DNA vs RNA: Structure, Sugar, Bases & Polymerase Calculator

DNA vs RNA comes down to three differences: DNA has the sugar deoxyribose, RNA has ribose; DNA uses thymine (T), RNA uses uracil (U); DNA is usually a double helix, RNA usually a single strand.

Because of these differences, DNA is the stable, long-term store of genetic information, while RNA is a short-lived working copy that carries the message, builds proteins and regulates genes. Both are nucleic acids made of nucleotides with A, G and C, a phosphate backbone and the same five elements.

Rule of thumb: the same sequence weighs about 3 to 5% more as RNA than as single-stranded DNA, and RNA polymerase makes roughly 10,000 times more errors per base than DNA replication.

DNA vs RNA, explained: DNA (deoxyribonucleic acid) and RNA (ribonucleic acid) are the two kinds of nucleic acid. Both are chains of nucleotides, and each nucleotide has a phosphate, a five-carbon sugar and a nitrogenous base. The difference between DNA and RNA lies in which sugar, which bases and how many strands they use, and those small chemical differences give them very different jobs in the cell.

DNA vs RNA comparison chart

DNA vs RNA sugar: deoxyribose vs ribose

The sugar in DNA vs the sugar in RNA differs by a single oxygen atom. Both are five-carbon sugars (pentoses) in a ring. Ribose, the RNA sugar, carries a hydroxyl group (OH) on carbon 2′; deoxyribose, the DNA sugar, carries only a hydrogen there, which is what "deoxy" means. That one OH group makes RNA more reactive and less stable, and it is why the same sequence is heavier as RNA.

DNA vs RNA bases and base pairing

DNA and RNA share three nitrogenous bases: adenine (A), guanine (G) and cytosine (C). DNA's fourth base is thymine (T); RNA's is uracil (U). Thymine is uracil with an extra methyl group (CH₃) on carbon 5. Both pair with adenine using two hydrogen bonds, so the pairing rules are A–T and G–C in DNA, and A–U and G–C in RNA.

DNA vs RNA nucleotides

A DNA nucleotide is a deoxyribonucleotide: phosphate + deoxyribose + A, G, C or T. An RNA nucleotide is a ribonucleotide: phosphate + ribose + A, G, C or U. The cell builds DNA from deoxyribonucleoside triphosphates (dNTPs) and RNA from ribonucleoside triphosphates (NTPs), and the two polymerases are strict about which they use.

DNA vs RNA similarities

• Both are nucleic acids: polymers of nucleotides joined by phosphodiester bonds.
• Both have a sugar-phosphate backbone with a 5′ and a 3′ end, and both are made 5′ to 3′.
• Both use adenine, guanine and cytosine, and both follow complementary base pairing.
• Both contain the same five elements, C, H, O, N and P, and no sulfur.
• Both absorb UV light most strongly at 260 nm.
• Both can be the genetic material of a virus.

DNA vs RNA function

The function of DNA is to store the genetic instructions and pass them to new cells and offspring. The function of RNA is to use those instructions: messenger RNA (mRNA) carries a gene's code to the ribosome, transfer RNA (tRNA) brings amino acids, ribosomal RNA (rRNA) joins them into a protein, and small RNAs switch genes on and off. In the central dogma, DNA is copied by replication, transcribed into RNA, and RNA is translated into protein (Crick, 1970). Some viruses reverse the second step: reverse transcriptase copies RNA back into DNA (Baltimore, 1970; Temin and Mizutani, 1970).

A worked one-liner: the DNA coding strand ATGGCTTGGAAACGTGAATAA becomes the RNA AUGGCUUGGAAACGUGAAUAA. Its 5 T become 5 U and its 21 deoxyriboses become riboses, so the molecule gains 21 oxygen atoms, loses 5 CH₂ groups and gets 265.84 g/mol heavier.

Who uses this: students who need to compare and contrast DNA vs RNA (the same three differences taught in popular class videos such as the Amoeba Sisters' DNA vs RNA lesson), teachers making a DNA vs RNA chart or card sort, lab scientists converting between an RNA target and its cDNA or working out RNA vs DNA concentrations from one A260 reading, and anyone reading about DNA vs RNA viruses or RNA-seq.

What did you paste?

