An intermediate course for future biochemists, biological technicians and medical laboratory technologists, about the core methods of a molecular biology lab and the data they produce. The first module covers nucleic acids: how DNA and RNA extraction works and how to judge yield and purity, how PCR works and how to check a primer pair, and how to compute relative gene expression from qPCR with the delta-delta-Ct method. The second module covers separation and detection: reading an agarose gel against a ladder, each step of SDS-PAGE and Western blotting, and calculating concentrations from an ELISA standard curve. The third module covers sequencing and bioinformatics: Sanger compared with next-generation sequencing, finding motifs and GC content in a DNA sequence with Python, and running a BLAST search and reading its results. Python activities are fill-in-the-blank and use only the standard library, so no programming background is needed. Every activity is a paper, data or web exercise: nothing here involves handling chemicals, biological material or patient samples at home. Bench work needs supervised training in a teaching or employer lab, and these careers have real entry requirements, such as a degree with lab coursework, and for medical laboratory scientists a NAACLS-accredited program and ASCP certification.

Extract nucleic acids, run PCR and qPCR, read gels, Western blots and ELISAs, compare sequencing methods, and work with sequences and BLAST.

Molecular Biology Methods

Download a spin-column kit handbook and find the protocol for one sample type (for example, cultured cells or blood).

Read the whole protocol once without writing anything.

On a printout or in a notebook, label each step as lyse, bind, wash, dry or elute.

Mark every step that needs preparation before the day (buffer reconstitution, pre-heating, thawing).

Circle each point where you keep the flow-through versus discard it, and each safe stopping point.

Write down where an extraction blank fits in and how you would label your tubes (sample ID, date, initials).

Calculate concentration, A260/A280 and A260/A230 for three DNA readings: (a) A260 0.84, A280 0.46, A230 0.41; (b) A260 0.50, A280 0.33, A230 0.48; (c) A260 0.30, A280 0.16, A230 0.12. Decide which extracts you would use.

If your teaching lab runs an extraction practical, compare your annotated protocol with the one your instructor uses and ask about any differences.

## Why it matters

Almost every molecular method in this course starts with clean nucleic acid. PCR, qPCR, sequencing and cloning all fail, or quietly give wrong answers, when the DNA or RNA going in is degraded, contaminated or too dilute. A biological technician may run dozens of extractions a day, and a medical laboratory technologist may run them on automated platforms for infectious disease or genetic tests. In every case the job is the same: get the nucleic acid out of the cells, away from everything else, and prove it is good enough to use.

## The core idea: lyse, bind, wash, elute

Most modern extractions use a **silica spin column** or **magnetic silica beads**. The steps are the same in almost every kit:

1. **Lyse.** Break open cells and nuclei with a lysis buffer (detergent plus salts), often with an enzyme such as proteinase K to digest proteins. Tough samples (plant tissue, bacteria with thick walls) may need mechanical disruption too.
2. **Bind.** Add a high concentration of a chaotropic salt (and usually ethanol). Under these conditions nucleic acids stick to silica, while most proteins and other debris do not.
3. **Wash.** Spin wash buffers (usually ethanol-based) through the column to remove salts and leftover contaminants. A final "dry spin" removes residual ethanol, which would inhibit later reactions.
4. **Elute.** Add a small volume of low-salt buffer or nuclease-free water. Nucleic acid releases from the silica and collects in a clean tube.

Older methods, such as phenol-chloroform extraction (and its RNA cousin, TRIzol), separate nucleic acids by partitioning into a water layer. They work well but use hazardous solvents, so they are done only in a fume hood after specific training.

## DNA versus RNA

RNA is far less forgiving than DNA:

- **RNases are everywhere** (skin, dust, many reagents) and do not need cofactors. RNA work uses RNase-free tubes and tips, fresh gloves, a clean bench and often a dedicated area.
- **Work fast and cold.** Samples are kept on ice or put into a stabilising reagent immediately.
- **Remove DNA.** RNA preps for gene expression are usually treated with **DNase** so genomic DNA does not show up in later qPCR.

## Checking what you got

You always measure yield and purity before using an extract, usually on a microvolume UV spectrophotometer:

| Measurement | What it tells you | Typical target |
|---|---|---|
| A260 | Concentration. An A260 of 1.0 is about 50 ng/uL for double-stranded DNA and about 40 ng/uL for RNA | Depends on the method downstream |
| A260/A280 | Protein contamination | about 1.8 for DNA, about 2.0 for RNA |
| A260/A230 | Salt, chaotrope or phenol carryover | about 2.0 to 2.2 |

**Worked example.** A DNA eluate (undiluted, 1 cm path equivalent) reads A260 = 0.84, A280 = 0.46, A230 = 0.41.

- Concentration = 0.84 x 50 ng/uL = **42 ng/uL**.
- A260/A280 = 0.84 / 0.46 = **1.83**: little protein.
- A260/A230 = 0.84 / 0.41 = **2.05**: little salt carryover.

This extract looks usable. If A260/A230 had been 0.9, you would suspect chaotropic salt carryover, often from a skipped dry spin or wash, and expect PCR to struggle.

For RNA, purity ratios are not enough: integrity matters too. Labs check it on an automated electrophoresis instrument that gives an **RNA integrity number (RIN)** from 1 (degraded) to 10 (intact).

## Following a protocol

A kit protocol is a controlled procedure, not a suggestion. Before you start, read it once end to end, and mark:

- **Steps you must prepare in advance**, such as adding ethanol to a wash buffer concentrate or pre-heating a block.
- **Steps that are easy to get wrong**, such as which wash buffer goes first or whether to discard the flow-through.
- **Stopping points**, where the sample can be safely stored.
- **Your controls**: an extraction blank (no sample) shows whether reagents or technique introduced contamination.

