projects

I am actively developing and maintaining multiple open-source software tools, typically applied within the fields of cancer precision medicine and high-throughput cancer biology. Technically, I use mostly R and Python.

I try to make all tools I develop available and documented through GitHub repositories, allowing users to file bug reports, ask questions regarding functionality etc., and potentially contribute code. Some of the main ongoing projects are briefly outlined below.

Personal Cancer Genome Reporter (PCGR)

We have developed the Personal Cancer Genome Reporter (PCGR), a stand-alone software package for translation of individual tumor genomes for precision cancer medicine. Through the integration of a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, PCGR generates a comprehensive variant report that acts as decision support for identification of novel treatment targets and strategies.

Cancer Predisposition Sequencing Reporter (CPSR)

The Cancer Predisposition Sequencing Reporter (CPSR) is a computational workflow that interprets inherited DNA variation identified from next-generation sequencing in the context of cancer predisposition and inherited cancer syndromes. Importantly, CPSR classifies the pathogenicity of DNA variants in cancer predisposition genes trough a refined set of ACMG/AMP variant classification criteria.

oncoEnrichR

Genome-scale screening experiments produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogating these gene lists is a time-consuming and challenging undertaking. oncoEnrichR is a user-friendly reporting framework that portrays the cancer relevance of candidate hits in a comprehensive manner, allowing researchers to efficiently gather evidence when picking candidates for in-depth follow-up experiments. oncoEnrichR comes as an R package, but is also accessible through a web interface in Galaxy.

pharmOncoX

pharmOncoX is an R package that provides access to targeted and non-targeted cancer drugs, including comprehensive annotations per target, drug mechanism-of-action, approval dates, clinical trial phases for various indications etc. Drugs are further classified according to the Anatomical Therapeutic Chemical (ATC) Classification System, enabling a filtering of cancer drugs according to their main types of action. Some of the functionality is outlined here.

geneOncoX

Which human genes are implicated in tumor development? geneOncoX is an R package that address this question through the integration of a number of resources with respect to cancer gene annotations, providing up-to-date information on tumor suppressive/proto-oncogenic roles of human genes, predicted cancer drivers, known cancer predisposition genes, and more.

phenOncoX

An ontological definition of disease enables each type of disease to be singularly classified in a formalized structure. Multiple ontology frameworks have been developed for human cancers/diseases, examples being OncoTree, Experimental Factor Ontology (EFO), Disease Ontology (DO), MeSH, and ICD. However, these ontologies are typically used to different extents across knowledge resources in the oncology domain, such as gene-disease, drug-disease, or variant-disease associations. phenOncoX is an R package that cross-references disease entries across multiple ontologies, with a sole focus on the oncology domain. Its purpose is thus to bridge the various disease ontologies used for human cancers, and to simplify data integration procedures in this context.

gvanno - generic variant annotation pipeline

The generic variant annotator (gvanno) is a software package intended for basic analysis and interpretation of human DNA variants. Variants and genes are annotated with disease-related and functional associations. Technically, the workflow is built with the Docker technology, and it can also be installed through the Singularity framework.

lscarisk.org

Using data from the Prospective Lynch Syndrome Database (PLSD), we have developed an interactive web application (lscarisk.org) that allows users to retrieve estimated cancer risks in carriers of mismatch repair (MMR) gene variants - by gene, age, and gender. The application has been developed through the Shiny framework.