Cancer Bioinformatics

Why we focus on Cancer Bioinformatics

Bioinformatics is a new interdisciplinary area involving biological, statistical and computational sciences. Bioinformatics will enable cancer researchers not only to manage, analyse, mine and understand the currently accumulated, valuable, high-throughput data, but also to integrate these in their current research programmes. The need for bioinformatics will become ever more important as new technologies increase the already exponential rate at which cancer data are generated.

What we do

  • We work alongside clinical and basic scientists to support the cancer projects within BCI.  This is an ideal partnership between scientific experts, who know the research questions that will be relevant from a cancer biologist or clinician’s perspective, and bioinformatics experts, who know how to develop the proposed methods to provide answers.
  • We also conduct independent bioinformatics research, focussing on the development of computational and integrative methods, algorithms, databases and tools to tackle the analysis of the high volumes of cancer data. 
  • We also are actively involved in the development of bioinformatics educational courses at BCI. Our courses offer a unique opportunity for biologists to gain a basic understanding in the use of bioinformatics methods to access and harness large complicated high-throughput data and uncover meaningful information that could be used to understand molecular mechanisms and develop novel targeted therapeutics/diagnostic tools.

For more details, please visit the Cancer Bioinformatics Unit page.

Key Publications

  • Tawana K et al. Disease evolution and outcomes in familial AML with germline CEBPA mutations. (2015) Blood 126(10):1214-23. 
  • Speirs V et al. Animal research: share surplus animal tissue. (2015) Nature 522(7555):156
  • Smedley D et al.  The BioMart community portal: an innovative alternative to large, centralized data repositories. (2015) Nucleic Acids Research 1;43(W1):W589-98.
  • Haider S et al. A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma. (2014) Genome Medicine, 3;6(12).
  • Kadaba et al. Imbalance of desmoplastic stromal cell numbers drives aggressive cancer processes. (2013) The Journal of pathology ;230(1):107-17.
  • Ullah et al. SNPnexus: a web server for functional annotation of novel and publicly known genetic variants (2012 update). Nucleic Acids Res 40 (Web Server issue):W65-70.
  • Cutts et al. O-miner: an integrative platform for automated analysis and mining of -omics data. (2012) Nucleic Acids Res ; 40 (Web Server issue):W560-8.
  • Gadaleta et al. A global insight into a cancer transcriptional space using pancreatic data: importance, findings and flaws. (2011) Nucleic Acids Res ; 39: 7900-7.
  • Cutts et al. The Pancreatic Expression database: 2011 update. (2011) Nucleic Acids Res 39 (Database issue): D1023-8.
  • Gadaleta et al. Online resources of cancer data: barriers, benefits and lessons. (2011) Briefings in Bioinformatics 12: 52-63.
  • Froeling et al. Retinoic Acid-Induced Pancreatic Stellate Cell Quiescence Reduces Paracrine Wnt-<beta>-Catenin Signaling to Slow Tumor Progression. (2011) Gastroenterology 141(4):1486-97.

Who does the research

→ Click here for BCI researchers working on cancer bioinformatics

Major Funders

  • Breast Cancer Campaign
  • Cancer Research UK
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