Nucleic Acid Calculator: Structure, Monomers, MW & Elements

A nucleic acid is a polymer of nucleotides that stores and uses genetic information. The two types are DNA and RNA. Each monomer, a nucleotide, has a phosphate, a five-carbon sugar and a nitrogenous base.

Nucleotides join by phosphodiester bonds into a sugar-phosphate backbone with the bases sticking out. DNA is usually a double helix held together by A–T and G–C base pairs; RNA is usually single-stranded and uses U instead of T. Nucleic acids contain five elements: carbon, hydrogen, oxygen, nitrogen and phosphorus, and no sulfur.

Rule of thumb: one DNA nucleotide weighs about 309 g/mol (618 g/mol per base pair; about 321 g/mol per RNA nucleotide), and 1 A260 unit is about 50 ng/µL of dsDNA, 40 of RNA and 33 of ssDNA.

Nucleic acid definition (biology): a nucleic acid is a large biological molecule, a polymer made of nucleotide monomers, that carries the genetic information of a cell or virus. The name comes from where they were first found: in 1869 Friedrich Miescher isolated a phosphorus-rich acid from cell nuclei and called it nuclein (Dahm, 2005). In simple terms, nucleic acids are the molecules that hold the instructions for life and help carry them out.

What is in a nucleic acid? The building blocks

Every nucleic acid is built from nucleotides, and every nucleotide has three parts:

1. A phosphate group (PO₄), which gives nucleic acids their negative charge and their acidity.
2. A five-carbon sugar (a pentose): deoxyribose in DNA and ribose in RNA. Ribose has an OH group on carbon 2′; deoxyribose has only an H there.
3. A nitrogenous base: adenine (A), guanine (G), cytosine (C), and thymine (T) in DNA or uracil (U) in RNA.

Nucleic acid monomer and polymer

Nucleic acids are polymers of nucleotides. During polymerisation, the 3′-OH of one nucleotide reacts with the 5′-phosphate of the next, releasing water (and, in the cell, pyrophosphate from a nucleoside triphosphate). The link is a phosphodiester bond. Repeating this builds a chain with a free 5′-phosphate at one end and a free 3′-OH at the other, so every nucleic acid has a direction. A chain of n nucleotides has n − 1 phosphodiester bonds; a closed circle, such as a plasmid, has n.

What are the two types of nucleic acids?

The two types of nucleic acids are DNA and RNA. So yes, DNA is a nucleic acid and RNA is a nucleic acid. They differ in three ways: the sugar (deoxyribose vs ribose), one base (thymine vs uracil) and usually the shape (double helix vs single strand).

What is the function of nucleic acids?

The main function of nucleic acids is to store and transmit genetic information and use it to make proteins. In more detail, the functions of nucleic acids are:

1. Storing genetic information. DNA holds the instructions for every protein and RNA a cell can make.
2. Passing it on. DNA is copied before cell division and passed to offspring, which is the basis of heredity.
3. Carrying the message. Messenger RNA copies a gene and carries it to the ribosome.
4. Building proteins. Transfer RNA brings amino acids and ribosomal RNA joins them; see the protein synthesis steps.
5. Catalysis. Some RNAs are enzymes (ribozymes), including the peptide-bond-forming centre of the ribosome (Cech, 2000).
6. Regulating genes. microRNAs and siRNAs switch genes down (Fire et al., 1998).

Two functions of nucleic acids that cover the rest: storing information (DNA) and expressing it (RNA).

How are proteins and nucleic acids related?

Nucleic acids encode proteins, and proteins make nucleic acids. The DNA sequence of a gene sets the order of amino acids in a protein, three bases per amino acid. In turn, proteins such as DNA polymerase and RNA polymerase copy and read nucleic acids, and histone proteins package DNA. Each needs the other.

A worked one-liner: the 20-base single-stranded DNA primer ACGTGCTAGCTAGGCATCAG has 5 A, 5 C, 6 G and 4 T (55% GC), the formula C₁₉₅H₂₄₅N₇₈O₁₁₇P₁₉, a molecular weight of 6,142.04 g/mol and 19 phosphodiester bonds.

Who uses this: students learning nucleic acid structure, function and monomers; lab scientists working out primer and DNA concentrations from a NanoDrop reading; molecular biologists checking the mass and melting temperature of an oligo before PCR; and teachers who need a labeled nucleic acid picture and worked numbers.

The molecule

The A260 reading (concentration)

4.1 Length and the monomer count

Length is the number of monomers (nucleotides) in one strand, written nt for single strands and bp (base pairs) for double strands. A primer is 18 to 25 nt, a PCR product a few hundred bp, a plasmid a few thousand bp, and a human genome about 3.1 billion bp per set of chromosomes (International Human Genome Sequencing Consortium, 2001).

