chemistry

Genome mining

Genome mining is a chemistry topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Genome mining rather than just read about it. In short: Genome mining describes the exploitation of genomic information for the discovery of biosynthetic pathways of natural products and their possible interactions. It depends on computational technology and bioinformatics tools.

Key takeaways

Genome mining belongs to chemistry; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Genome mining to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Genome mining from memory before moving on to harder problems.

Reference excerpt

Genome mining describes the exploitation of genomic information for the discovery of biosynthetic pathways of natural products and their possible interactions. It depends on computational technology and bioinformatics tools. The mining process relies on a huge amount of data (represented by DNA sequences and annotations) accessible in genomic databases. By applying data mining algorithms, the data can be used to generate new knowledge in several areas of medicinal chemistry, such as discovering novel natural products.

History In the mid- to late 1980s, researchers have increasingly focused on genetic studies with the advancing sequencing technologies. The GenBank database was established in 1982 for the collection, management, storage, and distribution of DNA sequence data due to the increasing availability of DNA sequences. With the increasing number of genetic data, biotechnological companies have been able to use human DNA sequence to develop protein and antibody drugs through genome mining since 1992. In the late 1990s, many companies, such as Amgen, Immunec, Genentech were able to develop drugs that progressed to the clinical stage by adopting genome mining. Since the Human Genome Project was completed in the early 2000, researchers have been sequencing the genomes of many microorganisms. Subsequently, many of these genomes have been carefully studied to identify new genes and biosynthetic pathways.

Algorithms As large quantities of genomic sequence data began to accumulate in public databases, genetic algorithms became important to decipher the enormous collection of genomic data. They are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. The followings are commonly used genetic algorithms:

AntiSMASH (Antibiotics and Secondary Metabolite Analysis Shell) addresses secondary metabolite genome pipelines. BiGSCAPE Large-scale network analysis and classification of Biosynthetic Gene Clusters. PRISM (Prediction Informatics for Secondary Metabolites) is a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. SIM (Statistically based sequence similarity) method, such as FASTA or PSI-BLAST, infer orthologous homology. BLAST (Basic local alignment search tool) is an approach for rapid sequence comparison.

Applications Genome mining applies on the discovery of natural product by facilitating the characterization of novel molecules and biosynthetic pathways.

Natural product discovery The production of natural products is regulated by the biosynthetic gene clusters (BGCs) encoded in the microorganism. By adopting genome mining, the BGCs that produce the target natural product can be predicted. Some important enzymes responsible for the formation of natural products are polyketide synthases (PKS), non-ribosomal peptide synthases (NRPS), ribosomally and post-translationally modified peptides (RiPPs), and terpenoids, and many more. Mining for enzymes, researchers can figure out the classes that BGCs encode and compare target gene clusters to known gene clusters. To verify the relation between the BGCs and natural products, the target BGCs can be expressed by suitable host through the use of molecular cloning.

Databases and tools Genetic data has been accumulated in databases. Researchers are able to utilize algorithms to decipher the data accessible from databases for the discovery of new processes, targets, and products. The following are databases and tools:

GenBank database provides genomic datasets for analysis. UCSC Genome Browser AntiSMASH-DB allows comparing the sequences of newly sequenced BGCs against those of previously predicted and experimentally characterized ones. BIG-FAM is a biosynthetic gene cluster family database. DoBISCUIT is a database of secondary metabolite biosynthetic gene clusters. MIBiG (Minimum Information about a Biosynthetic Gene cluster specification) provides a standard for annotations and metadata on biosynthetic gene clusters and their molecular products. Interactive tree of life (iTOL) is a web-based tool for the display, manipulation and annotation of phylogenetic trees.

References

Illustrations

Worked examples

Example 1 — a first encounter with Genome mining

Start with the simplest possible case. Write down what Genome mining claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In chemistry, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Genome mining before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Genome mining ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Genome mining

In research: Genome mining appears in chemistry research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Genome mining in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Genome mining is common in secondary-school and first-year university syllabi. It links to neighbouring topics DNA, Genomics techniques, Medicinal chemistry, so understanding it makes those chapters shorter.
In everyday life: Look for Genome mining outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Genome mining in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Genome mining means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Genome mining out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Genome mining in simple terms?

Why does Genome mining matter?

Because it connects several chemistry ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Genome mining?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Genome mining.