Type:	Package
Title:	A Calculator for Melting Temperature of Nucleic Acid Sequences
Version:	1.0.4
Date:	2026-03-18
Description:	A comprehensive R package for calculating melting temperatures of nucleic acid sequences. Implements three calculation methods: 1. Wallace rule (Thein & Wallace, 1986) 2. Empirical formulas based on GC content (Marmur, 1962; Schildkraut, 2010; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001) 3. Nearest neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1998; SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; Sugimoto, 1995; Allawi, 1997; Santalucia, 2005) Includes corrections for: - Salt ions (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008) - Chemical compounds (dimethyl sulfoxide, formamide) Supports both direct sequence input and FASTA file input.
BugReports:	https://github.com/JunhuiLi1017/TmCalculator/issues
License:	MIT + file LICENSE
Depends:	R (≥ 3.5)
VignetteBuilder:	knitr
Imports:	seqinr, BSgenome, Biostrings, GenomicRanges, IRanges, S4Vectors, GenomeInfoDb, Gviz, dplyr, ggbio, ggplot2, karyoploteR, viridis, rlang, plotly
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, devtools, roxygen2, BSgenome.Hsapiens.UCSC.hg38
NeedsCompilation:	no
Repository:	CRAN
RoxygenNote:	7.3.2
LazyData:	true
Encoding:	UTF-8
Packaged:	2026-03-20 04:00:13 UTC; lij11
Author:	Junhui Li [cre, aut]
Maintainer:	Junhui Li <ljh.biostat@gmail.com>
Date/Publication:	2026-03-20 16:50:30 UTC

TmCalculator: A Calculator for Melting Temperature of Nucleic Acid Sequences

Description

A comprehensive R package for calculating melting temperatures of nucleic acid sequences. Implements three calculation methods: 1. Wallace rule (Thein & Wallace, 1986) 2. Empirical formulas based on GC content (Marmur, 1962; Schildkraut, 2010; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001) 3. Nearest neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1998; SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; Sugimoto, 1995; Allawi, 1997; Santalucia, 2005) Includes corrections for: - Salt ions (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008) - Chemical compounds (dimethyl sulfoxide, formamide) Supports both direct sequence input and FASTA file input.

Author(s)

Maintainer: Junhui Li ljh.biostat@gmail.com (ORCID)

See Also

Useful links:

• Report bugs at https://github.com/JunhuiLi1017/TmCalculator/issues

convert a vector of characters into a string

Description

Simply convert a vector of characters such as c("H","e","l","l","o","W","o","r","l","d") into a single string "HelloWorld".

Usage

c2s(characters)

Arguments

Value

Retrun a strings

Author(s)

Junhui Li

References

citation("TmCalculator")

Filter invalid bases in nucleotide sequences

Description

This function processes nucleotide sequences by converting characters to uppercase and replacing invalid bases with "". based on the specified method. The function preserves the sequence length and attributes (name and Tm) of each sequence.

Usage

check_filter_seq(seq_list, method)

Arguments

Value

Returns a list of sequences with the same structure as input, where invalid bases are replaced with ""

Author(s)

Junhui Li

References

citation("TmCalculator")

Corrections of melting temperature with chemical substances

Description

Apply corrections to melting temperature calculations based on the presence of DMSO and formamide. These corrections are rough approximations and should be used with caution.

Usage

chem_correct(
  DMSO = 0,
  formamide_value_unit = list(value = 0, unit = "percent"),
  dmso_factor = 0.75,
  formamide_factor = 0.65,
  pt_gc = NULL
)

Arguments

Details

When formamide_value_unit$unit = "percent": Correction = - factor * percentage_of_formamide

When formamide_value_unit$unit = "molar": Correction = (0.453 * GC/100 - 2.88) * formamide

Author(s)

Junhui Li

References

von Ahsen N, Wittwer CT, Schutz E, et al. Oligonucleotide melting temperatures under PCR conditions: deoxynucleotide Triphosphate and Dimethyl sulfoxide concentrations with comparison to alternative empirical formulas. Clin Chem 2001, 47:1956-1961.

Examples

# DMSO correction
chem_correct(DMSO = 3)

# Formamide correction (percent)
chem_correct(formamide_value_unit = list(value = 1.25, unit = "percent"), pt_gc = 50)

# Formamide correction (molar)
chem_correct(formamide_value_unit = list(value = 1.25, unit = "molar"), pt_gc = 50)

Convert genomic coordinate strings to GenomicRanges object with sequences

Description

This function converts genomic coordinate strings in the format "chr:start-end:strand:species[:name]" to a GenomicRanges object containing the corresponding sequences from the specified reference genome.

Usage

coor_to_genomic_ranges(input_seq)

Arguments

Value

A GenomicRanges object containing: - GRanges information (seqnames, ranges, strand) - sequence data from the reference genome - Names either from the optional name parameter or auto-generated as "1", "2", etc.

