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3<head>
4<title>obiMiRNA</title>
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9<body>
10<h1>obiMiRNA</h1>
11<!--   <index name="modules/gene ontology GO"> --> 
12<p>The module obiMiRNA allows the user to work with data about microRNAs <a href="http://en.wikipedia.org/wiki/MicroRNA">(miRNAs)</a>.
13It has an internal library that is loaded with the installation and updated on Orange server. The entries of the library are accessed by the identifier ID,
14thus the name of the miRNA.
15The reference information for each miRNA in the library is retrieved form <a href="http://www.mirbase.org/">miRBase</a>, while the target genes are taken
16from <a href="http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_50">Target Scan</a>.
17</p>
18<p>The module is also used through Orange Bioinformatics for unifying organisms' and genes' name across different modules.</p>
19
20
21<p class=section>Functions</p>
22<dl class=attributes>
23    <dt>ids(org=None)</dt>
24    <dd>Return a list of the identifiers for all the miRNAs in the library or just for the introduced organism <code>org</code>, if present.</dd>
25   
26    <dt>get_info(objectID,type='mat')</dt>
27    <dd>Create an instance of a mature <code>(mat_miRNA)</code> or pre-<code>(pre_miRNA)</code> miRNA.</dd>
28   
29    <dt>cluster(clusterID, type='name')</dt>
30    <dd>Take a cluster identifier (if type='num') or a pre-miRNA identifier and return the list of premiRNAs referred to that cluster
31    or clustered together with that pre-miRNA.</dd>
32   
33    <dt>fromACC_toID(accession)</dt>
34    <dd>Take a miRNA accession number and return a miRNA identifier.</dd>
35   
36    <dt>get_geneMirnaLib(org=None)</dt>
37    <dd>Build dictionary gene:[miRNAs] for all the organisms or just for the specified one.</dd>
38   
39    <dt>get_GO(mirna_list, annotations, enrichment=False, pval=0.1, goSwitch=True)</dt>
40    <dd>Take as input a list of miRNAs of the organism for which the annotations are defined.
41    If <code>goSwitch</code> is False, get_GO() returns a dictionary that has miRNAs as keys and GO IDs as values;
42    in the other case it returns a dictionary with GO IDs as keys and miRNAs as values.</dd>
43   
44    <dt>filter_GO(mirna_goid, annotations, treshold=0.04, reverse=True)</dt>
45    <dd>Take as input a dictionary like {mirna:[list of GO_IDs]} and
46    remove the most common GO IDs in each list using the TF-IDF criterion.</dd>
47   
48    <dt>get_pathways(mirna_list, organism='hsa', enrichment=False, pVal=0.1, pathSwitch=True)</dt>
49    <dd>Take as input a list of miRNAs and return a dictionary that has miRNAs as keys
50    and pathways IDs as values; if the switch is set on True,
51    then return a dictionary with pathways IDs as keys and miRNAs as values.</dd>
52   
53    <dt>removeOldMirnas(mirna_list, getOnlyMature=False)</dt>
54    <dd>Take a list of miRNAs as input and divide them in two lists, according if they're still present on miRBase or not.</dd>
55           
56</dl>
57
58<h2>mat_miRNA</h2>
59<p>Mature miRNA.</p>
60<p class=section>Attributes</p>
61<dl class=attributes>
62    <dt>matACC</dt>
63    <dd>Accession code on miRBase.</dd>
64</dl>
65<dl class=attributes>
66    <dt>matID</dt>
67    <dd>Identifier on miRBase.</dd>
68</dl>
69<dl class=attributes>
70    <dt>matSQ</dt>
71    <dd>Sequence of ~20 nt long.</dd>
72</dl>
73<dl class=attributes>
74    <dt>pre_forms</dt>
75    <dd>Identifier(s) of the pre-miRNA(s) from which the mature miRNA can originate.</dd>
76</dl>
77<dl class=attributes>
78    <dt>targets</dt>
79    <dd>Target genes of the mature miRNA, retrieved from TargetScan.</dd>
80</dl>
81
82<h2>pre_miRNA</h2>
83<p>Pre-form of the miRNA.</p>
84<p class=section>Attributes</p>
85<dl class=attributes>
86    <dt>preACC</dt>
87    <dd>Accession code on miRBase.</dd>
88</dl>
89<dl class=attributes>
90    <dt>preID</dt>
91    <dd>Identifier on miRBase.</dd>
92</dl>
93<dl class=attributes>
94    <dt>preSQ</dt>
95    <dd>Sequence of ~70 nt long.</dd>
96</dl>
97<dl class=attributes>
98    <dt>matACCs</dt>
