4924, -4.4725, -10.2831,
-8.1045, -21.6798
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_621, "salt_mM") <- 621
# AOP-OW04 at 1020 mM [Na+]
# source: AOP-OW04-1020.par
DNA_NN_Weber_OW04_1020 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 6.4573, 16.9991, 4.8550, 11.8191, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -8.6278, -24.3084,
-2.1846, -5.6406, -13.8317, -40.2750, -15.0245, -41.9995, -1.7673, -3.2100,
-6.9985, -18.5138, -9.5341, -26.2352, -14.4129, -38.8461, -6.7989, -15.8173,
-11.0161, -29.2985
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_1020, "salt_mM") <- 1021
# ---- DNA, general purpose ----
# AOP-CMB, combined dataset (recommended DNA set)
# source: AOP-CMB.par
DNA_NN_Weber_2015 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 2.5326, 5.6637, 0.4900, -0.6757, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -7.2009, -20.2175,
-5.6921, -15.8947, -4.0774, -11.3388, -8.5782, -23.1654, -7.2564, -19.3191,
-6.4646, -17.0925, -7.1036, -18.9155, -9.7028, -24.8796, -9.5938, -24.4371,
-8.7684, -22.7375
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_2015, "salt_mM") <- 1020
# ---- RNA, measured at five salts, VIF scheme ----
# AOP-VIF at 71 mM [Na+]
# source: AOP-VIFRW-71.par
RNA_NN_Weber_VIF_71 <- .complete_nn_rc(matrix(c(
3.4085, 0.2886, 4.6722, 12.4606, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.3315, -14.8085,
-10.2716, -29.6132, -10.3296, -31.9802, -10.3505, -29.3566, -12.4315, -32.9656,
-11.5553, -31.1152, -12.1350, -33.8251, -10.6616, -27.9859, -14.2091, -36.2688,
-12.8534, -32.1972
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_71, "salt_mM") <- 71
# AOP-VIF at 121 mM [Na+]
# source: AOP-VIFRW-121.par
RNA_NN_Weber_VIF_121 <- .complete_nn_rc(matrix(c(
2.3170, 0.2578, 5.6296, 15.1423, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -9.5363, -28.9960,
-11.4066, -32.4378, -10.5677, -33.7691, -10.1748, -29.8826, -12.2827, -32.2623,
-11.3606, -30.2829, -12.8637, -37.1318, -10.8907, -29.1438, -13.9826, -35.8758,
-11.6163, -28.9123
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_121, "salt_mM") <- 121
# AOP-VIF at 221 mM [Na+]
# source: AOP-VIFRW-221.par
RNA_NN_Weber_VIF_221 <- .complete_nn_rc(matrix(c(
2.4417, -1.2287, 5.2434, 14.1869, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -7.2734, -20.9687,
-11.0415, -30.9510, -9.4127, -29.3164, -11.1475, -32.0746, -11.7833, -30.3817,
-12.4938, -33.5013, -11.9556, -33.3326, -11.9159, -31.8045, -13.5035, -33.7292,
-11.8934, -29.0209
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_221, "salt_mM") <- 221
# AOP-VIF at 621 mM [Na+]
# source: AOP-VIFRW-621.par
RNA_NN_Weber_VIF_621 <- .complete_nn_rc(matrix(c(
2.0381, -0.7355, 4.7624, 12.6860, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.0673, -13.7959,
-12.0086, -33.5987, -9.4331, -29.1261, -12.3988, -36.0577, -13.1418, -34.7699,
-11.6446, -30.8224, -11.9930, -33.4713, -11.8000, -31.5396, -13.1341, -32.6632,
-11.4641, -27.8822
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_621, "salt_mM") <- 621
# AOP-VIF at 1021 mM [Na+]
# source: AOP-VIFRW-1021.par
RNA_NN_Weber_VIF_1021 <- .complete_nn_rc(matrix(c(
5.2375, 1.0265, 1.8683, 4.8400, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -6.9088, -18.9939,
-12.3315, -35.5068, -11.1870, -31.5678, -9.2476, -22.8267, -13.0887, -34.4079,
-11.4727, -29.7037, -13.2535, -34.7600, -12.2039, -30.9536, -15.5716, -38.5593,
-15.0523, -36.9921
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_1021, "salt_mM") <- 1021
# ---- RNA, measured at five salts, FIF scheme ----
# AOP-FIF at 71 mM [Na+]
# source: AOP-FIFRW-71.par
RNA_NN_Weber_FIF_71 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.6583, -15.6834,
-8.5043, -25.9600, -10.5270, -30.0115, -11.0010, -29.8012, -11.8700, -31.8195,
-10.7268, -29.1405, -12.8979, -34.6410, -10.5983, -27.3390, -14.6024, -36.9660,
-13.3232, -33.1294
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_71, "salt_mM") <- 71
# AOP-FIF at 121 mM [Na+]
# source: AOP-FIFRW-121.par
RNA_NN_Weber_FIF_121 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -10.6106, -31.9905,
-7.5798, -22.7640, -11.5058, -32.8953, -11.7860, -32.2437, -11.1365, -29.0034,
-9.6600, -25.3309, -14.8961, -40.8555, -10.9362, -28.2149, -15.0954, -38.0510,
-12.9669, -31.8119
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_121, "salt_mM") <- 121
# AOP-FIF at 221 mM [Na+]
# source: AOP-FIFRW-221.par
RNA_NN_Weber_FIF_221 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -7.7054, -22.0235,
-8.4715, -24.8591, -10.0105, -28.2728, -12.2437, -33.5058, -11.0624, -28.5283,
-11.3271, -30.2921, -13.3287, -35.6580, -12.0183, -31.3994, -14.3687, -35.5373,
-13.0358, -31.6893
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_221, "salt_mM") <- 221
# AOP-FIF at 621 mM [Na+]
# source: AOP-FIFRW-621.par
RNA_NN_Weber_FIF_621 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.8155, -15.6770,
-9.0920, -26.3098, -10.2523, -28.5906, -13.3474, -36.6460, -12.7358, -33.5822,
-10.4105, -27.1001, -13.6846, -36.4177, -11.7763, -30.4166, -14.5406, -35.7457,
-13.1793, -31.8911
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_621, "salt_mM") <- 621
# AOP-FIF at 1021 mM [Na+]
# source: AOP-FIFRW-1021.par
RNA_NN_Weber_FIF_1021 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -6.4314, -17.6766,
-12.7794, -37.0377, -13.5099, -39.1452, -8.3871, -20.6134, -12.5166, -33.0215,
-10.0627, -25.7313, -13.4641, -35.8415, -10.8365, -27.2504, -14.7595, -36.6631,
-13.6056, -33.2045
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_1021, "salt_mM") <- 1021
# ---- DNA/RNA hybrid ----
# AOP-DRFT, curve fitting (recommended high salt)
# source: AOP-DRFT.par
RNA_DNA_NN_Weber_2019_FT <- matrix(c(
1.4943, -8.5131, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -8.2694, -25.2054,
-9.3923, -25.2281, -10.8693, -31.0383, -9.8990, -28.5875, -9.7935, -26.2840,
-11.9087, -28.2205, -12.6023, -33.6818, -12.2978, -33.1193, -11.5436, -33.0019,
-13.4801, -34.4699, -12.4230, -32.8537, -11.7433, -31.0498, -9.5595, -28.8683,
-9.7656, -25.4503, -11.4676, -32.1090, -10.2758, -29.8576
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