Examples

## Not run: 
# Example with multiple coordinates
coords <- c(
  "chr1:1898000-1898050:+:BSgenome.Hsapiens.UCSC.hg38:exon1",
  "chr2:2563000-2563050:-:BSgenome.Hsapiens.UCSC.hg38:exon2"
)
gr <- coor_to_genomic_ranges(coords)

## End(Not run)

Convert FASTA file to GenomicRanges object

Description

This function reads sequences from a FASTA file and converts them to a GenomicRanges object. If named with format ">chr2:1-10:[+|-]:[seq_name]", the name will be parsed into GRanges components.

Usage

fa_to_genomic_ranges(input_seq)

Arguments

Value

A GenomicRanges object containing: - GRanges information (seqnames, ranges, strand) - sequence data from FASTA file - Complementary sequences (if provided) - Names from FASTA headers

Examples

# Example with single FASTA file
input_seq <- system.file("extdata", "example1.fasta", package = "TmCalculator")
gr <- fa_to_genomic_ranges(input_seq)

Calculate G and C content of nucleotide sequences

Description

Calculate G and C content of nucleotide sequences. The function calculates the percentage of G and C bases relative to the total number of A, T, G, and C bases in the sequence.

Usage

gc(input_seq, ambiguous = FALSE)

Arguments

Value

Content of G and C as a percentage (range from 0 to 100

Author(s)

Junhui Li

Examples

# Calculate GC content of a DNA sequence
gc(c("a","t","c","t","g","g","g","c","c","a","g","t","a"))  # 53.85%

# Calculate GC content including ambiguous bases
gc("GCATSWSYK", ambiguous = TRUE)  # 55.56%

Generate complementary sequence

Description

Generate the complementary sequence of a nucleic acid sequence, with an option to reverse it.

Usage

generate_complement(input_seq, reverse = FALSE)

Arguments

Value

Returns the complementary sequence(s) in the specified direction.

Author(s)

Junhui Li

References

citation("TmCalculator")

Examples

# Generate complementary sequence in same direction (5' to 3')
generate_complement("ATGCG", reverse = FALSE)

# Generate complementary sequence in reverse direction (3' to 5')
generate_complement("ATGCG", reverse = TRUE)

Plot Tm values as Genome Browser Tracks using Gviz

Description

This function generates Gviz plots displaying Tm values as DataTracks alongside genome axes and ideograms for specified chromosomes. Tm values are visualized using a heatmap-like color gradient.

Usage

plot_tm_genome_tracks(
  gr,
  chromosome_to_plot,
  genome_assembly = NULL,
  tm_track_title = "Melting Temperature (°C)",
  color_palette = c("viridis", "magma", "plasma", "inferno", "cividis"),
  show_ideogram = TRUE,
  zoom = NULL
)

Arguments

Value

Invisible NULL. The function generates a plot directly.

Examples

## Not run: 
library(GenomicRanges)
library(Gviz)
# Example 1: Generate sample data with 150 sequences
set.seed(123)

# Generate 100 sequences for chr1
chr1_starts <- sort(sample(1:249250621, 100))  # chr1 length in hg19
chr1_lengths <- sample(50:200, 100, replace=TRUE)
chr1_ends <- chr1_starts + chr1_lengths
chr1_tms <- runif(100, min=60, max=80)

# Generate 50 sequences for chr2
chr2_starts <- sort(sample(1:243199373, 50))   # chr2 length in hg19
chr2_lengths <- sample(50:200, 50, replace=TRUE)
chr2_ends <- chr2_starts + chr2_lengths
chr2_tms <- runif(50, min=60, max=80)

# Create GRanges object
tm_results <- GRanges(
  seqnames = Rle(c(rep("chr1", 100), rep("chr2", 50))),
  ranges = IRanges(
    start = c(chr1_starts, chr2_starts),
    end = c(chr1_ends, chr2_ends)
  ),
  strand = Rle(sample(c("+", "-"), 150, replace=TRUE)),
  Tm = c(chr1_tms, chr2_tms)
)

# Plot single chromosome with zoom
plot_tm_genome_tracks(
  gr = tm_results,
  chromosome_to_plot = "chr1",
  genome_assembly = "hg19",
  tm_track_title = "DNA Sequence Tm",
  zoom = "chr1:10062800-20000000"
)

# Example with custom color palette and no zoom
plot_tm_genome_tracks(
  gr = tm_results,
  chromosome_to_plot = "chr2",
  genome_assembly = "hg19",
  color_palette = "plasma"
)

## End(Not run)

Plot Tm values as a heatmap using ggbio

Description

This function generates a heatmap visualization of Tm values across chromosomes using the ggbio package. It supports both karyogram and faceted plot types.

Usage

plot_tm_heatmap(
  gr,
  genome_assembly = NULL,
  chromosome_to_plot = NULL,
  plot_type = c("karyogram", "faceted"),
  color_palette = c("viridis", "magma", "plasma", "inferno", "cividis"),
  title_name = NULL,
  zoom = NULL
)

Arguments

Value

A ggplot object displaying Tm values across genomic coordinates.

