2024-03-28T19:02:14Zhttp://oai-repositori.upf.edu/oai/requestoai:repositori.upf.edu:10230/246452018-02-19T12:31:38Zcom_10230_5963col_10230_24644
Low-complexity regions in proteins as a source of evolutionary innovation [research data]
Radó i Trilla, Núria, 1985-
Capítol 1: Adaptive evolution of orthologs: role of low-complexity regions: Cap1_SupMat: Document amb el material suplementari d’aquest capítol; Cap1_Data: Excel amb totes les dades utilitzades per l’elaboració de l’article que forma aquest capítol. Capítol 2: LCRs as a mechanism of new coding sequences formation: Cap2_SupMat: Document amb el material suplementari d’aquest capítol; Cap2_Data: Excel amb totes les dades utilitzades per l’elaboració de l’article que forma aquest capítol. Capítol 3: LCRs as a mechanism of protein diversification: Cap3_Data: Excel amb tots els identificadors classificats utilitzats per l’elaboració del capítol; Cap3_Exp: Tots els resultats experimentals que se citen al capítol
Dades primàries de la tesi “Low-complexity regions as a source of evolutionary innovation”/nde Núria Radó-Trilla http://hdl.handle.net/10803/113603
2013
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/24645
eng
http://hdl.handle.net/10803/113603
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
CC0 1.0 Universal
oai:repositori.upf.edu:10230/256082016-01-20T17:02:31Zcom_10230_5963col_10230_24644
A functional study of the conserved LSM proteins in C. elegans reveals their involvement in the stress response of metazoans [datasets]
Cornes Maragliano, Eric, 1987-
Table S1. RNAi phenotypes of lsm genes retrieved from wormmart./nWormmart is a tool available at wormbase [S1]. RNAi assays represented in this table were performed following diverse protocols and using distinct genetic backgrounds: /nRNAi by feeding starting at L4 [S2], RNAi by feeding starting at L1 [S3], RNAi in RNAi hypersensitive backgrounds [S4, S5], RNAi by injection [S6], or RNAi by soaking [S7]. Other RNAi screens referred here were focused in specific phenotypes as aging, cell proliferation, endocytosis, innate immunity or transgene silencing [S8–13]./n/nTable S2. Yeast lsm phenome./nTable shows all the yeast lsm phenotypes retrieved from the Saccharomyces Genome Database (SGD) [S13]. All the information available for S. cerevisiae mutant strains for lsm genes is shown. Non-viable phenotypes are highlighted in red. /n/nTable S3. RNA-Seq analyses of lsm-1 mutants. /nSheet 1: Statistical analyses including FPKM (fragments per kilobase of exon per million fragments mapped) for each gene mapped, and fold change between lsm-1 mutants and wild type animals. /nSheet 2: Genes significantly upregulated (p value ≤ 0.05). /nSheet 3: Genes significantly downregulated (p value ≤ 0.05). /nSheets 4 and 5: Additional information about genes up and down regulated in lsm-1 mutants. Gene description was retrieved from wormbase using the tool wormmart.
Research data from the thesis “A functional study of the conserved LSM proteins in C. elegans reveals their involvement in the stress response of metazoans” by Eric Cornes http://hdl.handle.net/10803/315473
2015
info:eu-repo/semantics/other
http://hdl.handle.net/10230/25608
eng
http://hdl.handle.net/10803/315473
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
CC0 1.0 Universal
oai:repositori.upf.edu:10230/276362016-12-19T08:52:29Zcom_10230_5963col_10230_24644
Cancer bioMarkers database
Tamborero Noguera, David
Rubio Pérez, Carlota
Déu Pons, Jordi
Schroeder, Michael Philipp, 1986-
Vivancos Prellezo, Ana
Rovira Guerín, Ana
Tusquets, Ignasi
Albanell Mestres, Joan
Rodon, Jordi
Tabernero Cartula, Josep
Dienstmann, Rodrigo
González-Pérez, Abel
López Bigas, Núria
1 TSV (tab-separated values) file
The cancer bioMarkers database is curated and maintained by several clinical and scientific experts in the field of precision oncology supported by the European Union’s Horizon 2020 funding. This database is currently being integrated with knowledge databases of other institutions in a collaborative effort of the Global Alliance for Genomics and Health.
2016-10-17
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/27636
eng
https://www.cancergenomeinterpreter.org/biomarkers
Més informació: Cancer bioMarkers database (Cancer Genome Interpreter)
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
Creative Commons CC0
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/277842017-02-27T11:44:32Zcom_10230_5963col_10230_24644
OncoPaD
Tamborero Noguera, David
López Bigas, Núria
González-Pérez, Abel
Rubio Pérez, Carlota
Déu Pons, Jordi
Software: Docker 1.12+.
A tool aimed at the rational design of cancer gene panels. It estimates the cost-effectiveness of the designed panel on a cohort of tumors and provides reports on the importance of individual mutations for tumorigenesis or therapy.
2016-10-16
info:eu-repo/semantics/other
http://hdl.handle.net/10230/27784
eng
http://hdl.handle.net/10230/27942
https://bitbucket.org/bbglab/oncopad
Més informació: OncoPAD (Bitbucket)
Rubio-Perez C, Deu-Pons J, Tamborero D, Lopez-Bigas N, Gonzalez-Perez A. Rational design of cancer gene panels with OncoPaD. Genome Med. 2016; 8(98). DOI: 10.1186/s13073-016-0349-1 http://hdl.handle.net/10230/27942
http://bitbucket.org/bbglab/oncopad/raw/619011cedb025d4542b7abc54d41b69636264575/LICENSE
info:eu-repo/semantics/openAccess
OncoPAD is the property of the Universitat Pompeu Fabra (UPF), which hold the copyright thereto. Copyright® 2012-2014 Universitat Pompeu Fabra.
OncoPAD is made available to the general public subject to certain conditions described in its license. For the avoidance of doubt, you may use the software and any data accessed through UPF software for academic, non-commercial and personal use only, and you may not copy, distribute, transmit, duplicate, reduce or alter in any way for commercial purposes, or for the purpose of redistribution, without a license from the Universitat Pompeu Fabra (UPF). Requests for information regarding a license for commercial use or redistribution of IntOGen Mutations Analysis may be sent via e-mail to innovacio@upf.edu.
Third Party Software - Third Party Data
All rights in any third-party data and/or any third-party software, including all ownership rights, are reserved and remain with the respective third parties. You agree that these third parties may enforce their rights under this agreement against you directly in their own name.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/278102017-02-03T12:21:32Zcom_10230_5963col_10230_24644
IntOGen - Pipeline
González-Pérez, Abel
Pérez Llamas, Christian, 1976-
Tamborero Noguera, David
Schroeder, Michael Philipp, 1986-
Jené i Sanz, Alba, 1984-
Santos, Alberto
López Bigas, Núria
Déu Pons, Jordi
Requirements: IntOGen depends on **Python 3.4** or above and some python libraries. If you don't have Python 3.4 already installed, the easiest way to install all this software stack is using the well known [Anaconda Python distribution](http://continuum.io/downloads#34). /nAlso **Perl 5.10** (with DBI module installed) or above has to be available at PATH to be able to run VEP scripts./n By default MutsigCV is disabled. If you want to enable it you have to first download and install [Matlab Runtime](http://es.mathworks.com/products/compiler/mcr/) and MutsigCV](https://www.broadinstitute.org/cancer/cga/mutsig) and then edit the IntOGen configuration file that by default it's at /.intogen/system.conf (parameters: mutsig_enabled, mutsig_path and matlab_mcr) /nInstallation: To install or update to the last stable version of IntOGen you need to run: /n $ pip install intogen pandas=0.17/nAfter this you will have the `intogen` script available at your path and if this is the first time that you install IntOGen you need to run the setup to download all the data dependencies. This setup will download 3.6Gb of data that after uncompress it will need 9Gb of free space. /n $ intogen --setup/n**TIP**: By default the IntOGen configuration files are in `/.intogen` if you want to change this folder you need to define/nthe system environment variable **INTOGEN_HOME** using the `export` command. Also, all the datasets are downloaded by/ndefault at `/.bgdata` if you want to change this folder you need to define the system environment variable **BGDATA_LOCAL**./nRun an example:/nDownload and extract some samples VCF files:/n $ wget https://bitbucket.org/intogen/intogen-pipeline/downloads/intogen-samples.tar.gz/n $ tar xvzf intogen-samples.tar.gz /nRun IntOGen using the default tasks configuration./n $ intogen -i sample1.vcf -i sample2.vcf -i sample3.vcf -i sample4.vcf /nBrowse the results at the `output` folder./n /nCustom configuration:/nAt `/.intogen/task.conf` you can check the default task configuration values. If you want to run the pipeline /nusing different parameters you can change the default values or create a `.smconfig` file for each project. /nThe `.smconfig` files are a copy of `/.intogen/task.conf` but adding `id` and `files` parameters. The `id` is the name /nof the project and the `files` is a list separated by comma of all the files (MAF, VCF or tab format) that contain /nsamples for that project. /nYou can create a `.smconfig` file like this:/n $ echo -e "id = allsamples/nfiles = sample1.vcf,sample2.vcf,sample3.vcf,sample4.vcf/n" > allsamples.smconfig/n $ cat /.intogen/task.conf >> allsamples.smconfig/nTo run it again, you need to delete or move the previous output and run using the `.smconfig` file as input./n $ rm -rf output/n $ intogen -i allsamples.smconfig /nIf you want to run multiple projects at once you can create multiple `.smconfig` files in one folder and then give that/nfolder as input.