Record lot numbers, the protocol version and any deviation in your notebook, as you learned in the lab data course.

## Safety and real-world training

Real samples can be infectious (clinical specimens, cultured pathogens) and lysis reagents are often irritant or toxic. Extraction is done only in a teaching or employer lab, at the right biosafety level, after institution training and under supervision. The CDC's *Biosafety in Microbiological and Biomedical Laboratories* (BMBL) explains biosafety levels: https://www.cdc.gov/labs/bmbl/index.html (Checked on 2026-10-23.) Nothing in this course asks you to extract nucleic acids at home.

## Common mistakes

- Skipping the dry spin, so ethanol carries over into the eluate.
- Mixing up flow-through and column: discarding the column instead of the flow-through.
- Overloading the column with too much starting material, which lowers purity and yield.
- Touching tube rims and tips during RNA work.
- Trusting A260 alone without checking the ratios.

## Quick recap

- Silica methods: lyse, bind (chaotropic salt), wash (ethanol), dry, elute (low salt).
- RNA needs RNase-free technique, speed, cold and usually DNase treatment.
- Check concentration (A260) and purity (A260/A280 about 1.8 DNA or 2.0 RNA; A260/A230 about 2.0 to 2.2).
- Run an extraction blank and record lots and deviations.

## Try it

Open the **Do** tab to annotate a real kit protocol and judge three sets of spectrophotometer readings, on paper.


Follow the logic of a DNA or RNA extraction protocol and judge the yield and purity of what you get.

DNA/RNA extraction

DNA extraction explained | biology animation

How to isolate RNA from tissue or cells

## Why it matters

The polymerase chain reaction (PCR) makes millions of copies of one short stretch of DNA in about an hour. It is behind genotyping, cloning, pathogen detection, forensic profiling and most sequencing library preparation. Biological technicians set up PCRs constantly, and most failures trace back to two things: badly designed primers and careless reaction setup.

## The core idea

PCR repeats three temperature steps, usually 25 to 35 times, in a thermal cycler:

1. **Denature** at about 94 to 98 C: the two DNA strands separate.
2. **Anneal** at about 50 to 65 C: two short **primers** bind to their complementary sites on opposite strands, flanking the target.
3. **Extend** at about 72 C: a heat-stable DNA polymerase (such as Taq) adds nucleotides to the 3' end of each primer.

Each cycle roughly doubles the target, so after n perfect cycles one copy becomes 2^n copies. In practice efficiency is below 100 percent and the reaction plateaus as reagents run out, which is why plain PCR is good for "is it there, and how big is it?" but not for measuring how much was there to begin with. That is what qPCR (next lesson) is for.

## What goes in the tube

A typical 25 uL reaction contains:

| Component | Role |
|---|---|
| Template DNA | The DNA containing your target (often 1 to 100 ng genomic DNA) |
| Forward and reverse primers | Define the start and end of the product (often 0.1 to 0.5 uM each) |
| dNTPs | The A, C, G, T building blocks |
| Polymerase buffer with Mg2+ | Magnesium is a required cofactor; its concentration affects specificity |
| DNA polymerase | Copies the DNA |
| Nuclease-free water | Brings the volume up |

Labs usually make a **master mix** of everything except template, split it into tubes, then add template last. Always include a **no-template control (NTC)**, with water instead of template, to catch contamination, and a **positive control** known to work.

## Designing primers

Primers are short single-stranded DNA pieces. Common rules of thumb:

- **Length** 18 to 25 nucleotides.
- **GC content** 40 to 60 percent.
- **Melting temperature (Tm)** roughly 52 to 65 C, with the two primers **within about 5 C** of each other.
- A G or C in the last one or two bases at the 3' end (a "GC clamp") helps the polymerase start, but avoid more than three G or C in the last five bases.
- Avoid runs of four or more of one base, and avoid primers that pair with themselves or each other (hairpins and primer dimers).
- The reverse primer is written 5' to 3' and matches the **reverse complement** of the top strand.

A quick Tm estimate for short primers is the **Wallace rule**:

```text
Tm = 2 x (number of A + T) + 4 x (number of G + C)
```

**Worked example.** Primer `AGCTGACCTGAAGGCTCTTG` has 20 bases: A 4, T 5, G 6, C 5. A + T = 9 and G + C = 11, so Tm = 2 x 9 + 4 x 11 = **62 C**. GC content is 11 / 20 = **55 percent**. Both are in range.

The Wallace rule is only a rough guide (it ignores salt and the order of bases). Real design uses nearest-neighbour calculators such as Primer-BLAST from NCBI (https://www.ncbi.nlm.nih.gov/tools/primer-blast/, checked on 2026-10-23), which also checks that the primers match only your intended target. A common starting annealing temperature is about 3 to 5 C below the lower primer Tm, refined with a gradient PCR.

## Reading the result

PCR products are usually checked on an agarose gel (module 2). You expect one sharp band at the predicted size, nothing in the NTC, and a band in the positive control. Extra bands suggest non-specific priming (try a higher annealing temperature); a smear suggests too much template or too many cycles; a band in the NTC means contamination, so the whole run is suspect.

## Avoiding contamination

Because PCR is so sensitive, one stray product molecule can give a false positive. Good labs separate **pre-PCR** (reagent and setup) and **post-PCR** (gel) areas, use filter tips, never bring opened products back to the setup area, and clean benches before setup. Clinical molecular labs follow these rules strictly and are inspected on them.

