4 title: "Querying the Metadata Database"
7 This tutorial introduces the Arvados Metadata Database. The Metadata Database stores information about files in Keep. This example will use the Python SDK to find public WGS (Whole Genome Sequencing) data for people who have reported a certain medical condition.
9 *This tutorial assumes that you are "logged into an Arvados VM instance":{{site.baseurl}}/user/getting_started/ssh-access.html#login, and have a "working environment.":{{site.baseurl}}/user/getting_started/check-environment.html*
11 In the tutorial examples, three angle brackets (>>>) will be used to denote code to enter at the interactive Python prompt.
13 Start by running Python.
16 <pre><code>~$ <span class="userinput">python</span>
17 Python 2.7.3 (default, Jan 2 2013, 13:56:14)
19 Type "help", "copyright", "credits" or "license" for more information.
24 If everything is set up correctly, you will be able to import the arvados SDK.
26 notextile. <pre><code>>>> <span class="userinput">import arvados</span></pre></code>
28 This tutorial will also use the regular expression (re) python module:
31 <pre><code>>>> <span class="userinput">import re</span>
37 notextile. <pre><code>>>> <span class="userinput">all_traits = arvados.api().traits().list(limit=1000).execute()</span></code></pre>
39 * @arvados.api()@ gets an object that provides access to the Arvados API server
40 * @.traits()@ gets an object that provides access to the "traits" resource on the Arvados API server
41 * @.list(limit=1000)@ constructs a query to list all elements of the "traits" resource, with a limit of 1000 entries returned
42 * @.execute()@ executes the query and returns the result, which we assign to "all_traits"
44 notextile. <pre><code>>>> <span class="userinput">cancer_traits = filter(lambda t: re.search('cancer', t['name']), all_traits['items'])</span></code></pre>
46 * @lambda t: re.search('cancer', t['name'])@ is an inline function that takes a parameter @t@ and uses a simple regular expression to test if @t['name']@ contains the substring 'cancer'
47 * @all_traits['items']@ is the input sequence of traits
48 * @filter@ tests each element @t@ and constructs a new sequence consisting only of the elements that pass the filter
49 * @cancer_traits@ gets the result of @filter@
52 <pre><code>>>> <span class="userinput">for t in cancer_traits: print(t['uuid'], t['name'])</span>
54 qr1hi-q1cn2-8q57g2diohwnzm0 Cervical cancer
55 qr1hi-q1cn2-vqp4243janpjbyj Breast cancer
56 qr1hi-q1cn2-v6usijujcpwqrn1 Non-melanoma skin cancer
61 In this tutorial wil will use "Non-melanoma skin cancer" trait with uuid @qr1hi-q1cn2-v6usijujcpwqrn1@.
63 notextile. <pre><code>>>> <span class="userinput">non_melanoma_cancer = 'qr1hi-q1cn2-v6usijujcpwqrn1'</code></pre>
65 h2. Finding humans with the selected trait
67 We query the "links" resource to find humans that report the selected trait. Links are directional connections between Arvados data items, for example, from a human to their reported traits.
70 <pre><code>>>> <span class="userinput">trait_query = {
71 'link_class': 'human_trait',
72 'tail_kind': 'arvados#human',
73 'head_uuid': non_melanoma_cancer
78 * @'link_class'@ queries for links that describe the traits of a particular human.
79 * @'tail_kind'@ queries for links where the tail of the link is a human.
80 * @'head_uuit'@ queries for links where the head of the link is a specific data item.
