Answers.Body: <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>
  
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Answers.CreationDate: 2019-04-26T11:47:08.337Z
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Answers.UserDisplayName: llrs
Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-29T11:34:49.473Z
Answers.LastActivityDate: 2019-05-29T11:34:49.473Z
Answers.UserDisplayName: TW93
Body: <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>
  
Tags: <proteins><gene-ontology>
Title: How do i map subcellular localizations to lower the number of classes to predict
ThreadText: # 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
  
  
  ---
  
  ## 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.
  
  
  ---
  
  ### 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.
  
  
  ---
  
  