## Common mistakes

- Forgetting the NTC, so contamination goes unnoticed.
- Writing the reverse primer as the top-strand sequence instead of its reverse complement.
- Primer Tm values 10 C apart, so one primer barely binds at the chosen temperature.
- Adding template to the master mix before splitting it.
- Opening post-PCR tubes in the setup area.

## Quick recap

- Denature, anneal, extend; each cycle roughly doubles the target.
- Master mix, template last, NTC and positive control every time.
- Primers: 18 to 25 nt, 40 to 60 percent GC, Tm within 5 C, no runs or dimers.
- Wallace rule Tm = 2(A+T) + 4(G+C) is a quick estimate; use Primer-BLAST for real design.

## Try it

Open the **Do** tab. A Python primer checker is ready except for the Tm formula, which you write.


Understand how PCR copies a target and check a primer pair before you set up a reaction.

PCR (Polymerase Chain Reaction)

How to Design Primers for PCR

## Why it matters

Quantitative PCR (qPCR, also called real-time PCR) measures how much of a target sequence was in a sample, not just whether it was there. Research labs use it to compare gene expression between treated and untreated cells. Clinical labs use it to detect and quantify pathogens (viral load, for example). If you work in a molecular lab, you will read Ct values and fold changes every week.

## The core idea: watch the reaction as it happens

A qPCR instrument is a thermal cycler that measures fluorescence in each well after every cycle. The fluorescence comes from either:

- a **DNA-binding dye** such as SYBR Green, which glows when bound to double-stranded DNA, or
- a **probe** (for example a hydrolysis or "TaqMan" probe), which glows only when the specific target is copied.

Plotting fluorescence against cycle number gives an S-shaped **amplification curve**. The software sets a **threshold** in the exponential part of the curve. The **Ct** (cycle threshold, also called Cq) is the cycle at which a well's curve crosses it.

The key fact: **more starting template means a lower Ct**. If efficiency is near 100 percent, the target doubles each cycle, so a sample with twice as much template crosses one cycle earlier. A difference of 3.3 cycles is about a 10-fold difference.

## Relative expression: the delta-delta-Ct method

To compare expression of a **target gene** between a **treated** and a **control** sample, you also measure a **reference gene** (for example GAPDH or ACTB) whose expression should not change. It corrects for differences in how much RNA went into each reaction. The Livak method:

1. **Delta Ct** for each sample: Ct(target) - Ct(reference).
2. **Delta-delta Ct**: delta Ct(treated) - delta Ct(control).
3. **Fold change** = 2^(-delta-delta Ct).

**Worked example.** Means of triplicate wells:

| Sample | Ct target | Ct reference | Delta Ct |
|---|---|---|---|
| Control | 25.0 | 18.0 | 7.0 |
| Treated | 23.0 | 18.2 | 4.8 |

Delta-delta Ct = 4.8 - 7.0 = **-2.2**. Fold change = 2^2.2 = **4.6**. The target is about 4.6 times higher in treated cells. A positive delta-delta Ct gives a fold change below 1, meaning lower expression.

## When the method is valid

The 2^-delta-delta-Ct formula assumes:

- **Both assays have efficiency close to 100 percent** (about 90 to 110 percent), checked with a dilution series standard curve. If they differ, use an efficiency-corrected method (such as Pfaffl).
- **The reference gene really is stable** under your conditions. Many labs validate two or three reference genes.
- **Ct values are in a reliable range.** Very late Ct values (often above about 35) are noisy and may be non-specific.

Good practice is summarised in the **MIQE guidelines** (Bustin and colleagues, *Clinical Chemistry*, 2009, https://doi.org/10.1373/clinchem.2008.112797, checked on 2026-10-23), which list what a qPCR experiment must report.

## Controls and quality checks

- **No-template control (NTC):** should show no amplification, or a Ct far later than any sample.
- **No-reverse-transcriptase control (no-RT):** for RNA work, shows whether genomic DNA is giving signal.
- **Technical replicates:** usually triplicates. Replicate Ct values more than about 0.3 to 0.5 cycles apart suggest pipetting problems.
- **Melt curve (dye-based assays):** one clean peak means one product; extra peaks mean primer dimers or off-target products.

## Absolute quantification

Clinical and environmental labs often need actual copy numbers. They run a **standard curve** of known copy numbers and read unknowns off it. The slope of Ct against log10(copies) gives efficiency: E = 10^(-1/slope) - 1. A slope of -3.32 means 100 percent efficiency.

## Common mistakes

- Subtracting in the wrong order, so the fold change is inverted.
- Averaging fold changes instead of averaging Ct values first.
- Using a reference gene that the treatment actually changes.
- Ignoring a positive NTC or no-RT control.
- Reporting fold changes from Ct values near 38.

## Quick recap

- Lower Ct = more starting target; about 1 cycle = 2-fold at 100 percent efficiency.
- Delta Ct = target - reference; delta-delta Ct = treated - control; fold = 2^(-delta-delta Ct).
- Check efficiency, reference stability, replicates, NTC, no-RT and melt curves.
- Follow MIQE when you report results.

## Try it

Open the **Do** tab. A Python function computes delta Ct for you; you write the delta-delta-Ct and fold-change line.


Read Ct values from quantitative PCR and compute relative gene expression with the delta-delta-Ct method.

qPCR

qPCR and PCR Explained: Real-Time PCR Animation

How To Perform The Delta-Delta Ct Method (In Excel)

Follow a DNA or RNA extraction protocol, set up a PCR with a checked primer pair, and compute relative expression from qPCR with delta-delta-Ct.

