Resultados totales (Incluyendo duplicados): 77
Encontrada(s) 8 página(s)
Encontrada(s) 8 página(s)
CORA.Repositori de Dades de Recerca
doi:10.34810/data407
Dataset. 2023
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
Analyses somatic mutations in thousands of tumor genomes to identify cancer driver genes.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data408
Dataset. 2016
GITOOLS
- Pérez Llamas, Christian, 1976-
- López Bigas, Núria
- Schroeder, Michael Philipp, 1986-
- Déu Pons, Jordi
Gitools is a framework for analysis and visualization of multidimensional genomic data using interactive heat-maps.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data409
Dataset. 2023
ONCODRIVEFML
- Mularoni, Loris
- Sabarinathan, Radhakrishnan
- González-Pérez, Abel
- López Bigas, Núria
- Déu Pons, Jordi
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.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data412
Dataset. 2023
ONCODRIVECLUST
- Tamborero Noguera, David
- González-Pérez, Abel
- López Bigas, Núria
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.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data413
Dataset. 2023
ONCODRIVEFM
- González-Pérez, Abel
- López Bigas, Núria
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 dif?culty to estimate background mutation rate, and the fact that they usually fail to identify lowly recurrently mutated driver genes.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data416
Dataset. 2023
ONCODRIVEROLE
- Schroeder, Michael Philipp, 1986-
- Rubio Pérez, Carlota
- Tamborero Noguera, David
- González-Pérez, Abel
- López Bigas, Núria
Machine-learning based approach to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data418
Dataset. 2023
C10-HDAC7
- Barneda Zahonero, Bruna
- Román González, Lidia
- Collazo, Olga
- Rafati, Haleh
- Islam, Abul
- Bussmann, Lars
- Di Tullio, Alessandro
- Andrés, Luisa De
- Graf, T. (Thomas)
- López Bigas, Núria
- Mahmoudi, Tokameh
- Parra, Maribel
HDAC7 is a repressor of myeloid genes whose downregulation in pre-B cells is required for transdifferentiation into macrophages.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data419
Dataset. 2023
ONCODRIVE-CIS
- Tamborero Noguera, David
- López Bigas, Núria
- González-Pérez, Abel
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.
Proyecto: //
CORA.Repositori de Dades de Recerca
doi:10.34810/data420
Dataset. 2012
SVGMAP
- Rafael Palou, Xavier
- Schroeder, Michael Philipp, 1986-
- López Bigas, Núria
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.
Proyecto: //
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