4.2 Molecular weight

The molecular weight is the sum of the nucleotide residues plus the atoms at the ends. A synthetic oligo usually has a 5′-OH, so it has one phosphate fewer than nucleotides; DNA from PCR or a cell usually has a 5′-phosphate. The value is for the free acid; the sodium salt you buy weighs about 22 g/mol more per phosphate. Biopython's molecular_weight assumes a 5′-phosphate, so choose that option to match it.

4.3 What elements are found in nucleic acids?

Only five: carbon, hydrogen, oxygen, nitrogen and phosphorus (CHONP). Nitrogen comes from the bases, phosphorus only from the backbone, and carbon, hydrogen and oxygen from the sugar and bases. The element table always shows sulfur as 0: nucleic acids do not contain sulfur. That fact made the Hershey and Chase (1952) experiment work: they labelled phage protein with ³⁵S and phage DNA with ³²P, and only the ³²P entered the bacteria, showing that DNA is the genetic material.

4.4 GC content

GC content is the share of G and C bases. Each G–C pair has three hydrogen bonds and stacks more strongly than an A–T pair, so GC-rich DNA melts at a higher temperature. Primers work best between 40% and 60% GC. Whole genomes range from about 20% (Plasmodium) to over 70% (Streptomyces); human DNA averages about 41%. In double-stranded DNA, A = T and G = C (Chargaff, 1950).

4.5 The extinction coefficient

Bases absorb ultraviolet light most strongly at 260 nm, so A260 measures nucleic acid. The extinction coefficient (ε) here is the sum of each nucleotide's value. Real single strands absorb a little less because neighbouring bases stack (hypochromicity), so the nearest-neighbour method (Cavaluzzi and Borer, 2004; Tataurov et al., 2008) is more exact, by about 5 to 15% for short oligos.

4.6 Concentration in ng/µL, µM and copies

Concentration in ng/µL tells you how much to load; µM (the same number as pmol/µL) tells you how many molecules, which is what primers and probes need; copies per µL is used for qPCR standards. The verdict badge reads A260 IN RANGE between 0.1 and 1.0, the range where spectrophotometers are linear. Outside it the badge reads A260 TOO LOW or A260 TOO HIGH: dilute or concentrate and read again.

4.7 Melting temperature

Tm is the temperature at which half of a duplex has separated into single strands. The calculator picks the formula by length: the Wallace rule below 14 nt (Wallace et al., 1979), a GC formula for 14 to 49 nt, and a salt-adjusted formula for 50 nt and longer. For primers, set the annealing temperature about 3 to 5 °C below Tm. For critical work use nearest-neighbour Tm (SantaLucia, 1998) with your buffer's Mg²⁺.

4.8 Bonds in nucleic acids

Two kinds of bond hold a nucleic acid together. Covalent phosphodiester bonds join the sugar of one nucleotide to the phosphate of the next along each strand; hydrogen bonds hold the two strands together, two per A–T or A–U pair and three per G–C pair. A third link, the N-glycosidic bond, joins each base to its sugar. Base stacking between neighbouring bases adds more stability than the hydrogen bonds do.

4.9 Complement and reverse complement

The complementary strand pairs A with T (or U) and G with C and runs the other way. The reverse complement is that strand written 5′ to 3′, which is how you order a reverse primer or read the other strand of a sequencing result. The copy button above gives it ready to paste.

4.10 What this calculation cannot tell you

It cannot tell you purity: A260 counts every nucleic acid in the tube, including RNA in a DNA prep, free nucleotides and primer dimers. Check A260/A280 (about 1.8 for DNA, 2.0 for RNA) and A260/A230 (2.0 to 2.2), and run a gel to see the size and integrity. Modified bases, dyes and backbone changes are not included in the mass.

▶ Run the analysis above to auto-fill all five examples with your results.

This flow chart follows the worked example, the 20-nt DNA primer ACGTGCTAGCTAGGCATCAG with a 5′-OH end, read at A260 0.25 after a 1 in 10 dilution. The Excel, R and Python sections use the same numbers.

Check any box yourself: the Excel sheet in Section 9, the R script in Section 10 and the Python script in Section 11 all return 6,142.04 g/mol, 70.50 ng/µL and 53.83 °C for this primer.