attr(RNA_DNA_NN_Weber_2019_FT, "salt_mM") <- 1000
# AOP-DRVH, Van't Hoff
# source: AOP-DRVH.par
RNA_DNA_NN_Weber_2019_VH <- matrix(c(
1.5947, -8.6455, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -7.7206, -23.8767,
-11.4595, -31.6731, -10.5706, -30.0225, -9.8591, -28.5101, -10.6942, -29.0233,
-11.3728, -26.5422, -13.0108, -35.1695, -12.1751, -32.9612, -11.6522, -32.9572,
-14.0583, -35.7112, -12.4383, -32.9497, -10.5935, -27.3484, -10.2428, -30.8614,
-9.5993, -24.4245, -10.7358, -29.8654, -9.9268, -28.6058
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
attr(RNA_DNA_NN_Weber_2019_VH, "salt_mM") <- 1000
# AOP-DRLS, low salt
# source: AOP-DRLS.par
RNA_DNA_NN_Weber_2019_LS <- matrix(c(
0.9630, -6.5119, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -10.8561, -35.9011,
-10.2316, -29.0019, -8.6009, -22.9547, -9.4736, -30.5491, -9.5454, -25.1770,
-11.8223, -30.8244, -11.9288, -29.9238, -12.2281, -33.2457, -8.7011, -25.6312,
-8.4153, -22.4119, -7.4720, -19.3490, -12.7010, -37.8656, -9.9307, -30.7749,
-12.2396, -33.9475, -13.8697, -37.9789, -10.6987, -32.0137
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
attr(RNA_DNA_NN_Weber_2019_LS, "salt_mM") <- 100
# -- Return assembled list ------------------------------------------------
list(
DNA_NN_Breslauer_1986 = DNA_NN_Breslauer_1986,
DNA_NN_Sugimoto_1996 = DNA_NN_Sugimoto_1996,
DNA_NN_Allawi_1998 = DNA_NN_Allawi_1998,
DNA_NN_SantaLucia_2004 = DNA_NN_SantaLucia_2004,
RNA_NN_Freier_1986 = RNA_NN_Freier_1986,
RNA_NN_Xia_1998 = RNA_NN_Xia_1998,
RNA_NN_Chen_2012 = RNA_NN_Chen_2012,
RNA_DNA_NN_Sugimoto_1995 = RNA_DNA_NN_Sugimoto_1995,
DNA_IMM_Peyret_1999 = DNA_IMM_Peyret_1999,
DNA_TMM_Bommarito_2000 = DNA_TMM_Bommarito_2000,
DNA_DE_Bommarito_2000 = DNA_DE_Bommarito_2000,
RNA_DE_Turner_2010 = RNA_DE_Turner_2010,
GC_VARTAB = GC_VARTAB,
# -- Weber group sets ---------------------------------------------------
DNA_NN_Weber_OW04_69 = DNA_NN_Weber_OW04_69,
DNA_NN_Weber_OW04_119 = DNA_NN_Weber_OW04_119,
DNA_NN_Weber_OW04_220 = DNA_NN_Weber_OW04_220,
DNA_NN_Weber_OW04_621 = DNA_NN_Weber_OW04_621,
DNA_NN_Weber_OW04_1020 = DNA_NN_Weber_OW04_1020,
DNA_NN_Weber_2015 = DNA_NN_Weber_2015,
RNA_NN_Weber_VIF_71 = RNA_NN_Weber_VIF_71,
RNA_NN_Weber_VIF_121 = RNA_NN_Weber_VIF_121,
RNA_NN_Weber_VIF_221 = RNA_NN_Weber_VIF_221,
RNA_NN_Weber_VIF_621 = RNA_NN_Weber_VIF_621,
RNA_NN_Weber_VIF_1021 = RNA_NN_Weber_VIF_1021,
RNA_NN_Weber_FIF_71 = RNA_NN_Weber_FIF_71,
RNA_NN_Weber_FIF_121 = RNA_NN_Weber_FIF_121,
RNA_NN_Weber_FIF_221 = RNA_NN_Weber_FIF_221,
RNA_NN_Weber_FIF_621 = RNA_NN_Weber_FIF_621,
RNA_NN_Weber_FIF_1021 = RNA_NN_Weber_FIF_1021,
RNA_DNA_NN_Weber_2019_FT = RNA_DNA_NN_Weber_2019_FT,
RNA_DNA_NN_Weber_2019_VH = RNA_DNA_NN_Weber_2019_VH,
RNA_DNA_NN_Weber_2019_LS = RNA_DNA_NN_Weber_2019_LS
)
}
# =============================================================================
# VERIFICATION - run this after building to confirm completion
# =============================================================================
.verify_nn_tables <- function(tbl_list) {
# The 6 RC pairs that should now be present in every standard DNA/RNA NN table
expected_rc_pairs <- c("TT/AA","TG/AC","TC/AG","AG/TC","AC/TG","CC/GG")
standard_tables <- c("DNA_NN_Breslauer_1986","DNA_NN_Sugimoto_1996",
"DNA_NN_Allawi_1998","DNA_NN_SantaLucia_2004",
"RNA_NN_Freier_1986","RNA_NN_Xia_1998",
"RNA_NN_Chen_2012","RNA_DNA_NN_Sugimoto_1995",
"DNA_NN_Weber_2015","DNA_NN_Weber_OW04_69",
"DNA_NN_Weber_OW04_119","DNA_NN_Weber_OW04_220",
"DNA_NN_Weber_OW04_621","DNA_NN_Weber_OW04_1020",
"RNA_NN_Weber_VIF_71","RNA_NN_Weber_VIF_121",
"RNA_NN_Weber_VIF_221","RNA_NN_Weber_VIF_621",
"RNA_NN_Weber_VIF_1021","RNA_NN_Weber_FIF_71",
"RNA_NN_Weber_FIF_121","RNA_NN_Weber_FIF_221",
"RNA_NN_Weber_FIF_621","RNA_NN_Weber_FIF_1021")
all_ok <- TRUE
for (nm in standard_tables) {
tbl <- tbl_list[[nm]]
missing <- expected_rc_pairs[!expected_rc_pairs %in% rownames(tbl)]
n_rows <- nrow(tbl)
status <- if (length(missing) == 0) "OK" else paste("MISSING:", paste(missing, collapse=", "))
cat(sprintf(" %-35s rows=%-3d %s\n", nm, n_rows, status))
if (length(missing) > 0) all_ok <- FALSE
}
# -- Weber sets must all carry a salt attribute --------------------------
weber <- grep("Weber", names(tbl_list), value = TRUE)
cat("\n salt attributes on Weber sets:\n")
for (nm in weber) {
s <- attr(tbl_list[[nm]], "salt_mM")
cat(sprintf(" %-28s %8s mM %s\n", nm,
if (is.null(s)) "NA" else format(s),
if (!is.null(s) && !is.na(s)) "OK" else "MISSING"))
if (is.null(s) || is.na(s)) all_ok <- FALSE
}
# -- Published values that were previously mistranscribed -----------------
sl <- tbl_list$DNA_NN_SantaLucia_2004
ref <- list("TA/AT" = c(-7.2, -21.3), "GG/CC" = c(-8.0, -19.9))
cat("\n SantaLucia 2004 spot checks (vs P-SL04.par and P-SL98.par):\n")
for (k in names(ref)) {
ok <- isTRUE(all.equal(unname(sl[k, ]), ref[[k]], tolerance = 1e-8))
cat(sprintf(" %-28s %s\n", k, if (ok) "OK" else "FAIL"))
if (!ok) all_ok <- FALSE
}
if (all_ok) {
cat("\nAll tables complete. keys_fr lookups will always succeed.\n")
cat("The keys_rf / pos_nn_rf fallback in tm_nn_core() can now be removed.\n")
}
invisible(all_ok)
}
# -- Usage --------------------------------------------------------------------
# Build once at package development time:
# .TM_CONSTANTS <- .build_all_tm_tables()
# .verify_nn_tables(.TM_CONSTANTS)
# usethis::use_data(.TM_CONSTANTS, internal = TRUE, overwrite = TRUE)
#
# Expected verification output:
# DNA_NN_Breslauer_1986 rows=23 OK
# DNA_NN_Sugimoto_1996 rows=23 OK
# DNA_NN_Allawi_1998 rows=23 OK
# DNA_NN_SantaLucia_2004 rows=23 OK
# RNA_NN_Freier_1986 rows=23 OK
# RNA_NN_Xia_1998 rows=23 OK
# RNA_NN_Chen_2012 rows=28 OK (17 std + 11 wobble; 6 RC added to std)
# RNA_DNA_NN_Sugimoto_1995 rows=23 OK (already had all 6 RC pairs)
#
# After building, in tm_nn_core() the NN sum simplifies to:
# matched_nn <- keys_fr %in% rownames(nn_tbl)
# delta_h <- delta_h + sum(nn_tbl[keys_fr[matched_nn], 1])
# delta_s <- delta_s + sum(nn_tbl[keys_fr[matched_nn], 2])
# # No keys_rf, no pos_nn_rf, no which_nn_fr/rf needed.
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Defines functions .verify_nn_tables .build_all_tm_tables .complete_nn_rc