Analyses somatic mutations in thousands of tumor genomes to identify cancer driver genes.
2016-07-18
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/27810
eng
http://dx.doi.org/10.1038/nmeth.2642
https://bitbucket.org/intogen/intogen-pipeline
Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A, Santos A, Lopez-Bigas N. IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods. 2013; 10 (11): 1081-2. DOI: 10.1038/nmeth.2642
Més informació: IntOGen pipeline (Bitbucket)
http://bg.upf.edu/licenses/intogen-mutations-analysis-license.txt
info:eu-repo/semantics/openAccess
IntOGen Mutations Analysis is the property of the Universitat Pompeu Fabra (UPF), which hold the copyright thereto. Copyright® 2012-2014 Universitat Pompeu Fabra./nIntOGen Mutations Analysis is made available to the general public subject to certain conditions described in its license. For the avoidance of doubt, you may use the software and any data accessed through UPF software for academic, non-commercial and personal use only, and you may not copy, distribute, transmit, duplicate, reduce or alter in any way for commercial purposes, or for the purpose of redistribution, without a license from the Universitat Pompeu Fabra (UPF). Requests for information regarding a license for commercial use or redistribution of IntOGen Mutations Analysis may be sent via e-mail to innovacio@upf.edu./nThird Party Software - Third Party Data/nThe following software may be included in the code and, unless otherwise specified, is licensed under the licenses described below. The disclaimers and copyright notices provided are based on information made available to UPF by the third party licensors listed:/n- VEP/n- Ensembl/n- LiftOver/nRegarding third-party data, you agree to comply with the terms and conditions described in the licenses provided below, based on information made available to UPF by the third party licensors listed:/n- Ensembl genes: www.ensembl.org European Bioinformatic Institute./n- Mutation assessor 2: www.mutationassessor.org Computational Biology Center/n- Memorial Sloan Kettering Cancer Center./n- KEGG: http://www.genome.jp/kegg//n- Gene Ontology: http://www.geneontology.org//n- ICGC: http://icgc.org//n- TCGA: http://cancergenome.nih.gov//nAll rights in any third-party data and/or any third-party software, including all ownership rights, are reserved and remain with the respective third parties. You agree that these third parties may enforce their rights under this agreement against you directly in their own name.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/278782018-02-19T12:30:04Zcom_10230_5963col_10230_24644
Gitools
Pérez Llamas, Christian, 1976-
López Bigas, Núria
Schroeder, Michael Philipp, 1986-
Déu Pons, Jordi
Software: Java 7+
Gitools is a framework for analysis and visualization of multidimensional genomic data using interactive heat-maps
2016-07-05
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/27878
eng
http://hdl.handle.net/10230/23502
http://www.gitools.org/
Publicació relacionada: Pérez-Llamas C, López-Bigas N. Gitools: analysis and visualisation of genomic data using interactive heat-maps. PLoS ONE. 2011; 6(5): e19541. DOI 10.1371/journal.pone.0019541 http://hdl.handle.net/10230/23502
Més informació: Gitools website
http://www.gnu.org/licenses/gpl-3.0.txt
info:eu-repo/semantics/openAccess
GNU General Public License v3.0. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/279282017-01-18T11:54:06Zcom_10230_5963col_10230_24644
OncodriveFML
Mularoni, Loris
Sabarinathan, Radhakrishnan
González-Pérez, Abel
López Bigas, Núria
Déu Pons, Jordi
Software: Python3+
Method to identify genomic regions, both coding and non-coding, bearing mutations with significant shift towards high functional impact across a cohort of tumos (FMbias), which are candidates to function as cancer drivers, through a local test.
2016-06-06
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/27928
eng
http://hdl.handle.net/10230/27626
http://bitbucket.org/bbglab/oncodrivefml
Més informació: OncodriveFML (Bitbucket)
Mularoni L, Sabarinathan R, Déu Pons J, González-Pérez A, López Bigas N. OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations. Genome Biology. 2016; 17:128. DOI: 10.1186/s13059-016-0994-0 http://hdl.handle.net/10230/27626
info:eu-repo/semantics/openAccess
OncodriveFML is the property of the Universitat Pompeu Fabra (UPF), which hold the copyright thereto./nCopyright® 2012-2014 Universitat Pompeu Fabra./nOncodriveFML is made available to the general public subject to certain conditions described in its license./nFor the avoidance of doubt, you may use the software and any data accessed through UPF software for academic,/nnon-commercial and personal use only, and you may not copy, distribute, transmit, duplicate, reduce or alter/nin any way for commercial purposes, or for the purpose of redistribution, without a license from the/nUniversitat Pompeu Fabra (UPF). Requests for information regarding a license for commercial use or/nredistribution of OncodriveFML may be sent via e-mail to innovacio@upf.edu./nThird Party Software/nThe third-party software listed below is downloaded directly from its homepages and, unless otherwise specified,/nis licensed under the licenses described below. The disclaimers and copyright notices provided are based on/ninformation made available to UPF by the third party licensors listed:/nNumpy: http://www.numpy.org//nScipy: http://www.scipy.org//nPandas: https://github.com/pydata/pandas/nStatmodels: http://statsmodels.sourceforge.net//nAll rights in any third-party data and/or any third-party software, including all ownership rights, are reserved/nand remain with the respective third parties. You agree that these third parties may enforce their rights under/nthis agreement against you directly in their own name./nConsulteu les condicions d'ús específiques dins del document.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/279372017-01-19T09:56:53Zcom_10230_5963col_10230_24644
itab
Déu Pons, Jordi
Software: Pyton 3+
Python tab files parsing and validating schema tools.
2016-04
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/27937
eng
https://github.com/bbglab/itab
Més informació: itab (GitHub)
http://www.apache.org/licenses/LICENSE-2.0
info:eu-repo/semantics/openAccess
Copyright 2015 Universitat Pompeu Fabra/nLicensed under the Apache License, Version 2.0 (the "License");/nyou may not use this file except in compliance with the License./nYou may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0/nUnless required by applicable law or agreed to in writing, software/ndistributed under the License is distributed on an "AS IS" BASIS,/nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied./nSee the License for the specific language governing permissions and/nlimitations under the License.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/279382017-03-15T09:12:48Zcom_10230_5963col_10230_24644
Mutations Needle Plot (muts-needle-plot)
Schroeder, Michael Philipp, 1986-
Software: Javascript.
A needle-plot (aka stem-plot or lollipop-plot) plots each data point as a big dot and adds a vertical line that makes it appear like a needle.