82 The query will return links that match all three conditions.
85 <pre><code>>>> <span class="userinput">trait_links = arvados.api().links().list(limit=1000, where=trait_query).execute()</span>
89 * @arvados.api()@ gets an object that provides access to the Arvados API server
90 * @.links()@ gets an object that provides access to the "links" resource on the Arvados API server
91 * @.list(limit=1000, where=query)@ constructs a query to elements of the "links" resource that match the criteria discussed above, with a limit of 1000 entries returned
92 * @.execute()@ executes the query and returns the result, which we assign to "trait_links"
95 <pre><code>>>> <span class="userinput">human_uuids = map(lambda l: l['tail_uuid'], trait_links['items'])</span>
96 >>> <span class="userinput">human_uuids</span>
97 [u'1h9kt-7a9it-c0uqa4kcdh29wdf', u'1h9kt-7a9it-x4tru6mn40hc6ah',
98 u'1h9kt-7a9it-yqb8m5s9cpy88i8', u'1h9kt-7a9it-46sm75w200ngwny',
99 u'1h9kt-7a9it-gx85a4tdkpzsg3w', u'1h9kt-7a9it-8cvlaa8909lgeo9',
100 u'1h9kt-7a9it-as37qum2pq8vizb', u'1h9kt-7a9it-14fph66z2baqxb9',
101 u'1h9kt-7a9it-e9zc7i4crmw3v69', u'1h9kt-7a9it-np7f35hlijlxdmt',
102 u'1h9kt-7a9it-j9hqyjwbvo9cojn', u'1h9kt-7a9it-lqxdtm1gynmsv13',
103 u'1h9kt-7a9it-zkhhxjfg2o22ywq', u'1h9kt-7a9it-nsjoxqd33lzldw9',
104 u'1h9kt-7a9it-ytect4smzcgd4kg', u'1h9kt-7a9it-y6tl353b3jc4tos',
105 u'1h9kt-7a9it-98f8qave4f8vbs5', u'1h9kt-7a9it-gd72sh15q0p4wq3',
106 u'1h9kt-7a9it-zlx25dscak94q9h', u'1h9kt-7a9it-8gronw4rbgmim01',
107 u'1h9kt-7a9it-wclfkjcb23tr5es', u'1h9kt-7a9it-rvp2qe7szfz4dy6',
108 u'1h9kt-7a9it-50iffhmpzsktwjm', u'1h9kt-7a9it-ul412id5y31a5o8',
109 u'1h9kt-7a9it-732kwkfzylmt4ik', u'1h9kt-7a9it-v9zqxegpblsbtai',
110 u'1h9kt-7a9it-kmaraqduit1v5wd', u'1h9kt-7a9it-t1nwtlo1hru5vvq',
111 u'1h9kt-7a9it-q3w6j9od4ibpoyl', u'1h9kt-7a9it-qz8vzkuuz97ezwv',
112 u'1h9kt-7a9it-t1v8sjz6dm9jmjf', u'1h9kt-7a9it-qe8wrbyvuqs5jew']
116 * @lambda l: l['tail_uuid']@ is an inline function that returns the 'tail_uuid' attribute of 'l'
117 * @trait_links['items']@ is the input set from the query
118 * @map@ converts each item in a sequence into a different item using the embedded function, in this case to produce a sequence of uuids which refer to humans that have the specified trait.
120 h2. Find Personal Genome Project identifiers from Arvados UUIDs
123 <pre><code>>>> <span class="userinput">human_query = {
124 "link_class": "identifier",
125 "head_uuid": human_uuids
127 >>> <span class="userinput">pgpid_links = arvados.api('v1').links().list(limit=1000, where=human_query).execute()</span>
128 >>> <span class="userinput">map(lambda l: l['name'], pgpid_links['items'])</span>
129 [u'hu01024B', u'hu11603C', u'hu15402B', u'hu174334', u'hu1BD549', u'hu237A50',
130 u'hu34A921', u'hu397733', u'hu414115', u'hu43860C', u'hu474789', u'hu553620',
131 u'hu56B3B6', u'hu5917F3', u'hu599905', u'hu5E55F5', u'hu602487', u'hu633787',
132 u'hu68F245', u'hu6C3F34', u'hu7260DD', u'hu7A2F1D', u'hu94040B', u'hu9E356F',
133 u'huAB8707', u'huB1FD55', u'huB4883B', u'huD09050', u'huD09534', u'huD3A569',
134 u'huDF04CC', u'huE2E371']
138 These PGP IDs let us find public profiles, for example:
140 * "https://my.personalgenomes.org/profile/huE2E371":https://my.personalgenomes.org/profile/huE2E371
141 * "https://my.personalgenomes.org/profile/huDF04CC":https://my.personalgenomes.org/profile/huDF04CC
144 h2. Find genomic data from specific humans
