Author: | Collin Tokheim, Rachel Karchin |
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Contact: | ctokhei1 # alumni DOT jh DOT edu |
Lab: | Karchin Lab |
Source code: | GitHub |
Q&A: | Biostars (tag: CHASMplus) |
Large-scale DNA sequencing studies of patients' tumors have revealed that most driver mutations occur only in a few patients, which presents a challenge for precision medicine. CHASMplus is a machine learning method that accurately distinguishes between driver and passenger missense mutations, even for those found at low frequencies or are cancer type-specific. Unlike previous approaches that focus on identifying driver genes, CHASMplus identifies whether individual mutations are cancer drivers. CHASMplus can be used by both bioinformaticians and biolgists by using a graphical user interface or a command line tool.
Note
You can run CHASMplus without installing anything by submitting your data to the OpenCRAVAT webserver (details here). After creating a user account, you'll just need to check the box for CHASMplus and hit the annotate button (OpenCRAVAT webserver). Also, you can install locally a graphical user interface [see the :ref:`quickstart-ref`]
Prominent papers using CHASMplus:
- Reiter et al., Minimal functional driver gene heterogeneity among untreated metastases. Science
- Anagnostou, Niknafs et al., Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer. Nature Cancer
- Saito et al., Landscape and function of multiple mutations within individual oncogenes. Nature
- Reiter et al., An analysis of genetic heterogeneity in untreated cancers. Nature Reviews Cancer
- Hu et al., Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nature Genetics
- Sakomoto et al., The Evolutionary Origins of Recurrent Pancreatic Cancer, Cancer Discovery
Contents:
.. toctree:: :maxdepth: 3 quickstart_opencravat models download installation faq
Please cite our paper:
Tokheim and Karchin, CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers, Cell Systems (2019), https://doi.org/10.1016/j.cels.2019.05.005