Genomes, Proteomes, and Sequencing Methods Revision Notes | A Level AQA Biology

1. The Genome-to-Proteome Problem

Here is a question that stumped biologists for years: if we have the complete DNA sequence of a human, why can't we read off every protein the body makes? The answer reveals something profound about how gene expression actually works — and it is the central puzzle of this lesson.

The genome is the full DNA instruction manual of an organism. The proteome is the complete collection of proteins that are actually produced. You might expect a neat, one-to-one relationship between the two — but in complex organisms, the translation from genome to proteome is far from straightforward.

Think of the genome as a vast library containing millions of books. The proteome is only the selection of books that are actually being read at any one moment, in any one room of the library. The same library exists in every room, but what gets read — and when — differs enormously. Two reasons explain why this gap between genome and proteome is so wide in complex organisms: non-coding DNA and regulatory genes.

2. Non-Coding DNA: More Than Junk

For decades, sequences that did not code for proteins were dismissed as 'junk DNA'. That view has been overturned. In humans, only around 1.5% of the genome codes for proteins — the vast majority is non-coding, and much of it has critical functions.

• Introns: non-coding sequences within genes that are transcribed into pre-mRNA but then spliced out before translation. Different exons can be joined in different combinations (alternative splicing), meaning one gene can produce multiple different proteins.
• Regulatory sequences: regions such as promoters and enhancers that control whether, when, and how strongly a gene is transcribed. They do not code for protein but profoundly affect the proteome.
• Repetitive sequences: large stretches of repeated DNA whose roles include maintaining chromosome structure and contributing to regulatory networks.
• Genes encoding non-coding RNAs: sequences transcribed into functional RNA molecules (such as microRNAs and siRNAs) that regulate gene expression post-transcriptionally without ever becoming protein.

The consequence for the genome-to-proteome relationship is significant. Knowing the DNA sequence alone does not tell you which exons will be included in the final mRNA, nor which regulatory sequences will be active in a particular cell. The same genomic sequence can give rise to different proteins in a liver cell compared with a neuron — even though both cells carry identical DNA.

Alternative splicing is a particularly powerful illustration of why genome knowledge alone is insufficient. The human genome contains roughly 20,000 protein-coding genes, yet the human proteome is estimated to contain well over 100,000 distinct proteins. Alternative splicing — where different combinations of exons are joined — is a major reason for this discrepancy. One gene, many proteins.

3. Regulatory Genes and the Control of Expression

Non-coding DNA sets the stage, but regulatory genes are the directors. A regulatory gene encodes a product — often a transcription factor protein or a regulatory RNA — that controls the expression of other genes. These regulatory products bind to specific DNA sequences and either activate or repress transcription of their target genes.

Consider what this means for proteome prediction. Even if you know every gene in the genome, you cannot predict the proteome without also knowing which transcription factors are present, which regulatory sequences they bind, and how those interactions change across different cell types and developmental stages. The regulatory network adds a layer of complexity that sits entirely on top of the raw sequence information.

Regulatory genes also interact with one another in cascades. One transcription factor may activate several others, each of which activates further downstream targets. A single change in one regulatory gene can therefore ripple through the entire proteome. This combinatorial complexity means the relationship between genome and proteome is not linear — it is a network.

4. Why This Matters: Genome Projects in Context

Genome projects — large-scale efforts to sequence the complete DNA of organisms — have transformed biology. The Human Genome Project, completed in 2003, was the landmark example: an international collaboration that sequenced all 3 billion base pairs of the human genome. Since then, thousands of other species have had their genomes sequenced.

The data from genome projects has enormous value. Identifying genes associated with inherited diseases allows earlier diagnosis and the development of targeted therapies. Comparing genomes across species reveals evolutionary relationships and conserved functional regions. Agricultural applications include identifying genes that confer disease resistance or improved yield in crop plants.

Yet the genome-to-proteome gap means that genome sequence data is a starting point, not a complete answer. Understanding disease requires proteomics — the large-scale study of proteins — alongside genomics. A mutation in a regulatory gene, for instance, may not alter any protein-coding sequence but can dramatically change which proteins are expressed and in what quantities. Genome projects revealed this complexity rather than resolving it.

5. Sequencing Methods: From Manual to Automated

To appreciate the genome-to-proteome problem fully, it helps to understand how genome sequences are actually obtained. Sequencing methods have undergone a revolution since the first DNA sequences were read in the 1970s, and they continue to be updated and refined.