2015-11
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/27938
eng
https://github.com/bbglab/muts-needle-plot
Més informació: muts-needle-plot (GitHub)
info:eu-repo/semantics/openAccess
Copyright 2015 Universitat Pompeu Fabra/nLicensed under the Apache License, Version 2.0 (the "License");/nyou may not use this file except in compliance with the License./nYou may obtain a copy of the License at/nhttp://www.apache.org/licenses/LICENSE-2.0/nUnless required by applicable law or agreed to in writing, software/ndistributed under the License is distributed on an "AS IS" BASIS,/nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied./nSee the License for the specific language governing permissions and/nlimitations under the License.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/279832017-03-08T10:46:44Zcom_10230_5963col_10230_24644
OncodriveCLUST
Tamborero Noguera, David
González-Pérez, Abel
López Bigas, Núria
OncodriveCLUST depends on Python 3 and some external libraries, numpy, scipy, pandas and statsmodels./nThe easiest way to install all this software stack is using the well known Anaconda Python distribution./nThen to get OncodriveCLUST installed run the following command:/n(env) $ pip install oncodriveclust/nAnd that's all. The following command will allow you to check that is correctly installed by showing the command help:/n(env) $ oncodriveclust --help/nusage: oncodriveclust [-h] [--version] [-o PATH] [--cgc PATH] [-m INT] [-c]/n [-p INT]/n NON-SYN-PATH SYN-PATH GENE-TRANSCRIPTS/nRun OncodriveCLUST analysis/npositional arguments:/n NON-SYN-PATH The path to the NON-Synonymous mutations file to be/n checked/n SYN-PATH The path to the Synonymous mutations file to construct/n the background model/n GENE-TRANSCRIPTS The path of a file containing transcripts length for/n genes/noptional arguments:/n -h, --help show this help message and exit/n --version show program's version number and exit/n -o PATH, --out PATH Define the output file path/n --cgc PATH The path of a file containing CGC data/n -m INT, --muts INT Minimum number of mutations of a gene to be included/n in the analysis ('5' by default)/n -c, --coord Use this argument for printing cluster coordinates in/n the output file/n --pos INT AA position column index ('-1' by default)/n -d INT, --dist INT Intra cluster maximum distance ('5' by default)/n -p FLOAT, --prob FLOAT/n Probability of the binomial model to find cluster/n seeds ('0.01' by default)/n --dom PATH The path of a file containing gene domains/n -L LEVEL, --log-level LEVEL/n Define the loggging level
OncodriveCLUST is a method aimed to identify genes whose mutations are biased towards a large spatial clustering. This method is designed to exploit the feature that mutations in cancer genes, especially oncogenes, often cluster in particular positions of the protein. We consider this as a sign that mutations in these regions change the function of these proteins in a manner that provides an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumours, and this property can thus be used to nominate novel candidate driver genes./nThe method does not assume that the baseline mutation probability is homogeneous across all gene positions but it creates a background model using silent mutations. Coding silent mutations are supposed to be under no positive selection and may reflect the baseline clustering of somatic mutations. Given recent evidences of non-random mutation processes along the genome, the assumption of homogenous mutation probabilities is likely an oversimplication introducing bias in the detection of meaningful events.
2015-11
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/27983
eng
http://dx.doi.org/10.1093/bioinformatics/btt395
http://bitbucket.org/bbglab/oncodriveclust
Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics. 2013; 19(18): 2238-44. DOI: 10.1093/bioinformatics/btt395
Més informació: OncodriveCLUST (Bitbucket)
info:eu-repo/semantics/openAccess
Universitat Pompeu Fabra Free Source Code License Agreement. Consulteu les condicions d'ús específiques dins del document.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/279942017-03-08T10:47:20Zcom_10230_5963col_10230_24644
OncodriveFM
González-Pérez, Abel
López Bigas, Núria
OncodriveFM depends on Python 3 and some external libraries, numpy, scipy, pandas and statsmodels./nThe easiest way to install all this software stack is using the well known Anaconda Python distribution./nThen to get OncodriveFM installed run the following command:/n(env) $ pip install oncodrivefm/nAnd that's all. The following command will allow you to check that is correctly installed by showing the command help:/n(env) $ oncodrivefm --help/nusage: oncodrivefm [-h] [-o PATH] [-n NAME] [--output-format FORMAT]/n [-N NUMBER] [-e ESTIMATOR] [--gt THRESHOLD]/n [--pt THRESHOLD] [-s SLICES] [-m PATH] [--save-data]/n [--save-analysis] [-j CORES] [-D KEY=VALUE] [-L LEVEL]/n DATA/nCompute the FM bias for genes and pathways/npositional arguments:/n DATA File containing the data matrix in TDM format/noptional arguments:/n -h, --help show this help message and exit/n -o PATH, --output-path PATH/n Directory where output files will be written/n -n NAME Analysis name/n --output-format FORMAT/n The FORMAT for the output file/n -N NUMBER, --samplings NUMBER/n Number of samplings to compute the FM bias pvalue/n -e ESTIMATOR, --estimator ESTIMATOR/n Test estimator for computation./n --gt THRESHOLD, --gene-threshold THRESHOLD/n Minimum number of mutations per gene to compute the FM/n bias/n --pt THRESHOLD, --pathway-threshold THRESHOLD/n Minimum number of mutations per pathway to compute the/n FM bias/n -s SLICES, --slices SLICES/n Slices to process separated by commas/n -m PATH, --mapping PATH/n File with mappings between genes and pathways to be/n analysed/n --save-data The input data matrix will be saved/n --save-analysis The analysis results will be saved/n -j CORES, --cores CORES/n Number of cores to use for calculations. Default is 0/n that means all the available cores/n -D KEY=VALUE Define external parameters to be saved in the results/n -L LEVEL, --log-level LEVEL/n Define log level: debug, info, warn, error, critical,/n notset
OncodriveFM detects candidate cancer driver genes and pathways from catalogs of somatic mutations in a cohort of tumors by computing the bias towards the accumulation of functional mutations (FM bias).This novel approach avoids some known limitations of recurrence-based approaches, such as the difficulty to estimate background mutation rate, and the fact that they usually fail to identify lowly recurrently mutated driver genes.
2015-10
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/27994
eng
http://hdl.handle.net/10230/23344
https://bitbucket.org/bbglab/oncodrivefm
Gonzalez-Perez A, Lopez-Bigas N. Functional impact bias reveals cancer drivers. Nucleic Acids Research. 2012; 40(21): e169. DOI 10.1093/nar/gks743 http://hdl.handle.net/10230/23344
Més informació: OncodriveFM (Bitbucket)
info:eu-repo/semantics/openAccess
Universitat Pompeu Fabra Free Source Code License Agreement. Consulteu les condicions d'ús específiques dins del document.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280492017-03-10T02:30:28Zcom_10230_5963col_10230_24644
IntOGen - Cancer Drivers Database (2014)
Rubio Pérez, Carlota
Tamborero Noguera, David
Schroeder, Michael Philipp, 1986-
Antolín Hernández, Albert, 1984-
Déu Pons, Jordi
Pérez Llamas, Christian, 1976-
Mestres i López, Jordi
González-Pérez, Abel
López Bigas, Núria
File contents/n-----------------/nTumor_cohort_details.tsv/nDatasets of somatic mutations employed in the analysis to detect drivers/nCNA_drivers_per_tumor_type.tsv/nList of 29 CNA cancer driver genes in TCGA cohort./nFusion_drivers_per_tumor_type.tsv/nList of 10 fusion driver genes in TCGA cohort./nMutational_drivers_per_tumor_type.tsv/nList of 459 mutation driver genes in full cohort./nMutational_drivers_project_detection.tsv/nList of 459 mutation driver genes detected by project./nMutational_drivers_signals.tsv/nList of genes with at least 1 signal of positive selection across the 6792 samples./nMutational_drivers_count_frequency.tsv/n List of 459 mutational drivers the count of mutated samples across all tumor types./nDrivers_type_role.tsv/nList of 475 drivers, driver type (mutational, CNA and/or fusion driver) and its role in cancer/nDrivers_cloncal_frequency.tsv/nList of 666 genes for which we were able to compute clonal frequency
This database contains information on the genes identified as drivers in Rubio-Perez and Tamborero et al. (2015). It contains information on driver identification at mutational, CNA and gene fusion level. Additional ancillary information about the role and major clonality of drivers is also included. A table is also provided with the list of datasets used for mutational driver identification.
