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Genome-based peptide fingerprint scanning

Genome-based peptide fingerprint scanning is a science 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-based peptide fingerprint scanning rather than just read about it. In short: Genome-based peptide fingerprint scanning (GFS) is a system in bioinformatics analysis that attempts to identify the genomic origin (that is, what species they come from) of sample proteins by scanning their peptide-mass fingerprint against the theoretical translation and proteolytic digest of an entire genome. This method is an improvement from previous methods because it compares the peptide fingerprints to an ent…

Key takeaways

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

Reference excerpt

Genome-based peptide fingerprint scanning (GFS) is a system in bioinformatics analysis that attempts to identify the genomic origin (that is, what species they come from) of sample proteins by scanning their peptide-mass fingerprint against the theoretical translation and proteolytic digest of an entire genome. This method is an improvement from previous methods because it compares the peptide fingerprints to an entire genome instead of comparing it to an already annotated genome. This improvement has the potential to improve genome annotation and identify proteins with incorrect or missing annotations.

History and background GFS was designed by Michael C. Giddings (University of North Carolina, Chapel Hill) et al., and released in 2003. Giddings expanded the algorithms for GFS from earlier ideas. Two papers were published in 1993 explaining the techniques used to identify proteins in sequence databases. These methods determined the mass of peptides using mass spectrometry, and then used the mass to search protein databases to identify the proteins In 1999 a more complex program was released called Mascot that integrated three types of protein/database searches: peptide molecular weights, tandem mass spectrometry from one or more peptide, and combination mass data with amino acid sequence. The fallback with this widely used program is that it is unable to detect alternative splice sites that are not currently annotated, and it not usually able to find proteins that have not been annotated. Giddings built upon these sources to create GFS which would compare peptide mass data to entire genomes to identify the proteins. Giddings system is able to find new annotations of genes that have not been found, such as undocumented genes and undocumented alternative splice sites.

Research examples In 2012 research was published where genes and proteins were found in a model organism that could not have been found without GFS because they had not been previously annotated. The planarian Schmidtea mediterranea has been used in research for over 100 years. This planarian is capable of regenerating missing body parts and is therefore emerging as potential model organism for stem cell research. Planarians are covered in mucus which aids in locomotion, in protecting them from predation, and in helping their immune system. The genome of Schmidtea mediterranea is sequenced but mostly un-annotated making it a prime candidate for genome-based peptide fingerprint scanning. When the proteins were analyzed with GFS 1,604 proteins were identified. These proteins had mostly not been annotated before they were found with GFS They were also able to find the mucous subproteome (all the genes associated with mucus production). They found that this proteome was conserved in the sister species Schmidtea mansoni. The mucous subproteome is so conserved that 119 orthologs of planarians are found in humans. Due to the similarity in these genes the planarian can now be used as a model to study mucous protein function in humans. This is relevant for infections and diseases related to mucous aberrancies such as cystic fibrosis, asthma, and other lung diseases. These genes could not have been found without GFS because they had not been previously annotated. In February 2013, proteogenomic mapping research was done with ENCODE to identify translational regions in the human genome. They applied peptide fingerprint scanning and MASCOT to the protein data to find regions that may not have been previously annotated as translated in the human genome. This search against the whole genome revealed that approximately 4% of unique peptide that they found were outside of previously annotated regions. Also the comparison of the whole genome revealed 15% more hits than from a protein database search (such as MASCOT) alone. GFS can be used as a complementary method for annotation due to the fact that you can find new genes or splice sites that have not been annotated before. However it is important to remember that the whole genome approach used by GFS can be less sensitive than programs that look only at annotated regions.

References

External links Genome-based Peptide Fingerprint Scanning (GFS) Documentation Archived 27 September 2013 at the Wayback Machine Facebook link to "Genome-based Peptide Fingerprint Scanning" Explanation of MS/MS in relation to MASCOT

Worked examples

Example 1 — a first encounter with Genome-based peptide fingerprint scanning

Start with the simplest possible case. Write down what Genome-based peptide fingerprint scanning claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, 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-based peptide fingerprint scanning 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-based peptide fingerprint scanning 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-based peptide fingerprint scanning

In research
Genome-based peptide fingerprint scanning appears in science 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-based peptide fingerprint scanning 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-based peptide fingerprint scanning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Bioinformatics, Genomics techniques, so understanding it makes those chapters shorter.
In everyday life
Look for Genome-based peptide fingerprint scanning 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-based peptide fingerprint scanning in 20 minutes

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

Frequently asked questions

What is Genome-based peptide fingerprint scanning in simple terms?

Genome-based peptide fingerprint scanning (GFS) is a system in bioinformatics analysis that attempts to identify the genomic origin (that is, what species they come from) of sample proteins by scanning their peptide-mass fingerprint against the theoretical translation and proteolytic digest of an e…

Why does Genome-based peptide fingerprint scanning matter?

Because it connects several science 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-based peptide fingerprint scanning?

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-based peptide fingerprint scanning.

Tags

  • Bioinformatics
  • Genomics techniques

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