1. Paste your DNA or RNA sequence. Paste the sequence 5 prime to 3 prime into the box. FASTA headers, spaces, numbers and lowercase are cleaned automatically, and the live readout shows the length, type (DNA or RNA) and GC content.
2. Or load a sample. Ten built-in samples cover a PCR primer, a PCR product, an siRNA, an mRNA with a poly-A tail, a plasmid, GC-rich and AT-rich DNA, a telomere repeat, an over-range reading and a failed read.
3. Or upload a file, or type base counts. On the Upload tab choose a FASTA or TXT file, or a CSV or Excel file and the column that holds the sequence. On the Manual tab type how many of each base the molecule has.
4. Choose the nucleic acid type. Pick single- or double-stranded DNA or RNA. Double strands add the complementary strand to the mass, extinction coefficient and bond counts.
5. Choose the shape and 5 prime end. Pick linear or circular, and a 5 prime hydroxyl (most synthetic oligos) or 5 prime phosphate (PCR products and natural DNA). The end changes the mass by 79.98 g/mol.
6. Enter the A260 reading. Type the absorbance at 260 nm, the dilution factor, the path length and, if you want the total yield, the volume. Leave the method on automatic unless you have a reason to change it.
7. Set the salt. Enter the sodium concentration of your buffer in mM. It is used by the salt-adjusted melting temperature formula and the Tm chart.
8. Press Calculate Nucleic Acid Properties. Nothing is calculated until you press the button. Changing any input clears the old results so you never read stale numbers.
9. Read the results. Check the cards for length, molecular weight, formula, GC content, concentration and Tm, then the element table (with sulfur at zero), the base table, the sequence views and the four charts.
10. Report and export. Copy one of the five ready-made report sentences, copy the reverse complement, or download the full report as a text document or PDF.

Excel can count bases, add up the molecular weight and convert an A260 reading into a concentration with a handful of text and maths functions. Base counting uses the LEN minus SUBSTITUTE trick, the mass is a SUMPRODUCT of counts and residue masses, and concentration is one line of Beer-Lambert arithmetic. What trips people up is forgetting the end correction, mixing up ng/µL and µM, and lowercase letters that SUBSTITUTE does not see.

9.1 The functions you need

9.2 Step by step

9.3 The awkward cases

Double strands. Count the complementary strand too: its A count is your T count and so on. Add both strands' masses, or use the shortcut MW ≈ bp × 617.96 + 36.04 for a 5′-phosphate duplex. For concentration, use the factor of 50 rather than summed extinction coefficients, which ignore base stacking.

RNA. Use U instead of T, the RNA residue masses (329.209, 305.183, 345.208, 306.167) and 9,900 for U.

N bases. Count them with =LEN(B2)-SUM(E2:E5) and decide whether to ignore them or replace them with an average residue.

9.4 Why your Excel answer might differ from R, Python or a supplier sheet

9.5 Charting the result in Excel

1. Select the base letters and counts (D2:E5) and choose Insert → Clustered Column.
2. Colour the bars A green, C blue, G amber and T fuchsia to match the base colours used on this page.
3. For element mass %, make a column of atoms × atomic weight ÷ MW and insert a doughnut chart.
4. Put the length, GC% and MW in the chart title so the figure stands on its own.

9.6 Excel errors you will probably hit

In R, Biostrings handles the sequence: DNAString() or RNAString() holds it, letterFrequency() counts the monomers and reverseComplement() gives the other strand. The script below builds the chemical formula atom by atom, so the elements, molecular weight, extinction coefficient, concentration and Tm all match this calculator.

Biostrings is a Bioconductor package: install it with install.packages("BiocManager") then BiocManager::install("Biostrings").

10.1 The complete script

R with Biostrings, saves nucleic_acid_plot.png

# Nucleic acid calculator in R: monomers, formula, elements, MW, GC, extinction,
# concentration from A260, Tm and bonds. Matches the StatsUnlock calculator.
library(Biostrings)

# ---- 1. Your data -------------------------------------------------------
seq_txt   <- "ACGTGCTAGCTAGGCATCAG"   # 5' to 3', DNA or RNA
mol       <- "ssDNA"                  # ssDNA, dsDNA, ssRNA or dsRNA
five_p    <- FALSE                    # TRUE if the 5' end carries a phosphate
circular  <- FALSE                    # TRUE for a plasmid
a260      <- 0.25; dilution <- 10; path_cm <- 1; vol_ul <- 50; na_mM <- 50

# ---- 2. Clean the sequence ---------------------------------------------
s   <- toupper(gsub("[^A-Za-z]", "", seq_txt))
rna <- grepl("RNA$", mol); ds <- grepl("^ds", mol)
s   <- if (rna) chartr("T", "U", s) else chartr("U", "T", s)
x   <- if (rna) RNAString(s) else DNAString(s)
n   <- nchar(s)
bases <- if (rna) c("A", "C", "G", "U") else c("A", "C", "G", "T")
cnt <- letterFrequency(x, bases)