One A260 reading, read as DNA and as RNA

DNA polymerase vs RNA polymerase

4.1 The conversion: transcription vs reverse transcription

From the DNA coding strand, the RNA is the same sequence with every T changed to U, because RNA polymerase reads the other strand, the template, and builds a complementary copy. From the template strand, the RNA is its reverse complement. From RNA, the cDNA is the same sequence with U changed to T, which is what reverse transcriptase produces after its second strand. The strand view shows all four forms, with T and U highlighted.

4.2 The sugar difference in numbers

Every ribose has one more oxygen than a deoxyribose, so an RNA of n nucleotides has n more oxygen atoms than the same sequence as DNA. That alone adds 15.999 g/mol per nucleotide. The "+O" figure on the mass card is exactly the number of known nucleotides in your sequence.

4.3 The base difference in numbers

Each thymine in DNA becomes a uracil in RNA, which removes a methyl group: one carbon and two hydrogens, 14.027 g/mol. So an AT-rich sequence has a smaller DNA-to-RNA mass gap than a GC-rich one of the same length, and a sequence with no T at all gains the full 15.999 g/mol per nucleotide.

4.4 Single strand vs double helix

DNA usually exists as two antiparallel strands in a right-handed B-form helix, about 2 nm wide with 10.5 base pairs per turn (Watson and Crick, 1953; Franklin and Gosling, 1953). RNA is usually single-stranded but folds back on itself into hairpins and loops. When two RNA strands do pair, as in double-stranded RNA viruses or the first poly(A)·poly(U) duplex described by Rich and Davies (1956), the 2′-OH pushes the helix into the A-form: shorter, wider and about 11 base pairs per turn. RNA double helix vs DNA double helix is therefore a real structural difference, not just a strand count.

4.5 One A260, two concentrations

DNA and RNA both absorb at 260 nm, so a spectrophotometer cannot tell them apart. The same reading means 50 ng/µL per unit for double-stranded DNA, 40 for RNA and 33 for single-stranded DNA. Choose the right molecule in your instrument; reading RNA as dsDNA overestimates it by 25%. The sequence-specific values in the results use each molecule's own extinction coefficient.

4.6 Why RNA is less stable than DNA

The 2′-OH of ribose can attack the neighbouring phosphodiester bond and cut the RNA backbone, especially at high pH or with metal ions (Li and Breaker, 1999). DNA has no 2′-OH, so it survives alkali and lasts for years in a freezer, and even millennia in bones. RNA is also attacked by RNase enzymes, which are everywhere. That is why DNA is the long-term store and RNA a short-lived message.

4.7 DNA polymerase vs RNA polymerase

DNA polymerase copies DNA into DNA during replication; RNA polymerase copies DNA into RNA during transcription. DNA polymerase needs a primer, uses dNTPs, copies both strands of the whole genome once per cell cycle and checks its work with a 3′ to 5′ exonuclease. RNA polymerase needs no primer, uses NTPs, copies one strand of one gene at a time, as many times as the cell needs, and proofreads only by backing up and cutting (Sydow and Cramer, 2009). The copy-time card uses the speeds you set.

4.8 Error rates and why they differ

Replication must be nearly perfect because a mistake becomes a permanent mutation passed to every daughter cell. After proofreading and mismatch repair, about one error remains per billion to ten billion bases copied (Kunkel, 2004). A transcription error only spoils one of many RNA copies, so RNA polymerase can afford roughly one error per hundred thousand bases. The errors-per-copy card multiplies your length by each rate.

4.9 What this means for DNA vs RNA viruses

RNA viruses copy their genomes with RNA-dependent RNA polymerases or reverse transcriptases that lack proofreading, so their mutation rates are about 10⁻⁶ to 10⁻⁴ per nucleotide per cell infection, against roughly 10⁻⁸ to 10⁻⁶ for DNA viruses (Sanjuán et al., 2010; Drake, 1993). That is why influenza and SARS-CoV-2 change quickly. Whether a DNA virus or an RNA virus is more dangerous depends on the virus, not its nucleic acid: smallpox and some papillomaviruses are DNA viruses, while influenza, HIV and Ebola are RNA viruses.

4.10 What this comparison cannot tell you

It compares the same sequence as DNA and as RNA. It does not predict RNA folding, chemical modifications such as methylated bases or pseudouridine, splicing, or how long a particular RNA lasts in a cell. Copying times and error rates are averages; real polymerases pause, stall and vary along a gene. Use the numbers to understand the difference, then measure your own system.