# =============================================================================
# .build_all_tm_tables() - updated with reverse complement completion (Bug 9 fix)
#
# WHAT CHANGED:
# Added .complete_nn_rc() helper that adds the 6 missing reverse complement
# rows to any standard 17-row DNA/RNA NN table.
#
# The 16 Watson-Crick NN pairs split into:
# 10 asymmetric pairs: each has a distinct RC partner → 6 unique new rows needed
# AA/TT ↔ TT/AA (TT/AA missing)
# CA/GT ↔ TG/AC (TG/AC missing)
# GA/CT ↔ TC/AG (TC/AG missing)
# CT/GA ↔ AG/TC (AG/TC missing)
# GT/CA ↔ AC/TG (AC/TG missing)
# GG/CC ↔ CC/GG (CC/GG missing)
# 6 self-complementary palindromes: RC maps to themselves → no new rows needed
# AT/TA, TA/AT, CG/GC, GC/CG, CA/GT→already, GC/CG→already
# (Actually: AT/TA, TA/AT, CG/GC, GC/CG are true palindromes)
#
# RNA_DNA_NN_Sugimoto_1995 already contains all 16 + 6 RC rows → skip.
# RNA_NN_Chen_2012 has extra wobble rows → complete only the standard 16.
# =============================================================================
# -- Helper: add the 6 missing reverse complement rows to a standard NN table --
#
# There are exactly 16 valid Watson-Crick dinucleotide keys (XY/X'Y' where
# X' = complement of X, Y' = complement of Y). Published NN tables list only
# 10 of these - the 6 missing ones are thermodynamically equivalent to an
# existing row when read from the other strand direction.
#
# The complete mapping (verified by enumeration):
# TT/AA <- same ΔH/ΔS as AA/TT (homodimer palindrome)
# AC/TG <- same ΔH/ΔS as CA/GT (RC pair)
# AG/TC <- same ΔH/ΔS as GA/CT (RC pair)
# TC/AG <- same ΔH/ΔS as CT/GA (RC pair)
# TG/AC <- same ΔH/ΔS as GT/CA (RC pair)
# CC/GG <- same ΔH/ΔS as GG/CC (homodimer palindrome)
#
# @param tbl Matrix with dimnames - a standard NN table (17+ rows).
# @param skip_rows Row names to exclude from completion (init/sym rows and any
# extra wobble rows like in RNA_NN_Chen_2012).
# @return The input matrix with up to 6 new rows appended.
#
.complete_nn_rc <- function(tbl,
skip_rows = c("init", "init_A/T", "init_G/C",
"init_oneG/C", "init_allA/T",
"init_5T/A", "sym")) {
# Fixed mapping: new key -> source key to copy values from
# This is the complete, verified set - do not derive algorithmically
# (TT/AA and CC/GG are self-palindromes under the RC formula and would
# be missed by a naive "RC != self" filter).
missing_map <- c(
"TT/AA" = "AA/TT",
"AC/TG" = "CA/GT",
"AG/TC" = "GA/CT",
"TC/AG" = "CT/GA",
"TG/AC" = "GT/CA",
"CC/GG" = "GG/CC"
)
# Only add rows not already present (handles RNA_DNA_NN_Sugimoto_1995
# which was published with the full 16-pair set)
to_add <- missing_map[!names(missing_map) %in% rownames(tbl)]
if (length(to_add) == 0L) return(tbl) # already complete
# Verify source rows exist before copying
missing_sources <- to_add[!to_add %in% rownames(tbl)]
if (length(missing_sources) > 0L)
stop(sprintf("Source rows missing from table: %s",
paste(missing_sources, collapse=", ")))
new_rows <- tbl[to_add, , drop = FALSE]
rownames(new_rows) <- names(to_add)
rbind(tbl, new_rows)
}
# -- Main table builder --------------------------------------------------------
.build_all_tm_tables <- function() {
nn_col <- c("left", "right")
nn_row_std <- c("init", "init_A/T", "init_G/C", "init_oneG/C", "init_allA/T",
"init_5T/A", "sym",
"AA/TT", "AT/TA", "TA/AT", "CA/GT", "GT/CA",
"CT/GA", "GA/CT", "CG/GC", "GC/CG", "GG/CC")
# -- DNA NN Tables -----------------------------------------------------------
DNA_NN_SantaLucia_2004_bug <- .complete_nn_rc(matrix(c(
0.2,-5.7, 2.2,6.9, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-7.6,-21.3, -7.2,-20.4, -7.2,-21.3, -8.5,-22.7, -8.4,-22.4,
-7.8,-21.0, -8.2,-22.2, -10.6,-27.2, -9.8,-24.4, -8.0,-19.9
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_SantaLucia_2004 <- .complete_nn_rc(matrix(c(
0.2,-5.7, 2.2,6.9, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-7.6,-21.3, -7.2,-20.4, -7.2,-21.3, -8.5,-22.7, -8.4,-22.4,
-7.8,-21.0, -8.2,-22.2, -10.6,-27.2, -9.8,-24.4, -8.0,-19.9
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_Breslauer_1986 <- .complete_nn_rc(matrix(c(
0,0, 0,0, 0,0, 0,-16.8, 0,-20.1, 0,0, 0,-1.3,
-9.1,-24.0, -8.6,-23.9, -6.0,-16.9, -5.8,-12.9, -6.5,-17.3,
