The CCBC has access to high-performance servers and has experience in processing and analyzing high-throughput omics data, see above for current services.
For CCBC assistance or computational resources, please submit your project proposal or e-mail us directly.
We store, query and integrate various public cancer data sources to unravel the mechanisms of cancer development. Moreover, we store vast amounts of locally generated Next Generation Sequencing (NGS) and Proteomics data, which are, subsequently, integrated with various databases.
We are building a pipeline to detect mono-allelic transcription factor binding sites using ChIP-Seq and mono-allelic gene-expression using RNA-Seq. In this project we collaboration with the Erasmus MC - Developmental Biology.
An advanced in-house database for mouse xenografts has been developed. An in-house stand-alone mouse xenograft database was migrated to a MySQL environment with an advanced web-accessable interface, complete with user authentication and CRUD tables.
An advanced database and web-interface for the storage and retrieval of meta-data on (large-scale) -omics projects within the Erasmus MC. This database has been developed in collaboration with the Department of Genetics and is hosted by Erasmus IT.
Click here to go to the EMC-omicsDB.
Advances in NGS technologies has made it possible to quantitatively measure various levels of cellular content. This makes it possible to combine DNA and RNA data with proteomics data to find novel gene & protein variants. The CCBC is involved in an EMC collaboration for the development of pipelines to facilitate this usage of data.
An open-source application (with web interface) which builds sophisticated and uniform Bash scripts and pipelines out of YAML-structured files. Termed Python YAML Pipeline Evaluator (PYPE). This allows for easier distribution of pipelines within the Erasmus MC and provides. This application can easily installed via pip.
Detection of allelic imbalances in a multitude of cancers using RNA sequencing data. We have developed our custom algorithms and R package to perform this efficiently for large sample-sets. This package will soon be publicly available in the BioConductor suite.
Alternative transcription initiation can lead to novel transcripts in cancer by altering the transcriptome. We are involved in detecting these genetic aberrations using DNA and RNA-sequencing data.
We have developed an open-source R/Shiny package to display loss of heterozygosity (LOH) in tumor samples using VCF files as input. Used by the Department of Pathology to view regions of LOH by Targeted Sequencing using Ion Torrent Next-Generation Sequencing. We hope to publish this tool shortly.