99    <dd>Identifier(s) of the mature miRNA(s) that can originate.</dd>
100</dl>
101<dl class=attributes>
102    <dt>pubIDs</dt>
103    <dd>Identifier code(s) on PubMed.</dd>
104</dl>
105<dl class=attributes>
106    <dt>clusters</dt>
107    <dd>Identifiers of the pre-miRNAs that belong to the same cluster.</dd>
108</dl>
109<dl class=attributes>
110    <dt>web_addr</dt>
111    <dd>Link to the web-page of the pre-miRNA on miRBase.</dd>
112</dl>
113
114<p class=section>Examples</p>
115<a href='mirnaExample1.py'>mirnaExample1.py</a>
116<xmp class=code>import random
117import obimiRNA
118
119miRNAs = obimiRNA.ids()
120
121print 'miRNA name\tAccession_Number\t\tSequence\t\tPre-forms\n'
122for m in random.sample(miRNAs, 10):
123    accession = obimiRNA.get_info(m).matACC
124    sequence = obimiRNA.get_info(m).matSQ
125    preForms = obimiRNA.get_info(m).pre_forms
126    print '%s\t%s\t\t%s\t\t%s' % (m, accession, sequence, preForms)
127</xmp>
128Output:
129<xmp class=code>miRNA name  Accession_Number        Sequence        Pre-forms
130
131mmu-miR-711 MIMAT0003501        gggacccggggagagauguaag      mmu-mir-711
132hsa-miR-885-5p  MIMAT0004947        uccauuacacuacccugccucu      hsa-mir-885
133bta-miR-10a MIMAT0003786        uacccuguagauccgaauuugug     bta-mir-10a
134dre-miR-144 MIMAT0001841        uacaguauagaugauguacu        dre-mir-144
135rno-miR-292-5p  MIMAT0000896        acucaaacugggggcucuuuug      rno-mir-292
136bta-miR-338 MIMAT0009292        uccagcaucagugauuuuguuga     bta-mir-338
137hsa-miR-487b    MIMAT0003180        aaucguacagggucauccacuu      hsa-mir-487b
138hsa-miR-34b*    MIMAT0000685        uaggcagugucauuagcugauug     hsa-mir-34b
139mmu-miR-101a*   MIMAT0004526        ucaguuaucacagugcugaugc      mmu-mir-101a
140rno-miR-193*    MIMAT0004736        ugggucuuugcgggcaagauga      rno-mir-193
141</xmp>
142<a href='mirnaExample2.py'>mirnaExample2.py</a>
143<xmp class=code>import random
144import obimiRNA
145
146mirnaHSA = obimiRNA.ids('hsa')
147
148for pm in reduce(lambda x,y: x+y, [obimiRNA.get_info(m).pre_forms.split(',') for m in random.sample(mirnaHSA,3)]):                                   
149    pre_miRNA = obimiRNA.get_info(pm,type='pre')
150    print
151    print 'Pre-miRNA name: %s' % pm
152    print 'Accession Number: %s' % pre_miRNA.preACC
153    print 'Accession Number of mature form(s): %s' % pre_miRNA.matACCs
154    print 'PubMed accession number(s): %s' % pre_miRNA.pubIDs
155    print 'Pre-miRNAs clustered together with %s: %s' % (pm, pre_miRNA.clusters)
156    print 'Link to miRBase: %s' % pre_miRNA.web_addr
157</xmp>
158Output:
159<xmp class=code>Pre-miRNA name: hsa-mir-33a
160Accession Number: MI0000091
161Accession Number of mature form(s): MIMAT0000091,MIMAT0004506
162PubMed accession number(s): 11679670,17604727
163Pre-miRNAs clustered together with hsa-mir-33a: None
164Link to miRBase: http://www.mirbase.org/cgi-bin/mirna_entry.pl?acc=MI0000091
165
166Pre-miRNA name: hsa-mir-30c-1
167Accession Number: MI0000736
168Accession Number of mature form(s): MIMAT0000244,MIMAT0004674
169PubMed accession number(s): 12007417,15325244,15634332,15978578,17604727,17616659
170Pre-miRNAs clustered together with hsa-mir-30c-1: hsa-mir-30e
171Link to miRBase: http://www.mirbase.org/cgi-bin/mirna_entry.pl?acc=MI0000736
172
173Pre-miRNA name: hsa-let-7c
174Accession Number: MI0000064
175Accession Number of mature form(s): MIMAT0000064,MIMAT0004483
176PubMed accession number(s): 11679670,14573789,17604727,17616659
177Pre-miRNAs clustered together with hsa-let-7c: hsa-mir-99a
178Link to miRBase: http://www.mirbase.org/cgi-bin/mirna_entry.pl?acc=MI0000064
179</xmp>
180<a href='mirnaExample3.py'>mirnaExample3.py</a>
181<xmp class=code>import random
182import obiGO
183import obimiRNA
184
185annotations = obiGO.Annotations('hsa',obiGO.Ontology())
186miRNAs = random.sample(obimiRNA.ids('hsa'),10)
187
188print 'miRNA\tNumber of annotations\tGO_IDs\n'
189for mi,goList in obimiRNA.get_GO(miRNAs, annotations, goSwitch=False).items():
190    if goList:
191        print '%s\t%d\t%s' % (mi, len(goList), ','.join(goList[0:4])+'...')