# ---- 3. Formula and molecular weight -----------------------------------
aw  <- c(C = 12.011, H = 1.008, N = 14.007, O = 15.999, P = 30.974)
res <- if (rna) list(A = c(10, 12, 5, 6, 1), C = c(9, 12, 3, 7, 1), G = c(10, 12, 5, 7, 1), U = c(9, 11, 2, 8, 1)) else
                list(A = c(10, 12, 5, 5, 1), C = c(9, 12, 3, 6, 1), G = c(10, 12, 5, 6, 1), T = c(10, 13, 2, 7, 1))
strand_formula <- function(k) {
  f <- Reduce(`+`, Map(function(b, m) res[[b]] * m, names(k), k))
  if (!circular) { f <- f + c(0, 2, 0, 1, 0); if (!five_p) f <- f - c(0, 1, 0, 3, 1) }
  setNames(f, names(aw))
}
comp <- complement(x)
cnt2 <- letterFrequency(comp, bases)
form <- strand_formula(cnt) + if (ds) strand_formula(cnt2) else 0
mw   <- sum(form * aw)
gc   <- (cnt["G"] + cnt["C"]) / n * 100

# ---- 4. Extinction coefficient and concentration ------------------------
eps_nt <- c(A = 15400, C = 7400, G = 11500, T = 8700, U = 9900)
eps    <- sum(cnt * eps_nt[bases]) + if (ds) sum(cnt2 * eps_nt[bases]) else 0
factor <- c(dsDNA = 50, ssDNA = 33, ssRNA = 40, dsRNA = 40)[mol]
conc   <- if (ds) a260 * factor * dilution / path_cm else a260 / (eps * path_cm) * dilution * mw * 1000
uM     <- conc * 1000 / mw
copies <- uM * 1e-12 * 6.02214076e23

# ---- 5. Melting temperature and bonds -----------------------------------
gcn <- unname(cnt["G"] + cnt["C"])
tm  <- if (n < 14) 2 * (n - gcn) + 4 * gcn else if (n < 50) 64.9 + 41 * (gcn - 16.4) / n else 81.5 + 16.6 * log10(na_mM / 1000) + 0.41 * gc - 600 / n
pdb <- (if (circular) n else n - 1) * (if (ds) 2 else 1)

cat("Length:", n, "nt | composition:", paste(bases, cnt, collapse = ", "), "\n")
cat("Formula:", paste0(names(form), form, collapse = " "), "(no sulfur)\n")
cat(sprintf("MW = %.2f g/mol | GC = %.2f%% | eps260 = %d\n", mw, gc, eps))
cat(sprintf("Concentration = %.2f ng/uL = %.2f uM | copies/uL = %.3g | total = %.2f ug\n", conc, uM, copies, conc * vol_ul / 1000))
cat(sprintf("Tm = %.2f C | phosphodiester bonds = %d\n", tm, pdb))
cat("Reverse complement:", as.character(reverseComplement(x)), "\n")

# ---- 6. Figure: base counts and element mass % ---------------------------
png("nucleic_acid_plot.png", width = 2400, height = 1100, res = 300)
par(mfrow = c(1, 2), mar = c(4, 4, 3, 1))
barplot(cnt, col = c("#16a34a", "#2563eb", "#d97706", "#a21caf"), ylab = "Count",
        main = sprintf("%d nt, GC %.1f%%", n, gc))
pct <- form * aw / mw * 100
barplot(pct, col = c("#475569", "#0891b2", "#2563eb", "#c2410c", "#d97706"), ylab = "Mass %",
        main = sprintf("MW %.2f g/mol", mw))
dev.off()

Run it with Rscript nucleic_acid.R. For the worked example it prints C195 H245 N78 O117 P19, MW = 6142.04 g/mol, GC = 55.00%, eps260 = 217800, 70.50 ng/uL = 11.48 uM and Tm = 53.83 C.

10.2 What each part does

10.3 What the figure shows

The left panel shows how many of each base the molecule has, in the page's base colours. The right panel shows the share of the molecular weight from each element: carbon is the largest, phosphorus is about 9 to 10%, and there is no bar for sulfur.

10.4 Common R problems and fixes

10.5 Useful one-liners

# Reverse complement and RNA version
reverseComplement(DNAString("ACGTGCTAGCTAGGCATCAG"))
RNAString(DNAString("ACGTGCTAGCTAGGCATCAG"))
# GC content as a fraction
letterFrequency(DNAString("ACGTGCTAGCTAGGCATCAG"), "GC", as.prob = TRUE)
# Dinucleotide counts (for nearest-neighbour extinction or Tm)
dinucleotideFrequency(DNAString("ACGTGCTAGCTAGGCATCAG"))
# Read every sequence in a FASTA file
seqs <- readDNAStringSet("primers.fasta"); width(seqs)

Biopython's Seq object holds the sequence, and Bio.SeqUtils has molecular_weight and MeltingTemp. The script below builds the formula the same way as the R version, prints the same numbers, and then cross-checks the mass and Wallace Tm against Biopython's own functions.

Install once with pip install biopython matplotlib.

