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BCCTBbp: the Breast Cancer Campaign Tissue Bank bioinformatics portal.

Cutts RJ, Guerra-Assunção JA, Gadaleta E, Dayem Ullah AZ, Chelala C - Nucleic Acids Res. (2014)

Bottom Line: By recording a large number of annotations on samples and studies, and linking to other databases, such as NCBI, Ensembl and Reactome, a wide variety of different investigations can be carried out.Additionally, BCCTBbp has a dedicated analytical layer allowing researchers to further analyse stored datasets.A future important role for BCCTBbp is to make available all data generated on BCCTB tissues thus building a valuable resource of information on the tissues in BCCTB that will save repetition of experiments and expand scientific knowledge.

View Article: PubMed Central - PubMed

Affiliation: Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.

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Related in: MedlinePlus

Analytical layer and integrated modules. The analytical tools can be accessed from the menu bar at http://bioinformatics.breastcancertissuebank.org/analysisTools.html. First one could choose the dataset of interest by pressing the radio-buttons to the left of the dataset title. For this example we are selecting the dataset from (A). Ivshina et al. as it has the largest sample size and contains survival information (http://bioinformatics.breastcancertissuebank.org/analysisTools.html?dset=Ivshina). (B) Selecting the ‘Transcriptomics Analysis’ option, and giving a gene name, MELK in this example, will result in the analysis of MELK gene (Entrez Gene ID: 9833) expression per molecular subtype (C) Returning to the analysis screen and selecting ‘Survival analysis by gene of interest’ for the MELK gene will produce the survival figure for two sub-groups automatically selected by the median value of MELK gene expression. Risk group assignment is presented with RG1 (black) for low expression and RG2 (red) for high expression of MELK.
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Figure 3: Analytical layer and integrated modules. The analytical tools can be accessed from the menu bar at http://bioinformatics.breastcancertissuebank.org/analysisTools.html. First one could choose the dataset of interest by pressing the radio-buttons to the left of the dataset title. For this example we are selecting the dataset from (A). Ivshina et al. as it has the largest sample size and contains survival information (http://bioinformatics.breastcancertissuebank.org/analysisTools.html?dset=Ivshina). (B) Selecting the ‘Transcriptomics Analysis’ option, and giving a gene name, MELK in this example, will result in the analysis of MELK gene (Entrez Gene ID: 9833) expression per molecular subtype (C) Returning to the analysis screen and selecting ‘Survival analysis by gene of interest’ for the MELK gene will produce the survival figure for two sub-groups automatically selected by the median value of MELK gene expression. Risk group assignment is presented with RG1 (black) for low expression and RG2 (red) for high expression of MELK.

Mentions: Four analysis categories can be performed: Molecular Classification; Tumour Purity; Gene Expression; and Survival. Each of the included dataset samples can be classified based on the PAM50 set of markers (15) or the hormonal receptor status can be inferred from the transcriptome using the MCLUST R package (http://cran.r-project.org/web/packages/mclust/). Cancer samples frequently contain a small proportion of normal adjacent tissue that might confuse sample analysis. A method to infer sample cancer purity is implemented using ESTIMATE (16). Gene expression plots can be obtained for a gene of interest (Figure 3). Finally, for datasets that contain patient survival information, survival analysis can be performed using the ‘survival’ package in R (http://cran.r-project.org/web/packages/survival/). Two modes are available, survival based on sample sub-groups within the dataset or survival based on the expression value of a gene of interest. In the gene-based survival analysis, two classes of gene expression values (high and low) are determined based on the median expression value (Figure 3).


BCCTBbp: the Breast Cancer Campaign Tissue Bank bioinformatics portal.

Cutts RJ, Guerra-Assunção JA, Gadaleta E, Dayem Ullah AZ, Chelala C - Nucleic Acids Res. (2014)

Analytical layer and integrated modules. The analytical tools can be accessed from the menu bar at http://bioinformatics.breastcancertissuebank.org/analysisTools.html. First one could choose the dataset of interest by pressing the radio-buttons to the left of the dataset title. For this example we are selecting the dataset from (A). Ivshina et al. as it has the largest sample size and contains survival information (http://bioinformatics.breastcancertissuebank.org/analysisTools.html?dset=Ivshina). (B) Selecting the ‘Transcriptomics Analysis’ option, and giving a gene name, MELK in this example, will result in the analysis of MELK gene (Entrez Gene ID: 9833) expression per molecular subtype (C) Returning to the analysis screen and selecting ‘Survival analysis by gene of interest’ for the MELK gene will produce the survival figure for two sub-groups automatically selected by the median value of MELK gene expression. Risk group assignment is presented with RG1 (black) for low expression and RG2 (red) for high expression of MELK.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4384036&req=5

Figure 3: Analytical layer and integrated modules. The analytical tools can be accessed from the menu bar at http://bioinformatics.breastcancertissuebank.org/analysisTools.html. First one could choose the dataset of interest by pressing the radio-buttons to the left of the dataset title. For this example we are selecting the dataset from (A). Ivshina et al. as it has the largest sample size and contains survival information (http://bioinformatics.breastcancertissuebank.org/analysisTools.html?dset=Ivshina). (B) Selecting the ‘Transcriptomics Analysis’ option, and giving a gene name, MELK in this example, will result in the analysis of MELK gene (Entrez Gene ID: 9833) expression per molecular subtype (C) Returning to the analysis screen and selecting ‘Survival analysis by gene of interest’ for the MELK gene will produce the survival figure for two sub-groups automatically selected by the median value of MELK gene expression. Risk group assignment is presented with RG1 (black) for low expression and RG2 (red) for high expression of MELK.
Mentions: Four analysis categories can be performed: Molecular Classification; Tumour Purity; Gene Expression; and Survival. Each of the included dataset samples can be classified based on the PAM50 set of markers (15) or the hormonal receptor status can be inferred from the transcriptome using the MCLUST R package (http://cran.r-project.org/web/packages/mclust/). Cancer samples frequently contain a small proportion of normal adjacent tissue that might confuse sample analysis. A method to infer sample cancer purity is implemented using ESTIMATE (16). Gene expression plots can be obtained for a gene of interest (Figure 3). Finally, for datasets that contain patient survival information, survival analysis can be performed using the ‘survival’ package in R (http://cran.r-project.org/web/packages/survival/). Two modes are available, survival based on sample sub-groups within the dataset or survival based on the expression value of a gene of interest. In the gene-based survival analysis, two classes of gene expression values (high and low) are determined based on the median expression value (Figure 3).

Bottom Line: By recording a large number of annotations on samples and studies, and linking to other databases, such as NCBI, Ensembl and Reactome, a wide variety of different investigations can be carried out.Additionally, BCCTBbp has a dedicated analytical layer allowing researchers to further analyse stored datasets.A future important role for BCCTBbp is to make available all data generated on BCCTB tissues thus building a valuable resource of information on the tissues in BCCTB that will save repetition of experiments and expand scientific knowledge.

View Article: PubMed Central - PubMed

Affiliation: Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.

Show MeSH
Related in: MedlinePlus