Examples

## Not run: 
# Create example GRanges object  
gr_tm <- GenomicRanges::GRanges(
  seqnames = c("chr1", "chr2", "chr1", "chr2", "chr1"),
  ranges = IRanges::IRanges(
    start = c(100, 200, 300, 400, 150),
    end = c(150, 250, 350, 450, 200)
  ),
  Tm = c(65.5, 68.2, 70.1, 63.8, 72.0)
)

# Plot with ideograms
plot_tm_heatmap(gr_tm, genome_assembly = "hg19", plot_type = "karyogram")

# Faceted plot by chromosome
plot_tm_heatmap(gr_tm, genome_assembly = "hg19", plot_type = "faceted")

# Plot with zoom
plot_tm_heatmap(gr_tm, genome_assembly = "hg19", plot_type = "faceted", zoom = "chr1:100-200")


## End(Not run)

Convert Tm plots to interactive plotly versions

Description

These functions convert the standard Tm plots to interactive plotly versions that can be used in Shiny applications or R Markdown documents.

These functions convert the standard Tm karyotype plots to interactive plotly versions that can be used in Shiny applications or R Markdown documents.

These functions convert the standard Tm genome tracks plots to interactive plotly versions that can be used in Shiny applications or R Markdown documents.

Usage

plot_tm_heatmap_interactive(
  gr,
  genome_assembly = NULL,
  chromosome_to_plot = NULL,
  plot_type = c("karyogram", "faceted"),
  color_palette = c("viridis", "magma", "plasma", "inferno", "cividis"),
  title_name = NULL,
  zoom = NULL
)

plot_tm_karyotype_interactive(
  gr,
  chromosomes = NULL,
  genome_assembly = NULL,
  colors = NULL,
  shapes = NULL,
  plot_type = 1,
  point_cex = 1.5,
  xaxis_cex = 0.7,
  yaxis_cex = 0.8,
  chr_cex = 1,
  tick_dist = 1e+07,
  zoom = NULL
)

plot_tm_genome_tracks_interactive(
  gr,
  chromosome_to_plot,
  genome_assembly = NULL,
  tm_track_title = "Melting Temperature (°C)",
  color_palette = c("viridis", "magma", "plasma", "inferno", "cividis"),
  show_ideogram = TRUE,
  zoom = NULL
)

Arguments

Value

A plotly object.

Plot Tm Values from GRanges with Per-Chromosome Colors and Shapes

Description

Creates a genome-wide plot of melting temperature (Tm) values from a GRanges object using the karyoploteR package. The x-axis represents genomic positions across chromosomes, and the y-axis represents Tm values. Points are plotted at the midpoints of genomic ranges, with customizable colors and shapes per chromosome.

Usage

plot_tm_karyotype(
  gr,
  chromosomes = NULL,
  genome_assembly = NULL,
  colors = NULL,
  shapes = NULL,
  plot_type = 1,
  point_cex = 1.5,
  xaxis_cex = 0.7,
  yaxis_cex = 0.8,
  chr_cex = 1,
  tick_dist = 1e+07,
  zoom = NULL
)

Arguments

Details

The function validates that gr is a GRanges object with a Tm column. It automatically sets the y-axis limits based on the range of Tm values, with slight padding (floor and ceiling). The plot includes chromosome names (via karyoploteR's default labeling), base pair positions, and a labeled y-axis. Points are colored and shaped according to the colors and shapes parameters, with defaults applied for unspecified chromosomes. The y-axis is placed on the left for plot_type = 1 and on the right for plot_type = 4 or 7, with the label positioned to the right of the y-axis, vertically centered in the middle of the y-axis range. Text sizes for x-axis labels, y-axis labels, and chromosome names can be customized using xaxis_cex, yaxis_cex, and chr_cex, respectively.

Value

Invisibly returns NULL. The function generates a plot as a side effect.

Examples

## Not run: 
library(GenomicRanges)
# Create a sample GRanges object
gr <- GRanges(
  seqnames = c("chr22", "chr1", "chr14", "chr22"),
  ranges = IRanges(
    start = c(13209021, 1, 13200, 13209150),
    end = c(13209099, 76, 13222, 13209200)
  ),
  strand = c("+", "*", "*", "-"),
  Tm = c(69.1147, 71.1160, 50.7169, 65.5000)
)
genome(seqinfo(gr)) <- "hg19"

# Plot with default settings (plot_type=1)
plot_tm_karyotype(gr)

# Plot with partial color and shape specification (plot_type=4)
plot_tm_karyotype(
  gr,
  genome_assembly="hg19",
  colors = c(chr1 = "#FF0000"),  # chr14, chr22 use default color
  shapes = c(chr1 = 16, chr14 = 17),  # chr22 uses pch=16
  plot_type = 4,
  xaxis_cex = 0.6,
  yaxis_cex = 0.9,
  chr_cex = 1.2
)

# Plot with full color and shape specification (plot_type=7)
plot_tm_karyotype(
  gr,
  genome_assembly="hg38",
  colors = c(chr1 = "#FF0000", chr14 = "#00FF00", chr22 = "#0000FF"),
  shapes = c(chr1 = 16, chr14 = 17, chr22 = 16),
  plot_type = 5,
  xaxis_cex = 0.8,
  yaxis_cex = 0.7,
  chr_cex = 0.8
)

# Plot with zoom into chr22
zoom_region <- GRanges("chr22:13200000-13220000")
plot_tm_karyotype(
  gr,
  genome_assembly="hg38",
  chromosomes = "chr22",
  zoom = zoom_region,
  xaxis_cex = 0.5,
  yaxis_cex = 1,
  chr_cex = 1.5
)

## End(Not run)

Prints melting temperature from a TmCalculator object

Description

print.TmCalculator prints to console the melting temperature value from an object of class TmCalculator.