11.1 The complete script

Python 3 with Biopython, saves nucleic_acid_plot.png

"""Nucleic acid calculator in Python: monomers, formula, elements, MW, GC, extinction,
concentration from A260, Tm and bonds. Matches the StatsUnlock calculator."""
import math, re
from collections import Counter
from Bio.Seq import Seq
from Bio.SeqUtils import molecular_weight
from Bio.SeqUtils import MeltingTemp as mt
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

# ---- 1. Your data -------------------------------------------------------
seq_txt = "ACGTGCTAGCTAGGCATCAG"   # 5' to 3', DNA or RNA
mol = "ssDNA"                      # ssDNA, dsDNA, ssRNA or dsRNA
five_p = False                     # True if the 5' end carries a phosphate
circular = False                   # True for a plasmid
a260, dilution, path_cm, vol_ul, na_mM = 0.25, 10, 1, 50, 50

# ---- 2. Clean the sequence ---------------------------------------------
s = re.sub(r"[^A-Za-z]", "", seq_txt).upper()
rna, ds = mol.endswith("RNA"), mol.startswith("ds")
s = s.replace("T", "U") if rna else s.replace("U", "T")
seq = Seq(s)
n = len(s)
bases = "ACGU" if rna else "ACGT"
cnt = Counter(s)

# ---- 3. Formula and molecular weight -----------------------------------
AW = dict(C=12.011, H=1.008, N=14.007, O=15.999, P=30.974)
RES = (dict(A=(10, 12, 5, 6, 1), C=(9, 12, 3, 7, 1), G=(10, 12, 5, 7, 1), U=(9, 11, 2, 8, 1)) if rna else
       dict(A=(10, 12, 5, 5, 1), C=(9, 12, 3, 6, 1), G=(10, 12, 5, 6, 1), T=(10, 13, 2, 7, 1)))

def strand_formula(k):
    f = [0, 0, 0, 0, 0]
    for b, m in k.items():
        if b in RES:
            f = [a + m * r for a, r in zip(f, RES[b])]
    if not circular:
        f[1] += 2; f[3] += 1                     # + H2O for the chain ends
        if not five_p:
            f[1] -= 1; f[3] -= 3; f[4] -= 1      # 5'-OH end: one phosphate fewer
    return dict(zip("CHNOP", f))

comp = seq.complement_rna() if rna else seq.complement()
cnt2 = Counter(str(comp))
form = strand_formula(cnt)
if ds:
    f2 = strand_formula(cnt2)
    form = {e: form[e] + f2[e] for e in form}
mw = sum(AW[e] * k for e, k in form.items())
gc = (cnt["G"] + cnt["C"]) / n * 100

# ---- 4. Extinction coefficient and concentration ------------------------
EPS = dict(A=15400, C=7400, G=11500, T=8700, U=9900)
eps = sum(EPS[b] * cnt[b] for b in bases) + (sum(EPS[b] * cnt2[b] for b in bases) if ds else 0)
FACTOR = dict(dsDNA=50, ssDNA=33, ssRNA=40, dsRNA=40)
conc = a260 * FACTOR[mol] * dilution / path_cm if ds else a260 / (eps * path_cm) * dilution * mw * 1000
uM = conc * 1000 / mw
copies = uM * 1e-12 * 6.02214076e23

# ---- 5. Melting temperature and bonds -----------------------------------
gcn = cnt["G"] + cnt["C"]
if n < 14:   tm = 2 * (n - gcn) + 4 * gcn
elif n < 50: tm = 64.9 + 41 * (gcn - 16.4) / n
else:        tm = 81.5 + 16.6 * math.log10(na_mM / 1000) + 0.41 * gc - 600 / n
pdb = (n if circular else n - 1) * (2 if ds else 1)

print(f"Length: {n} nt | composition:", ", ".join(f"{b} {cnt[b]}" for b in bases))
print("Formula:", " ".join(f"{e}{k}" for e, k in form.items()), "(no sulfur)")
print(f"MW = {mw:.2f} g/mol | GC = {gc:.2f}% | eps260 = {eps}")
print(f"Concentration = {conc:.2f} ng/uL = {uM:.2f} uM | copies/uL = {copies:.3g} | total = {conc * vol_ul / 1000:.2f} ug")
print(f"Tm = {tm:.2f} C | phosphodiester bonds = {pdb}")
print("Reverse complement:", seq.reverse_complement_rna() if rna else seq.reverse_complement())
# Cross-checks with Biopython's own functions (Biopython assumes a 5'-phosphate)
print(f"Biopython molecular_weight (5'-phosphate): {molecular_weight(seq, 'RNA' if rna else 'DNA', double_stranded=ds, circular=circular):.2f}")
print(f"Biopython Tm_Wallace: {mt.Tm_Wallace(seq):.2f}")

# ---- 6. Figure: base counts and element mass % ---------------------------
fig, ax = plt.subplots(1, 2, figsize=(8, 3.6))
ax[0].bar(list(bases), [cnt[b] for b in bases], color=["#16a34a", "#2563eb", "#d97706", "#a21caf"])
ax[0].set(ylabel="Count", title=f"{n} nt, GC {gc:.1f}%")
ax[1].bar(list(form), [AW[e] * k / mw * 100 for e, k in form.items()], color=["#475569", "#0891b2", "#2563eb", "#c2410c", "#d97706"])
ax[1].set(ylabel="Mass %", title=f"MW {mw:.2f} g/mol")
fig.tight_layout()
fig.savefig("nucleic_acid_plot.png", dpi=300)

11.2 What each part does

11.3 What the figure shows

The same two panels as the R figure: base counts on the left and element mass % on the right, with the length, GC content and molecular weight in the titles.