Nucleic Acids

Ladder bands (size in bp, distance from well in mm): 3000 at 9, 2000 at 12, 1500 at 14.5, 1000 at 18, 750 at 22, 500 at 28, 250 at 37, 100 at 46.

Plot log10(size) on the y-axis against distance on the x-axis. Draw a best-fit line through the middle bands (2000 to 250 bp).

Read off the sizes of three unknown bands at 16 mm, 25 mm and 33 mm.

Lane notes: lane 1 ladder, lane 2 NTC (no band), lane 3 positive control (band at 25 mm), lanes 4 to 6 samples (bands at 25 mm, 25 mm and 33 mm plus a faint fuzzy band at 50 mm). The expected product is 600 bp. Write one line of interpretation per lane.

Decide which samples you would report as positive, and what the 33 mm and 50 mm bands might be.

Sketch how you would label the saved gel image (lanes, ladder name and sizes, gel percentage, date, initials).

If your teaching lab runs a gel practical, ask your instructor to show you the lab's gel imaging and image-saving procedure.

## Why it matters

Agarose gel electrophoresis is the quickest way to see DNA. After a PCR, a restriction digest or an extraction, a gel tells you whether you have a product, how big it is, whether there is one band or several, and whether your controls behaved. Reading a gel correctly, and photographing and recording it properly, is a daily skill for biological technicians and an early step in many clinical molecular tests.

## The core idea

DNA has a negatively charged phosphate backbone, so in an electric field it moves toward the **positive electrode (anode)**. A common lab saying is "run to red", because the positive lead is usually red. An agarose gel is a mesh: small fragments slip through quickly, large fragments are slowed. After a run, fragments are sorted by size, smallest farthest from the wells.

To see the DNA, the gel contains (or is soaked in) a fluorescent stain. Many teaching labs use safer stains viewed with blue light; some labs still use ethidium bromide with UV light, which needs specific training and protection because the stain is a mutagen and UV burns skin and eyes. Either way, gel work is done in a lab with your instructor or supervisor, never at home.

## Choosing a gel

| Agarose | Good separation range (roughly) |
|---|---|
| 0.7 percent | about 1,000 to 20,000 bp |
| 1.0 percent | about 500 to 10,000 bp |
| 1.5 percent | about 200 to 3,000 bp |
| 2.0 percent | about 100 to 2,000 bp |

Higher percentages resolve small fragments better. The gel and the tank use the same running buffer (TAE or TBE).

## The ladder

A **DNA ladder** (size marker) is a mix of fragments of known sizes, loaded in its own lane. It is your ruler. Over the useful middle of the gel, the distance a fragment travels is roughly **linear in the logarithm of its size**. So to size an unknown band:

1. Measure the migration distance of each ladder band from the bottom of the well.
2. Plot log10(size in bp) against distance and draw the best straight line through the bands near your unknown.
3. Read the unknown's size from its distance.

**Worked example.** Ladder bands at 1,000 bp (18 mm), 750 bp (22 mm), 500 bp (28 mm) and 250 bp (37 mm). Your PCR band is at 25 mm, between the 750 and 500 bp bands. Interpolating in log space: log10(750) = 2.875 at 22 mm and log10(500) = 2.699 at 28 mm. At 25 mm, halfway, log10(size) is about 2.787, so the size is about **610 bp**. If your predicted product is 600 bp, that is a match. (Simply eyeballing "between 500 and 750" is often enough for a yes/no PCR check.)

## Reading a whole gel

Go lane by lane and write down what you see before you interpret it:

- **Expected lanes:** one sharp band at the predicted size.
- **No-template control:** empty. A band here means contamination.
- **Positive control:** the expected band. If it is missing, the run failed, not your samples.
- **Extra bands:** non-specific products or primer dimers (a faint, fuzzy band below about 100 bp).
- **Smears:** degraded DNA, too much DNA, or too many PCR cycles.
- **Uncut plasmid** can show several bands (supercoiled, nicked, linear) that run at different apparent sizes from the same molecule, which is why you size linear fragments against a linear ladder.

## Recording the result

Save the original gel image without editing it (adjusting overall brightness is usually allowed, editing individual bands never is). Label lanes, the ladder and its sizes, the gel percentage, the date and your initials, and attach the image to your notebook entry. This is the same data integrity you learned for any lab record.

## Common mistakes

- Running the gel backwards, so the DNA runs off the top into the buffer.
- Sizing against the wrong ladder (check which ladder lot you loaded and its band sizes).
- Reading sizes from the far ends of the gel, where the log-linear relationship breaks down.
- Skipping the controls when interpreting the samples.
- Cropping or editing images in ways that hide bands.

## Quick recap

- DNA runs toward the positive electrode; small fragments go farthest.
- Choose agarose percentage by fragment size.
- Size bands by interpolating log10(bp) against distance from a ladder.
- Check NTC and positive control first; record unedited, labelled images.

## Try it

Open the **Do** tab to size bands from a set of gel measurements on paper or graph paper.


Read an agarose gel against a DNA ladder and estimate the size of unknown bands.

Gel electrophoresis

Gel Electrophoresis

EdvoTech Tips: What is a DNA Ladder and how do you use it?

## Why it matters

Gels for DNA tell you about genes. Many questions, though, are about **proteins**: is this protein made, how big is it, and does a treatment raise or lower it? SDS-PAGE separates proteins by size, and a Western blot then detects one specific protein with antibodies. Biochemists, biological technicians and some clinical labs use these methods, and knowing why each step exists is what lets you troubleshoot a bad blot.