Usage

## S3 method for class 'TmCalculator'
print(x, ...)

Arguments

Value

The melting temperature value.

convert a string into a vector of characters

Description

Simply convert a single string such as "HelloWorld" into a vector of characters such as c("H","e","l","l","o","W","o","r","l","d")

Usage

s2c(strings)

Arguments

Value

Retrun a vector of characters

Author(s)

Junhui Li

References

citation("TmCalculator")

Corrections of melting temperature with salt concentration

Description

Apply corrections to melting temperature calculations based on salt concentrations. Different correction methods are available for various experimental conditions.

Usage

salt_correction(
  Na = 0,
  K = 0,
  Tris = 0,
  Mg = 0,
  dNTPs = 0,
  method = c("Schildkraut2010", "Wetmur1991", "SantaLucia1996", "SantaLucia1998-1",
    "SantaLucia1998-2", "Owczarzy2004", "Owczarzy2008"),
  input_seq,
  ambiguous = FALSE
)

Arguments

Details

Different correction methods are available for various experimental conditions:

- Schildkraut2010: Updated salt correction method that accounts for monovalent and divalent cations - Wetmur1991: Classic salt correction method for monovalent cations - SantaLucia1996: DNA-specific salt correction - SantaLucia1998-1: Improved DNA salt correction - SantaLucia1998-2: Alternative DNA salt correction (requires sequence information) - Owczarzy2004: Comprehensive salt correction including effects of divalent cations (requires sequence information) - Owczarzy2008: Updated comprehensive salt correction (requires sequence information)

Author(s)

Junhui Li

References

Schildkraut C, Lifson S. Dependence of the melting temperature of DNA on salt concentration. Biopolymers. 1965;3(2):195-208.

Wetmur JG. DNA Probes: Applications of the Principles of Nucleic Acid Hybridization. Critical Reviews in Biochemistry and Molecular Biology. 1991;26(3-4):227-259.

SantaLucia J. A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proceedings of the National Academy of Sciences. 1998;95(4):1460-1465.

Owczarzy R, Moreira BG, Manthey JA, et al. Predicting stability of DNA duplexes in solutions containing magnesium and monovalent cations. Biochemistry. 2008;47(19):5336-5353.

Examples

salt_correction(Na = 50, Mg = 1.5, method = "Owczarzy2008", 
               input_seq = "ATGCGATGCG")

Thermodynamic parameters for GC-based Tm calculation methods

Description

A data frame containing coefficients and parameters for different GC-based Tm calculation methods. Each row represents a different method with its specific coefficients (A, B, C, D) and salt correction method.

Usage

thermodynamic_gc_params

Format

A data frame with 8 rows and 5 columns:

A

Intercept coefficient

B

GC content coefficient

C

Length correction coefficient

D

Mismatch coefficient

salt_correction

Associated salt correction method

Details

The methods included are: - Chester1993: Tm = 69.3 + 0.41(Percentage_GC) - 650/N - QuikChange: Tm = 81.5 + 0.41(Percentage_GC) - 675/N - Percentage_mismatch - Schildkraut1965: Tm = 81.5 + 0.41(Percentage_GC) - 675/N + 16.6 x log[Na+] - Wetmur1991_MELTING: Tm = 81.5 + 0.41(Percentage_GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - Percentage_mismatch - Wetmur1991_RNA: Tm = 78 + 0.7(Percentage_GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - Percentage_mismatch - Wetmur1991_RNA/DNA: Tm = 67 + 0.8(Percentage_GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - Percentage_mismatch - Primer3Plus: Tm = 81.5 + 0.41(Percentage_GC) - 600/N + 16.6 x log[Na+] - vonAhsen2001: Tm = 77.1 + 0.41(Percentage_GC) - 528/N + 11.7 x log[Na+]

Thermodynamic Tables for Nucleic Acid Hybridization

Description

A comprehensive collection of thermodynamic parameters used for calculating melting temperatures of nucleic acid duplexes. The dataset includes parameters for DNA/DNA, RNA/RNA, and RNA/DNA hybridizations, as well as parameters for mismatches and dangling ends.

