Limits...
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.

Show MeSH

Related in: MedlinePlus

Accessing the BCCTBbp data through the MartView web query interface. In this first example our goal was to identify differentially regulated genes in Infiltrating Lobular Carcinoma (ILC), related pathways and check if any of these genes appear in the Parker PAM 50 gene set. The MartView interface can be accessed by following the ‘Advanced/BioMart Search’ button on the main bioinformatics portal web page (http://bioinformatics.breastcancertissuebank.org). The query starts by choosing the ‘BCCTB Bioinformatics Portal’ from the ‘Choose Database’ drop-down selection in the right panel. Users will be automatically directed to choose a dataset from the ‘Choose Dataset’ drop-down menu. (A) The ‘BCCTB Bioinformatics Gene Data’ dataset can be chosen from the dropdown menus. The left panel will refresh automatically displaying the ‘Filters’ and ‘Attributes’ nodes with their default settings. The next step involves choosing the appropriate attributes and filters to restrict the query. Clicking on the ‘Filters’ or ‘Attributes’ nodes on the left will display the related page on the right where ‘Filters’ or ‘Attributes’ are arranged into sections, which can be expanded/collapsed using the ‘+/−’ box. To choose an attribute or a filter, users can simply click on the checkbox next to its description. A summary of the selected filters and attributes is automatically displayed in the left panel. We will click on Filters in the left panel and then expand the ‘SELECT GENES FROM TRANSCRIPTOMICS PROFILING EXPERIMENTS:’ section. From the rich list of possible options, we will select ‘Specimen histology’ then ‘ILC versus normal lobular’ from the related list. Clicking on the ‘Count’ button in the tool bar at any time when constructing the query will return the number of genes satisfying the pre-selected criteria. (B) Clicking on the Attributes tab in the left panel allows the user to select which attributes of the data will be returned in the results. On the right panel, these are arranged into six modules for ‘Study Data’, ‘Features’, ‘Structures’, ‘Transcript Event’, ‘Homologs’, ‘Variation’ and ‘Sequences’. In order to select the output content, the ‘Attributes’ node on the left needs to be clicked on and the attribute page on the right needs to be chosen. In this example we are interested in exploring the de-regulated pathways. From the ‘Study Data’ attribute page, a selection is made of Pathway URL and Pathway name options. Finally, pressing the results button on the top left of the page will produce a table containing the requested information (C). One can select the option to download the results to a file at the top of the result page and export them using the ‘GO’ button. Again, there are options to change the format (‘HTML’, ‘CSV’ for comma separated values, ‘TSV’ for tab separated values, ‘XLS’ for Excel, ‘ADF’ for array description format) and whether to make the results unique. One can select a compressed file output and the query will run in the background to be downloaded later. One needs to provide an email address to receive an URL in a notification email that allows the query results to be downloaded. There is an option to produce a Perl script to repeat the query programmatically with BioMart Perl API. Due to the nature of the database and the one-to-many relationship between genes and transcripts, some results may appear more than once. To address that there is a button that can be used to present only unique results and the transcript attribute could be removed from the final results.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4384036&req=5