-7.8,-20.8, -5.6,-13.5, -11.9,-27.8, -11.1,-26.7, -11.0,-26.6
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_Sugimoto_1996 <- .complete_nn_rc(matrix(c(
0.6,-9.0, 0,0, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-8.0,-21.9, -5.6,-15.2, -6.6,-18.4, -8.2,-21.0, -9.4,-25.5,
-6.6,-16.4, -8.8,-23.5, -11.8,-29.0, -10.5,-26.4, -10.9,-28.4
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
DNA_NN_Allawi_1998 <- .complete_nn_rc(matrix(c(
0,0, 2.3,4.1, 0.1,-2.8, 0,0, 0,0, 0,0, 0,-1.4,
-7.9,-22.2, -7.2,-20.4, -7.2,-21.3, -8.5,-22.7, -8.4,-22.4,
-7.8,-21.0, -8.2,-22.2, -10.6,-27.2, -9.8,-24.4, -8.0,-19.9
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
# -- RNA NN Tables -----------------------------------------------------------
RNA_NN_Freier_1986 <- .complete_nn_rc(matrix(c(
0,-10.8, 0,0, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-6.6,-18.4, -5.7,-15.5, -8.1,-22.6, -10.5,-27.8, -10.2,-26.2,
-7.6,-19.2, -13.3,-35.5, -8.0,-19.4, -14.2,-34.9, -12.2,-29.7
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
RNA_NN_Xia_1998 <- .complete_nn_rc(matrix(c(
3.61,-1.5, 3.72,10.5, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-6.82,-19.0, -9.38,-26.7, -7.69,-20.5, -10.44,-26.9, -11.40,-29.5,
-10.48,-27.1, -12.44,-32.5, -10.64,-26.7, -14.88,-36.9, -13.39,-32.7
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
# Chen_2012 has extra wobble rows - complete_nn_rc skips non-standard rows
# automatically and only processes the 16 standard Watson-Crick rows.
chen_rows <- c(nn_row_std,
"GT/TG","GG/TT","AG/TT","TG/AT","TT/AG","TG/GT",
"AT/TG","CG/GT","CT/GG","GG/CT","GT/CG")
RNA_NN_Chen_2012 <- .complete_nn_rc(matrix(c(
6.40,6.99, 3.85,11.04, 0,0, 0,0, 0,0, 0,0, 0,-1.4,
-7.09,-19.8, -9.11,-25.8, -8.50,-22.9, -11.03,-28.8, -11.98,-31.3,
-10.90,-28.5, -13.21,-34.9, -10.88,-27.4, -16.04,-40.6, -14.18,-35.0,
-13.83,-46.9, -17.82,-56.7, -3.96,-11.6, -0.96,-1.8, -10.38,-31.8,
-12.64,-38.9, -7.39,-21.0, -5.56,-13.9, -9.44,-24.7, -7.03,-16.8,
-11.09,-28.8
), ncol=2, byrow=TRUE, dimnames=list(chen_rows, nn_col)),
# Pass the extra wobble rows as skip so they are not RC-completed
skip_rows = c("init","init_A/T","init_G/C","init_oneG/C","init_allA/T",
"init_5T/A","sym",
"GT/TG","GG/TT","AG/TT","TG/AT","TT/AG","TG/GT",
"AT/TG","CG/GT","CT/GG","GG/CT","GT/CG"))
# -- RNA/DNA Hybrid - already complete (22 rows including all RC pairs) ------
# RNA_DNA_NN_Sugimoto_1995 was published with the full symmetric set.
# .complete_nn_rc() will find nothing to add (returns tbl unchanged).
hybrid_rows <- c("init","init_A/T","init_G/C","init_oneG/C","init_allA/T",
"init_5T/A","sym",
"AA/TT","AC/TG","AG/TC","AT/TA",
"CA/GT","CC/GG","CG/GC","CT/GA",
"GA/CT","GC/CG","GG/CC","GT/CA",
"TA/AT","TC/AG","TG/AC","TT/AA")
RNA_DNA_NN_Sugimoto_1995 <- matrix(c(
1.9,-3.9, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0,
-11.5,-36.4, -7.8,-21.6, -7.0,-19.7, -8.3,-23.9,
-10.4,-28.4, -12.8,-31.9, -16.3,-47.1, -9.1,-23.5,
-8.6,-22.9, -8.0,-17.1, -9.3,-23.2, -5.9,-12.3,
-7.8,-23.2, -5.5,-13.5, -9.0,-26.1, -7.8,-21.9
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
# No .complete_nn_rc() needed - already has all 16 unique pairs
# -- IMM, TMM, DE tables (unchanged) -----------------------------------------
imm_rows <- c(
"AG/TT","AT/TG","CG/GT","CT/GG","GG/CT","GG/TT","GT/CG","GT/TG",
"TG/AT","TG/GT","TT/AG","AA/TG","AG/TA","CA/GG","CG/GA","GA/CG",
"GG/CA","TA/AG","TG/AA","AC/TT","AT/TC","CC/GT","CT/GC","GC/CT",
"GT/CC","TC/AT","TT/AC","AA/TC","AC/TA","CA/GC","CC/GA","GA/CC",
"GC/CA","TA/AC","TC/AA","AA/TA","CA/GA","GA/CA","TA/AA","AC/TC",
"CC/GC","GC/CC","TC/AC","AG/TG","CG/GG","GG/CG","TG/AG","AT/TT",
"CT/GT","GT/CT","TT/AT","AI/TC","TI/AC","AC/TI","TC/AI","CI/GC",
"GI/CC","CC/GI","GC/CI","AI/TA","TI/AA","AA/TI","TA/AI","CI/GA",
"GI/CA","CA/GI","GA/CI","AI/TT","TI/AT","AT/TI","TT/AI","CI/GT",
"GI/CT","CT/GI","GT/CI","AI/TG","TI/AG","AG/TI","TG/AI","CI/GG",
"GI/CG","CG/GI","GG/CI","AI/TI","TI/AI","CI/GI","GI/CI"
)
DNA_IMM_Peyret_1999 <- matrix(c(
1.0,0.9, -2.5,-8.3, -4.1,-11.7, -2.8,-8.0, 3.3,10.4,
5.8,16.3, -4.4,-12.3, 4.1,9.5, -0.1,-1.7, -1.4,-6.2,
-1.3,-5.3, -0.6,-2.3, -0.7,-2.3, -0.7,-2.3, -4.0,-13.2,