11.4 Common Python problems and fixes

11.5 Useful one-liners

# Reverse complement and transcription
Seq("ACGTGCTAGCTAGGCATCAG").reverse_complement()      # CTGATGCCTAGCTAGCACGT
Seq("ACGTGCTAGCTAGGCATCAG").transcribe()              # ACGUGCUAGCUAGGCAUCAG
# GC content (Biopython 1.80 or newer)
from Bio.SeqUtils import gc_fraction
gc_fraction("ACGTGCTAGCTAGGCATCAG")                   # 0.55
# Nearest-neighbour melting temperature
from Bio.SeqUtils import MeltingTemp as mt
mt.Tm_NN("ACGTGCTAGCTAGGCATCAG", Na=50)
# Double-stranded and circular molecular weight
from Bio.SeqUtils import molecular_weight
molecular_weight("ACGTGCTAGCTAGGCATCAG", "DNA", double_stranded=True)

Run it with python nucleic_acid.py from a terminal. In Jupyter, drop the savefig line and add plt.show().

12.1 The monomers of nucleic acids: nucleotide values used here

What to conclude: every nucleotide has exactly one phosphorus atom, so a strand's phosphorus count equals its phosphate count, and G is the heaviest base while C is the lightest. Atomic weights are IUPAC standard values; extinction coefficients are the nucleotide values used for summed ε (Cavaluzzi and Borer, 2004).

12.2 The four macromolecules: carbohydrates, lipids, proteins and nucleic acids

What to conclude: all four are built by condensation (water released) and broken down by hydrolysis. Of the four macromolecules, only nucleic acids carry heritable information.

12.3 Examples of nucleic acids

What to conclude: two examples of nucleic acids are DNA and RNA, but each comes in many forms. All of them are polymers of nucleotides with the same backbone.

12.4 Nucleotide vs nucleoside vs nucleic acid (and is ATP a nucleic acid?)

What to conclude: nucleotide vs nucleic acid is monomer vs polymer. When RNA polymerase adds ATP to a chain, two phosphates are released and only AMP stays in the RNA.

12.5 Foods with nucleic acids

Every food made of cells contains nucleic acids, because every cell has DNA and RNA. Foods made of many small, active cells contain the most per gram. Your gut breaks dietary nucleic acids into nucleotides, nucleosides and bases; the purine bases (A and G) are broken down to uric acid, which is why people with gout are told to limit high-purine foods.

What to conclude: an example of a nucleic acid in food is the DNA and RNA in sardines or liver. Nucleic acids are not an essential nutrient, because the body makes its own nucleotides. This table is a general guide, not dietary advice; ask a doctor or dietitian about a low-purine diet.

12.6 A260 conversion factors and purity ratios

What to conclude: a low A260/A280 suggests protein or phenol; a low A260/A230 suggests guanidine salts, EDTA or carbohydrate carry-over. The ratios depend on the pH and salt of the buffer (Wilfinger et al., 1997).

Eight worked examples, from a PCR primer to a plasmid. Every number below came from this calculator, and each can be loaded from the sample list in Section 2.

Study design: UV absorbance at 260 nm is the quickest way to measure how much nucleic acid you have. This protocol gives the A260 reading the calculator turns into ng/µL, µM and copies (Desjardins and Conklin, 2010).

Prepare

1. Know your molecule. Decide whether it is dsDNA, ssDNA or RNA, and for oligos have the sequence ready to paste here for the extinction coefficient.
2. Mix the sample. Flick or pipette gently; vortexing shears genomic DNA. Spin down briefly. Thaw RNA on ice.
3. Choose the blank. Use exactly the buffer the sample is in (water, TE or elution buffer), because EDTA and salts change the reading.

Set up

4. Warm up and clean the instrument. Wipe the pedestal with water and a lint-free tissue, or use a clean quartz or UV-transparent cuvette.
5. Blank the instrument. Load 1 to 2 µL of buffer on a microvolume spectrophotometer (or 1 mL in a cuvette) and set the zero.
6. Plan a dilution. For a cuvette, dilute so A260 falls between 0.1 and 1.0, for example 5 µL in 495 µL (dilution factor 100).