Figure 1: Accessing the BCCTBbp data through the MartView web query interface. In this first example our goal was to identify differentially regulated genes in Infiltrating Lobular Carcinoma (ILC), related pathways and check if any of these genes appear in the Parker PAM 50 gene set. The MartView interface can be accessed by following the ‘Advanced/BioMart Search’ button on the main bioinformatics portal web page (http://bioinformatics.breastcancertissuebank.org). The query starts by choosing the ‘BCCTB Bioinformatics Portal’ from the ‘Choose Database’ drop-down selection in the right panel. Users will be automatically directed to choose a dataset from the ‘Choose Dataset’ drop-down menu. (A) The ‘BCCTB Bioinformatics Gene Data’ dataset can be chosen from the dropdown menus. The left panel will refresh automatically displaying the ‘Filters’ and ‘Attributes’ nodes with their default settings. The next step involves choosing the appropriate attributes and filters to restrict the query. Clicking on the ‘Filters’ or ‘Attributes’ nodes on the left will display the related page on the right where ‘Filters’ or ‘Attributes’ are arranged into sections, which can be expanded/collapsed using the ‘+/−’ box. To choose an attribute or a filter, users can simply click on the checkbox next to its description. A summary of the selected filters and attributes is automatically displayed in the left panel. We will click on Filters in the left panel and then expand the ‘SELECT GENES FROM TRANSCRIPTOMICS PROFILING EXPERIMENTS:’ section. From the rich list of possible options, we will select ‘Specimen histology’ then ‘ILC versus normal lobular’ from the related list. Clicking on the ‘Count’ button in the tool bar at any time when constructing the query will return the number of genes satisfying the pre-selected criteria. (B) Clicking on the Attributes tab in the left panel allows the user to select which attributes of the data will be returned in the results. On the right panel, these are arranged into six modules for ‘Study Data’, ‘Features’, ‘Structures’, ‘Transcript Event’, ‘Homologs’, ‘Variation’ and ‘Sequences’. In order to select the output content, the ‘Attributes’ node on the left needs to be clicked on and the attribute page on the right needs to be chosen. In this example we are interested in exploring the de-regulated pathways. From the ‘Study Data’ attribute page, a selection is made of Pathway URL and Pathway name options. Finally, pressing the results button on the top left of the page will produce a table containing the requested information (C). One can select the option to download the results to a file at the top of the result page and export them using the ‘GO’ button. Again, there are options to change the format (‘HTML’, ‘CSV’ for comma separated values, ‘TSV’ for tab separated values, ‘XLS’ for Excel, ‘ADF’ for array description format) and whether to make the results unique. One can select a compressed file output and the query will run in the background to be downloaded later. One needs to provide an email address to receive an URL in a notification email that allows the query results to be downloaded. There is an option to produce a Perl script to repeat the query programmatically with BioMart Perl API. Due to the nature of the database and the one-to-many relationship between genes and transcripts, some results may appear more than once. To address that there is a button that can be used to present only unique results and the transcript attribute could be removed from the final results.

Mentions: A typical query is shown in Figure 1 where we can investigate differences in specimen histological types using the transcriptomics profiling module. Here we investigate the differences between Invasive Lobular Carcinoma (ILC) and normal lobular cells and obtain a list of 175 transcripts of significance and related pathways (Figure 1). The portal allows multiple further filtering to enhance this initial query if required. For example, we can intersect this output to see if any of these genes appear in the Parker PAM 50 gene set (17) and extract related upstream sequences (Supplementary Figure S1).


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)

