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cancer

Graphic showing the to hallmarks of cancer as a circular ring with different colored segments with different icons{:.sc-intro-left}

Welcome to the Cancer Galaxy Workbench!

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The Cancer Galaxy workbench is a comprehensive set of analysis tools and consolidated workflows that to enable reproducible analysis of cancer multiomics data. The current implementation comprises more than ## bioinformatics tools including those form existing Galaxy workbenches (e.g. singlecell.workbench, HCA galaxy) that are utilized in cancer research for biomarker discovery and patient stratification. We have implemented and included a comprehensive suite of tools for somatic variant detection (e.g. SNV, CNV and SV) and dedicated workflows to reproduce state-of-the art cancer analysis  

This service is a joint project between different groups from the Erasmus Medical Center{:target="_blank"}, the Open University{:target="_blank"}, OHSU, and the University of Freiburg{:target="_blank"}. The server is part if the European Galaxy server and is maintained by the RNA Bioinformatics Center (RBC){:target="_blank"} as part of de.NBI{:target="_blank"} and ELIXIR{:target="_blank"}.

Content

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  1. TOC {:toc}

Get started

Are you new to Galaxy, or returning after a long time, and looking for help to get started? Take [a guided tour]({{ page.website }}/tours/core.galaxy_ui){:target="_blank"} through Galaxy's user interface.

Training and Workshops

Want to learn more about cancer analysis in Galaxy? Look for a training event near you, or check out some of the GTN tutorials that cover the topic. For example:

GTN Topic Tutorial
Epigenetics Identification of the binding sites of the Estrogen receptor
Genome Annotation CRISPR screen analysis
Imaging Analyse HeLa fluorescence siRNA screen
Proteomics Machine Learning Modeling of Anticancer Peptides
Proteomics MaxQuant and MSstats for the analysis of TMT data
Proteomics MaxQuant and MSstats for the analysis of label-free data
Statistics & Machine Learning PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis
Statistics & Machine Learning Machine learning: classification and regression
Transcriptomics CLIP-Seq data analysis from pre-processing to motif detection
Variant Analysis Identification of somatic and germline variants from tumor and normal sample pairs
Variant Analysis Somatic Variant Discovery from WES Data Using Control-FREEC
Variant Analysis Trio Analysis using Synthetic Datasets from RD-Connect GPAP
Visualisation Visualisation with Circos
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Tip: Access the GTN tutorials via the graduation cap icon in the top menu of Galaxy.

Available Tools

In this section we highlight some tools that have been integrated in the the cancer analysis workbench. The list is likely to grow as soon as further tools and workflows are contributed.

Tool Description References
Arriba detect gene fusions from STAR aligned RNA-Seq data Uhrig et al. 2021
Delly Structural variant discovery by integrated paired-end and split-read analysis Rausch et al. 2012
FuMa Match detected fusion genes based on gene names (in particular for RNA-Seq) Hoogstrate et al. 2015
GEMINI A flexible framework for exploring genome variation Paila et al. 2013
LUMPY A probabilistic framework for structural variant discovery Layer et al. 2014
Mutect2 (GATK) Call somatic SNVs and indels via local assembly of haplotypes McKenna et al. 2010
Pathifier Quantify deregulation of pathways in cancer Drier et al. 2013
Personal Cancer Genome Reporter (beta) Functional annotation and translation of individual tumor genomes for precision cancer medicine. Nakken et al. 2017
Pizzly fast fusion detection using kallisto Melsted et al. 2017
STAR-fusion detect fusion genes in RNA-Seq data Haas et al.
Strelka Small variant caller for germline or somatic variation Kim et al. 2018
VarScan copynumber Determine relative tumor copy number from tumor-normal pileups Koboldt et al. 2012
VarScan somatic Call germline/somatic and LOH variants from tumor-normal sample pairs Koboldt et al. 2012
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..and many more!

Workflows

Coming Soon!