Run

7. Read A260, A280 and A230. Measure each sample in duplicate with a fresh drop each time, and wipe between samples.
8. Check the spectrum. A clean nucleic acid shows a peak at 260 nm and a dip at 230 nm. A peak shifted towards 270 nm or a high 230 nm value means contamination.
9. Enter the values here. Type A260, the dilution factor and path length into Section 2 and press Calculate.

Record

10. Record concentration and purity. Note ng/µL, µM, A260/A280 and A260/A230 for each tube.
11. Confirm on a gel or with a dye. For low concentrations (A260 under 0.1) or when RNA may contaminate DNA, use a fluorescent dye assay specific to dsDNA or RNA.

Replicates: read each sample at least twice; if the two readings differ by more than 10%, mix the tube and read again.

Lab notebook: sample name, buffer used as blank, instrument, A260, A280, A230, dilution, path length, calculated concentration and the method (factor or extinction coefficient).

Common mistakes that spoil a nucleic acid measurement:

• Blanking with water when the sample is in TE or elution buffer.
• Reading a sample above A260 1.0 instead of diluting it.
• Using the dsDNA factor of 50 for a short primer.
• A bubble or a dried drop on the pedestal.
• Treating an RNA-contaminated DNA prep as pure DNA: A260 counts both.

Use it when:

• ✓ You need the molecular weight, formula or elements of a DNA or RNA sequence.
• ✓ You have an A260 reading and need ng/µL, µM or copies per µL.
• ✓ You are making a primer stock and need the micromolar concentration.
• ✓ You want GC content, Tm, complement or reverse complement in one place.
• ✓ You are learning nucleic acid structure and want real numbers for monomers and bonds.

Use something else when:

• ✗ You need a precise primer Tm with Mg²⁺ and dNTPs: use a nearest-neighbour Tm tool.
• ✗ Your oligo has modifications (dyes, phosphorothioates, LNA): use the supplier's mass.
• ✗ Your sample is very dilute or impure: use a fluorescent dye assay or qPCR.
• ✗ You need the protein a gene makes: use a translation or protein synthesis tool.

Real-world examples:

1. Biology class: working out the elements and bonds in a short DNA strand.
2. PCR set-up: diluting a primer from its A260 to 10 µM.
3. qPCR standards: converting a plasmid concentration to copies per µL.
4. Cloning: getting picomoles of insert and vector for a ligation.

Experimental design: calculate the oligo's MW and ε when you order it, measure A260 when it arrives, and recalculate after every dilution you store.

Decision tree: Do you have a sequence? No → use the Manual Base Counts tab, or for long dsDNA the factor of 50. Yes → Is it a short oligo? Yes → use the sequence-specific ε. No → Double-stranded? Yes → factor 50 (DNA) or 40 (RNA). No → use this nucleic acid calculator with the ε method.

Assumptions

1. The sequence is complete and unmodified. Requires only A, C, G, T or U. When it fails (modified bases, dyes), the mass and ε are wrong by the mass of the modification.
2. The ends are as selected. Requires the right 5′ end and shape. When it fails, the mass is off by 79.98 g/mol per strand (5′-OH vs phosphate).
3. Free acid form. Requires reporting the neutral molecule. Sodium salts weigh about 22 g/mol more per phosphate, so a 20-mer salt is about 420 g/mol heavier.
4. Extinction coefficients add up. Requires no base stacking. Real strands absorb a little less than the sum, so the summed ε is slightly too high and the concentration slightly too low; short oligos differ by about 5 to 15% from nearest-neighbour values.
5. A260 comes only from this molecule. Requires a pure sample. Contaminating RNA, nucleotides or phenol all add absorbance, so the concentration is overestimated.
6. Simple Tm models. Require standard salt and no Mg²⁺. In PCR buffer, real Tm is often several degrees higher than these estimates.

Limitations

1. No nearest-neighbour thermodynamics. Use a dedicated primer design tool for precise Tm and hairpins.
2. No secondary structure. RNA folding and DNA hairpins are not predicted.
3. No modifications. Methylation, fluorophores and backbone changes are ignored.
4. No purity check. Enter purity ratios in your notebook and check on a gel.
5. Sequences up to a few megabases. For whole genomes, use R or Python.
6. Average atomic weights. For mass spectrometry, calculate monoisotopic masses.

A nucleic acid is a polymer of nucleotides, built from five elements, held together by phosphodiester bonds along each strand and hydrogen bonds between strands. DNA stores genetic information and RNA puts it to use. This calculator turns any DNA or RNA sequence into its monomers, formula, molecular weight, concentration, melting temperature and bonds.

What was found

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What it means at the bench

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Cautions

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Next steps

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Whether you are learning what nucleic acids are made of or making a primer stock, the logic is the same: count the monomers, add up the atoms, and convert absorbance into amount.