146 Now we want to find collections in Keep that were provided by these humans. We search the "links" resource for "provenance" links that point to subjects in list of humans with the non-melanoma skin cancer trait:
149 <pre><code>>>> <span class="userinput">provenance_links = arvados.api().links().list(limit=1000, where={
150 "link_class": "provenance",
152 "tail_uuid": human_uuids
154 collection_uuids = map(lambda l: l['head_uuid'], provenance_links['items'])
156 # build map of human uuid -> PGP ID
158 for pgpid_link in pgpid_links['items']:
159 pgpid[pgpid_link['head_uuid']] = pgpid_link['name']
161 # build map of collection uuid -> PGP ID
162 for p_link in provenance_links['items']:
163 pgpid[p_link['head_uuid']] = pgpid[p_link['tail_uuid']]
165 # get details (e.g., list of files) of each collection
166 collections = arvados.api('v1').collections().list(where={
167 "uuid": collection_uuids
170 # print PGP public profile links with file locators
171 for c in collections['items']:
173 print "https://my.personalgenomes.org/profile/%s %s %s%s" % (pgpid[c['uuid']], c['uuid'], ('' if f[0] == '.' else f[0]+'/'), f[1])
175 https://my.personalgenomes.org/profile/hu43860C a58dca7609fa84c8c38a7e926a97b2fc var-GS00253-DNA_A01_200_37-ASM.tsv.bz2
176 https://my.personalgenomes.org/profile/huB1FD55 ea30eb9e46eedf7f05ed6e348c2baf5d var-GS000010320-ASM.tsv.bz2
177 https://my.personalgenomes.org/profile/huDF04CC 4ab0df8f22f595d1747a22c476c05873 var-GS000010427-ASM.tsv.bz2
178 https://my.personalgenomes.org/profile/hu7A2F1D 756d0ada29b376140f64e7abfe6aa0e7 var-GS000014566-ASM.tsv.bz2
179 https://my.personalgenomes.org/profile/hu553620 7ed4e425bb1c7cc18387cbd9388181df var-GS000015272-ASM.tsv.bz2
180 https://my.personalgenomes.org/profile/huD09534 542112e210daff30dd3cfea4801a9f2f var-GS000016374-ASM.tsv.bz2
181 https://my.personalgenomes.org/profile/hu599905 33a9f3842b01ea3fdf27cc582f5ea2af var-GS000016015-ASM.tsv.bz2
182 https://my.personalgenomes.org/profile/hu43860C a58dca7609fa84c8c38a7e926a97b2fc+302 var-GS00253-DNA_A01_200_37-ASM.tsv.bz2
183 https://my.personalgenomes.org/profile/huB1FD55 ea30eb9e46eedf7f05ed6e348c2baf5d+291 var-GS000010320-ASM.tsv.bz2
184 https://my.personalgenomes.org/profile/huDF04CC 4ab0df8f22f595d1747a22c476c05873+242 var-GS000010427-ASM.tsv.bz2
185 https://my.personalgenomes.org/profile/hu7A2F1D 756d0ada29b376140f64e7abfe6aa0e7+242 var-GS000014566-ASM.tsv.bz2
186 https://my.personalgenomes.org/profile/hu553620 7ed4e425bb1c7cc18387cbd9388181df+242 var-GS000015272-ASM.tsv.bz2
187 https://my.personalgenomes.org/profile/huD09534 542112e210daff30dd3cfea4801a9f2f+242 var-GS000016374-ASM.tsv.bz2
188 https://my.personalgenomes.org/profile/hu599905 33a9f3842b01ea3fdf27cc582f5ea2af+242 var-GS000016015-ASM.tsv.bz2
189 https://my.personalgenomes.org/profile/hu599905 d6e2e57cd60ba5979006d0b03e45e726+81 Witch_results.zip
190 https://my.personalgenomes.org/profile/hu553620 ea4f2d325592a1272f989d141a917fdd+85 Devenwood_results.zip
191 https://my.personalgenomes.org/profile/hu7A2F1D 4580f6620bb15b25b18373766e14e4a7+85 Innkeeper_results.zip
192 https://my.personalgenomes.org/profile/huD09534 fee37be9440b912eb90f5e779f272416+82 Hallet_results.zip
196 h3. Search for a variant
198 Now we will use crunch to issue a 'grep' job to look for variant rs1126809 in each of the "var-" files (these contain variant calls from WGS data).