## SDS-PAGE: separating proteins by size

Proteins differ in shape and charge, so on their own they would not separate cleanly by size. SDS-PAGE fixes that:

1. **Sample preparation.** Proteins are mixed with a sample buffer containing **SDS** (sodium dodecyl sulfate), a detergent that unfolds proteins and coats them with negative charge roughly in proportion to their length. A **reducing agent** (such as DTT or beta-mercaptoethanol) breaks disulfide bonds. Heating completes denaturation.
2. **Loading.** Equal amounts of total protein (measured beforehand with an assay such as BCA or Bradford) go into each well, plus a **pre-stained protein ladder** of known molecular weights in kilodaltons (kDa).
3. **The gel.** A polyacrylamide gel with a short **stacking gel** on top, which concentrates samples into sharp bands, and a **resolving gel** below, where separation happens. Higher acrylamide percentages resolve smaller proteins.
4. **Running.** Proteins migrate toward the positive electrode; smaller ones move farther.

Afterwards you can stain the whole gel (for example with Coomassie) to see every protein, or move on to a Western blot. Unpolymerised acrylamide is a neurotoxin, which is one reason labs buy precast gels or train people before letting them pour gels.

## Western blot: finding one protein

1. **Transfer.** An electric current moves the proteins out of the gel onto a **membrane** (nitrocellulose or PVDF), keeping their positions. A reversible stain such as Ponceau S confirms the transfer worked.
2. **Blocking.** The membrane is soaked in a protein solution (non-fat milk or BSA) so antibodies do not stick everywhere.
3. **Primary antibody.** An antibody that recognises your target protein binds to it on the membrane.
4. **Washing.** Buffer washes (often TBS or PBS with a little Tween detergent) remove unbound antibody.
5. **Secondary antibody.** An antibody that recognises the primary antibody, linked to an enzyme (such as HRP) or a fluorescent dye. It amplifies and reports the signal.
6. **Washing** again.
7. **Detection.** For HRP, a chemiluminescent substrate produces light captured by an imager; fluorescent secondaries are imaged directly.

## Reading a blot

- **Size check.** Is the band at the expected kDa, judged against the ladder? Modifications such as glycosylation or phosphorylation can shift apparent size.
- **Loading control.** A protein expected to be constant (for example beta-actin, GAPDH or a total-protein stain) shows whether lanes were loaded evenly. Compare the target band to it, never the raw band alone.
- **Controls.** A positive control lysate known to contain the protein, and ideally a negative control (for example a knockdown) that should lack it.
- **Quantification.** Densitometry gives band intensities. Results are semi-quantitative and only valid when the signal is not saturated.

**Worked example.** Target band intensity: control 12,000, treated 6,000. Loading control: control 20,000, treated 10,000. Normalised: control 0.60, treated 0.60. The apparent halving was just uneven loading: there is no real change.

## Troubleshooting by step

| Symptom | Step to check first |
|---|---|
| No bands at all, ladder transferred | Primary antibody, target abundance, detection reagent |
| Ladder also missing from membrane | Transfer |
| Dark background everywhere | Blocking, washing, antibody too concentrated |
| Many extra bands | Antibody specificity, too much antibody, protein degradation |
| Bands smile or smear | Running conditions, overloading, sample prep |

## Common mistakes

- Unequal protein loading, then interpreting band strength without a loading control.
- Saturated signal, which hides real differences.
- Skipping the transfer check.
- Reusing diluted antibody without tracking it.
- Over-editing blot images. Journals and quality systems require original, unedited images.

## Quick recap

- SDS plus reducing agent plus heat: proteins separate by size on polyacrylamide.
- Western: transfer, block, primary, wash, secondary, wash, detect.
- Judge bands against the ladder, controls and a loading control.
- Troubleshoot by asking which step could cause the symptom.

## Try it

The **Do** tab has a quiz on the order and purpose of each step.


Explain what each step of SDS-PAGE and Western blotting does and how to judge a blot.

SDS-PAGE and Western blot

SDS-PAGE explained - Protein Separation Technique

Western Blot / Protein Immunoblot explained

## Why it matters

The enzyme-linked immunosorbent assay (ELISA) measures how much of a specific protein, hormone, antibody or other molecule is in a sample. Research labs use it for cytokines and biomarkers; clinical and public health labs use ELISA-type immunoassays for infections and antibodies; biomanufacturing uses it to measure product and impurities such as host-cell proteins. Running the plate is half the job. The other half is turning optical density readings into concentrations you can defend.

## The core idea: a sandwich ELISA

The most common format for measuring a protein is the **sandwich ELISA** in a 96-well plate:

1. **Capture antibody** is coated on the bottom of each well.
2. **Blocking** with a protein solution covers the remaining plastic.
3. **Sample or standard** is added; the target binds the capture antibody. Washing removes everything else.
4. **Detection antibody**, which binds a different part of the target, is added. It is linked to an enzyme (often HRP), directly or through a biotin and streptavidin step.
5. **Substrate** (often TMB) is added. The enzyme turns it blue; more target means more enzyme and more colour.
6. **Stop solution** (an acid) turns the colour yellow and stops the reaction, and a plate reader measures **optical density (OD)** at 450 nm.

Other formats exist. A **direct** or **indirect** ELISA detects target stuck to the plate; a **competitive** ELISA, used for small molecules, gives *less* signal when there is *more* target.

## The standard curve

OD is not a concentration. You convert it using **standards**: known concentrations of the target, run on the same plate. Typical good practice:

- Run standards, samples and blanks in **duplicate or triplicate**, and use the mean OD.
- **Subtract the blank** (zero standard) OD from everything.
- Fit a curve to the standards. Many ELISAs are sigmoidal over their full range and are fitted with a **four-parameter logistic (4PL)** model in plate-reader software. Over a narrower range the curve is often close to a straight line, which is what this lesson's activity uses.
- **Read unknowns only inside the range of the standards.** A result above the top standard is reported as "above range" and the sample is diluted and rerun; you never extrapolate.