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

This flow chart follows the worked example, the 21-base DNA coding strand ATGGCTTGGAAACGTGAATAA with a 5′-OH, read at A260 = 0.5. 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 6,518.31, 6,784.16 and 265.84 g/mol for this sequence.

1. Paste your DNA or RNA sequence. Paste the sequence 5 prime to 3 prime. FASTA headers, spaces, numbers and lowercase are cleaned automatically, and the live readout says whether it looks like DNA or RNA.
2. Or load a sample. Ten built-in samples cover a short gene, its template strand, an siRNA, an mRNA, a 1,200-nt gene, GC-rich and AT-rich DNA, telomere DNA and its RNA (TERRA), and a messy 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 there are.
4. Say what you pasted. Choose the DNA coding strand, the DNA template strand or RNA, or let the tool detect it: a U means RNA. The tool transcribes DNA to RNA or reverse-transcribes RNA to cDNA.
5. Choose the 5 prime end. Pick 5 prime hydroxyl for synthetic oligos or 5 prime phosphate for natural and enzymatic molecules. The same end is used for both, so the mass gap does not change.
6. Enter one A260 reading. Type the absorbance, dilution factor and path length. The tool shows what that one reading means if the sample is double-stranded DNA, single-stranded DNA or RNA.
7. Set the polymerases. Pick E. coli or human cells for typical DNA polymerase and RNA polymerase speeds, or enter your own speeds and error rates.
8. Press Compare DNA vs RNA. Nothing is calculated until you press the button. Changing any input clears the old results so you never read stale numbers.
9. Read the comparison. Check the cards, the aligned DNA and RNA strands, the formula block, the side-by-side table, the element table and the four charts.
10. Report and export. Copy the RNA or DNA sequence, copy one of the five ready-made report sentences, or download the full report as a text document or PDF.

Excel can convert DNA to RNA with one SUBSTITUTE, count the bases with the LEN minus SUBSTITUTE trick, and add up both molecular weights with SUMPRODUCT. What trips people up is lowercase letters, forgetting the end correction, and using the DNA residue masses for RNA.

9.1 The functions you need

9.2 Step by step

9.3 The awkward cases

Template strand. Reverse the text with =CONCAT(MID(A2,SEQUENCE(LEN(A2),,LEN(A2),-1),1)) in Excel 365, complement it with the lowercase-placeholder SUBSTITUTE chain, then change T to U.

RNA in, DNA out. Swap the roles: count U in the RNA, and the DNA has the same number of T.

Long sequences. A cell holds 32,767 characters. For whole transcriptomes or genomes, use R or Python.

9.4 Why your Excel answer might differ from R, Python or Biopython

9.5 Charting the result in Excel

1. Make a table of element counts with DNA and RNA columns.
2. Select it and choose Insert → Clustered Column.
3. Colour DNA blue and RNA fuchsia, the colours used on this page.
4. Put the mass difference in the chart title so the figure stands on its own.

9.6 Excel errors you will probably hit

Biostrings converts between the two molecules for you: RNAString(dna) transcribes and DNAString(rna) reverse-transcribes. The script below then builds both chemical formulas atom by atom, so the masses, the mass gap, the extinction coefficients and the concentrations 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 dna_vs_rna_plot.png

# DNA vs RNA calculator in R: convert, then compare formula, mass, extinction,
# A260 concentration, copy time and errors. Matches the StatsUnlock calculator.
library(Biostrings)

# ---- 1. Your data -------------------------------------------------------
seq_txt  <- "ATGGCTTGGAAACGTGAATAA"   # 5' to 3'
in_type  <- "dna_coding"              # dna_coding, dna_template or rna
five_p   <- FALSE                     # TRUE if the 5' end carries a phosphate
a260 <- 0.5; dilution <- 1; path_cm <- 1
v_dna <- 1000; v_rna <- 50            # polymerase speeds, nt per second
e_dna <- 1e-9; e_rna <- 1e-5          # errors per nucleotide

# ---- 2. Convert DNA <-> RNA ---------------------------------------------
s <- toupper(gsub("[^A-Za-z]", "", seq_txt))
if (in_type == "rna") {
  rna <- RNAString(chartr("T", "U", s)); dna <- DNAString(rna)        # reverse transcription: U -> T
} else if (in_type == "dna_template") {
  dna <- reverseComplement(DNAString(s)); rna <- RNAString(dna)       # template: reverse complement, then T -> U
} else {
  dna <- DNAString(s); rna <- RNAString(dna)                          # coding strand: T -> U
}
n  <- length(dna)
nT <- letterFrequency(dna, "T")

