.gc_vec

Mirrors <code>gc_content()</code> exactly, including its ambiguity apportioning and its
<code>GC/(A+C+G+T)</code> denominator, but counts every base in a single compiled
pass per sequence (<code>cpp_base_counts()</code>) instead of splitting each
sequence into a character vector with <code>s2c()</code> and scanning it five
times. The apportioning arithmetic stays here, vectorised over the count
matrix, so the published formula remains the readable one.

internal

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 multiple input formats including sequence strings, FASTA files, genomic coordinates. The implementation provides three Tm calculation methods: the Wallace rule (Thein & Wallace, 1986), empirical GC‑content formulas (Marmur, 1962; Schildkraut, 2010; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001), and 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; 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. Corrections are otherwise supported for salt ions (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008) and for chemical conditions such as dimethyl sulfoxide and formamide. A compiled C++ core and parallel task partitioning 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.

Junhui Li

TmCalculator

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

Lihua Julie Zhu

.gc_vec function

<dl><dt>input_seq</dt>
<dd>Character vector of sequences.</dd>
<dt>ambiguous</dt>
<dd>Logical; apportion ambiguous IUPAC codes as <code>gc_content()</code> does.</dd></dl>

Arguments

Vectorized GC percent over a character vector of sequences — .gc_vec

<dl>

<dt>input_seq</dt>
<dd>Character vector of sequences.</dd>


<dt>ambiguous</dt>
<dd>Logical; apportion ambiguous IUPAC codes as <code>gc_content()</code> does.</dd>

</dl>

.gc_vec: Vectorized GC percent over a character vector of sequences

Description

Usage

Value

Arguments