-0.6,-1.0, 0.5,3.2, 0.7,0.7, 3.0,7.4, 0.7,0.2,
-1.2,-6.2, -0.8,-4.5, -1.5,-6.1, 2.3,5.4, 5.2,13.5,
1.2,0.7, 1.0,0.7, 2.3,4.6, 5.3,14.6, 1.9,3.7,
0.6,-0.6, 5.2,14.2, -0.7,-3.8, 3.4,8.0, 7.6,20.2,
1.2,1.7, -0.9,-4.2, -2.9,-9.8, 4.7,12.9, 0.0,-4.4,
-1.5,-7.2, 3.6,8.9, 6.1,16.4, -3.1,-9.5, -4.9,-15.3,
-6.0,-15.8, 1.6,3.6, -2.7,-10.8, -5.0,-15.8, -2.2,-8.4,
0.2,-1.5, -8.9,-25.5, -5.9,-17.4, -8.8,-25.4, -4.9,-13.9,
-5.4,-13.7, -6.8,-19.1, -8.3,-23.8, -5.0,-12.6, -8.3,-25.0,
-3.4,-11.2, -0.7,-2.6, -1.3,-4.6, 2.6,8.9, -7.8,-21.1,
-7.0,-20.0, -7.6,-20.2, 0.49,-0.7, -6.5,-22.0, -5.6,-18.7,
-0.8,-4.3, -1.0,-2.4, -3.5,-10.6, 0.1,-1.0, -4.3,-12.1,
-4.9,-15.8, -1.9,-8.5, 0.1,-1.8, 1.0,1.0, 7.1,21.3,
-1.1,-3.2, 5.8,16.9, -7.6,-22.0, -3.3,-11.9, 0.1,-2.3,
1.3,3.0, -0.5,-1.3
), ncol=2, byrow=TRUE, dimnames=list(imm_rows, nn_col))
tmm_rows <- c(
"AA/TA","TA/AA","CA/GA","GA/CA","AC/TC","TC/AC","CC/GC","GC/CC",
"AG/TG","TG/AG","CG/GG","GG/CG","AT/TT","TT/AT","CT/GT","GT/CT",
"AA/TC","AC/TA","CA/GC","CC/GA","GA/CC","GC/CA","TA/AC","TC/AA",
"AC/TT","AT/TC","CC/GT","CT/GC","GC/CT","GT/CC","TC/AT","TT/AC",
"AA/TG","AG/TA","CA/GG","CG/GA","GA/CG","GG/CA","TA/AG","TG/AA",
"AG/TT","AT/TG","CG/GT","CT/GG","GG/CT","GT/CG","TG/AT","TT/AG"
)
DNA_TMM_Bommarito_2000 <- matrix(c(
-3.1,-7.8, -2.5,-6.3, -4.3,-10.7, -8.0,-22.5, -0.1,0.5,
-0.7,-1.3, -2.1,-5.1, -3.9,-10.6, -1.1,-2.1, -1.1,-2.7,
-3.8,-9.5, -0.7,-19.2, -2.4,-6.5, -3.2,-8.9, -6.1,-16.9,
-7.4,-21.2, -1.6,-4.0, -1.8,-3.8, -2.6,-5.9, -2.7,-6.0,
-5.0,-13.8, -3.2,-7.1, -2.3,-5.9, -2.7,-7.0, -0.9,-1.7,
-2.3,-6.3, -3.2,-8.0, -3.9,-10.6, -4.9,-13.5, -3.0,-7.8,
-2.5,-6.3, -0.7,-1.2, -1.9,-4.4, -2.5,-5.9, -3.9,-9.6,
-6.0,-15.5, -4.3,-11.1, -4.6,-11.4, -2.0,-4.7, -2.4,-5.8,
-3.2,-8.7, -3.5,-9.4, -3.8,-9.0, -6.6,-18.7, -5.7,-15.9,
-5.9,-16.1, -3.9,-10.5, -3.6,-9.8
), ncol=2, byrow=TRUE, dimnames=list(tmm_rows, nn_col))
de_dna_rows <- c(
"AA/.T","AC/.G","AG/.C","AT/.A","CA/.T","CC/.G","CG/.C","CT/.A",
"GA/.T","GC/.G","GG/.C","GT/.A","TA/.T","TC/.G","TG/.C","TT/.A",
".A/AT",".C/AG",".G/AC",".T/AA",".A/CT",".C/CG",".G/CC",".T/CA",
".A/GT",".C/GG",".G/GC",".T/GA",".A/TT",".C/TG",".G/TC",".T/TA"
)
DNA_DE_Bommarito_2000 <- matrix(c(
0.2,2.3, -6.3,-17.1, -3.7,-10.0, -2.9,-7.6, 0.6,3.3,
-4.4,-12.6, -4.0,-11.9, -4.1,-13.0, -1.1,-1.6, -5.1,-14.0,
-3.9,-10.9, -4.2,-15.0, -6.9,-20.0, -4.0,-10.9, -4.9,-13.8,
-0.2,-0.5, -0.7,-0.8, -2.1,-3.9, -5.9,-16.5, -0.5,-1.1,
4.4,14.9, -0.2,-0.1, -2.6,-7.4, 4.7,14.2, -1.6,-3.6,
-3.9,-11.2, -3.2,-10.4, -4.1,-13.1, 2.9,10.4, -4.4,-13.1,
-5.2,-15.0, -3.8,-12.6
), ncol=2, byrow=TRUE, dimnames=list(de_dna_rows, nn_col))
de_rna_rows <- c(
".T/AA",".T/CA",".T/GA",".T/TA",".G/AC",".G/CC",".G/GC",".G/TC",
".C/AG",".C/CG",".C/GG",".C/TG",".T/AG",".T/CG",".T/GG",".T/TG",
".A/AT",".A/CT",".A/GT",".A/TT",".G/AT",".G/CT",".G/GT",".G/TT",
"AT/.A","CT/.A","GT/.A","TT/.A","AG/.C","CG/.C","GG/.C","TG/.C",
"AC/.G","CC/.G","GC/.G","TC/.G","AT/.G","CT/.G","GT/.G","TT/.G",
"AA/.T","CA/.T","GA/.T","TA/.T","AG/.T","CG/.T","GG/.T","TG/.T"
)
RNA_DE_Turner_2010 <- matrix(c(
-4.9,-13.20, -0.9,-1.30, -5.5,-15.10, -2.3,-5.50,
-9.0,-23.50, -4.1,-10.60, -8.6,-22.20, -7.5,-20.31,
-7.4,-20.30, -2.8,-7.70, -6.4,-16.40, -3.6,-9.70,
-4.9,-13.20, -0.9,-1.30, -5.5,-15.10, -2.3,-5.50,
-5.7,-16.10, -0.7,-1.90, -5.8,-16.40, -2.2,-6.80,
-5.7,-16.10, -0.7,-1.90, -5.8,-16.40, -2.2,-6.80,
-0.5,-0.60, 6.9,22.60, 0.6,2.60, 0.6,2.60,
-1.6,-4.50, 0.7,3.20, -4.6,-14.80, -0.4,-1.30,
-2.4,-6.10, 3.3,11.60, 0.8,3.20, -1.4,-4.20,
-0.5,-0.60, 6.9,22.60, 0.6,2.60, 0.6,2.60,
1.6,6.10, 2.2,8.10, 0.7,3.50, 3.1,10.60,
1.6,6.10, 2.2,8.10, 0.7,3.50, 3.1,10.60
), ncol=2, byrow=TRUE, dimnames=list(de_rna_rows, nn_col))
# -- GC Content Coefficient Table (unchanged) -----------------------------
GC_VARTAB <- data.frame(
A = c(69.3, 81.5, 81.5, 81.5, 78.0, 67.0, 81.5, 77.1),
B = c(0.41, 0.41, 0.41, 0.41, 0.70, 0.80, 0.41, 0.41),
C = c(650, 675, 675, 500, 500, 500, 600, 528),
D = rep(1, 8),
salt_correct = c(NA, NA, "Schildkraut2010",
rep("Wetmur1991", 3),
"Schildkraut2010", "SantaLucia1998-1"),
row.names = c("Chester1993","QuikChange","Schildkraut1965",
"Wetmur1991_MELTING","Wetmur1991_RNA","Wetmur1991_RNA/DNA",
"Primer3Plus","vonAhsen2001"),
stringsAsFactors = FALSE
)