2015-03
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/28049
eng
http://dx.doi.org/10.1016/j.ccell.2015.02.007
https://www.intogen.org/downloads
Més informació: IntOGen (web)
Rubio-Perez C, Tamborero D, Schroeder MP, Aantolín AA, Deu-Pons J, Perez-Llamas C, Mestres J, Gonzalez-Perez A, Lopez-Bigas N. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell. 2015; 27(3): 382-96. DOI: 10.1016/j.ccell.2015.02.007
http://www.intogen.org/terms
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280502018-01-24T08:33:12Zcom_10230_5963col_10230_24644
IntOGen - Cancer Drivers Actionability Database
Rubio Pérez, Carlota
Tamborero Noguera, David
Schroeder, Michael Philipp, 1986-
Antolín Hernández, Albert, 1984-
Déu Pons, Jordi
Pérez Llamas, Christian, 1976-
Mestres i López, Jordi
González-Pérez, Abel
López Bigas, Núria
File contents/n-----------------/nProtein_Drug_Interactions.tsv/nInteractions of 96/475 cancer driver genes in Drivers Database with therapeutic agents/nProtein_druggability.tsv/nExclusvie druggability of 157/475 driver genes according interactions in Protein_Drug_Interactions.tsv/nDrug_details.tsv/nDescription of each drug/nDrug_FDAapproved_rules.tsv/nRules for prescription of FDA approved drugs to genomic alterations/nDrug_ClinicalTrials_rules.tsv/nRules for prescription of drugs in clinical trials to genomic alterations/nDrug_resistances_rules.tsv/nRules for not prescribing drugs to samples bearing genomic alterations of primary resistance/n/nSpecificities/n-----------------/n*In most of the files tumor types are represented through its acronyms (see Drivers Database)/n*Specific genomic dependencies/evidences/resistances are codified in the same unified code in the Columns () in the files (Drug_resistances_rules.tsv, Drug_FDAapproved_rules.tsv, Drug_ClinicalTrials_rules.tsv). /nThe coding rules are:/n- All of them are divided in mutations/CNAs/fusions. /n- For each one, different genes with alterations are semicolon (;) divided./n- Each gene differs from its alterions with colon(:) and its different alterations are comma (,) divided. /ni.e BRAF:V600E,V600K (two diferent alterations)/n- For CNAs A stants for Amplification and D for delention./ni.e. FGFR1:A/n- For mutations all of them are specified at protein level lik referenceAA+proteinposition+alteredAA./ni.e. BRAF:V600E,V600K/n except mutational requirements not based on AA change but specific consequence type, which are specified by ::CT:: after the gene./ni.e. NOTCH1::CT::missense_variant:2380-2445;CT::feature_truncation:2380-2445/n- Fusion partners are divided with dash (-)./ni.e. BCR-ABL1/n- A dot (.) represents any./ni.e. BRAF:V600. (any alteredAA)/ni.e. BRAF:. (any PAM mutation)
This database contains data on the interactions with therapeutic agents an driver genes contained in Cancer Drivers Database (2014.12). It characterizes the interacting therapeutic agents in terms of clinical phase and cancer prescription, among other features. Additionally, it contains ancillary information on specific genomic alterations associated to drug effectiveness which are FDA approved or clinically being tested together with data on other genomic alterations known to be responsible of drug primary resistance.
2015-03
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/28050
eng
http://dx.doi.org/10.1016/j.ccell.2015.02.007
https://www.intogen.org/downloads
Més informació: IntOGen (web)
Rubio-Perez C, Tamborero D, Schroeder MP, Aantolín AA, Deu-Pons J, Perez-Llamas C, Mestres J, Gonzalez-Perez A, Lopez-Bigas N. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell. 2015; 27(3): 382-96. DOI: 10.1016/j.ccell.2015.02.007
http://www.intogen.org/terms
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280092017-01-27T10:43:02Zcom_10230_5963col_10230_24644
Onexus
Déu Pons, Jordi
Software: Java 7+
Onexus is a modular framework to manage the complete life cycle of data analyses. Data analyses follow these steps: analysis definition, analysis execution, results storing, results browsing and finally results publishing.
2014-11
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28009
eng
https://github.com/onexus/onexus
Més informació: Onexus (GitHub)
http://www.apache.org/licenses/LICENSE-2.0
info:eu-repo/semantics/openAccess
Copyright 2012 Universitat Pompeu Fabra./n Licensed under the Apache License, Version 2.0 (the "License");/n you may not use this file except in compliance with the License./n You may obtain a copy of the License at/n http://www.apache.org/licenses/LICENSE-2.0/n Unless required by applicable law or agreed to in writing, software/n distributed under the License is distributed on an "AS IS" BASIS,/n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied./n See the License for the specific language governing permissions and/n limitations under the License./nConsulteu les condicions d'ús específiques dins del document.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280252017-02-01T10:04:00Zcom_10230_5963col_10230_24644
Wok
Pérez Llamas, Christian, 1976-
Workflows in Wok are defined in an xml file with the .flow extension. This definition includes:/n- the different modules (or pieces of processing)/n- the interconnections between modules (i.e. the input of module B links with the output of module A)/n- explicit dependencies (i.e. module A cannot be executed until module B has finished)/n- descriptions that can be used to generate documentation automatically or to create web forms/nEach module corresponds with a piece of software that has to be run in order to process some input and generate an output. For now, only Python scripts are allowed, but they can be used to execute software written in other languages./nWorkflows in Wok can be treated as any software project and managed with version control system tools and the IDE of your choice./nWok can be used as a terminal script or can be run in server mode./nThe execution of a workflow in the terminal is done using the wok-run script which allows few options:/n- An instance name (-n name), which allows to run the same workflow many times simultaneously independently/n- Configuration files (-c file.conf), the configuration can be splitted in as much files as desired/n- Configuration parameters (-D param=value), which overwrite any previous configuration in configuration files/nThe workflow definition file (i.e. myworkflow.flow) is passed as the first argument./nTo monitor the execution of the workflow there are different resources available:/n- The web server that allows to interact with the engine in a very straightforward way. Recommended!./n- The logs emited by the wok-run through the standard output,/n- The intermediate files generated by Wok (i.e. the tasks output files)/nIt has been designed for workflow developers who feel more confortable programming than doing hundred of clicks and drag & drop's, and also for those who want infraestructure flexibility and full control and monitorization of the execution.
Wok is a workflow management system implemented in Python that makes very easy to structure the workflows, parallelize their execution and monitor its progress among other things. It is designed in a modular way allowing to adapt it to different infraestructures./nFor the time being it is strongly focused on clusters implementing any DRMAA compatible resource manager (i.e. Oracle Grid Engine) which working nodes have a shared folder in common. Other, more flexible infrastructures (such as the Amazon EC2) are considered for future implementations.
2014-06
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28025
eng
http://github.com/bbglab/wok
Més informació: Wok (GitHut)
http://www.gnu.org/licenses/gpl-3.0.en.html
info:eu-repo/semantics/openAccess
GNU General Public License v3.0
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280302017-02-01T11:56:15Zcom_10230_5963col_10230_24644
OncodriveRole
Schroeder, Michael Philipp, 1986-
Rubio Pérez, Carlota
Tamborero Noguera, David
González-Pérez, Abel
López Bigas, Núria
Software: R
Machine-learning based approach to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development.
