<p>One way to do this would be to plot the first 10 GO categories of the the CC sub ontology (CC from cellular component). Or you can just visit <a href="http://amigo.geneontology.org/amigo/term/GO:0005575" rel="nofollow noreferrer">the official</a> webpage to see the list of children terms of them.</p>

<p>However there aren't only 4 or 5 categories, so you could arbitrary pick some of those. </p>

CC BY-SA 4.0
2019-04-26T11:47:08.337Z
2019-04-26T11:47:08.337Z
llrs
<p>This is probably one of those things where as long as you can justify your choice somewhat.</p>

<p>It might be worth seeing what others have done (see <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086075/" rel="nofollow noreferrer">loctree3</a> and <a href="https://academic.oup.com/bioinformatics/article/26/13/1608/201357" rel="nofollow noreferrer">PSORT</a>) and seeing how well they match your case, then add in any subdomains you have a particular interest in.</p>

CC BY-SA 4.0
2019-05-29T11:34:49.473Z
2019-05-29T11:34:49.473Z
TW93
<p>For a project, I want to predict the subcellular localization of some proteins. I am a student in software Informatics at the University of applied Science HEIG-VD.</p>

<p>Subcellular localization prediction is a common problem in the bioinformatics, but I have a problem</p>

<p>With the data I found. In this <a href="http://locate.imb.uq.edu.au/downloads.shtml" rel="nofollow noreferrer">database</a>: of human and mouse proteins, we can <a href="http://locate.imb.uq.edu.au/faq.shtml" rel="nofollow noreferrer">see</a> that there are more than 30 different localizations.</p>

<p>At my level, and for the use of my exercise, 30 classes to predict is too much.</p>

<p>Where can I find a mapping to reduce those 30 classes to 4 or 5?</p>

<p>I tried QuickGO, to see the ancestor tree of the different classes, but nothing conclusive. Somebody might have done that, but I lack knowledge on bioinformatic web research.</p>

<p>There are the classes I wanted to map to reduce the number of classes :</p>

<ul>
<li>Cytoplasm (GO:0005737) </li>
<li>Cytoplasmic Vesicles (GO:0016023) </li>
<li>Endoplasmic Reticulum (GO:0005783) </li>
<li>Endosomes (GO:0005768) </li>
<li>Extracellular (GO:0005576) </li>
<li>Golgi Apparatus (GO:0005794) </li>
<li>Lipid Particles (GO:0005811) </li>
<li>Lysosomes (GO:0005764) </li>
<li>Melanosome (GO:0042470)</li>
<li>Mitochondria (GO:0005739) </li>
<li>Nucleus (GO:0005634) </li>
<li>Peroxisome (GO:0005777) </li>
<li>Plasma Membrane (GO:0005886) </li>
<li>Synaptic Vesicles (GO:0008021) </li>
<li>Cellular Component Unknown (GO:0008372) </li>
<li>Apical Plasma Membrane (GO:0016324) </li>
<li>Basolateral Plasma Membrane (GO:0016323)</li>
<li>Centrosome (GO:0005813) </li>
<li>Golgi Cis Cisterna (GO:0000137) </li>
<li>Cytoskeleton (GO:0005856) </li>
<li>Early Endosomes (GO:0005769) </li>
<li>ERGIC (GO:0005793) </li>
<li>Inner Mitochondrial Membrane (GO:0005743) </li>
<li>Late Endosomes (GO:0005770)</li>
<li>Medial-Golgi (GO:0005797) </li>
<li>Outer Mitochondrial Membrane (GO:0005741)</li>
<li>Secretory Granule (GO:0030141) </li>
<li>Golgi Trans Cisterna (GO:0000137)</li>
<li>Golgi Trans Face (GO:0005802) </li>
<li>Tight Junction (GO:0005923) </li>
<li>Transport Vesicle (GO:0030133)</li>
</ul>

<proteins><gene-ontology>
How do i map subcellular localizations to lower the number of classes to predict
# How do i map subcellular localizations to lower the number of classes to predict

(Asked by: username_0 on 2019-04-26)

For a project, I want to predict the subcellular localization of some proteins. I am a student in software Informatics at the University of applied Science HEIG-VD.

Subcellular localization prediction is a common problem in the bioinformatics, but I have a problem

With the data I found. In this [database](http://locate.imb.uq.edu.au/downloads.shtml): of human and mouse proteins, we can [see](http://locate.imb.uq.edu.au/faq.shtml) that there are more than 30 different localizations.

At my level, and for the use of my exercise, 30 classes to predict is too much.

Where can I find a mapping to reduce those 30 classes to 4 or 5?

I tried QuickGO, to see the ancestor tree of the different classes, but nothing conclusive. Somebody might have done that, but I lack knowledge on bioinformatic web research.

There are the classes I wanted to map to reduce the number of classes :

* Cytoplasm (GO:0005737)
* Cytoplasmic Vesicles (GO:0016023)
* Endoplasmic Reticulum (GO:0005783)
* Endosomes (GO:0005768)
* Extracellular (GO:0005576)
* Golgi Apparatus (GO:0005794)
* Lipid Particles (GO:0005811)
* Lysosomes (GO:0005764)
* Melanosome (GO:0042470)
* Mitochondria (GO:0005739)
* Nucleus (GO:0005634)
* Peroxisome (GO:0005777)
* Plasma Membrane (GO:0005886)
* Synaptic Vesicles (GO:0008021)
* Cellular Component Unknown (GO:0008372)
* Apical Plasma Membrane (GO:0016324)
* Basolateral Plasma Membrane (GO:0016323)
* Centrosome (GO:0005813)
* Golgi Cis Cisterna (GO:0000137)
* Cytoskeleton (GO:0005856)
* Early Endosomes (GO:0005769)
* ERGIC (GO:0005793)
* Inner Mitochondrial Membrane (GO:0005743)
* Late Endosomes (GO:0005770)
* Medial-Golgi (GO:0005797)
* Outer Mitochondrial Membrane (GO:0005741)
* Secretory Granule (GO:0030141)
* Golgi Trans Cisterna (GO:0000137)
* Golgi Trans Face (GO:0005802)
* Tight Junction (GO:0005923)
* Transport Vesicle (GO:0030133)

**Tags:** proteins, gene-ontology

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## 2 answers

### Answer by username_1 on 2019-04-26. Score: 1

One way to do this would be to plot the first 10 GO categories of the the CC sub ontology (CC from cellular component). Or you can just visit [the official](http://amigo.geneontology.org/amigo/term/GO:0005575) webpage to see the list of children terms of them.

However there aren't only 4 or 5 categories, so you could arbitrary pick some of those.


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### Answer by username_2 on 2019-05-29. Score: 0

This is probably one of those things where as long as you can justify your choice somewhat.

It might be worth seeing what others have done (see [loctree3](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086075/) and [PSORT](https://academic.oup.com/bioinformatics/article/26/13/1608/201357)) and seeing how well they match your case, then add in any subdomains you have a particular interest in.


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