Usage

thermodynamic_nn_params

Format

A list containing 12 matrices of thermodynamic parameters:

DNA_NN_Breslauer_1986

DNA/DNA nearest neighbor parameters from Breslauer et al. (1986)

DNA_NN_Sugimoto_1996

DNA/DNA nearest neighbor parameters from Sugimoto et al. (1996)

DNA_NN_Allawi_1998

DNA/DNA nearest neighbor parameters from Allawi et al. (1998)

DNA_NN_SantaLucia_2004

DNA/DNA nearest neighbor parameters from SantaLucia (2004)

RNA_NN_Freier_1986

RNA/RNA nearest neighbor parameters from Freier et al. (1986)

RNA_NN_Xia_1998

RNA/RNA nearest neighbor parameters from Xia et al. (1998)

RNA_NN_Chen_2012

RNA/RNA nearest neighbor parameters from Chen et al. (2012)

RNA_DNA_NN_Sugimoto_1995

RNA/DNA nearest neighbor parameters from Sugimoto et al. (1995)

DNA_IMM_Peyret_1999

DNA internal mismatch parameters from Peyret et al. (1999)

DNA_TMM_Bommarito_2000

DNA terminal mismatch parameters from Bommarito et al. (2000)

DNA_DE_Bommarito_2000

DNA dangling end parameters from Bommarito et al. (2000)

RNA_DE_Turner_2010

RNA dangling end parameters from Turner et al. (2010)

Each matrix contains thermodynamic parameters (enthalpy and entropy) for different nucleic acid interactions. The parameters are used in the nearest neighbor model for calculating melting temperatures of nucleic acid duplexes.

Source

Various publications as cited in the references

References

Breslauer K J (1986) <doi:10.1073/pnas.83.11.3746> Sugimoto N (1996) <doi:10.1093/nar/24.22.4501> Allawi H (1998) <doi:10.1093/nar/26.11.2694> SantaLucia J (2004) <doi:10.1146/annurev.biophys.32.110601.141800> Freier S (1986) <doi:10.1073/pnas.83.24.9373> Xia T (1998) <doi:10.1021/bi9809425> Chen JL (2012) <doi:10.1021/bi3002709> Sugimoto N (1995) <doi:10.1016/S0048-9697(98)00088-6> Bommarito S (2000) <doi:10.1093/nar/28.9.1929> Peyret N (1999) <doi:10.1021/bi9825091> Allawi H T (1997) <doi:10.1021/bi962590c> Santalucia N (2005) <doi:10.1093/nar/gki918> Turner D H (2010) <doi:10.1093/nar/gkp892>

Examples

# Access DNA/DNA nearest neighbor parameters
thermodynamic_nn_params$DNA_NN_SantaLucia_2004

# Access RNA/RNA nearest neighbor parameters
thermodynamic_nn_params$RNA_NN_Chen_2012

# Access DNA internal mismatch parameters
thermodynamic_nn_params$DNA_IMM_Peyret_1999

Calculate melting temperature using multiple methods

Description

Calculates melting temperature using multiple methods: - Nearest Neighbor thermodynamics (tm_nn) - GC content-based method (tm_gc) - Wallace rule (tm_wallace)

Usage

tm_calculate(
  input_seq,
  method = c("tm_nn", "tm_gc", "tm_wallace"),
  complement_seq = NULL,
  ambiguous = FALSE,
  shift = 0,
  nn_table = c("DNA_NN_SantaLucia_2004", "DNA_NN_Breslauer_1986", "DNA_NN_Sugimoto_1996",
    "DNA_NN_Allawi_1998", "RNA_NN_Freier_1986", "RNA_NN_Xia_1998", "RNA_NN_Chen_2012",
    "RNA_DNA_NN_Sugimoto_1995"),
  tmm_table = "DNA_TMM_Bommarito_2000",
  imm_table = "DNA_IMM_Peyret_1999",
  de_table = c("DNA_DE_Bommarito_2000", "RNA_DE_Turner_2010"),
  dnac_high = 25,
  dnac_low = 25,
  self_comp = FALSE,
  variant = c("Primer3Plus", "Chester1993", "QuikChange", "Schildkraut1965",
    "Wetmur1991_MELTING", "Wetmur1991_RNA", "Wetmur1991_RNA/DNA", "vonAhsen2001"),
  userset = NULL,
  Na = 50,
  K = 0,
  Tris = 0,
  Mg = 0,
  dNTPs = 0,
  salt_corr_method = c("Schildkraut2010", "Wetmur1991", "SantaLucia1996",
    "SantaLucia1998-1", "Owczarzy2004", "Owczarzy2008"),
  DMSO = 0,
  formamide_value_unit = list(value = 0, unit = "percent"),
  dmso_factor = 0.75,
  formamide_factor = 0.65,
  mismatch = TRUE
)

Arguments

Details

The function calculates melting temperature using the specified method(s). For each method: - NN: Uses nearest neighbor thermodynamics with detailed sequence analysis - GC: Uses GC content-based calculation with various empirical formulas - Wallace: Uses the simple Wallace rule (2°C per A/T, 4°C per G/C)

The function processes the input sequence once and applies it to all selected methods, making it more efficient than calling each method separately.

