Computational Biology Group Members
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Dr Christina Curtis
Oncology
| Telephone: | +44 1223 40 4231 |
| Fax: | +44 1223 40 4199 |
| email: | christina.curtis@cancer.org.uk |
| Office Location: | Room 134, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE |
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| Background |
Christina completed a BSc (Departmental and College Hons) in Molecular, Cellular and Developmental Biology at the University of California, Los Angeles in 2001.
She was the recipient of a Deutscher Akademischer Austausch Dienst Fellowship, which enabled her to pursue an MSc in Molecular Biology at the
University of Heidelberg. In 2003, she began a PhD in Molecular and Computational Biology
at the University of Southern California with Professor Simon Tavaré. Her doctoral work focused on the analysis of high-density oligonucleotide
gene expression data with application to understanding the molecular mechanisms of aging and cancer. Christina joined the Computational Biology Group in the Department of
Oncology in October 2007.
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| Research Overview |
Christina's current research includes the following themes:
- The integrative analysis of multi-dimensional biological information including gene expression, genotype, copy number, epigenetic, and mutational data. My research in this area has been driven by METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), which aims to identify oncogenomic signatures that improve the classification of this heterogeneous disease. To this end, we are extensively characterizing the genomic and transcriptional landscape of 2000 tumors using novel statistical and computational techniques.
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Developing inference techniques for the systems-level analysis of disease biomarkers and genotype-phenotype associations.
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The development of experimental and computational tools for massively parallel single molecule haplotyping and methylation analysis. We developed bead-emulsion bisulfite sequencing (BBS) to enable the high-throughput, digital analysis of targeted CpG regions at low cost. This approach, in conjunction with statistical inference, is being used to reconstruct cell lineages in both normal and cancerous tissue. Adaptations of this method, which exploit the bead-emulsion amplification (BEA) technology, are being used to quantify differential allelic expression and rare DNA variants at targeted loci. This suite of approaches serves as a validation method for high-throughput sequencing technologies.
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Issues concerning the low-level analysis of microarray and sequencing technologies, including probe design and image analysis. Understanding potential biases in high-throughput sequence data such as those due to multiplexing across samples via barcoding.
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Design, pre-processing, and analysis of high-throughput microarray and sequence data for gene-expression, copy number, genotyping, methylation, and structural variant analysis.
| | Teaching |
MPhil in Computational Biology, University of Cambridge
Introductory courses on microarray analysis in Cambridge:
(CRI, Department of Genetics, EBI)
and overseas: (Lisbon)
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