**Worked example.** After blank subtraction, a linear fit of the standards gives slope 0.00208 OD per pg/mL and intercept 0.0 (these standards happen to fall on a perfect line). A sample diluted 1 in 2 reads OD 0.440, or 0.390 after subtracting the blank 0.050.

```text
concentration in well = (0.390 - 0.0) / 0.00208 = 187.5 pg/mL
original sample       = 187.5 x 2 (dilution factor) = 375 pg/mL
```

Forgetting the dilution factor is one of the most common reporting errors.

## Quality checks on a plate

- **Replicate agreement:** the coefficient of variation (CV = SD / mean x 100) of duplicate ODs is often required to be below about 15 to 20 percent. A high CV points to pipetting or washing problems.
- **Curve fit:** check R-squared, and back-calculate each standard from the curve; each should be close to its nominal value.
- **Controls:** kit or in-house controls with known values should fall in their acceptable ranges, just like the QC rules from the lab data course.
- **Edge effects and timing:** uneven temperature or adding substrate slowly across a plate can shift ODs, so follow incubation times exactly.

## Clinical and regulated context

Clinical immunoassays run on validated, regulated systems, and results are released only by qualified staff under CLIA in the US. Medical laboratory scientists qualify through a NAACLS-accredited program (https://www.naacls.org, checked on 2026-10-23) and ASCP Board of Certification exams (https://www.ascp.org/content/board-of-certification, checked on 2026-10-23). Plate work itself happens in a lab, under supervision.

## Common mistakes

- Forgetting to apply the dilution factor.
- Reporting values above the top standard or below the lowest standard as numbers.
- Not subtracting the blank, or subtracting it twice.
- Ignoring high CV between duplicates.
- Mixing standards from one kit lot with plates from another.

## Quick recap

- Sandwich ELISA: capture, block, sample, detection antibody, enzyme, substrate, stop, read OD.
- Convert OD to concentration with a standard curve on the same plate (4PL or, over a linear range, a straight line).
- Blank-correct, stay within the curve, apply dilution factors.
- Check replicate CV, the curve fit and controls before reporting.

## Try it

Open the **Do** tab. A Python script fits the standard curve; you write the line that turns a sample OD into a concentration.


Understand how an ELISA works and compute sample concentrations from its standard curve.

ELISA

ELISA (Enzyme-linked Immunosorbent Assay)

ELISA: How to make a standard curve

Read an agarose gel against a ladder, explain each step of SDS-PAGE and Western blotting, and compute concentrations from an ELISA standard curve.

Separation and Detection

## Why it matters

Reading DNA sequence is now routine: confirming a plasmid clone, checking a patient variant, identifying a microbe, profiling a tumour or measuring expression of every gene at once. Different jobs need different technologies, and choosing badly wastes money or misses the answer. Technicians prepare samples for sequencing and often do the first quality checks on the data, so you need to know what each method can and cannot do.

## Sanger sequencing

Sanger, or **chain-termination**, sequencing (1977) is still the standard for reading one short region with high accuracy.

1. A sequencing reaction resembles PCR with **one primer**, normal dNTPs and a small amount of fluorescently labelled **dideoxynucleotides (ddNTPs)**. A ddNTP has no 3' hydroxyl, so when the polymerase adds one, that strand stops growing.
2. The result is a mix of fragments ending at every position, each tagged with a colour for its last base.
3. **Capillary electrophoresis** separates fragments by length with single-base resolution, and a laser reads the colour of each as it passes.
4. The output is a **chromatogram** (trace) of coloured peaks, plus the base calls.

Strengths: very accurate, reads of about 700 to 1,000 bases, cheap and fast for a few samples. Limits: one template per reaction, so it does not scale to whole genomes, and it struggles to detect variants present in only a small fraction of cells (below roughly 15 to 20 percent).

Typical uses: confirming a cloned insert, checking a CRISPR edit at one site, confirming a variant found by NGS.

## Next-generation sequencing (NGS)

NGS (also called massively parallel or high-throughput sequencing) reads millions to billions of DNA fragments at once. The most widespread approach, **sequencing by synthesis** from Illumina, works like this:

1. **Library preparation:** DNA is fragmented and short **adapters** are added to both ends, often with **index** sequences (barcodes) so many samples can share one run.
2. **Cluster generation:** fragments bind to a flow cell and are copied in place into clusters of identical molecules.
3. **Sequencing:** fluorescent, reversibly terminated nucleotides are added one base per cycle and imaged, so every cluster is read in parallel.
4. The output is **short reads** (commonly 50 to 300 bases, often paired-end) in **FASTQ** files, which store each read with a quality score per base.

**Long-read** technologies (Pacific Biosciences and Oxford Nanopore) read single molecules thousands to over a hundred thousand bases long. They help with repeats, structural variants and assembling new genomes.

## Key numbers you will see

- **Phred quality (Q):** Q = -10 log10(error probability). Q20 means a 1 in 100 chance a base is wrong; **Q30** means 1 in 1,000, a common quality benchmark.
- **Coverage (depth):** how many reads cover each base on average.

```text
coverage = number of reads x read length / genome size
```

**Worked example.** 40 million reads of 150 bases on a 5 million base bacterial genome: 40,000,000 x 150 / 5,000,000 = **1,200x** coverage, far more than the roughly 30 to 100x usually wanted. You could multiplex many more samples on the same run.