Value

A list containing Tm values and options for each method used. The structure includes: - Tm: A list of sequences with updated Tm attributes - Options: A list containing calculation parameters and method information

Available Options

Method Selection:

• method: c("tm_nn", "tm_gc", "tm_wallace")

Nearest Neighbor (NN) Method Options:

• nn_table:

  • "DNA_NN_Breslauer_1986"

  • "DNA_NN_Sugimoto_1996"

  • "DNA_NN_Allawi_1998"

  • "DNA_NN_SantaLucia_2004" (default)

  • "RNA_NN_Freier_1986"

  • "RNA_NN_Xia_1998"

  • "RNA_NN_Chen_2012"

  • "RNA_DNA_NN_Sugimoto_1995"

• tmm_table (Terminal Mismatches):

  • "DNA_TMM_Bommarito_2000" (default)

• imm_table (Internal Mismatches):

  • "DNA_IMM_Peyret_1999" (default)

• de_table (Dangling Ends):

  • "DNA_DE_Bommarito_2000" (default)

  • "RNA_DE_Turner_2010"

GC Method Options:

• variant:

  • "Primer3Plus" (default)

  • "Chester1993"

  • "QuikChange"

  • "Schildkraut1965"

  • "Wetmur1991_MELTING"

  • "Wetmur1991_RNA"

  • "Wetmur1991_RNA/DNA"

  • "vonAhsen2001"

Salt Correction Options:

• salt_corr_method:

  • "Schildkraut2010" (default)

  • "Wetmur1991"

  • "SantaLucia1996"

  • "SantaLucia1998-1"

  • "SantaLucia1998-2"

  • "Owczarzy2004"

  • "Owczarzy2008"

Formamide Unit Options:

• formamide_value_unit$unit:

  • "percent" (default)

  • "molar"

Other Parameters:

• ambiguous: TRUE/FALSE (default: FALSE)

• shift: Integer value (default: 0)

• dnac_high: Numeric value in nM (default: 25)

• dnac_low: Numeric value in nM (default: 25)

• self_comp: TRUE/FALSE (default: FALSE)

• Na: Millimolar concentration (default: 50)

• K: Millimolar concentration (default: 0)

• Tris: Millimolar concentration (default: 0)

• Mg: Millimolar concentration (default: 0)

• dNTPs: Millimolar concentration (default: 0)

• DMSO: Percent concentration (default: 0)

• dmso_factor: Numeric value (default: 0.75)

• formamide_factor: Numeric value (default: 0.65)

• mismatch: TRUE/FALSE (default: TRUE)

Examples

# Calculate Tm using all methods
input_seq <- c("ATGCGATGCG")

# Calculate Tm with specific method parameters
result <- tm_calculate(
  input_seq,
  method = "tm_nn",
  nn_table = "DNA_NN_SantaLucia_2004",
  salt_corr_method = "Owczarzy2008"
)

Calculate the melting temperature using empirical formulas based on GC content

Description

Calculate the melting temperature using empirical formulas based on GC content with different options. The function returns a list of sequences with updated Tm attributes and calculation options.

Usage

tm_gc(
  gr_seq,
  ambiguous = FALSE,
  userset = NULL,
  variant = c("Primer3Plus", "Chester1993", "QuikChange", "Schildkraut1965",
    "Wetmur1991_MELTING", "Wetmur1991_RNA", "Wetmur1991_RNA/DNA", "vonAhsen2001"),
  Na = 50,
  K = 0,
  Tris = 0,
  Mg = 0,
  dNTPs = 0,
  salt_corr_method = c("Schildkraut2010", "Wetmur1991", "SantaLucia1996",
    "SantaLucia1998-1", "Owczarzy2004", "Owczarzy2008"),
  mismatch = TRUE,
  DMSO = 0,
  formamide_value_unit = list(value = 0, unit = "percent"),
  dmso_factor = 0.75,
  formamide_factor = 0.65
)

Arguments

Value

Returns a list with two components: - Tm: A list of sequences with updated Tm attributes - Options: A list containing calculation parameters and method information

Author(s)

Junhui Li

References

Marmur J, Doty P. Determination of the base composition of deoxyribonucleic acid from its thermal denaturation temperature. Journal of Molecular Biology, 1962, 5(1):109-118.

Schildkraut C. Dependence of the melting temperature of DNA on salt concentration. Biopolymers, 2010, 3(2):195-208.

Wetmur JG. DNA Probes: Applications of the Principles of Nucleic Acid Hybridization. CRC Critical Reviews in Biochemistry, 1991, 26(3-4):33.

Untergasser A, Cutcutache I, Koressaar T, et al. Primer3–new capabilities and interfaces. Nucleic Acids Research, 2012, 40(15):e115-e115.

von Ahsen N, Wittwer CT, Schutz E, et al. Oligonucleotide melting temperatures under PCR conditions: deoxynucleotide Triphosphate and Dimethyl sulfoxide concentrations with comparison to alternative empirical formulas. Clin Chem 2001, 47:1956-1961.