The original approach, developed by Frederick Sanger in the 1970s, is known as Sanger sequencing (or chain-termination sequencing). It was the gold standard for decades and was used to sequence the human genome. The principle relies on incorporating modified nucleotides — dideoxynucleotides (ddNTPs) — that terminate DNA synthesis at specific bases, producing fragments of different lengths that can be separated and read.

Worked example: How Sanger Sequencing Works: The Core Logic

Describe the key steps by which Sanger sequencing determines the order of bases in a DNA fragment.

1. Single-stranded DNA template + DNA polymerase + primer + normal dNTPs + a small proportion of fluorescently labelled ddNTPs (one colour per base: ddATP, ddCTP, ddGTP, ddTTP) — The ddNTPs lack a 3'-OH group. When one is incorporated, the chain cannot be extended further — synthesis terminates at that point. Each ddNTP carries a different fluorescent label so the terminating base can be identified.
2. DNA polymerase extends the primer, randomly incorporating either a normal dNTP (synthesis continues) or a ddNTP (synthesis stops). This produces a mixture of fragments of all possible lengths, each ending at a specific base. — Because termination is random, every possible length of fragment is generated — one ending at each position in the sequence.
3. Fragments are separated by gel electrophoresis (or capillary electrophoresis in automated systems). Smaller fragments travel further. Each fragment ends at a known base identified by its fluorescent colour. — Reading the colour of each band from smallest to largest gives the sequence of bases, one position at a time.
4. A laser detects the fluorescent signal from each fragment as it passes a detector. The output is a chromatogram showing peaks of different colours corresponding to A, T, C, or G at each position. — In automated systems, this detection and recording is performed by a computer, removing the need for manual gel reading and dramatically increasing throughput.

Answer: The sequence is read from the pattern of fluorescent peaks: each peak colour identifies the base at that position, from the shortest fragment (first base after the primer) to the longest (furthest from the primer).

Sanger sequencing is accurate and reliable, but it is relatively slow and expensive when applied to very large genomes. Sequencing the human genome using this method took over a decade and cost approximately $3 billion. This limitation drove the development of newer approaches.

6. Next-Generation Sequencing and Automation

From the mid-2000s onwards, a new generation of sequencing technologies emerged — collectively called next-generation sequencing (NGS) or high-throughput sequencing. The defining feature is massive parallelism: instead of sequencing one fragment at a time, NGS platforms sequence millions of DNA fragments simultaneously.

The cost and time reductions have been extraordinary. A human genome that cost $3 billion to sequence in 2003 can now be sequenced for under $1,000 in a matter of days. This has made genome sequencing accessible to routine clinical and research settings — something that would have been unimaginable at the time of the Human Genome Project.

Automation is central to the advance of sequencing technology. Modern NGS platforms use robotics, microfluidics, and sophisticated software to handle sample preparation, sequencing reactions, image capture, and base-calling with minimal human intervention. The data output is processed by bioinformatics pipelines — computer programmes that assemble millions of short sequence reads into a complete genome by identifying overlapping regions.

Sequencing methods continue to be updated. Third-generation sequencing technologies, such as nanopore sequencing, can read single DNA molecules directly and produce much longer reads — sometimes tens of thousands of base pairs — without the need for amplification. Each technological generation has expanded what is biologically knowable and reduced the barriers to applying that knowledge.

7. Exam Technique and Mark-Winning Strategies

Questions on this topic tend to fall into two categories: those asking you to explain the genome-to-proteome gap, and those asking you to describe or compare sequencing methods. Both reward precise use of terminology and clear causal reasoning.

When explaining why the genome cannot easily be translated into the proteome, examiners are looking for specific mechanisms — not vague statements about complexity. Name the mechanisms: alternative splicing of exons, the role of regulatory genes and transcription factors, and the presence of non-coding sequences that affect expression without coding for protein. Each mechanism should be linked to its consequence for the proteome.

For extended-response questions that ask you to evaluate the significance of genome projects, structure your answer to include: what genome projects have enabled (disease gene identification, evolutionary comparisons, agricultural applications), what limitations remain (the genome-to-proteome gap, the need for proteomics), and a conclusion that weighs these points. Avoid listing facts without linking them to the question's evaluative demand.

Taken together, the genome-to-proteome gap and the continuous development of sequencing technology are two sides of the same story: as our ability to read genomes has grown faster and cheaper, the biological complexity revealed by that data has made clear that the genome is the beginning of understanding an organism, not the end.