2014-06
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28030
eng
http://hdl.handle.net/10230/24768
http://bitbucket.org/bbglab/oncodriverole
Més informació: OncodriveRole (Bitbucket)
Schroeder MP, Rubio-Perez C, Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action. Bioinformatics. 2014 Sep 1; 30(17): i549-55. DOI: 10.1093/bioinformatics/btu467 http://hdl.handle.net/10230/24768
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Creative Commons Reconocimiento 3.0 España (CC BY 3.0 ES)
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280332017-02-02T10:37:01Zcom_10230_5963col_10230_24644
jHeatmap
Déu Pons, Jordi
Schroeder, Michael Philipp, 1986-
López Bigas, Núria
jHeatmap is designed for incorporation into web portals and applications so it has the above listed extension points which may be adapted to the developer's needs: data readers, cell decorators, data aggregation, sorting algorithms and filters./nThree data readers which allow to read tab separated data into the heatmap and annotations respectively are available. A decorator is a function that defines the color scale to use in the heatmap cells. jHeatmap comes with seven implementations fit for different data types. An aggregator is a function that collapses an array of numbers (rows or columns) into a single number. Aggregators are used in combination with the default sorter. Custom complex sorters are also possible, such as the already available MutualExclusiveSorter. Filters can be added to rows and columns as for example the non-significance filter will hide rows or columns that contain no significant p-values.
Javascript library to create interactive heatmaps within webpages.
2014-03
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28033
eng
http://doi.org/10.1093/bioinformatics/btu094
http://jheatmap.github.io/jheatmap/
Més informació: jHeatmap (web)
Deu-Pons J, Schroeder MP, Lopez-Bigas N. jHeatmap: an interactive heatmap viewer for the web. Bioinformatics. 2014; 30(12): 1757-8. DOI: 10.1093/bioinformatics/btu094
http://www.apache.org/licenses/LICENSE-2.0
info:eu-repo/semantics/openAccess
Copyright 2011 Universitat Pompeu Fabra (http://www.upf.edu)./n Licensed under the Apache License, Version 2.0 (the "License");/n you may not use this file except in compliance with the License./n You may obtain a copy of the License at/n <http://www.apache.org/licenses/LICENSE-2.0>/n Unless required by applicable law or agreed to in writing, software/n distributed under the License is distributed on an "AS IS" BASIS,/n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied./n See the License for the specific language governing permissions and/n limitations under the License.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280512017-03-10T02:30:38Zcom_10230_5963col_10230_24644
IntOGen - TCGA pan-cancer12 high confidence drivers
Tamborero Noguera, David
González-Pérez, Abel
Pérez Llamas, Christian, 1976-
Déu Pons, Jordi
Kandoth, Cyriac
Reimand, Jüri
Lawrence, Michael S.
Getz, Gad
Bader, Gary D.
Ding, Li
López Bigas, Núria
1 .txt file
This file lists the High Confidence Drivers identified as part of the pan-cancer12 initiative, published in the paper Comprehensive identification of mutational cancer driver genes across 12 tumor types" Scientific Reports 3:2650, 2013, doi:10.1038/srep02650
2013-10
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/28051
eng
http://hdl.handle.net/10230/24554
https://www.intogen.org/downloads
Més informació: IntOGen (web)
Tamborero D, González-Párez A, Pérez-Llamas C, Deu-Pons J, Kandoth C, Reimand J et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Scientific Reports. 2013; 3: 2650. DOI 10.1038/srep02650 http://hdl.handle.net/10230/24554
http://www.intogen.org/terms
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280482017-02-03T10:15:55Zcom_10230_5963col_10230_24644
C10-HDAC7
Barneda Zahonero, Bruna
Román González, Lidia
Collazo, Olga
Rafati, Haleh
Islam, Abul, 1978-
Bussmann, Lars
Di Tullio, Alessandro, 1982-
Andrés, Luisa De
Graf, T. (Thomas)
López Bigas, Núria
Mahmoudi, Tokameh
Parra, Maribel
Software: Docker 1.12+
HDAC7 is a repressor of myeloid genes whose downregulation in pre-B cells is required for transdifferentiation into macrophages.
2013-05
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28048
eng
http://hdl.handle.net/10230/23441
http://bg.upf.edu/projects/C10-HDAC7/
Més informació: C10-HDAC7 (web)
Barneda-Zahonero B, Roman-Gonzalez L, Collazo O, Rafati H, Islam ABMMK, Bussmann LH et al. HDAC7 is a repressor of myeloid genes whose downregulation is required for transdifferentiation of pre-B cells into macrophages. PLoS Genetics. 2013; 9(5): e1003503. DOI: 10.1371/journal.pgen.1003503 http://hdl.handle.net/10230/23441
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Aquest document es troba sota llicència Creative Commons Reconeixement 3.0 Espanya (CC BY 3.0 ES)
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280812017-03-18T02:30:44Zcom_10230_5963col_10230_24644
IntOGen - Cancer driver database (2013)
González-Pérez, Abel
Pérez Llamas, Christian, 1976-
Déu Pons, Jordi
Tamborero Noguera, David
Schroeder, Michael Philipp, 1986-
Jené i Sanz, Alba, 1984-
Santos, Alberto
López Bigas, Núria
1 .ZIP file
Mutations, genes and pathways involved in tumorigenesis across 4,623 cancer genomes/exomes from 13 cancer sites. IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods 10, 2013, doi:10.1038/nmeth.2642
2013-05
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/28081
eng
http://dx.doi.org/10.1038/nmeth.2642
https://www.intogen.org/downloads
Més informació: IntOGen (web)
Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A, Santos A, Lopez-Bigas N. IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods. 2013;10(11):1081-2. DOI: 10.1038/nmeth.2642
http://www.intogen.org/terms
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280822017-02-08T10:12:42Zcom_10230_5963col_10230_24644
Oncodrive-CIS
Tamborero Noguera, David
López Bigas, Núria
González-Pérez, Abel
How to install and run/nWe distribute a Python implementation of Oncodrive-CIS in a compressed file below. Oncodrive-CIS requires three input files containing:/nexpression values per sample and per gene/ncopy number status per sample and per gene/na sample file stating whether each sample identifier corresponds to either a normal or a tumor/nOncodrive-CIS is executed by the oncodrivecis.py script. It requires several arguments (some of them optional), which are displayed by typing -h (or --help):/n$ python src/oncodrivecis.py -h /nUsage: oncodrivecis.py [options] /nOptions: /n -h, --help show this help message and exit /n -e PATH, --expression=PATH /n Specifies the path to the exp file /n -c PATH, --cnv=PATH Specifies the path to the CNA file /n -s PATH, --samples=PATH /n Specifies the path to the samples file /n -o PATH, --output=PATH /n Specifies the output folder (by default the same than /n the samples file one) /n -i PATH, --identifier=PATH /n Specifies the gene id conversion file /n (optional) /n -n INT, --nsampling=INT /n Sampling number per gene (optional, 10000 by default) /n -a INT, --alterations=INT /n Minimum number of alterations per gene (2 by default) /nAmong the downloadable files we have included the gliobastoma multiforme data set (see the main manuscript for further details about these data) already formatted to be processed by Oncodrive-CIS. For using it, type the following:/n$ python src/oncodrivecis.py //n -e gbm_data/expression.per.gene.ens.gbm.tsv //n -c gbm_data/cnv.rae.ens.gbm.tsv //n -s gbm_data/samples_to_process.tsv //n -o output -i gbm_data/ensembl63_ensembl2hugo.tsv/nThe execution time for this example can be decreased by lowering the number of permutations performed to retrieve the Z score values by using the –n (--nsampling) argument or reduce the number of processed samples by modifying the 'samples_to_process.tsv' file./nNote that further details about Oncodrive-CIS execution, input files and produced output are contained in a User Manual which is available among the downloadable files.
Oncodrive-CIS is a method aimed to identify those copy number alterations (CNAs) leading to larger in cis expression changes that may be useful in elucidating the role of these aberrations in cancer. This is based on the hypothesis that a gene driving oncogenesis through copy number changes is more prone to bias towards overexpression (or underexpression) as compared to bystanders. The effect of the gene dosage is assessed by observing expression changes not only among tumors but also taking into account normal samples data, when available./nOncodrive-CIS has several potential benefits: first, it did not examine the frequency of the CNAs across samples and therefore the detection of low-recurrent driver alterations was not impaired. Second, amplifications and deletions were evaluated separately to obtain a fair ranking of genes, because the expression change measured in deletions was lower than the one obtained from multi-copy amplifications. Third, the expression of genes in tumor samples was analyzed according to the copy number status but was also compared to normal samples, thus better revealing the gene misregulation role of CNAs in cancer cells. And finally, it should be emphasized that the relationship between expression changes and their functional impact is complex, thus Oncodrive-CIS is proposed as a method to elucidate the role of CNAs in cancer which may be complementary to analyses based on other criteria.