Examples

# Example with multiple sequences
input_seq <- c("ATCGTGCGTAGCAGTACGATCAGTAG", "ATCGTGCGTAGCAGTACGATCAGTAG")
gr_seq <- to_genomic_ranges(input_seq)
out <- tm_gc(gr_seq, ambiguous = TRUE, variant = "Primer3Plus", Na = 50, mismatch = TRUE)
out
out$Options

Calculate melting temperature using nearest neighbor thermodynamics

Description

Calculate melting temperature using nearest neighbor thermodynamics. The function checks if all sequence combinations in the input sequence are present in the thermodynamic parameter tables before performing calculations.

Usage

tm_nn(
  gr_seq,
  ambiguous = FALSE,
  shift = 0,
  nn_table = c("DNA_NN_SantaLucia_2004", "DNA_NN_Breslauer_1986", "DNA_NN_Sugimoto_1996",
    "DNA_NN_Allawi_1998", "RNA_NN_Freier_1986", "RNA_NN_Xia_1998", "RNA_NN_Chen_2012",
    "RNA_DNA_NN_Sugimoto_1995"),
  tmm_table = "DNA_TMM_Bommarito_2000",
  imm_table = "DNA_IMM_Peyret_1999",
  de_table = c("DNA_DE_Bommarito_2000", "RNA_DE_Turner_2010"),
  dnac_high = 25,
  dnac_low = 25,
  self_comp = FALSE,
  Na = 50,
  K = 0,
  Tris = 0,
  Mg = 0,
  dNTPs = 0,
  salt_corr_method = c("Schildkraut2010", "Wetmur1991", "SantaLucia1996",
    "SantaLucia1998-1", "Owczarzy2004", "Owczarzy2008"),
  DMSO = 0,
  formamide_value_unit = list(value = 0, unit = "percent"),
  dmso_factor = 0.75,
  formamide_factor = 0.65
)

Arguments

Details

DNA_NN_Breslauer_1986: Breslauer K J (1986) <doi:10.1073/pnas.83.11.3746>

DNA_NN_Sugimoto_1996: Sugimoto N (1996) <doi:10.1093/nar/24.22.4501>

DNA_NN_Allawi_1998: Allawi H (1998) <doi:10.1093/nar/26.11.2694>

DNA_NN_SantaLucia_2004: SantaLucia J (2004) <doi:10.1146/annurev.biophys.32.110601.141800>

RNA_NN_Freier_1986: Freier S (1986) <doi:10.1073/pnas.83.24.9373>

RNA_NN_Xia_1998: Xia T (1998) <doi:10.1021/bi9809425>

RNA_NN_Chen_2012: Chen JL (2012) <doi:10.1021/bi3002709>

RNA_DNA_NN_Sugimoto_1995: Sugimoto N (1995)<doi:10.1016/S0048-9697(98)00088-6>

DNA_TMM_Bommarito_2000: Bommarito S (2000) <doi:10.1093/nar/28.9.1929>

DNA_IMM_Peyret_1999: Peyret N (1999) <doi:10.1021/bi9825091> & Allawi H T (1997) <doi:10.1021/bi962590c> & Santalucia N (2005) <doi:10.1093/nar/gki918>

DNA_DE_Bommarito_2000: Bommarito S (2000) <doi:10.1093/nar/28.9.1929>

RNA_DE_Turner_2010: Turner D H (2010) <doi:10.1093/nar/gkp892>

Author(s)

Junhui Li

References

Breslauer K J , Frank R , Blocker H , et al. Predicting DNA duplex stability from the base sequence.[J]. Proceedings of the National Academy of Sciences, 1986, 83(11):3746-3750.

Sugimoto N , Nakano S , Yoneyama M , et al. Improved Thermodynamic Parameters and Helix Initiation Factor to Predict Stability of DNA Duplexes[J]. Nucleic Acids Research, 1996, 24(22):4501-5.

Allawi, H. Thermodynamics of internal C.T mismatches in DNA[J]. Nucleic Acids Research, 1998, 26(11):2694-2701.

Hicks L D , Santalucia J . The thermodynamics of DNA structural motifs.[J]. Annual Review of Biophysics & Biomolecular Structure, 2004, 33(1):415-440.

Freier S M , Kierzek R , Jaeger J A , et al. Improved free-energy parameters for predictions of RNA duplex stability.[J]. Proceedings of the National Academy of Sciences, 1986, 83(24):9373-9377.

Xia T , Santalucia , J , Burkard M E , et al. Thermodynamic Parameters for an Expanded Nearest-Neighbor Model for Formation of RNA Duplexes with Watson-Crick Base Pairs,[J]. Biochemistry, 1998, 37(42):14719-14735.

Chen J L , Dishler A L , Kennedy S D , et al. Testing the Nearest Neighbor Model for Canonical RNA Base Pairs: Revision of GU Parameters[J]. Biochemistry, 2012, 51(16):3508-3522.

Bommarito S, Peyret N, Jr S L. Thermodynamic parameters for DNA sequences with dangling ends[J]. Nucleic Acids Research, 2000, 28(9):1929-1934.

Turner D H , Mathews D H . NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure[J]. Nucleic Acids Research, 2010, 38(Database issue):D280-D282.

