DNA sequencing and DNA fingerprinting are core biotechnology tools for reading DNA and distinguishing samples. They rely on predictable base pairing and natural DNA variation to produce comparable, interpretable genetic information.

DNA sequencing: determining nucleotide order

DNA sequencing identifies the exact order of nucleotides (A, T, C, G) in a DNA molecule, producing a DNA sequence that can be stored, searched, and compared across samples.

What sequencing output looks like

This diagram summarizes Sanger (dideoxy) sequencing, showing how chain-terminating ddNTPs generate a set of DNA fragments that differ by one nucleotide. The fragments are separated by electrophoresis and detected as a color-coded chromatogram, which is then translated into a base-by-base sequence (A, T, C, G). Source

Sequencing generates a digital representation of bases (a string of letters). Depending on the method, you may also see:

• Read data: many short sequences (“reads”) from the same sample

• Consensus sequence: the most supported base at each position after combining reads

• Variant calls: positions where a sample differs from a reference or another sample

Why sequencing is useful in comparisons

Because sequence data are explicit, comparisons can be direct:

• Match: the same bases at the same positions (within expected error)

• Difference: substitutions or length differences (often in repetitive regions)

• Relatedness inference: more shared variants usually indicates closer relationship (interpretation depends on context and sampling)

DNA fingerprinting: identifying and comparing samples

DNA fingerprinting uses variable regions of DNA to generate a pattern or profile that can be compared between samples (for identity testing or relatedness assessment).

This STR electropherogram shows how DNA fingerprinting produces a peak pattern across multiple loci, where each peak corresponds to an allele (a fragment size/repeat count) at a specific STR marker. In mixture samples, additional peak sets can appear, making interpretation depend on allele compatibility and relative peak heights. The resulting multi-locus peak pattern is the “profile” used to compare biological samples. Source

A key idea is that many genomic regions are shared by all humans, but some regions vary enough to distinguish individuals.

Sources of variation used for fingerprints

High-utility fingerprint regions typically include:

This diagram illustrates gel electrophoresis, the classic method for separating DNA fragments by size to create a banding pattern. Smaller fragments migrate farther through the gel matrix than larger fragments, producing distinct bands that can be compared between samples. This size-based separation underlies many fingerprint-style profiles, including those based on variable repeat regions. Source

• Short tandem repeats (STRs): short motifs (e.g., 2–6 bases) repeated a variable number of times

• Variable number tandem repeats (VNTRs): longer repeat units with variable repeat counts

• Single-nucleotide polymorphisms (SNPs): single-base differences (often used in large panels)

How sequencing data can generate DNA fingerprints

Sequence information can be converted into a fingerprint by focusing analysis on selected variable loci and then comparing the results between samples.

Conceptual workflow (sequencing-based fingerprinting)

• Choose a set of highly variable loci (commonly STRs or SNP panels)

• Obtain sequence reads covering those loci

• Determine the alleles present at each locus (e.g., repeat counts for STRs, specific bases for SNPs)

• Compile alleles into a profile (the “fingerprint”)

• Compare profiles between samples to assess:

  • Consistency (same alleles across loci)

  • Exclusion (mismatched alleles at one or more loci)

  • Strength of match (more matching loci generally increases confidence)

What “comparing samples” means in practice

Comparisons are most informative when:

• Multiple independent loci are analysed (reduces chance of coincidental matches)

• The same loci and analysis rules are used across all samples

• Data quality is sufficient to avoid false differences from sequencing error

Interpreting fingerprints: what can and cannot be concluded

Fingerprint interpretation depends on whether profiles are:

• Complete (all loci successfully typed)

• Partial (missing loci due to limited or degraded DNA)

• Mixed (DNA from more than one individual)

Key interpretive outcomes include:

• Match supports same source: profiles are consistent across tested loci

• Exclusion supports different sources: at least one locus clearly incompatible

• Inconclusive: insufficient loci/quality to decide confidently

Data reliability and common pitfalls

Even with strong methods, results can be affected by:

• Contamination: foreign DNA introduced during collection or processing

• Degradation: fragmented DNA reduces recoverable loci and read quality

• Sequencing error: incorrect base calls can create apparent differences

• Repeat-region challenges: repeats (STRs/VNTRs) can be harder to read accurately than non-repetitive DNA

Good practice typically includes:

• Negative controls (to detect contamination)

• Replicates (to confirm uncertain calls)

• Clear rules for calling alleles and handling low-quality data