To see how alleles of a gene combine in offspring, try the genotype calculator.

The nucleic acid structure, the history of its discovery and the formulas for molecular weight, extinction, concentration and melting temperature on this page rest on the peer-reviewed literature below. Every DOI was checked before publication.

1. Avery, O. T., MacLeod, C. M., & McCarty, M. (1944). Studies on the chemical nature of the substance inducing transformation of pneumococcal types. Journal of Experimental Medicine, 79(2), 137–158. https://doi.org/10.1084/jem.79.2.137
2. Cavaluzzi, M. J., & Borer, P. N. (2004). Revised UV extinction coefficients for nucleoside-5′-monophosphates and unpaired DNA and RNA. Nucleic Acids Research, 32(1), e13. https://doi.org/10.1093/nar/gnh015
3. Cech, T. R. (2000). The ribosome is a ribozyme. Science, 289(5481), 878–879. https://doi.org/10.1126/science.289.5481.878
4. Chargaff, E. (1950). Chemical specificity of nucleic acids and mechanism of their enzymatic degradation. Experientia, 6, 201–209. https://doi.org/10.1007/BF02173653
5. Dahm, R. (2005). Friedrich Miescher and the discovery of DNA. Developmental Biology, 278(2), 274–288. https://doi.org/10.1016/j.ydbio.2004.11.028
6. Desjardins, P., & Conklin, D. (2010). NanoDrop microvolume quantitation of nucleic acids. Journal of Visualized Experiments, (45), e2565. https://doi.org/10.3791/2565
7. Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature, 391, 806–811. https://doi.org/10.1038/35888
8. Franklin, R. E., & Gosling, R. G. (1953). Molecular configuration in sodium thymonucleate. Nature, 171, 740–741. https://doi.org/10.1038/171740a0
9. Hershey, A. D., & Chase, M. (1952). Independent functions of viral protein and nucleic acid in growth of bacteriophage. Journal of General Physiology, 36(1), 39–56. https://doi.org/10.1085/jgp.36.1.39
10. International Human Genome Sequencing Consortium. (2001). Initial sequencing and analysis of the human genome. Nature, 409, 860–921. https://doi.org/10.1038/35057062
11. Kibbe, W. A. (2007). OligoCalc: An online oligonucleotide properties calculator. Nucleic Acids Research, 35, W43–W46. https://doi.org/10.1093/nar/gkm234
12. Kruger, K., Grabowski, P. J., Zaug, A. J., Sands, J., Gottschling, D. E., & Cech, T. R. (1982). Self-splicing RNA: Autoexcision and autocyclization of the ribosomal RNA intervening sequence of Tetrahymena. Cell, 31(1), 147–157. https://doi.org/10.1016/0092-8674(82)90414-7
13. Meselson, M., & Stahl, F. W. (1958). The replication of DNA in Escherichia coli. Proceedings of the National Academy of Sciences, 44(7), 671–682. https://doi.org/10.1073/pnas.44.7.671
14. Owczarzy, R., You, Y., Moreira, B. G., Manthey, J. A., Huang, L., Behlke, M. A., & Walder, J. A. (2004). Effects of sodium ions on DNA duplex oligomers: Improved predictions of melting temperatures. Biochemistry, 43(12), 3537–3554. https://doi.org/10.1021/bi034621r
15. SantaLucia, J., Jr. (1998). A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proceedings of the National Academy of Sciences, 95(4), 1460–1465. https://doi.org/10.1073/pnas.95.4.1460
16. Tataurov, A. V., You, Y., & Owczarzy, R. (2008). Predicting ultraviolet spectrum of single stranded and double stranded deoxyribonucleic acids. Biophysical Chemistry, 133, 66–70. https://doi.org/10.1016/j.bpc.2007.12.004
17. Wallace, R. B., Shaffer, J., Murphy, R. F., Bonner, J., Hirose, T., & Itakura, K. (1979). Hybridization of synthetic oligodeoxyribonucleotides to ΦX174 DNA: The effect of single base pair mismatch. Nucleic Acids Research, 6(11), 3543–3557. https://doi.org/10.1093/nar/6.11.3543
18. Watson, J. D., & Crick, F. H. C. (1953). Molecular structure of nucleic acids: A structure for deoxyribose nucleic acid. Nature, 171, 737–738. https://doi.org/10.1038/171737a0
19. Wilfinger, W. W., Mackey, K., & Chomczynski, P. (1997). Effect of pH and ionic strength on the spectrophotometric assessment of nucleic acid purity. BioTechniques, 22(3), 474–481. https://doi.org/10.2144/97223st01
20. Wilkins, M. H. F., Stokes, A. R., & Wilson, H. R. (1953). Molecular structure of deoxypentose nucleic acids. Nature, 171, 738–740. https://doi.org/10.1038/171738a0