2013-02
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28082
eng
http://hdl.handle.net/10230/23565
http://bg.upf.edu/group/projects/oncodrive-cis.php
Més informació: Oncodrive-CIS (Biomedical Genomics)
Tamborero D, López-Bigas N, González-Pérez A. Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression. PLoS ONE. 2013; 8(2): e55489. DOI 10.1371/journal.pone.0055489 http://hdl.handle.net/10230/23565
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Dades sita llicència Creative Commons Reconocimiento 3.0 España (CC BY 3.0 ES)
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280952017-02-09T09:10:26Zcom_10230_5963col_10230_24644
SVGMap
Rafael Palou, Xavier
Schroeder, Michael Philipp, 1986-
López Bigas, Núria
Execution of SVGMap/nThis project provides a Jetty Server to run SVGMap web, which needs Sun Java 1.6 or higher installed on your machine./nIf your Operating System is a Linux or Mac OS X, then execute run.sh, in Windows use run.bat. We strongly recommend to run SVGMap from a terminal by the commands listed below!/nWhen you run SVGMap a Webserver is created at the port 8095. Access to it by opening your favourite browser and direct yourself to the address: http://localhost:8095/nKeep in mind that you may execute SVGMap only once per machine with the same port, otherwise it may create conflicts. The same applies if you already have another service running on that port. You can change the default port in the run.sh/run.bat on the following lines./nLinux/OS X: run.sh/njava -server -Djetty.port=8095 -Dsvgmap.home=data -jar lib/start-6.1.22.jar/nWindows: run.bat/njava -server -Djetty.port=8095 -jar lib/start-6.1.22.jar/nMore information at https://bg.upf.edu/forge/projects/svgmap/wiki/User_documentation
The aim of SVGMap is helping in the visualisation of experimental data which are associated with some graphical representation. Thus SVGMap browser allows to generate images with colored areas corresponding to the chosen data and color scale./nThe data is represented as a table and is searchable. All data as well as the generated images/figures can be exported easily through the interface./nAdditionally the tool allows to manage (add, edit or delete) experiments and configure the front-end user search appearance such as the number of images to be displayed, the scale types to use and more.
2012-01
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28095
eng
http://dx.doi.org/10.1093/bioinformatics/btr581
http://bitbucket.org/bbglab/svgmap/wiki/Home
Més informació: SVGMap (Bitbucket)
Rafael-Palou X, Schroeder MP, Lopez-Bigas N. SVGMap: configurable image browser for experimental data. Bioinformatics. 2011;28(1):119-20. DOI: 10.1093/bioinformatics/btr581
http://www.opensource.org/licenses/osl-3.0
info:eu-repo/semantics/openAccess
Copyright 2011 Universitat Pompeu Fabra./nThis software is open source and is licensed under the Open Software License version 3.0./nYou may obtain a copy of the License at/nhttp://www.opensource.org/licenses/osl-3.0/nThis software also includes image examples licensed under Creative Commons license./nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied./nSee the License for the specific language governing permissions and limitations under the License.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/280972017-02-09T11:52:49Zcom_10230_5963col_10230_24644
p27
Pippa, Raffaella
Espinosa Blay, Lluís
Gundem, Gunes
Garcia Escudero, Ramon
Dominguez, Ana
Orlando, S.
Gallastegui, E.
Saiz, Cristina
Besson, Arnaud
Pujol, Maria Jesus
López Bigas, Núria
Paramio, Jesus M.
Bigas Salvans, Anna
Bachs Valldeneu, Oriol
Software: Docker 1.12+
The cyclin-cdk (cyclin-dependent kinase) inhibitor p27(Kip1) (p27) has a crucial negative role on cell cycle progression. In addition to its classical role as a cyclin-cdk inhibitor, it also performs cyclin-cdk-independent functions as the regulation of cytoskeleton rearrangements and cell motility. p27 deficiency has been associated with tumor aggressiveness and poor clinical outcome, although the mechanisms underlying this participation still remain elusive. We report here a new cellular function of p27 as a transcriptional regulator in association with p130/E2F4 complexes that could be relevant for tumorigenesis. We observed that p27 associates with specific promoters of genes involved in important cellular functions as processing and splicing of RNA, mitochondrial organization and respiration, translation and cell cycle. On these promoters p27 co-localizes with p130, E2F4 and co-repressors as histone deacetylases (HDACs) and mSIN3A. p27 co-immunoprecipitates with these proteins and by affinity chromatography, we demonstrated a direct interaction of p27 with p130 and E2F4 through its carboxyl-half. We have also shown that p130 recruits p27 on the promoters, and there p27 is needed for the subsequent recruitment of HDACs and mSIN3A. Expression microarrays and luciferase assays revealed that p27 behaves as transcriptional repressor of these p27-target genes (p27-TGs). Finally, in human tumors, we established a correlation with overexpression of p27-TGs and poor survival. Thus, this new function of p27 as a transcriptional repressor could have a role in the major aggressiveness of tumors with low levels of p27.
2011-12
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28097
eng
http://dx.doi.org/10.1038/onc.2011.582
http://bg.upf.edu/p27/home/
Més informació: Transcriptional regulatory role of p27Kip1 (web)
Pippa R, Espinosa L, Gundem G, García-Escudero R, Dominguez A, Orlando S, Gallastegui E, Saiz C, Besson A, Pujol MJ, López-Bigas N, Paramio JM, Bigas A, Bachs O. p27Kip1 represses transcription by direct interaction with p130/E2F4 at the promoters of target genes. Oncogene. 2012;31(38):4207-20. DOI: 10.1038/onc.2011.582
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Dades sota llicència Creative Commons Reconocimiento 3.0 España (CC BY 3.0 ES)
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/281072017-03-18T02:30:45Zcom_10230_5963col_10230_24644
IntOGen - Arrays
Gundem, Gunes
Pérez Llamas, Christian, 1976-
Jené i Sanz, Alba, 1984-
Kedzierska, Anna
Islam, Abul, 1978-
Déu Pons, Jordi
Furney, Simon J.
López Bigas, Núria
1 zip file
Genes and pathways affected by expression and copy number changes in tumors across projects and cancer types.
2010-02
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/28107
eng
http://dx.doi.org/10.1038/nmeth0210-92
https://www.intogen.org/downloads
Més informació: IntOGen (web)
Gundem G, Perez-Llamas C, Jene-Sanz A, Kedzierska A, Islam A, Deu-Pons J, Furney SJ, Lopez-Bigas N. IntOGen: integration and data mining of multidimensional oncogenomic data. Nat Methods. 2010;7(2):92-3. DOI: 10.1038/nmeth0210-92
http://www.intogen.org/terms
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/281252017-02-20T09:55:00Zcom_10230_5963col_10230_24644
RBP2
López Bigas, Núria
Kisiel, Tomasz A.
DeWaal, Dannielle C.
Holmes, Katherine B.
Volkert, Tom L.
Gupta, Sumeet
Love, Jennifer
Murray, Heather L.
Young, Richard A.
Benevolenskaya, Elizaveta V.
1 WAR (Web application ARchive) file and 1 SQL (Structured Query Language) file.
Genome-wide analysis of the H3K4 histone demethylase RBP2 reveals a transcriptional program controlling differentiation.
2008-09
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28125
eng
http://dx.doi.org/10.1016/j.molcel.2008.08.004
Lopez-Bigas N, Kisiel TA, DeWaal DC, Holmes KB, Volkert TL, Gupta S, Love J, Murray HL, Young RA, Benevolenskaya EV. Genome-wide analysis of the H3K4 histone demethylase RBP2 reveals a transcriptional program controlling differentiation. Mol Cell. 2008; 31(4):520–30. DOI: 10.1016/j.molcel.2008.08.004
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Dades sota llicència Creative Commons Reconocimiento 3.0 España (CC BY 3.0 ES)
Universitat Pompeu Fabra
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CGProp
Furney, Simon J.
Madden, Stephen F.
Kisiel, Tomasz A.