# -- Weber group (UFMG) parameter sets, from VarGibbs 5.0 -----------------
# Generated from the shipped .par files, full precision. Mapping verified:
# P-SL98/P-SG96/P-XIA98/P-FR86 reproduce the tables above exactly, and
# P-PY99+P-AL97+P-AL98{,B,C}+P-WT05 reproduce all 87 IMM rows exactly.
# Each table carries attr 'salt_mM'; do not salt-correct a set twice.
# ---- DNA, salt-resolved (Weber 2015; Fig. 3 data, never tabulated) ----
# AOP-OW04 at 69 mM [Na+]
# source: AOP-OW04-69.par
DNA_NN_Weber_OW04_69 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 4.8549, 15.0479, 2.7622, 8.5772, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -6.8315, -20.4105,
-3.0941, -8.7078, -10.4021, -32.1233, -14.7626, -43.1062, -0.0815, 1.5161,
-7.4408, -20.5458, -6.1987, -17.9548, -14.9050, -41.4616, -1.8135, -2.2130,
-9.0220, -24.7968
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_69, "salt_mM") <- 69
# AOP-OW04 at 119 mM [Na+]
# source: AOP-OW04-119.par
DNA_NN_Weber_OW04_119 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 4.0569, 11.1000, 2.2427, 5.4788, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -7.5348, -22.2100,
-0.4697, -0.6289, -13.8995, -42.2140, -16.2807, -46.9669, 0.2914, 2.7872,
-6.2247, -16.5012, -8.2538, -23.7549, -15.3115, -41.9659, -3.1981, -6.0332,
-10.4625, -28.5610
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_119, "salt_mM") <- 119
# AOP-OW04 at 220 mM [Na+]
# source: AOP-OW04-220.par
DNA_NN_Weber_OW04_220 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 4.3309, 12.6063, 2.4710, 6.7606, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -7.4688, -21.6832,
-0.8398, -1.8294, -12.9102, -38.6391, -15.5770, -44.4811, 0.7896, 4.1875,
-6.7601, -18.0953, -7.5420, -21.2968, -15.6929, -43.0770, -2.5826, -4.1353,
-9.8508, -26.5768
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_220, "salt_mM") <- 220
# AOP-OW04 at 621 mM [Na+]
# source: AOP-OW04-621.par
DNA_NN_Weber_OW04_621 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 2.8755, 10.9634, 1.4790, 6.6027, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -5.7538, -16.2941,
-0.4605, -0.5974, -10.7988, -31.9130, -10.7744, -30.3081, -0.8610, -0.9626,
-4.1898, -10.7677, -7.2483, -20.2750, -10.5953, -28.4924, -4.4725, -10.2831,
-8.1045, -21.6798
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_621, "salt_mM") <- 621
# AOP-OW04 at 1020 mM [Na+]
# source: AOP-OW04-1020.par
DNA_NN_Weber_OW04_1020 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 6.4573, 16.9991, 4.8550, 11.8191, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -8.6278, -24.3084,
-2.1846, -5.6406, -13.8317, -40.2750, -15.0245, -41.9995, -1.7673, -3.2100,
-6.9985, -18.5138, -9.5341, -26.2352, -14.4129, -38.8461, -6.7989, -15.8173,
-11.0161, -29.2985
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_OW04_1020, "salt_mM") <- 1021
# ---- DNA, general purpose ----
# AOP-CMB, combined dataset (recommended DNA set)
# source: AOP-CMB.par
DNA_NN_Weber_2015 <- .complete_nn_rc(matrix(c(
0.0000, 0.0000, 2.5326, 5.6637, 0.4900, -0.6757, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -0.5873, -7.2009, -20.2175,
-5.6921, -15.8947, -4.0774, -11.3388, -8.5782, -23.1654, -7.2564, -19.3191,
-6.4646, -17.0925, -7.1036, -18.9155, -9.7028, -24.8796, -9.5938, -24.4371,
-8.7684, -22.7375
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(DNA_NN_Weber_2015, "salt_mM") <- 1020
# ---- RNA, measured at five salts, VIF scheme ----
# AOP-VIF at 71 mM [Na+]
# source: AOP-VIFRW-71.par
RNA_NN_Weber_VIF_71 <- .complete_nn_rc(matrix(c(
3.4085, 0.2886, 4.6722, 12.4606, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.3315, -14.8085,
-10.2716, -29.6132, -10.3296, -31.9802, -10.3505, -29.3566, -12.4315, -32.9656,
-11.5553, -31.1152, -12.1350, -33.8251, -10.6616, -27.9859, -14.2091, -36.2688,
-12.8534, -32.1972
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_71, "salt_mM") <- 71
# AOP-VIF at 121 mM [Na+]
# source: AOP-VIFRW-121.par
RNA_NN_Weber_VIF_121 <- .complete_nn_rc(matrix(c(
2.3170, 0.2578, 5.6296, 15.1423, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -9.5363, -28.9960,
-11.4066, -32.4378, -10.5677, -33.7691, -10.1748, -29.8826, -12.2827, -32.2623,
-11.3606, -30.2829, -12.8637, -37.1318, -10.8907, -29.1438, -13.9826, -35.8758,
-11.6163, -28.9123
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_121, "salt_mM") <- 121
# AOP-VIF at 221 mM [Na+]
# source: AOP-VIFRW-221.par
RNA_NN_Weber_VIF_221 <- .complete_nn_rc(matrix(c(
2.4417, -1.2287, 5.2434, 14.1869, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -7.2734, -20.9687,
-11.0415, -30.9510, -9.4127, -29.3164, -11.1475, -32.0746, -11.7833, -30.3817,
-12.4938, -33.5013, -11.9556, -33.3326, -11.9159, -31.8045, -13.5035, -33.7292,
-11.8934, -29.0209
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_221, "salt_mM") <- 221