## Choosing a method

| Question | Usual choice |
|---|---|
| Is my 800 bp plasmid insert correct? | Sanger |
| Which variants does this patient carry across 50 cancer genes? | Targeted NGS panel |
| Which genes changed expression after treatment? | RNA-seq (NGS) |
| What microbes are in this sample? | 16S amplicon or shotgun NGS |
| Assemble a new genome with long repeats | Long reads, often combined with short reads |

Clinical sequencing tests are validated and run under laboratory regulations and accreditation (in the US, CLIA and often CAP), and results are reviewed by qualified staff. Sample handling happens in the lab under supervision.

## Common mistakes

- Sending a mixed template (two PCR products) for Sanger and getting an unreadable trace.
- Expecting Sanger to detect a variant present in 5 percent of cells.
- Ignoring the first and last bases of a Sanger read, where quality is usually poor.
- Planning NGS without calculating the coverage you need.
- Mixing up sample indexes, which assigns reads to the wrong sample.

## Quick recap

- Sanger: ddNTP chain termination plus capillary electrophoresis; one region, very accurate, about 1 kb.
- NGS: library prep, clusters, sequencing by synthesis; millions of short reads in FASTQ.
- Long reads help with repeats and structural variants.
- Know Phred scores (Q30 = 1 in 1,000) and coverage = reads x length / genome size.

## Try it

The **Do** tab has a quiz on choosing and interpreting sequencing methods.


Compare Sanger sequencing with next-generation sequencing and choose the right one for a job.

Sanger and NGS

What Is Sanger Sequencing?

Overview of Illumina Sequencing by Synthesis Workflow | Standard SBS chemistry

## Why it matters

Sequencing produces text: long strings of A, C, G and T. Before and after every experiment you will handle sequences: checking a primer site, finding a restriction site before cloning, measuring GC content of a region that is hard to amplify, or confirming that a read contains an expected motif. Doing this by eye is slow and error-prone. A few lines of Python do it exactly, every time, and the same ideas power the big bioinformatics tools.

## Sequences are strings

In Python, a DNA sequence is just a string. Real sequences arrive messy, with lowercase letters, spaces and line breaks (for example when copied from a FASTA file), so the first step is always to **clean** them:

```python
def clean(seq):
    return "".join(seq.split()).upper()

clean("gaat tc\nGGA")   # 'GAATTCGGA'
```

`seq.split()` splits on any whitespace and `"".join(...)` glues the pieces back together.

## The FASTA format

Most sequence files and databases use FASTA: a header line starting with `>`, then sequence lines.

```text
>sample_01 partial gene region
GATATATGCATATACTT
```

Reading a FASTA file means skipping the `>` line and joining the rest.

## GC content

GC content is the percentage of bases that are G or C. It matters because G-C pairs have three hydrogen bonds and A-T pairs two, so GC-rich DNA melts at higher temperature, is harder to amplify and can form stable secondary structures.

```python
def gc_content(seq):
    s = clean(seq)
    return 100 * (s.count("G") + s.count("C")) / len(s)
```

**Worked example.** `GATATATGCATATACTT` has 17 bases: G 2, C 2. GC content = 4 / 17 x 100 = **23.5 percent**: AT-rich.

## Reverse complement

DNA is double-stranded and the two strands run in opposite directions. The other strand, read 5' to 3', is the **reverse complement**: swap A with T and G with C, then reverse.

```python
pairs = {"A": "T", "T": "A", "G": "C", "C": "G"}
"".join(pairs[b] for b in reversed("ATGCC"))   # 'GGCAT'
```

Many restriction sites are palindromes: `GAATTC` (EcoRI) is its own reverse complement, so the enzyme recognises it on both strands.

## Finding a motif

A **motif** is a short sequence of interest: a restriction site, a primer binding site, a promoter element. You often need **every** position where it occurs, including overlapping matches. Python's `str.find` and `str.count` skip overlaps, so a sliding window is safer:

```python
for i in range(len(s) - len(m) + 1):
    if s[i:i + len(m)] == m:
        positions.append(i + 1)   # biologists count from 1
```

**Worked example.** In `GATATATGCATATACTT`, the motif `ATAT` starts at positions **2, 4 and 10** (1-based). The matches at 2 and 4 overlap. `"GATATATGCATATACTT".count("ATAT")` returns only 2, which would be wrong for this task.

Notice the off-by-one detail: Python counts from 0, but sequence coordinates in biology (and in tools such as BLAST and genome browsers) count from 1. Always state which you use.

## Libraries for real work

For bigger jobs, bioinformaticians use libraries such as **Biopython**, which reads FASTA and FASTQ files, translates sequences, runs alignments and talks to NCBI. The ideas are the ones above; the library handles edge cases and file formats. Learning the plain-Python version first means you understand what the library is doing. (Biopython is not available in this site's activity runner, so the activity uses plain Python.)

Remember also to treat sequence data with the same care as any lab record: keep the original file, note where it came from, and never edit a sequence by hand without recording why.

## Common mistakes

- Forgetting to remove whitespace and line breaks, which distorts length and GC content.
- Mixing uppercase and lowercase, so `"atg" == "ATG"` is False.
- Counting non-overlapping matches when you need all of them.
- Reporting 0-based positions as if they were 1-based.
- Writing the complement but forgetting to reverse it.

## Quick recap

- Clean sequences first: remove whitespace, uppercase.
- GC content = (G + C) / length x 100.
- Reverse complement = complement each base, then reverse.
- Find motifs with a sliding window to catch overlaps, and report 1-based positions.