Higgins, Desmond G.
López Bigas, Núria
# Content
cgprop.war -> Java Website application (deploy in tomcat)
cgprop.sql -> MySQL database
# Example Apache Proxy Pass configuration
ProxyPass /cgprop http://bg.upf.edu:8080/cgprop
ProxyPassReverse /cgprop http://bg.upf.edu:8080/cgprop
Cancer gene properties.
2007-12
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28126
eng
http://hdl.handle.net/10230/16420
Furney SJ, Higgins DG, Ouzounis CA, López-Bigas N. Structural and functional properties of genes involved in human cancer. BMC Genomics. 2006; 7: 3. DOI: 10.1186/1471-2164-7-3 http://hdl.handle.net/10230/16420
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oai:repositori.upf.edu:10230/281272017-02-21T10:23:29Zcom_10230_5963col_10230_24644
funcSTAR
Shikhagaie, Medya
López Bigas, Núria
Software: Docker 1.12+
Web tool for the selection of SNPs from the STAR project with potential functional effect.
2007-10
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28127
eng
http://dx.doi.org/10.1038/ng.124
http://bg.upf.edu/funcSTAR/
Més informació: funcSTAR (web)
STAR Consortium. SNP and haplotype mapping for genetic analysis in the rat. Nat Genet. 2008;40(5):560-6. DOI: 10.1038/ng.124
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
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Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/281282017-02-21T11:17:48Zcom_10230_5963col_10230_24644
Evolvavility
López Bigas, Núria
De, Subhajyoti
Teichmann, Sarah A.
1 SQL (Structured Query Language) file and 1 WAR (Web application ARchive) file.
Mutations, genes and pathways involved in tumorigenesis across 4,623 cancer genomes/exomes from 13 cancer sites. IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods 10, 2013, doi:10.1038/nmeth.2642
2007-10
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28128
eng
http://dx.doi.org/10.1186/gb-2008-9-2-r33
Lopez-Bigas N, De S, Teichmann SA. Functional protein divergence in the evolution of Homo sapiens. Genome Biol. 2008; 9:R33. DOI: 10.1186/gb-2008-9-2-r33
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info:eu-repo/semantics/openAccess
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oai:repositori.upf.edu:10230/281292017-02-21T11:56:13Zcom_10230_5963col_10230_24644
iDGP
Furney, Simon J.
Albà Soler, Mar
López Bigas, Núria
# Content
dom_rec_prediction.sql -> MySQL database
web/* -> PHP website
Database of human genes prioritized for their probability of involvement in dominant or recessive hereditary diseases.
2006-07
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28129
eng
http://hdl.handle.net/10230/16419
Furney SJ, Albà MM, López-Bigas N. Differences in the evolutionary history of disease genes affected by dominant or recessive mutations. BMC Genomics. 2006; 7: 165. DOI: 10.1186/1471-2164-7-165 http://hdl.handle.net/10230/16419
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oai:repositori.upf.edu:10230/281312017-03-18T02:33:04Zcom_10230_5963col_10230_24644
OncodriveMUT
Tamborero Noguera, David
Rubio Pérez, Carlota
Déu Pons, Jordi
Schroeder, Michael Philipp, 1986-
Vivancos Prellezo, Ana
Rovira Guerín, Ana
Tusquets, Ignasi
Albanell Mestres, Joan
Rodon, Jordi
Tabernero Cartula, Josep
Dienstman, Rodrigo
González-Pérez, Abel
López Bigas, Núria
INSTALL
# Create a new Anaconda python environment with all the required dependencies
$ conda create -c bioconda -c bbglab -n oncodrivemut python=3.5 numpy=1.11 pandas=0.17 colorama=0.3 ago requests=2.11 pytabix=0.1 itab=0.9 bgconfig=0.3
$ source activate oncodrivemut
$ pip install oncodrivemut-1.0.0.tar.gz --no-deps
# The first time that you run OncodriveMUT it will download all the required datasets using bgdata (https://bitbucket.org/bgframework/bgdata)
# by default bgdata downloads all the files at ~/.bgdata check the documentation if you can to change this behaviour
$ oncodrivemut -h
usage: oncodrivemut [-h] -i INPUT_FILE [-o OUTPUT_FOLDER] [-t TUMOR_TYPE]
[-c CONFIG_FILE] [-s SAMPLE] [--force] [--debug]
[--extended]
optional arguments:
-h, --help show this help message and exit
-i INPUT_FILE, --input INPUT_FILE
Variants file
-o OUTPUT_FOLDER, --output OUTPUT_FOLDER
Output folder. Default to regions file name without
extensions.
-t TUMOR_TYPE, --tumor TUMOR_TYPE
Specify the tumor type of the sample(s) under analysis
-c CONFIG_FILE, --config CONFIG_FILE
Configuration file. Default to 'oncodrivemut.conf' in
the current folder if exists or to
~/.bbglab/oncodrivemut.conf if not.
-s SAMPLE, --sample SAMPLE
Default identifier of the sample
--force Run the commands and overwrite results although output
files already exist
--debug Show more progress details
--extended Computational expensive metrics are also calculated
for non coding mutations
Bioinformatics method to identify individual driver mutations.
2016-01
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28131
eng
http://bitbucket.org/intogen/intogen-pipeline/raw/a2569e57124108eabd6695009e8a6a682154e49e/LICENSE
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/281332017-03-18T02:33:06Zcom_10230_5963col_10230_24644
TCGI prescription
Tamborero Noguera, David
Rubio Pérez, Carlota
Déu Pons, Jordi
Schroeder, Michael Philipp, 1986-
Vivancos Prellezo, Ana
Rovira Guerín, Ana
Tusquets, Ignasi
Albanell Mestres, Joan
Rodon, Jordi
Tabernero Cartula, Josep
Dienstman, Rodrigo
González-Pérez, Abel
López Bigas, Núria
Software: Anaconda Python 3.5
CGI drug prescription assigns targeted drugs to a tumor, based on its genomic alterations, according different levels of evidence (from pre-clinical assays to clinical guidelines).
2016-10
info:eu-repo/semantics/other
Software
http://hdl.handle.net/10230/28133
eng
http://bitbucket.org/intogen/intogen-pipeline/raw/a2569e57124108eabd6695009e8a6a682154e49e/LICENSE
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/281382017-03-18T02:33:03Zcom_10230_5963col_10230_24644
Intogen - Catalog of driver mutations
Tamborero Noguera, David
Rubio Pérez, Carlota
Déu Pons, Jordi
Schroeder, Michael Philipp, 1986-
Vivancos Prellezo, Ana
Rovira Guerín, Ana
Tusquets, Ignasi
Albanell Mestres, Joan
Tabernero Cartula, Josep
Dienstman, Rodrigo
González-Pérez, Abel
López Bigas, Núria
Collection of 28 tab-separated values (TSV) files.
This database contains the results of the driver analysis performed by the Cancer Genome Interpreter across 6,792 exomes of a pan-cancer cohort of 28 tumor types. Validated oncogenic mutations are identified according to the state-of-the-art clinical and experimental data, whereas the effect of the mutations of unknown significance is predicted by the OncodriveMUT method.
2016-10
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/28138
eng
https://www.intogen.org/downloads
Més informació: IntOGen (web).
http://www.intogen.org/terms
info:eu-repo/semantics/openAccess
Universitat Pomper Fabra License Agreement. Consulteu les condicions d'ús específiques dins del document.
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/282012021-12-22T07:54:40Zcom_10230_5963col_10230_34003col_10230_24644
Performance of low-cost monitors to assess household air pollution [dataset]
Curto Tirado, Ariadna, 1987-
Donaire González, David
Barrera Gómez, Jose
Marshall, Julian D.
Nieuwenhuijsen, Mark J.
Wellenius, Gregory A.
Tonne, Cathryn
Sampling was conducted in a non-smoking private single-family house in the municipality of Terrassa (Spain) during February-March 2016.
There are 16 files of .txt format and 7 files of .csv format. The lightest file is 3KB and the heaviest 371KB. Counting all 23 files , there are 2,01MB.
