chemistry

Golm Metabolome Database

Golm Metabolome Database 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 Golm Metabolome Database rather than just read about it. In short: The Golm Metabolome Database (GMD) is a gas chromatography (GC) – mass spectrometry (MS) reference library dedicated to metabolite profiling experiments and comprises mass spectral and retention index (RI) information for non-annotated mass spectral tags (MSTs, mass spectral information with retention time attached indices) together with data of a multitude of already identified metabolites and reference substances…

Key takeaways

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

Reference excerpt

The Golm Metabolome Database (GMD) is a gas chromatography (GC) – mass spectrometry (MS) reference library dedicated to metabolite profiling experiments and comprises mass spectral and retention index (RI) information for non-annotated mass spectral tags (MSTs, mass spectral information with retention time attached indices) together with data of a multitude of already identified metabolites and reference substances. The GMD is hosted at the Max Planck Institute of Molecular Plant Physiology in Golm district of Potsdam, Germany.

Background Gas chromatography (GC) coupled to mass spectrometry (MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel biomarkers in metabolomics. However, the majority of MSTs currently measured in plant metabolomic profiling experiments remains unidentified due to the lack of authenticated pure reference substances and the expensive and time-consuming effort to maintain mass spectral RI libraries required for compound identification by GC-MS. As the communication of analytical results and other approach-related details such as mass spectral and RI reference information within the scientific community is becoming increasingly popular, open access platforms for information exchange, such as the GMD, are obligatory. Due to the lack of mandatory standards it remains difficult to compare individual mass spectrums. While the different mass detector technologies, namely quadrupole, ion trap and time of flight, can be deemed irrelevant, the chromatography settings such as temperature programming, type of capillary column and choice of column manufacturer heavily affect the empirically determined RI properties. Procedures for the transfer of RI properties between chromatography variants are, therefore, highly relevant for a shared library use. The GMD assesses the accuracy of RI transfer between chromatography variants and implements means to transfer empirically determined RI properties. Aiming at the classification and identification of un-identified MSTs, the GMD accesses the information on available reference compounds. These compounds serve as training set of data to apply decision trees (DT) as a supervised machine learning approach. Structural feature extraction was applied to classify the metabolite space of the GMD prior to DT training. DT-based predictions of the most frequent substructures classify low resolution GC-MS mass spectra of the linked (potentially unknown) metabolite with respect to the presence or absence of the chemical moieties. The web-based frontend supports conventional mass spectral and RI comparison by ranked hit lists as well as advanced DT supported substructure prediction. Batch processing is enabled via Simple Object Access Protocol (SOAP)-based web services while web-based data access services expose particular data base entities adapting Representational State Transfer (ReST) principles and mass spectral standards such as NIST-MSP and JCAMP-DX. The GMD visualise quantitative metabolite pool size changes data.

See also Human Metabolome Database MassBank

References

External links GMD website

Illustrations

profile of a single metabolite across replicates of different experiments

profile of all metabolites in a single experiments

Worked examples

Example 1 — a first encounter with Golm Metabolome Database

Start with the simplest possible case. Write down what Golm Metabolome Database 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 Golm Metabolome Database 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 Golm Metabolome Database 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 Golm Metabolome Database

In research: Golm Metabolome Database 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 Golm Metabolome Database 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: Golm Metabolome Database is common in secondary-school and first-year university syllabi. It links to neighbouring topics Chemical databases, Mass spectrometry software, Metabolomic databases, so understanding it makes those chapters shorter.
In everyday life: Look for Golm Metabolome Database 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 Golm Metabolome Database in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Golm Metabolome Database 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 Golm Metabolome Database out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Golm Metabolome Database in simple terms?

Why does Golm Metabolome Database 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 Golm Metabolome Database?

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 Golm Metabolome Database.