201 <pre><code>>>> <span class="userinput">job = {}
202 for c in collections['items']:
203 if [] != filter(lambda f: re.search('^var-.*\.tsv\.bz2', f[1]), c['files']):
204 job[c['uuid']] = arvados.api('v1').jobs().create(body={
206 'script_parameters': {'input': c['uuid'], 'pattern': "rs1126809\\b"},
207 'script_version': 'e7aeb42'
209 print "%s %s" % (pgpid[c['uuid']], job[c['uuid']]['uuid'])
211 hu43860C qr1hi-8i9sb-wbf3uthbhkcy8ji
212 huB1FD55 qr1hi-8i9sb-scklkiy8dc27dab
213 huDF04CC qr1hi-8i9sb-pg0w4rfrwfd9srg
214 hu7A2F1D qr1hi-8i9sb-n7u0u0rj8b47168
215 hu553620 qr1hi-8i9sb-k7gst7vyhg20pt1
216 huD09534 qr1hi-8i9sb-4w65pm48123fte5
217 hu599905 qr1hi-8i9sb-wmwa5b5r3eghnev
218 hu43860C qr1hi-8i9sb-j1mngmakdh8iv9o
219 huB1FD55 qr1hi-8i9sb-4j6ehiatcolaoxb
220 huDF04CC qr1hi-8i9sb-n6lcmcr3lowqr5u
221 hu7A2F1D qr1hi-8i9sb-0hwsdtojfcxjo40
222 hu553620 qr1hi-8i9sb-cvvqzqea7jhwb0i
223 huD09534 qr1hi-8i9sb-d0y0qtzuwzbrjj0
224 hu599905 qr1hi-8i9sb-i9ec9g8d7rt70xg
229 Monitor job progress by refreshing the Jobs page in Workbench, or by using the API:
232 <pre><code>>>> <span class="userinput">map(lambda j: arvados.api('v1').jobs().get(uuid=j['uuid']).execute()['success'], job.values())
233 [None, True, None, None, None, None, None, None, None, None, None, None, None, None]
237 Unfinished jobs will appear as None, failed jobs as False, and completed jobs as True.
239 After the jobs have completed, check output file sizes.
242 <pre><code>>>> <span class="userinput">for collection_uuid in job:
243 job_uuid = job[collection_uuid]['uuid']
244 job_output = arvados.api('v1').jobs().get(uuid=job_uuid).execute()['output']
245 output_files = arvados.api('v1').collections().get(uuid=job_output).execute()['files']
246 # Test the output size. If greater than zero, that means 'grep' found the variant
247 if output_files[0][2] > 0:
248 print("%s has variant rs1126809" % (pgpid[collection_uuid]))
250 print("%s does not have variant rs1126809" % (pgpid[collection_uuid]))
252 hu553620 does not have variant rs1126809
253 hu43860C does not have variant rs1126809
254 hu599905 has variant rs1126809
255 huD09534 has variant rs1126809
256 hu553620 does not have variant rs1126809
257 huB1FD55 does not have variant rs1126809
258 huDF04CC has variant rs1126809
259 hu7A2F1D has variant rs1126809
260 hu7A2F1D has variant rs1126809
261 hu599905 has variant rs1126809
262 huDF04CC has variant rs1126809
263 huB1FD55 does not have variant rs1126809
264 huD09534 has variant rs1126809
265 hu43860C does not have variant rs1126809
269 Thus, of the 14 WGS results available for PGP participants reporting non-melanoma skin cancer, 8 include the rs1126809 variant.