# AOP-VIF at 621 mM [Na+]
# source: AOP-VIFRW-621.par
RNA_NN_Weber_VIF_621 <- .complete_nn_rc(matrix(c(
2.0381, -0.7355, 4.7624, 12.6860, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.0673, -13.7959,
-12.0086, -33.5987, -9.4331, -29.1261, -12.3988, -36.0577, -13.1418, -34.7699,
-11.6446, -30.8224, -11.9930, -33.4713, -11.8000, -31.5396, -13.1341, -32.6632,
-11.4641, -27.8822
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_621, "salt_mM") <- 621
# AOP-VIF at 1021 mM [Na+]
# source: AOP-VIFRW-1021.par
RNA_NN_Weber_VIF_1021 <- .complete_nn_rc(matrix(c(
5.2375, 1.0265, 1.8683, 4.8400, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -6.9088, -18.9939,
-12.3315, -35.5068, -11.1870, -31.5678, -9.2476, -22.8267, -13.0887, -34.4079,
-11.4727, -29.7037, -13.2535, -34.7600, -12.2039, -30.9536, -15.5716, -38.5593,
-15.0523, -36.9921
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_VIF_1021, "salt_mM") <- 1021
# ---- RNA, measured at five salts, FIF scheme ----
# AOP-FIF at 71 mM [Na+]
# source: AOP-FIFRW-71.par
RNA_NN_Weber_FIF_71 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.6583, -15.6834,
-8.5043, -25.9600, -10.5270, -30.0115, -11.0010, -29.8012, -11.8700, -31.8195,
-10.7268, -29.1405, -12.8979, -34.6410, -10.5983, -27.3390, -14.6024, -36.9660,
-13.3232, -33.1294
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_71, "salt_mM") <- 71
# AOP-FIF at 121 mM [Na+]
# source: AOP-FIFRW-121.par
RNA_NN_Weber_FIF_121 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -10.6106, -31.9905,
-7.5798, -22.7640, -11.5058, -32.8953, -11.7860, -32.2437, -11.1365, -29.0034,
-9.6600, -25.3309, -14.8961, -40.8555, -10.9362, -28.2149, -15.0954, -38.0510,
-12.9669, -31.8119
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_121, "salt_mM") <- 121
# AOP-FIF at 221 mM [Na+]
# source: AOP-FIFRW-221.par
RNA_NN_Weber_FIF_221 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -7.7054, -22.0235,
-8.4715, -24.8591, -10.0105, -28.2728, -12.2437, -33.5058, -11.0624, -28.5283,
-11.3271, -30.2921, -13.3287, -35.6580, -12.0183, -31.3994, -14.3687, -35.5373,
-13.0358, -31.6893
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_221, "salt_mM") <- 221
# AOP-FIF at 621 mM [Na+]
# source: AOP-FIFRW-621.par
RNA_NN_Weber_FIF_621 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -5.8155, -15.6770,
-9.0920, -26.3098, -10.2523, -28.5906, -13.3474, -36.6460, -12.7358, -33.5822,
-10.4105, -27.1001, -13.6846, -36.4177, -11.7763, -30.4166, -14.5406, -35.7457,
-13.1793, -31.8911
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_621, "salt_mM") <- 621
# AOP-FIF at 1021 mM [Na+]
# source: AOP-FIFRW-1021.par
RNA_NN_Weber_FIF_1021 <- .complete_nn_rc(matrix(c(
3.6100, -1.5000, 3.7200, 10.5000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.4000, -6.4314, -17.6766,
-12.7794, -37.0377, -13.5099, -39.1452, -8.3871, -20.6134, -12.5166, -33.0215,
-10.0627, -25.7313, -13.4641, -35.8415, -10.8365, -27.2504, -14.7595, -36.6631,
-13.6056, -33.2045
), ncol=2, byrow=TRUE, dimnames=list(nn_row_std, nn_col)))
attr(RNA_NN_Weber_FIF_1021, "salt_mM") <- 1021
# ---- DNA/RNA hybrid ----
# AOP-DRFT, curve fitting (recommended high salt)
# source: AOP-DRFT.par
RNA_DNA_NN_Weber_2019_FT <- matrix(c(
1.4943, -8.5131, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -8.2694, -25.2054,
-9.3923, -25.2281, -10.8693, -31.0383, -9.8990, -28.5875, -9.7935, -26.2840,
-11.9087, -28.2205, -12.6023, -33.6818, -12.2978, -33.1193, -11.5436, -33.0019,
-13.4801, -34.4699, -12.4230, -32.8537, -11.7433, -31.0498, -9.5595, -28.8683,
-9.7656, -25.4503, -11.4676, -32.1090, -10.2758, -29.8576
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
attr(RNA_DNA_NN_Weber_2019_FT, "salt_mM") <- 1000
# AOP-DRVH, Van't Hoff
# source: AOP-DRVH.par
RNA_DNA_NN_Weber_2019_VH <- matrix(c(
1.5947, -8.6455, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -7.7206, -23.8767,
-11.4595, -31.6731, -10.5706, -30.0225, -9.8591, -28.5101, -10.6942, -29.0233,
-11.3728, -26.5422, -13.0108, -35.1695, -12.1751, -32.9612, -11.6522, -32.9572,
-14.0583, -35.7112, -12.4383, -32.9497, -10.5935, -27.3484, -10.2428, -30.8614,
-9.5993, -24.4245, -10.7358, -29.8654, -9.9268, -28.6058
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
attr(RNA_DNA_NN_Weber_2019_VH, "salt_mM") <- 1000
# AOP-DRLS, low salt
# source: AOP-DRLS.par
RNA_DNA_NN_Weber_2019_LS <- matrix(c(
0.9630, -6.5119, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -10.8561, -35.9011,
-10.2316, -29.0019, -8.6009, -22.9547, -9.4736, -30.5491, -9.5454, -25.1770,
-11.8223, -30.8244, -11.9288, -29.9238, -12.2281, -33.2457, -8.7011, -25.6312,
-8.4153, -22.4119, -7.4720, -19.3490, -12.7010, -37.8656, -9.9307, -30.7749,
-12.2396, -33.9475, -13.8697, -37.9789, -10.6987, -32.0137
), ncol=2, byrow=TRUE, dimnames=list(hybrid_rows, nn_col))
attr(RNA_DNA_NN_Weber_2019_LS, "salt_mM") <- 100
# -- Return assembled list ------------------------------------------------
list(