Accessing the BCCTBbp data through the MartView web query interface. In this first example our goal was to identify differentially regulated genes in Infiltrating Lobular Carcinoma (ILC), related pathways and check if any of these genes appear in the Parker PAM 50 gene set. The MartView interface can be accessed by following the ‘Advanced/BioMart Search’ button on the main bioinformatics portal web page (http://bioinformatics.breastcancertissuebank.org). The query starts by choosing the ‘BCCTB Bioinformatics Portal’ from the ‘Choose Database’ drop-down selection in the right panel. Users will be automatically directed to choose a dataset from the ‘Choose Dataset’ drop-down menu. (A) The ‘BCCTB Bioinformatics Gene Data’ dataset can be chosen from the dropdown menus. The left panel will refresh automatically displaying the ‘Filters’ and ‘Attributes’ nodes with their default settings. The next step involves choosing the appropriate attributes and filters to restrict the query. Clicking on the ‘Filters’ or ‘Attributes’ nodes on the left will display the related page on the right where ‘Filters’ or ‘Attributes’ are arranged into sections, which can be expanded/collapsed using the ‘+/−’ box. To choose an attribute or a filter, users can simply click on the checkbox next to its description. A summary of the selected filters and attributes is automatically displayed in the left panel. We will click on Filters in the left panel and then expand the ‘SELECT GENES FROM TRANSCRIPTOMICS PROFILING EXPERIMENTS:’ section. From the rich list of possible options, we will select ‘Specimen histology’ then ‘ILC versus normal lobular’ from the related list. Clicking on the ‘Count’ button in the tool bar at any time when constructing the query will return the number of genes satisfying the pre-selected criteria. (B) Clicking on the Attributes tab in the left panel allows the user to select which attributes of the data will be returned in the results. On the right panel, these are arranged into six modules for ‘Study Data’, ‘Features’, ‘Structures’, ‘Transcript Event’, ‘Homologs’, ‘Variation’ and ‘Sequences’. In order to select the output content, the ‘Attributes’ node on the left needs to be clicked on and the attribute page on the right needs to be chosen. In this example we are interested in exploring the de-regulated pathways. From the ‘Study Data’ attribute page, a selection is made of Pathway URL and Pathway name options. Finally, pressing the results button on the top left of the page will produce a table containing the requested information (C). One can select the option to download the results to a file at the top of the result page and export them using the ‘GO’ button. Again, there are options to change the format (‘HTML’, ‘CSV’ for comma separated values, ‘TSV’ for tab separated values, ‘XLS’ for Excel, ‘ADF’ for array description format) and whether to make the results unique. One can select a compressed file output and the query will run in the background to be downloaded later. One needs to provide an email address to receive an URL in a notification email that allows the query results to be downloaded. There is an option to produce a Perl script to repeat the query programmatically with BioMart Perl API. Due to the nature of the database and the one-to-many relationship between genes and transcripts, some results may appear more than once. To address that there is a button that can be used to present only unique results and the transcript attribute could be removed from the final results.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 1: Accessing the BCCTBbp data through the MartView web query interface. In this first example our goal was to identify differentially regulated genes in Infiltrating Lobular Carcinoma (ILC), related pathways and check if any of these genes appear in the Parker PAM 50 gene set. The MartView interface can be accessed by following the ‘Advanced/BioMart Search’ button on the main bioinformatics portal web page (http://bioinformatics.breastcancertissuebank.org). The query starts by choosing the ‘BCCTB Bioinformatics Portal’ from the ‘Choose Database’ drop-down selection in the right panel. Users will be automatically directed to choose a dataset from the ‘Choose Dataset’ drop-down menu. (A) The ‘BCCTB Bioinformatics Gene Data’ dataset can be chosen from the dropdown menus. The left panel will refresh automatically displaying the ‘Filters’ and ‘Attributes’ nodes with their default settings. The next step involves choosing the appropriate attributes and filters to restrict the query. Clicking on the ‘Filters’ or ‘Attributes’ nodes on the left will display the related page on the right where ‘Filters’ or ‘Attributes’ are arranged into sections, which can be expanded/collapsed using the ‘+/−’ box. To choose an attribute or a filter, users can simply click on the checkbox next to its description. A summary of the selected filters and attributes is automatically displayed in the left panel. We will click on Filters in the left panel and then expand the ‘SELECT GENES FROM TRANSCRIPTOMICS PROFILING EXPERIMENTS:’ section. From the rich list of possible options, we will select ‘Specimen histology’ then ‘ILC versus normal lobular’ from the related list. Clicking on the ‘Count’ button in the tool bar at any time when constructing the query will return the number of genes satisfying the pre-selected criteria. (B) Clicking on the Attributes tab in the left panel allows the user to select which attributes of the data will be returned in the results. On the right panel, these are arranged into six modules for ‘Study Data’, ‘Features’, ‘Structures’, ‘Transcript Event’, ‘Homologs’, ‘Variation’ and ‘Sequences’. In order to select the output content, the ‘Attributes’ node on the left needs to be clicked on and the attribute page on the right needs to be chosen. In this example we are interested in exploring the de-regulated pathways. From the ‘Study Data’ attribute page, a selection is made of Pathway URL and Pathway name options. Finally, pressing the results button on the top left of the page will produce a table containing the requested information (C). One can select the option to download the results to a file at the top of the result page and export them using the ‘GO’ button. Again, there are options to change the format (‘HTML’, ‘CSV’ for comma separated values, ‘TSV’ for tab separated values, ‘XLS’ for Excel, ‘ADF’ for array description format) and whether to make the results unique. One can select a compressed file output and the query will run in the background to be downloaded later. One needs to provide an email address to receive an URL in a notification email that allows the query results to be downloaded. There is an option to produce a Perl script to repeat the query programmatically with BioMart Perl API. Due to the nature of the database and the one-to-many relationship between genes and transcripts, some results may appear more than once. To address that there is a button that can be used to present only unique results and the transcript attribute could be removed from the final results.
Mentions: A typical query is shown in Figure 1 where we can investigate differences in specimen histological types using the transcriptomics profiling module. Here we investigate the differences between Invasive Lobular Carcinoma (ILC) and normal lobular cells and obtain a list of 175 transcripts of significance and related pathways (Figure 1). The portal allows multiple further filtering to enhance this initial query if required. For example, we can intersect this output to see if any of these genes appear in the Parker PAM 50 gene set (17) and extract related upstream sequences (Supplementary Figure S1).

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