## Try it

Open the **Do** tab. The GC and reverse-complement functions are written; you fill in the motif check.


Use Python to clean a DNA sequence, measure GC content and find every position of a motif.

Working with sequences

Python - DNA composition | Counting ATGC I GC content | Python 5

Open https://www.ncbi.nlm.nih.gov/nuccore/NM_000518 (human HBB mRNA). Note the accession and version and the gene name.

Click FASTA to show the sequence in FASTA format. Copy the header line and the first five lines of sequence (about 300 bases).

Open https://blast.ncbi.nlm.nih.gov/Blast.cgi, choose Nucleotide BLAST (blastn) and paste your sequence. Keep the default database and run the search.

For the top five hits, write down the description, query cover, percent identity, E-value and accession.

Open the alignment for the top hit and find the coordinates of your query on the subject (Sbjct) sequence.

Change three bases in your pasted sequence (write down which), run BLAST again, and compare percent identity and E-value with the first search.

Record the program, database, date and top accession for both searches, as you would in a lab notebook.

## Why it matters

You have a sequence. What is it, and what is it similar to? BLAST (Basic Local Alignment Search Tool) answers that in seconds by comparing your sequence against public databases holding hundreds of millions of sequences. Technicians use it to confirm a PCR product or Sanger read is the gene they expected, to identify a microbe, and to check that a primer will not bind somewhere unintended. It is free, runs in a browser and needs no account.

## Sequence databases

The main public databases are run by an international collaboration: **NCBI** (US National Center for Biotechnology Information), **ENA** (European Nucleotide Archive) and **DDBJ** (DNA Data Bank of Japan), which exchange data daily. At NCBI you will meet:

- **Nucleotide** and **GenBank**: submitted DNA and RNA sequences.
- **RefSeq**: curated reference sequences. Accessions start with a prefix such as `NM_` (mRNA), `NP_` (protein) or `NC_` (chromosome or complete genome).
- **Protein**: protein sequences.
- **Gene**: summaries that link a gene's sequences, names and literature.

Each record has an **accession number** with a version, such as `NM_000518.5`. Citing the accession and version makes your work reproducible.

## The BLAST programs

| Program | Query | Database |
|---|---|---|
| blastn | nucleotide | nucleotide |
| blastp | protein | protein |
| blastx | nucleotide, translated in all six frames | protein |
| tblastn | protein | nucleotide, translated |

Use blastn to identify a DNA sequence, blastp for a protein, and blastx when you have DNA and want to know which protein it might encode.

## Reading the results

BLAST lists **hits** (database sequences) with alignments. The columns that matter:

- **Query cover:** how much of your query is included in the alignment. A 98 percent identity over 20 percent of the query is not a convincing match for the whole sequence.
- **Percent identity:** identical positions within the aligned region.
- **E-value:** how many hits this good you would expect **by chance** in a database this size. Smaller is better: 1e-50 is overwhelmingly significant; values around 1 or higher are noise. It depends on database size and query length.
- **Max score (bit score):** alignment quality on a normalised scale; higher is better and comparable across searches.
- **Accession** and **description:** what the hit is.

**Worked example.** You BLAST a 600 bp PCR product. The top hit: "Homo sapiens hemoglobin subunit beta (HBB), mRNA", query cover 100 percent, identity 99.8 percent, E-value 0.0. One mismatch over 600 bases. Your product is HBB, and the mismatch is either a sequencing error (check the chromatogram at that position) or a real variant worth following up.

A different case: top hit with query cover 35 percent, identity 92 percent, E-value 2e-20. Part of your sequence matches, the rest does not, which might mean a chimeric product, vector sequence, or a region that is not in the database.

## The graphic and alignment views

The **graphic summary** shows each hit as a bar under your query, coloured by score, so you can see at a glance whether hits cover the whole query. The **alignment** view shows your query (`Query`) above the database sequence (`Sbjct`) with `|` for identical bases and gaps as `-`, and gives the coordinates on both, 1-based.

## Good habits

- Paste sequence in FASTA format, and trim low-quality ends of Sanger reads first.
- Choose the database deliberately (for example RefSeq for curated references, or the default nucleotide collection to cast a wide net).
- Record the program, database, date and top accession(s) in your notebook: databases grow daily, so a search repeated later can give different results.
- Never upload patient-identifiable data or confidential sequences to a public server; clinical labs use approved, often local, tools.

The NCBI BLAST service and its documentation are at https://blast.ncbi.nlm.nih.gov/Blast.cgi (Checked on 2026-10-23.)

## Common mistakes

- Trusting percent identity while ignoring query cover.
- Treating an E-value of 0.5 as a match.
- Using blastn on a protein sequence.
- Forgetting that the top hit's description can be wrong or vague if it is a poorly annotated submission.
- Not recording the database and date.

## Quick recap

- NCBI, ENA and DDBJ share sequence data; RefSeq holds curated references; cite accession.version.
- Pick the program by query and database type: blastn, blastp, blastx, tblastn.
- Read query cover, percent identity and E-value together.
- Record the program, database, date and hits; keep confidential data off public servers.

## Try it

Open the **Do** tab to fetch a real reference sequence from NCBI and BLAST it.


Run a BLAST search at NCBI and read the hit table: identity, coverage, E-value and score.

BLAST and databases

How to Use BLAST for Finding and Aligning DNA or Protein Sequences

Compare Sanger and next-generation sequencing, find motifs and GC content with Python, and run and read a BLAST search.

Molecular Biology Methods

About this course

Course content

Nucleic Acids

Separation and Detection

Sequencing and Bioinformatics

Where this course leads

Next course on the path

Careers that use it