Sugimoto N , Nakano S I , Katoh M , et al. Thermodynamic Parameters To Predict Stability of RNA/DNA Hybrid Duplexes[J]. Biochemistry, 1995, 34(35):11211-11216.

Allawi H, SantaLucia J: Thermodynamics and NMR of internal G-T mismatches in DNA. Biochemistry 1997, 36:10581-10594.

Santalucia N E W J . Nearest-neighbor thermodynamics of deoxyinosine pairs in DNA duplexes[J]. Nucleic Acids Research, 2005, 33(19):6258-67.

Peyret N , Seneviratne P A , Allawi H T , et al. Nearest-Neighbor Thermodynamics and NMR of DNA Sequences with Internal A-A, C-C, G-G, and T-T Mismatches, [J]. Biochemistry, 1999, 38(12):3468-3477.

Examples

input_seq <- c("AAAATTTTTTTCCCCCCCCCCCCCCGGGGGGGGGGGGTGTGCGCTGC",
"AAAATTTTTTTCCCCCCCCCCCCCCGGGGGGGGGGGGTGTGCGCTGC")
gr_seq <- to_genomic_ranges(input_seq)
out <- tm_nn(gr_seq, Na=50)
out
out$tm_nn$Options

Calculate the melting temperature using the 'Wallace rule'

Description

The Wallace rule is often used as rule of thumb for approximate melting temperature calculations for primers with 14 to 20 nt length.

Usage

tm_wallace(gr_seq, ambiguous = FALSE)

Arguments

Value

Returns a list of sequences with updated Tm attributes

Author(s)

Junhui Li

References

Thein S L , Lynch J R , Weatherall D J , et al. DIRECT DETECTION OF HAEMOGLOBIN E WITH SYNTHETIC OLIGONUCLEOTIDES[J]. The Lancet, 1986, 327(8472):93.

Examples

input_seq = c('acgtTGCAATGCCGTAWSDBSY','acgtTGCCCCGGCCGCGCCGTAWSDBSY') #for wallace rule
gr_seq <- to_genomic_ranges(input_seq)
out <- tm_wallace(gr_seq, ambiguous = TRUE)
out
out$Options

Convert input file into a GenomicRanges Object

Description

This function processes a vector of sequences string, a FASTA file, or a character vector with genomic coordinates into a GenomicRanges object, optionally including complementary sequences. sequence names are parsed based on their format: - If names have this pattern "chr:start-end:strand:species[:name]" (e.g., "chr1:1-5:+:seq_1"), parse components into seqnames, ranges, strand, and name. - If names have this pattern "chr:start-end:strand" (e.g., "chr1:1-5:+"), parse components into seqnames, ranges, and strand. - If names have this pattern "chr:start-end" (e.g., "chr1:1-5"), parse components into seqnames and ranges. - If no names are provided, use default values: seqnames = "chr1", start = 1, width = sequence length, strand = "*", name = "1", etc. Complementary sequences are either provided or automatically generated.

Usage

to_genomic_ranges(input_seq, complement_seq = NULL)

Arguments

Value

A GenomicRanges object with seqnames, ranges, strand, name, sequence, Complement, and Tm as metadata.

Author(s)

Junhui Li

Examples

# Using a character vector with auto-generated complementary sequences
seqs <- c("ATGCG", "GCTAG")
names(seqs) <- c("chr1:1-5:+:seq_1", "chr2:1-5:+")
gr <- to_genomic_ranges(seqs)
gr

# Using a character vector with provided complementary sequences
seqs <- c("ATGCG", "GCTAG")
comp_seqs <- c("TACGC", "CGTA")
gr <- to_genomic_ranges(seqs, comp_seqs)
gr

# Using a FASTA file
gr <- to_genomic_ranges(system.file("extdata", "example1.fasta", package = "TmCalculator"))
## Not run: 
# Using a character vector with genomic coordinates
seqs <- c(
  "chr1:1898000-1898050:+:BSgenome.Hsapiens.UCSC.hg38",
  "chr2:2563000-2563050:-:BSgenome.Hsapiens.UCSC.hg38"
)
gr <- to_genomic_ranges(seqs)
gr

## End(Not run)

Convert sequence strings to GenomicRanges object

Description

This function converts sequence strings to a GenomicRanges object, handling both named and unnamed sequences. It can also process complementary sequences if provided. sequence names can be in the format ">chr2:1-10:+:seq2" which will be parsed into chromosome, position, strand, and name components.

Usage

vec_to_genomic_ranges(input_seq)

Arguments

Value

A GenomicRanges object containing: - GRanges information (seqnames, ranges, strand) - sequence data - Complementary sequences - Names from input or auto-generated

Examples

# Example with named sequences in GRanges format
seqs <- c("ATGCG", "GCTAG")
names(seqs) <- c("chr1:1111-1115:+:seq1", "chr2:1221-1225:+")
gr <- vec_to_genomic_ranges(seqs)

# Example with unnamed sequences
seqs <- c("ATGCG", "GCTAG")
gr <- vec_to_genomic_ranges(seqs)