# ---- 3. Formulas and molecular weights ----------------------------------
aw <- c(C = 12.011, H = 1.008, N = 14.007, O = 15.999, P = 30.974)
res_dna <- 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))
res_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))
formula_of <- function(x, res) {
  k <- letterFrequency(x, names(res))
  f <- Reduce(`+`, Map(function(b, m) res[[b]] * m, names(k), k)) + c(0, 2, 0, 1, 0)
  if (!five_p) f <- f - c(0, 1, 0, 3, 1)
  setNames(f, names(aw))
}
f_dna <- formula_of(dna, res_dna); f_rna <- formula_of(rna, res_rna)
mw_dna <- sum(f_dna * aw); mw_rna <- sum(f_rna * aw)
d_rule <- 15.999 * n - 14.027 * nT

# ---- 4. Extinction and concentration ------------------------------------
eps_nt  <- c(A = 15400, C = 7400, G = 11500, T = 8700, U = 9900)
eps_dna <- sum(letterFrequency(dna, c("A", "C", "G", "T")) * eps_nt[c("A", "C", "G", "T")])
eps_rna <- sum(letterFrequency(rna, c("A", "C", "G", "U")) * eps_nt[c("A", "C", "G", "U")])
conc_dna <- a260 / (eps_dna * path_cm) * dilution * mw_dna * 1000
conc_rna <- a260 / (eps_rna * path_cm) * dilution * mw_rna * 1000

# ---- 5. Report ----------------------------------------------------------
cat("DNA:", as.character(dna), "\nRNA:", as.character(rna), "\n")
cat("DNA formula:", paste0(names(f_dna), f_dna, collapse = " "), "| RNA formula:", paste0(names(f_rna), f_rna, collapse = " "), "\n")
cat(sprintf("MW DNA = %.2f | MW RNA = %.2f | difference = %.2f (rule %.2f) g/mol\n", mw_dna, mw_rna, mw_rna - mw_dna, d_rule))
cat(sprintf("eps260 DNA = %d | RNA = %d\n", eps_dna, eps_rna))
cat(sprintf("A260 %.2f: ssDNA %.2f ng/uL, RNA %.2f ng/uL | factors: dsDNA %.2f, ssDNA %.2f, RNA %.2f\n",
            a260, conc_dna, conc_rna, a260 * 50 * dilution, a260 * 33 * dilution, a260 * 40 * dilution))
cat(sprintf("Copy time: DNA pol %.3f s, RNA pol %.3f s | errors per copy: %.2e vs %.2e\n", n / v_dna, n / v_rna, n * e_dna, n * e_rna))

# ---- 6. Figure: atoms and mass, DNA vs RNA ------------------------------
png("dna_vs_rna_plot.png", width = 2400, height = 1100, res = 300)
par(mfrow = c(1, 2), mar = c(4, 4, 3, 1))
barplot(rbind(DNA = f_dna, RNA = f_rna), beside = TRUE, col = c("#2563eb", "#a21caf"),
        legend.text = TRUE, args.legend = list(x = "topright", bty = "n"), ylab = "Atoms", main = "Atoms: DNA vs RNA")
barplot(c(DNA = mw_dna, RNA = mw_rna), col = c("#2563eb", "#a21caf"), ylab = "g/mol",
        main = sprintf("RNA is %.2f g/mol heavier", mw_rna - mw_dna))
dev.off()

Run it with Rscript dna_vs_rna.R. For the worked example it prints MW DNA = 6518.31, MW RNA = 6784.16, difference = 265.84 (rule 265.84), eps260 250500 vs 256500, and 13.01 vs 13.22 ng/uL.

10.2 What each part does

10.3 What the figure shows

The left panel compares the atoms in the DNA and RNA forms: the oxygen bars differ by one per nucleotide, the carbon and hydrogen bars slightly, and the nitrogen and phosphorus bars not at all. The right panel compares the two molecular weights, with the gap in the title.