192</xmp>
193Output:
194<xmp class=code>miRNA   Number of annotations   GO_IDs
195
196hsa-miR-1   1795    GO:0034605,GO:0034356,GO:0016358,GO:0019992...
197hsa-miR-296-3p  273 GO:0050804,GO:0051041,GO:0008589,GO:0042133...
198hsa-miR-339-3p  89  GO:0005783,GO:0005789,GO:0008469,GO:0008277...
199hsa-miR-1827    1310    GO:0004252,GO:0006888,GO:0006909,GO:0007409...
200hsa-miR-517a    137 GO:0030426,GO:0030425,GO:0030424,GO:0008270...
201hsa-miR-513a-5p 1867    GO:0034605,GO:0016358,GO:0034199,GO:0019992...
202hsa-miR-506 2575    GO:0046340,GO:0034605,GO:0016358,GO:0034199...
203hsa-miR-1204    77  GO:0090004,GO:0018107,GO:0032148,GO:0005125...
204hsa-miR-151-5p  31  GO:0045920,GO:0005887,GO:0030154,GO:0045449...
205</xmp>
206<a href='mirnaExample4.py'>mirnaExample4.py</a>
207<xmp class=code>import random
208import obiGO
209import obimiRNA
210
211annotations = obiGO.Annotations('hsa',obiGO.Ontology())
212
213miRNAs = random.sample(obimiRNA.ids('hsa'),10)
214
215dict_all = obimiRNA.get_GO(miRNAs, annotations, goSwitch=False)
216dict_enr = obimiRNA.get_GO(miRNAs, annotations, enrichment=True, goSwitch=False)
217
218dict_tfidf = obimiRNA.filter_GO(dict_all, annotations, reverse=False)
219
220print '#\tmiRNA name\t# All GO terms\t# Enriched GO terms\t# Filtred GO terms\n'
221for n,m in enumerate(miRNAs):
222    print '%d\t%s\t\t%d\t\t%d\t\t%d' % (n+1,m,len(dict_all[m]),len(dict_enr[m]),len(dict_tfidf[m]))
223</xmp>
224Output:
225<xmp class=code>#   miRNA name  # All GO terms  # Enriched GO terms # Filtred GO terms
226
2271   hsa-miR-1255b       453     53      71
2282   hsa-miR-1244        741     407     136
2293   hsa-miR-135b*       0       0       0
2304   hsa-miR-612     842     49      158
2315   hsa-miR-936     1010        175     165
2326   hsa-miR-27b*        0       0       0
2337   hsa-miR-220c        806     29      131
2348   hsa-let-7c*     0       0       0
2359   hsa-miR-215     474     0       79
23610  hsa-miR-361-5p      676     72      115
237</xmp>
238<a href='mirnaExample5.py'>mirnaExample5.py</a>
239<xmp class=code>import random
240import obimiRNA
241
242miRNAs = random.sample(obimiRNA.ids('hsa'),10)
243
244mirPath_all= obimiRNA.get_pathways(miRNAs,enrichment=False, pathSwitch=False)
245mirPath_enr = obimiRNA.get_pathways(miRNAs,enrichment=True, pathSwitch=False)
246
247print 'miRNA_name\t# of pathways\t# of enriched pathways\n'
248for m in miRNAs:
249    print '%s\t\t%d\t\t%d' % (m,len(mirPath_all[m]),len(mirPath_enr[m]))
250</xmp>
251Output:
252<xmp class=code>miRNA_name  # of pathways   # of enriched pathways
253
254hsa-miR-631     63      20
255hsa-miR-1324        99      26
256hsa-miR-1234        9       9
257hsa-miR-502-3p      62      35
258hsa-miR-196a        75      25
259hsa-miR-151-5p      2       2
260hsa-miR-589     54      10
261hsa-miR-200c        114     55
262hsa-miR-1469        0       0
263hsa-miR-644     41      10
264</xmp>
265<!-- <p class=section>Methods</p>
266<dl class=attributes>
267    <dt>method 1</dt>
268    <dd>...description...</dd> -->
269   
270</dl>
271
272<html>
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