DNA_NN_Breslauer_1986 = DNA_NN_Breslauer_1986,
DNA_NN_Sugimoto_1996 = DNA_NN_Sugimoto_1996,
DNA_NN_Allawi_1998 = DNA_NN_Allawi_1998,
DNA_NN_SantaLucia_2004 = DNA_NN_SantaLucia_2004,
RNA_NN_Freier_1986 = RNA_NN_Freier_1986,
RNA_NN_Xia_1998 = RNA_NN_Xia_1998,
RNA_NN_Chen_2012 = RNA_NN_Chen_2012,
RNA_DNA_NN_Sugimoto_1995 = RNA_DNA_NN_Sugimoto_1995,
DNA_IMM_Peyret_1999 = DNA_IMM_Peyret_1999,
DNA_TMM_Bommarito_2000 = DNA_TMM_Bommarito_2000,
DNA_DE_Bommarito_2000 = DNA_DE_Bommarito_2000,
RNA_DE_Turner_2010 = RNA_DE_Turner_2010,
GC_VARTAB = GC_VARTAB,
# -- Weber group sets ---------------------------------------------------
DNA_NN_Weber_OW04_69 = DNA_NN_Weber_OW04_69,
DNA_NN_Weber_OW04_119 = DNA_NN_Weber_OW04_119,
DNA_NN_Weber_OW04_220 = DNA_NN_Weber_OW04_220,
DNA_NN_Weber_OW04_621 = DNA_NN_Weber_OW04_621,
DNA_NN_Weber_OW04_1020 = DNA_NN_Weber_OW04_1020,
DNA_NN_Weber_2015 = DNA_NN_Weber_2015,
RNA_NN_Weber_VIF_71 = RNA_NN_Weber_VIF_71,
RNA_NN_Weber_VIF_121 = RNA_NN_Weber_VIF_121,
RNA_NN_Weber_VIF_221 = RNA_NN_Weber_VIF_221,
RNA_NN_Weber_VIF_621 = RNA_NN_Weber_VIF_621,
RNA_NN_Weber_VIF_1021 = RNA_NN_Weber_VIF_1021,
RNA_NN_Weber_FIF_71 = RNA_NN_Weber_FIF_71,
RNA_NN_Weber_FIF_121 = RNA_NN_Weber_FIF_121,
RNA_NN_Weber_FIF_221 = RNA_NN_Weber_FIF_221,
RNA_NN_Weber_FIF_621 = RNA_NN_Weber_FIF_621,
RNA_NN_Weber_FIF_1021 = RNA_NN_Weber_FIF_1021,
RNA_DNA_NN_Weber_2019_FT = RNA_DNA_NN_Weber_2019_FT,
RNA_DNA_NN_Weber_2019_VH = RNA_DNA_NN_Weber_2019_VH,
RNA_DNA_NN_Weber_2019_LS = RNA_DNA_NN_Weber_2019_LS
)
}
# =============================================================================
# VERIFICATION - run this after building to confirm completion
# =============================================================================
.verify_nn_tables <- function(tbl_list) {
# The 6 RC pairs that should now be present in every standard DNA/RNA NN table
expected_rc_pairs <- c("TT/AA","TG/AC","TC/AG","AG/TC","AC/TG","CC/GG")
standard_tables <- c("DNA_NN_Breslauer_1986","DNA_NN_Sugimoto_1996",
"DNA_NN_Allawi_1998","DNA_NN_SantaLucia_2004",
"RNA_NN_Freier_1986","RNA_NN_Xia_1998",
"RNA_NN_Chen_2012","RNA_DNA_NN_Sugimoto_1995",
"DNA_NN_Weber_2015","DNA_NN_Weber_OW04_69",
"DNA_NN_Weber_OW04_119","DNA_NN_Weber_OW04_220",
"DNA_NN_Weber_OW04_621","DNA_NN_Weber_OW04_1020",
"RNA_NN_Weber_VIF_71","RNA_NN_Weber_VIF_121",
"RNA_NN_Weber_VIF_221","RNA_NN_Weber_VIF_621",
"RNA_NN_Weber_VIF_1021","RNA_NN_Weber_FIF_71",
"RNA_NN_Weber_FIF_121","RNA_NN_Weber_FIF_221",
"RNA_NN_Weber_FIF_621","RNA_NN_Weber_FIF_1021")
all_ok <- TRUE
for (nm in standard_tables) {
tbl <- tbl_list[[nm]]
missing <- expected_rc_pairs[!expected_rc_pairs %in% rownames(tbl)]
n_rows <- nrow(tbl)
status <- if (length(missing) == 0) "OK" else paste("MISSING:", paste(missing, collapse=", "))
cat(sprintf(" %-35s rows=%-3d %s\n", nm, n_rows, status))
if (length(missing) > 0) all_ok <- FALSE
}
# -- Weber sets must all carry a salt attribute --------------------------
weber <- grep("Weber", names(tbl_list), value = TRUE)
cat("\n salt attributes on Weber sets:\n")
for (nm in weber) {
s <- attr(tbl_list[[nm]], "salt_mM")
cat(sprintf(" %-28s %8s mM %s\n", nm,
if (is.null(s)) "NA" else format(s),
if (!is.null(s) && !is.na(s)) "OK" else "MISSING"))
if (is.null(s) || is.na(s)) all_ok <- FALSE
}
# -- Published values that were previously mistranscribed -----------------
sl <- tbl_list$DNA_NN_SantaLucia_2004
ref <- list("TA/AT" = c(-7.2, -21.3), "GG/CC" = c(-8.0, -19.9))
cat("\n SantaLucia 2004 spot checks (vs P-SL04.par and P-SL98.par):\n")
for (k in names(ref)) {
ok <- isTRUE(all.equal(unname(sl[k, ]), ref[[k]], tolerance = 1e-8))
cat(sprintf(" %-28s %s\n", k, if (ok) "OK" else "FAIL"))
if (!ok) all_ok <- FALSE
}
if (all_ok) {
cat("\nAll tables complete. keys_fr lookups will always succeed.\n")
cat("The keys_rf / pos_nn_rf fallback in tm_nn_core() can now be removed.\n")
}
invisible(all_ok)
}
# -- Usage --------------------------------------------------------------------
# Build once at package development time:
# .TM_CONSTANTS <- .build_all_tm_tables()
# .verify_nn_tables(.TM_CONSTANTS)
# usethis::use_data(.TM_CONSTANTS, internal = TRUE, overwrite = TRUE)
#
# Expected verification output:
# DNA_NN_Breslauer_1986 rows=23 OK
# DNA_NN_Sugimoto_1996 rows=23 OK
# DNA_NN_Allawi_1998 rows=23 OK
# DNA_NN_SantaLucia_2004 rows=23 OK
# RNA_NN_Freier_1986 rows=23 OK
# RNA_NN_Xia_1998 rows=23 OK
# RNA_NN_Chen_2012 rows=28 OK (17 std + 11 wobble; 6 RC added to std)
# RNA_DNA_NN_Sugimoto_1995 rows=23 OK (already had all 6 RC pairs)
#
# After building, in tm_nn_core() the NN sum simplifies to:
# matched_nn <- keys_fr %in% rownames(nn_tbl)
# delta_h <- delta_h + sum(nn_tbl[keys_fr[matched_nn], 1])
# delta_s <- delta_s + sum(nn_tbl[keys_fr[matched_nn], 2])
# # No keys_rf, no pos_nn_rf, no which_nn_fr/rf needed.
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