10.4 Common R problems and fixes

10.5 Useful one-liners

# Transcribe and reverse-transcribe
RNAString(DNAString("ATGGCTTGGAAACGTGAATAA"))
DNAString(RNAString("AUGGCUUGGAAACGUGAAUAA"))
# RNA from a template strand
RNAString(reverseComplement(DNAString("TTATTCACGTTTCCAAGCCAT")))
# Base counts of DNA and RNA side by side
rbind(DNA = letterFrequency(DNAString("ATGGCTTGGAAACGTGAATAA"), c("A","C","G","T")),
      RNA = letterFrequency(RNAString("AUGGCUUGGAAACGUGAAUAA"), c("A","C","G","U")))

Biopython's Seq object has transcribe() and back_transcribe(), and molecular_weight handles both DNA and RNA. The script below mirrors the R version step for step, then cross-checks both masses against Biopython.

Install once with pip install biopython matplotlib.

11.1 The complete script

Python 3 with Biopython, saves dna_vs_rna_plot.png

"""DNA vs RNA calculator in Python: convert, then compare formula, mass, extinction,
A260 concentration, copy time and errors. Matches the StatsUnlock calculator."""
import re
from collections import Counter
from Bio.Seq import Seq
from Bio.SeqUtils import molecular_weight
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

# ---- 1. Your data -------------------------------------------------------
seq_txt = "ATGGCTTGGAAACGTGAATAA"   # 5' to 3'
in_type = "dna_coding"              # dna_coding, dna_template or rna
five_p = False                      # True if the 5' end carries a phosphate
a260, dilution, path_cm = 0.5, 1, 1
v_dna, v_rna = 1000, 50             # polymerase speeds, nt per second
e_dna, e_rna = 1e-9, 1e-5           # errors per nucleotide

# ---- 2. Convert DNA <-> RNA ---------------------------------------------
s = re.sub(r"[^A-Za-z]", "", seq_txt).upper()
if in_type == "rna":
    rna = Seq(s.replace("T", "U")); dna = rna.back_transcribe()        # U -> T (cDNA)
elif in_type == "dna_template":
    dna = Seq(s).reverse_complement(); rna = dna.transcribe()          # template -> coding -> RNA
else:
    dna = Seq(s); rna = dna.transcribe()                               # coding strand: T -> U
n, nT = len(dna), str(dna).count("T")

# ---- 3. Formulas and molecular weights ----------------------------------
AW = dict(C=12.011, H=1.008, N=14.007, O=15.999, P=30.974)
RES_DNA = 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))
RES_RNA = 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))

def formula_of(x, res):
    f = [0, 0, 0, 0, 0]
    for b, m in Counter(str(x)).items():
        if b in res:
            f = [a + m * r for a, r in zip(f, res[b])]
    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: one phosphate fewer
    return dict(zip("CHNOP", f))

f_dna, f_rna = formula_of(dna, RES_DNA), formula_of(rna, RES_RNA)
mw_dna = sum(AW[e] * k for e, k in f_dna.items())
mw_rna = sum(AW[e] * k for e, k in f_rna.items())
d_rule = 15.999 * n - 14.027 * nT

# ---- 4. Extinction and concentration ------------------------------------
EPS = dict(A=15400, C=7400, G=11500, T=8700, U=9900)
eps_dna = sum(EPS[b] for b in str(dna) if b in EPS)
eps_rna = sum(EPS[b] for b in str(rna) if b in EPS)
conc_dna = a260 / (eps_dna * path_cm) * dilution * mw_dna * 1000
conc_rna = a260 / (eps_rna * path_cm) * dilution * mw_rna * 1000

# ---- 5. Report ----------------------------------------------------------
print("DNA:", dna, "\nRNA:", rna)
print("DNA formula:", " ".join(f"{e}{k}" for e, k in f_dna.items()), "| RNA formula:", " ".join(f"{e}{k}" for e, k in f_rna.items()))
print(f"MW DNA = {mw_dna:.2f} | MW RNA = {mw_rna:.2f} | difference = {mw_rna - mw_dna:.2f} (rule {d_rule:.2f}) g/mol")
print(f"eps260 DNA = {eps_dna} | RNA = {eps_rna}")
print(f"A260 {a260:.2f}: ssDNA {conc_dna:.2f} ng/uL, RNA {conc_rna:.2f} ng/uL | factors: dsDNA {a260 * 50 * dilution:.2f}, "
      f"ssDNA {a260 * 33 * dilution:.2f}, RNA {a260 * 40 * dilution:.2f}")
print(f"Copy time: DNA pol {n / v_dna:.3f} s, RNA pol {n / v_rna:.3f} s | errors per copy: {n * e_dna:.2e} vs {n * e_rna:.2e}")
# Cross-check with Biopython's own masses (Biopython assumes a 5'-phosphate)
print(f"Biopython 5'-phosphate MW: DNA {molecular_weight(dna, 'DNA'):.2f}, RNA {molecular_weight(rna, 'RNA'):.2f}")

