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Tutorial OncodriveClust

luzgaral edited this page Jan 31, 2015 · 5 revisions
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INPUT


#### STEPS [1. Select your data](tutorial-oncodriveclust#select-your-data)
[2. Select priorizitation parameters](tutorial-oncodriveclust#select-priorizitation-parameters)
[3. Fill information job](tutorial-oncodriveclust#fill-information-job)
[4. Press *Launch job* button](tutorial-oncodriveclust#press-launch-job-button)

#### OUTPUT - [Input parameters](tutorial-oncodriveclust#input-parameters) - [Output files](tutorial-oncodriveclust#output-files)
#### CITE




INPUT

#####Input data

  • Input data should be a matrix upload as the data type VCF 4.0. See data types [here](Data Types).

#####Online example Here you can load a small dataset from our server. You can use them to run this example and see how the tool works. Click on the links to load the data: BRCA cancer set from TCGA.


### STEPS #####Select your data First step is to select your data to analyze.

#####Select priorizitation parameters

  • Select Human Genome version: GRCh37 or GRCh38

#####Fill information job

  • Select the output folder
  • Choose a job name
  • Specify a description for the job if desired.

#####Press Launch job button Press launch button and wait until the results is finished. A normal job may last approximately few minutes but the time may vary depending on the size of data. See the state of your job by clicking the jobs button in the top right at the panel menu. A box will appear at the right of the web browser with all your jobs. When the analysis is finished, you will see the label "Ready". Then, click on it and you will be redirected to the results page.


### OUTPUT #### Input parameters In this section you will find a reminder of the parameters or settings you have used to run the analysis.

Output files

Significant results

Statistical information for significant genes:

GENE	CGC	GENE_LEN	GENE_NUM_MUTS	MUTS_IN_CLUST	NUM_CLUSTERS	GENE_SCORE	ZSCORE	PVALUE	QVALUE
SIRPG		388	72	72	2	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
C20orf96		364	7	7	1	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
SIRPD		199	43	43	1	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
NRSN2		205	5	5	1	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
TBC1D20		404	3	3	1	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
DEFB129		184	6	6	1	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
SCRT2		308	7	7	1	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
DEFB127		100	47	47	2	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
SRXN1		138	3	3	1	1.0	5.54615384615	1.46010950776e-08	2.27128145651e-08
RAD21L1		557	77	76	3	0.987012987013	5.44625374625	2.57208688064e-08	3.6009216329e-08
DEFB128		94	36	35	1	0.972222222222	5.33247863248	4.84405862189e-08	6.16516551877e-08
TCF15		200	33	32	1	0.969696969697	5.31305361305	5.39016521274e-08	6.28852608152e-08
SIRPB1		399	157	157	7	0.917584836522	4.91219105017	4.50321147603e-07	4.84961235881e-07
DEFB132		96	3	3	1	0.784521187523	3.88862451941	5.04069720141e-05	5.04069720141e-05

CITE

Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics. 2013 Sep 15;29(18):2238-44. - Bioinformatics - PubMed - Site


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