TmCalculator

v1.1.0

Genome-wide nucleic acid melting temperature (Tm) profiling and multi-omics integration. Results are returned as GRanges objects, so Tm can be used directly as a quantitative genomic feature alongside ATAC-seq, RNA-seq, ChIP-seq and other assays.

1. install

install.packages("TmCalculator")

install dev version from github

pak::pkg_install("JunhuiLi1017/TmCalculator@dev")

2. usage and examples

Please see the vignetts for the details.

library(TmCalculator)

seqs <- to_genomic_ranges("AAAATTTTTTTCCCCCCCCCCCCCCGGGGGGGGGGGGTGTGCGCTGC")
tm_calculate(seqs, method = "tm_nn", nn_table = "DNA_NN_SantaLucia_2004", Na = 50)

3. thermodynamic parameter sets

Twenty-seven nearest-neighbor parameter sets are available, in two families.

Reference-salt sets were fitted at a single reference sodium concentration. Other conditions are reached through the salt_method correction formulas.

Condition-specific sets were fitted directly at the sodium concentration shown, by melting-temperature optimization. They are intended to replace salt correction rather than be corrected. When the requested Na matches the concentration a set was fitted at, salt correction is skipped automatically; when it does not, the correction is applied with a warning.

For RNA, the VIF (variable initiation factors) sets gave better cross-validation than FIF. For RNA/DNA hybrids at high salt, ..._FT was the best-performing set in the source study.

# Fitted at 100 mM, so no salt correction is applied on top of it
res <- tm_calculate(seqs, method = "tm_nn",
                    nn_table = "RNA_DNA_NN_Weber_2019_LS", Na = 100)
res$options[["Salt correction applied"]]                    # FALSE
res$options[["Parameter set fitted at [Na+] (mM)"]]         # 100

Pick the set whose fitted salt is closest to your experimental condition rather than correcting a distant one. See ?tm_nn for the full list and citations.

4. launch an R shiny application

using R function TmCalculatorShiny::TmCalculator_shiny()

5. citation

If you use the melting-temperature-optimized parameter sets, please also cite the source studies:

• Weber G (2015) Bioinformatics 31:871–877. doi:10.1093/bioinformatics/btu751
• Ferreira I, Jolley EA, Znosko BM, Weber G (2019) Chemical Physics 521:69–76. doi:10.1016/j.chemphys.2019.01.016
• Basilio Barbosa V, de Oliveira Martins E, Weber G (2019) Biophysical Chemistry 251:106189. doi:10.1016/j.bpc.2019.106189