# ---- 6. Figure: atoms and mass, DNA vs RNA ------------------------------
fig, ax = plt.subplots(1, 2, figsize=(8, 3.6))
x = range(5)
ax[0].bar([i - 0.2 for i in x], list(f_dna.values()), 0.4, color="#2563eb", label="DNA")
ax[0].bar([i + 0.2 for i in x], list(f_rna.values()), 0.4, color="#a21caf", label="RNA")
ax[0].set_xticks(list(x), list("CHNOP")); ax[0].set(ylabel="Atoms", title="Atoms: DNA vs RNA"); ax[0].legend(frameon=False)
ax[1].bar(["DNA", "RNA"], [mw_dna, mw_rna], color=["#2563eb", "#a21caf"])
ax[1].set(ylabel="g/mol", title=f"RNA is {mw_rna - mw_dna:.2f} g/mol heavier")
fig.tight_layout()
fig.savefig("dna_vs_rna_plot.png", dpi=300)

11.2 What each part does

11.3 What the figure shows

The same two panels as the R figure: atoms in DNA and RNA side by side, and the two molecular weights with the gap in the title.

11.4 Common Python problems and fixes

11.5 Useful one-liners

from Bio.Seq import Seq
Seq("ATGGCTTGGAAACGTGAATAA").transcribe()            # AUGGCUUGGAAACGUGAAUAA
Seq("AUGGCUUGGAAACGUGAAUAA").back_transcribe()       # ATGGCTTGGAAACGTGAATAA
Seq("TTATTCACGTTTCCAAGCCAT").reverse_complement().transcribe()   # template to RNA
from Bio.SeqUtils import molecular_weight
molecular_weight("AUGGCUUGGAAACGUGAAUAA", "RNA") - molecular_weight("ATGGCTTGGAAACGTGAATAA", "DNA")

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

12.1 DNA nucleotides vs RNA nucleotides

What to conclude: every RNA residue is 15.999 g/mol heavier than its DNA partner (one more oxygen), except U, which is only 1.972 g/mol heavier than T because uracil lacks thymine's CH₂. Masses from IUPAC atomic weights; extinction coefficients as used by Cavaluzzi and Borer (2004).

12.2 DNA polymerase vs RNA polymerase

What to conclude: DNA polymerase is built for accuracy, RNA polymerase for flexibility and volume. The function of DNA polymerase vs RNA polymerase follows from what each product is for: a permanent copy of the genome versus a disposable message.

12.3 DNA viruses vs RNA viruses

What to conclude: an RNA virus vs a DNA virus is not automatically more or less dangerous. RNA viruses change faster; DNA viruses are more stable and can persist for life. Danger depends on the specific virus, the host and the treatment available.

12.4 Replication vs transcription vs translation

What to conclude: DNA replication vs RNA replication differ in who does it. Cells replicate only DNA; RNA replication is a virus trick.

12.5 DNA sequencing vs RNA sequencing (DNA-seq vs RNA-seq)

What to conclude: a DNA sequence vs an RNA sequence of the same gene differ in T vs U and, in eukaryotes, in the introns spliced out of the RNA. DNA-seq tells you what could be made; RNA-seq tells you what is being made.

12.6 DNA vs RNA card sort answers

What to conclude: if a card names a sugar, a base other than A, G or C, the number of strands, or a job, it belongs to one molecule; if it describes what a nucleotide or a nucleic acid is, it is "Both".

Eight worked comparisons, from a 21-base gene to telomere RNA. Every number below came from this calculator, and each can be loaded from the sample list in Section 2.

Study design: a spectrophotometer cannot tell DNA from RNA, because both absorb at 260 nm. Enzymes can. DNase I destroys DNA but not RNA; RNase A destroys RNA but not DNA. Treating three portions of the same sample and running them on a gel shows which molecule you have.