Files from the HAPEX device are those which start with “HAPEX”. The following numbers correspond respectively to sensor ID_Day_Month_Year_Hour_Minute_Second of the sampling start.
Files from the TZOA-R device are those which start with “TZOA”. The following 4 digits correspond to the sensor ID.
Files from the DustTrak device are those which end with “dusttrak”. From 22 Feb to 25 Feb the number of the files’ name corresponds to DayMonthYear (DDMMYY) and HourMinute (HHMM) of the sampling start. In contrast, from 29 Feb, the number of the files’ name corresponds to DayMonthYear (DDMMYYYY) of the sampling start.
Files from the EL-USB-CO devices are those which end with “co”. From 22 Feb to 25 Feb the number of the files’ name corresponds to DayMonthYear (DDMMYYYY) of the sampling start plus DayMonthYear (DDMMYYYY) of the sampling end. The numbers before “co” correspond to the sensor ID (e.g. “1co”). In contrast, from 29 Feb, the number of the files’ name corresponds to DayMonthYear (DDMMYYYY) of the sampling start only.
File from the Q-Trak device is the one that ends with “qtrak”. The number of the files’ name corresponds to DayMonthYear (DDMMYYYY) of the sampling start.
File from BGI concentrations is the one that start with “BGI”. This is a database where the variable corresponding to the PM2.5 concentration is called “PMconcentration” (reported in µg/m3).
Raw data of PM2.5 and CO from an indoor wood-combustion experiment.
We evaluated the performance of two low-cost sensors measuring fine particulate matter (PM2.5) (HAPEX Nano, Climate Solutions Consulting, and TZOA-R Model RD02, MyTZOA) and one measuring carbon monoxide (CO) (EL-USB-CO, Lascar Electronics Ltd.) in a real-world wood-combustion experiment. PM2.5 devices were compared against a DustTrak (Model 8534, TSI Inc.) and a BGI pump (BGI4004, BGI Inc.) and the EL-USB-CO data-logger was compared against a Q-Trak (Model 7575, TSI Inc.). Sampling was conducted in a single-family house in Terrassa (Spain) during five non-consecutive days. All devices were co-located 1 meter away from an indoor fireplace and 0.6 meters above the ground. Fire was set once per day with hardwood logs and kept burning for 12 hours including a minimum of 2 hours with an opened window. The data provided is the raw output from all the devices tested for the 5 sampling days aiming interested researchers to play with the data and reproduce our findings.
2016-02-22
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/28201
eng
http://hdl.handle.net/10230/33850
info:eu-repo/grantAgreement/EC/FP7/336167
Publicació relacionada: Curto A, Donaire-Gonzalez D, Barrera-Gómez J, Marshall JD, Nieuwenhuijsen MJ, Wellenius GA, Tonne C. Performance of low-cost monitors to assess household air pollution. Environ Res. 2018;163: 53-63. DOI: 10.1016/j.envres.2018.01.024 http://hdl.handle.net/10230/33850
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Dades sota llicència Creative Commons Reconocimiento 3.0 España (CC BY 3.0 ES)
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oai:repositori.upf.edu:10230/337432018-02-19T10:59:08Zcom_10230_5963col_10230_24644
Quantitative predictions of protein interactions with long noncoding RNAs
Cirillo, Davide
Long noncoding RNAs (lncRNAs, which comprise 68% of the human transcriptome with average length of 1,000 nt) interact with various RNA-binding proteins (RBPs) to mediate cellular functions. Here we introduce Global Score as a tool to predict protein interactions with lncRNAs (http://service.tartaglialab.com/new_submission/globalscore). We used enhanced CLIP (eCLIP) to test the binding of the lncRNA Xist to the RBPs HnrnpK (Global Score of 0.99), Ptbp1 (0.99), Lbr (0.79), HnrnpU (Saf-A) (0.66), Spen (Sharp) (0.59) and negative control Dkc1 (0.01). Global Score prediction correlates with the eCLIP binding profile (Pearson correlation = 0.93). As for the binding sites, Spen and HnrnpK, Ptbp1, and Lbr interact respectively with Xist A, B, and E repeats and adjacent regions, while HnrnpU binds across the whole transcript, and Dkc1 does not interact with Xist.
2016
info:eu-repo/semantics/other
Dataset
http://hdl.handle.net/10230/33743
eng
http://hdl.handle.net/10803/403537
Publicació relacionada: Cirillo, Davide. Prediction of protein and nucleic acid interactions. 2016. http://hdl.handle.net/10803/403537
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CC0 1.0 Universal
Universitat Pompeu Fabra
oai:repositori.upf.edu:10230/345072018-04-28T01:30:12Zcom_10230_5963col_10230_24644
The genetics of East African populations: a Nilo-Saharan component in the African genetic landscape [supplementary information]
Dobon, Begoña
Hassan, Hisham Y.
Laayouni, Hafid, 1968-
Luisi, Pierre, 1985-
Ricaño Ponce, Isis
Zhernakova, Alexandra
Wijmenga, Cisca
Tahir, Hanan
Comas, David, 1969-
Netea, Mihai G
Bertranpetit, Jaume, 1952-
Includes Supplementary Figures S1-S13, Supplementary Tables S1-S8 and Supplementary Methods. Suppl. Fig. S1: Principal component analysis of the new populations genotyped from the Sudanese region; Suppl. Fig. S2: Principal component analysis of six world–wide populations from 1000 Genomes Project using different number of SNPs; Suppl. Fig. S3: Principal component analysis of the new populations genotyped from the Sudanese region; Suppl. Fig. S4: Principal component analysis of the populations from the Sudanese region in the context of the African continent with 14 samples identified as outliers with respect to their populations of origin; Suppl. Fig. S5: Pairwise FST values between the 14 populations; Suppl. Fig. S6: Cross-validation error estimates of the new nine genotyped populations for the ADMIXTURE analysis; Suppl. Fig. S7: ADMIXTURE results for k = 2 through k = 10 for the Sudanese populations; Suppl. Fig. S8: Cross-validation error estimates of the 14 populations for the ADMIXTURE analysis; Suppl. Fig. S9: ADMIXTURE results for k = 2 through k = 10 for the 14 populations using all 921 individuals; Suppl. Fig. S10: ADMIXTURE results for k = 2 through k = 10 for populations from the Sudanese region in the context of other external populations; Suppl. Fig. S11: Principal component analysis of the populations from the Sudanese region in the context of the African continent with an European population added; Suppl. Fig. S12: ADMIXTURE results for k = 2 through k = 10 for populations from the Sudanese region in the context of other external populations; Suppl. Fig. S13: Sampling distribution of the sample mean Global FST between Sudanese populations. Suppl. Table S1: Detailed sample information of the populations analysed in the present study, including sampling location and total number of individuals; Suppl. Table S2: Pairwise FST comparisons among the Sudanese ethnolinguistic groups and neighbouring populations; Suppl. Table S3: Three–population test with Yoruba as outgroup to estimate mixing proportions; Suppl. Table S4: Three–population test with Luya as outgroup to estimate mixing proportions; Suppl. Table S5: List of genes related to resistance to malaria present in the Immunochip; Suppl. Table S6: List of genes belonging to pathways related to antibacterial host defence present in the Immunochip; Suppl. Table S7: List of genes belonging to fungi host defence present in the Immunochip; Suppl. Table S8: Summary statistics of SNPs of disease-related genes from African populations of 1000 Genomes Project compared to the portion of those SNPs genotyped in the Immunochip. The compressed file contains the Sudan Inmunochip dataset in XLSX, BED, BIM and FAM formats.
2018-04-27
info:eu-repo/semantics/other
http://hdl.handle.net/10230/34507
eng
http://hdl.handle.net/10230/25768
Publicació relacionada: Dobon B, Hassan HY, Laayouni H, Luisi P, Ricaño-Ponce I, Zhernakova A et al. The genetics of East African populations: a Nilo-Saharan component in the African genetic landscape. Scientific Reports. 2015; 5: 9996. DOI 10.1038/srep09996 http://hdl.handle.net/10230/25768
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info:eu-repo/semantics/openAccess
This work is licensed under a Creative Commons Attribution 4.0 International License.
Universitat Pompeu Fabra