Tissue Bank

BCNTB Bioinformatics


Our Philosophy
Our aim is to create an in silico bioinformatics niche from which you can access a network of bioinformatics resources. These will provide you with the tools you need to query, analyse and integrate findings from publicly available, in-house, and experimental data generated using samples supplied from the BCNTB.

BCNTB:Miner is a data-mining tool that stores genomic, methylomic, transcriptomic, proteomic and microRNA data (full study list available here) that has been curated from the literature and links this to pathways and mechanisms involved in breast cancer.

BCNTB:Analytics is an integrated analytical layer from which you can conduct exploratory and in-depth analyses of transcriptomic, sequencing, genomic and mutation data obtained from tissues and cell lines.

The full potential of the relationship between the BCNTB and its bioinformatic networks will become reality as experimental data generated by studies using BCNTB samples is returned and stored alongside its corresponding clinical information. Our in silico resource will allow you to link to the original data files and published findings, and access a host of bioinformatics tools to analyse and integrate data into your research.



It was a pleasure to meet you all and answer your questions at UK IBCS.

Gadaleta E*., Pirro S*., Dayem Ullah AZ., Marzec J., Chelala C. BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking. Nucleic Acids Res 2018;46:D1055-D1061

Cutts RJ., Guerra-Assuncao JA., Gadaleta E., Dayem Ullah AZ., Chelala C. BCCTBbp: The Breast Cancer Campaign Tissue Bank bioinformatics portal. Nucleic Acids Res 2015;43:D831-D836

BCNTB: Miner

Quick Search

Examples: ERBB2, BRCA1


BCNTB:Analytics holds 8,230 samples obtained from The Cancer Genome Atlas, the Gene Expression Omnibus and the Cancer Cell Line Encyclopedia.

You have access to comprehensive analytical modalities from which you can conduct principal component analyses; estimate tumour purity; call molecular subtypes/receptor status; view the expression of gene(s) of interest across different biological conditions; conduct correlation analyses; visualise copy number aberrations; conduct gene network analyses and investigate mutational profiles.

Multidimensional data is integrated so that you can view data generated by different technologies from a single plot. This is crucial in understanding alterations in isolation and the relationships between them.

We welcome feedback and suggestions from the breast cancer community.