Prepare

1. Work RNase-free. Wear gloves, use filter tips and RNase-free tubes and water, and clean the bench with an RNase decontamination spray.
2. Measure the sample. Read A260 and enter it in Section 2 to see the concentration if it is DNA and if it is RNA.
3. Plan three tubes with about 200 to 500 ng each: untreated, DNase I and RNase A.

Set up

4. DNase tube. Add 1 unit of RNase-free DNase I and its buffer (with Mg²⁺) to 10 µL.
5. RNase tube. Add RNase A to about 10 µg/mL in the same volume.
6. Untreated tube. Add buffer and water only, as the control.

Run

7. Incubate all three at 37 °C for 15 to 30 minutes.
8. Stop the reactions by adding loading dye with EDTA (and, for RNA, heat at 65 °C for 5 minutes).
9. Run a 1% agarose gel with a DNA ladder at about 100 V for 30 to 40 minutes and stain it.

Record

10. Read the result. A band that disappears with DNase is DNA; a band that disappears with RNase is RNA; a band that survives both may be protected or not a nucleic acid.
11. Photograph and label the gel with lanes, treatments and ladder sizes.

Replicates: repeat with a known DNA and a known RNA as positive controls, so you know both enzymes worked.

Lab notebook: sample, A260, enzyme lot numbers and units, incubation time and temperature, gel percentage and voltage, and the gel photo.

Common mistakes when comparing DNA and RNA samples:

• Using DNase I that contains RNase, so RNA disappears in both tubes.
• Reading RNA with the dsDNA setting on the spectrophotometer (25% too high).
• Leaving RNA at room temperature without RNase inhibitors.
• Skipping DNase treatment before RT-qPCR, so genomic DNA gives false signal.
• Expecting RNA to run at the same size as DNA of the same length on a native gel; secondary structure changes its mobility.

Use it when:

• ✓ You need to compare DNA vs RNA structure and chemistry on a real sequence.
• ✓ You want the RNA made from a DNA strand, or the cDNA of an RNA.
• ✓ You need the molecular weight or extinction coefficient of the same sequence as DNA and as RNA.
• ✓ You have one A260 reading and are not sure whether the sample is DNA or RNA.
• ✓ You want to show how much faster and more accurate replication is than transcription.

Use something else when:

• ✗ You need the protein a gene makes: use a translation or protein synthesis tool.
• ✗ You need RNA folding or secondary structure: use an RNA folding program.
• ✗ Your RNA has modified bases: add the modifications to the mass yourself.
• ✗ You need to know whether a sample is DNA or RNA: do the nuclease test in Section 14.

Real-world examples:

1. Biology class: filling in a DNA vs RNA Venn diagram, chart or card sort with real numbers.
2. RT-qPCR: converting an RNA target into the cDNA sequence for primer design.
3. siRNA work: getting the RNA mass and extinction coefficient right for dosing.
4. Virology teaching: showing why RNA viruses mutate faster than DNA viruses.

Experimental design: decide early whether you will measure DNA or RNA, set the spectrophotometer to that molecule, and keep RNA cold and RNase-free from the first step.

Decision tree: Does the sequence contain U? Yes → it is RNA: choose "RNA" to get the cDNA. No → it is DNA: is it the strand RNA polymerase reads? Yes → choose "template strand". No → choose "coding strand" and use this DNA vs RNA calculator.

Assumptions

1. Same sequence, two molecules. Requires comparing like with like. In cells, mRNA lacks introns that the gene contains, so a real gene and its mRNA differ in length too.
2. Unmodified nucleotides. Requires plain A, C, G, T and U. Modified RNA bases (pseudouridine, m6A) and methylated DNA change the masses slightly.
3. Same 5′ end on both. Requires matching ends. When they differ, the gap shifts by 79.98 g/mol.
4. Free acid form. Salts weigh about 22 g/mol more per phosphate in both molecules, so the gap is unchanged.
5. Summed extinction coefficients. Ignore base stacking, which lowers real absorbance by a few percent, more for double strands.
6. Average copying speeds and error rates. Real polymerases pause and vary by organism, gene and conditions; the defaults are order-of-magnitude values.

