Package: TmCalculator 1.1.2

TmCalculator: Genome-Wide Nucleic Acid Melting Temperature Profiling and Multi-Omics Integration

Accurate calculation of nucleic acid melting temperature (Tm) is fundamental to many molecular biology applications, and this software scales Tm analysis from individual sequences to genome‑wide thermodynamic profiling. This package extends Tm analysis from simple sequence level computation to comprehensive genome-wide thermodynamic profiling. It takes four input sources: sequence strings, a FASTA file, an installed 'BSgenome' package named by string, or a 'GRanges' carrying sequences. A 'regions' argument selects what to cover and 'window' and 'slide' set the resolution at which it is tiled. The implementation provides three Tm calculation methods: the Wallace rule (Thein & Wallace, 1986), empirical GC‑content formulas (Marmur, 1962; Schildkraut, 1965; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001), and nearest‑neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1997, 1998; SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; Sugimoto, 1995; Peyret, 1999; SantaLucia & Peyret, 2001; Watkins & SantaLucia, 2005; Zuber, 2022; Ghosh, 2020, 2023). Nearest-neighbor parameter sets are provided for DNA, RNA and RNA/DNA hybrid duplexes. These include sets obtained by melting-temperature optimization that are fitted directly at a stated sodium concentration (Weber, 2015; Ferreira, 2019; Basilio Barbosa, 2019; Banerjee, 2020), which replace salt correction rather than being corrected; salt correction is skipped automatically when the requested condition matches the one a set was fitted at. The Zuber (2022) set additionally replaces the single terminal-AU penalty with end terms that depend on the penultimate base pair, applied automatically at both duplex ends. Parameter sets measured under molecular crowding (Ghosh, 2020, 2023) are also provided for DNA and RNA duplexes, so that duplex stability can be evaluated under cell-like rather than dilute-solution conditions. Salt corrections are otherwise applied to the nearest-neighbor model (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008), while each empirical GC-content formula carries the salt term it was published with; corrections for chemical conditions such as dimethyl sulfoxide and formamide apply to both. A compiled C++ core, and task partitioning by region across 'BiocParallel' workers through a 'BPPARAM' argument, profile the human genome in 3 minutes on a six-core laptop. This package returns result as a GRanges object for interoperability with Bioconductor workflows and downstream multi-omics analyses. Data-level integration reconciles Tm windows with external multi-omics GRanges objects through overlap, nearest-feature, windowed-count, and binned-average strategies, returning a single unified GRanges object ready for downstream analysis. Visualization-level integration renders multiple feature layers as independent concentric tracks on a shared genomic axis, each retaining its native coordinate resolution. Group comparison supports Wilcoxon rank-sum and Student's t-tests with multiple available correction methods for contrasting Tm and other features across region classes.

Authors:Junhui Li [cre, aut], Lihua Julie Zhu [aut]