Limitations

1. No splicing. Paste the mature mRNA or cDNA if you need the processed RNA.
2. No folding. RNA secondary structure is not predicted.
3. No translation. Use a protein synthesis tool for the protein.
4. Single strands for RNA. Double-stranded RNA mass is not shown separately; add the complementary RNA strand computed the same way.
5. No purity check. Measure A260/A280 and A260/A230 separately.
6. Average atomic weights. For mass spectrometry use monoisotopic masses.

DNA vs RNA is a difference of one oxygen on the sugar, one methyl group on one base, and the number of strands, and those small changes decide which molecule stores genes and which one uses them. This calculator puts numbers on every difference for your own sequence: the conversion, the formulas, the masses, the concentrations and the polymerases.

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 comparing DNA and RNA for an exam or converting an RNA target into cDNA, the rule is the same: deoxyribose and thymine for DNA, ribose and uracil for RNA, and always say which one you mean.

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

The structures of DNA and RNA, the polymerases that make them, the viruses that use them and the formulas for mass, extinction and concentration on this page rest on the peer-reviewed literature below. Every DOI was checked before publication.

1. Azzalin, C. M., Reichenbach, P., Khoriauli, L., Giulotto, E., & Lingner, J. (2007). Telomeric repeat–containing RNA and RNA surveillance factors at mammalian chromosome ends. Science, 318(5851), 798–801. https://doi.org/10.1126/science.1147182
2. Baltimore, D. (1970). RNA-dependent DNA polymerase in virions of RNA tumour viruses. Nature, 226(5252), 1209–1211. https://doi.org/10.1038/2261209a0
3. Baltimore, D. (1971). Expression of animal virus genomes. Bacteriological Reviews, 35(3), 235–241. https://doi.org/10.1128/br.35.3.235-241.1971
4. 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
5. Cech, T. R. (2000). The ribosome is a ribozyme. Science, 289(5481), 878–879. https://doi.org/10.1126/science.289.5481.878
6. Cramer, P. (2019). Organization and regulation of gene transcription. Nature, 573(7772), 45–54. https://doi.org/10.1038/s41586-019-1517-4
7. Crick, F. (1970). Central dogma of molecular biology. Nature, 227(5258), 561–563. https://doi.org/10.1038/227561a0
8. Drake, J. W. (1993). Rates of spontaneous mutation among RNA viruses. Proceedings of the National Academy of Sciences, 90(9), 4171–4175. https://doi.org/10.1073/pnas.90.9.4171
9. Franklin, R. E., & Gosling, R. G. (1953). Molecular configuration in sodium thymonucleate. Nature, 171, 740–741. https://doi.org/10.1038/171740a0
10. Jonkers, I., & Lis, J. T. (2015). Getting up to speed with transcription elongation by RNA polymerase II. Nature Reviews Molecular Cell Biology, 16(3), 167–177. https://doi.org/10.1038/nrm3953
11. 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
12. Kunkel, T. A. (2004). DNA replication fidelity. Journal of Biological Chemistry, 279(17), 16895–16898. https://doi.org/10.1074/jbc.R400006200
13. Li, Y., & Breaker, R. R. (1999). Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2′-hydroxyl group. Journal of the American Chemical Society, 121(23), 5364–5372. https://doi.org/10.1021/ja990592p
14. Rich, A., & Davies, D. R. (1956). A new two stranded helical structure: Polyadenylic acid and polyuridylic acid. Journal of the American Chemical Society, 78(14), 3548–3549. https://doi.org/10.1021/ja01595a086
15. Sanger, F., Nicklen, S., & Coulson, A. R. (1977). DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences, 74(12), 5463–5467. https://doi.org/10.1073/pnas.74.12.5463
16. Sanjuán, R., Nebot, M. R., Chirico, N., Mansky, L. M., & Belshaw, R. (2010). Viral mutation rates. Journal of Virology, 84(19), 9733–9748. https://doi.org/10.1128/JVI.00694-10
17. Sydow, J. F., & Cramer, P. (2009). RNA polymerase fidelity and transcriptional proofreading. Current Opinion in Structural Biology, 19(6), 732–739. https://doi.org/10.1016/j.sbi.2009.10.009
18. Temin, H. M., & Mizutani, S. (1970). RNA-dependent DNA polymerase in virions of Rous sarcoma virus. Nature, 226(5252), 1211–1213. https://doi.org/10.1038/2261211a0
19. Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57–63. https://doi.org/10.1038/nrg2484
20. 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