KarchinLab/bigmhc

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Created about 2 years ago
Updated about 1 year ago

KarchinLab/bigmhc

KarchinLab

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BigMHC predicts MHC-I (neo)epitope presentation and immunogenicity

open-cravat

A modular annotation tool for genomic variants

2020plus

Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests

mhcnuggets

MHC Class I and Class II neoantigen binding prediction

SCHISM

Subclonal Hierarchy Inference from Somatic Mutations

CHASMplus

Predicts which missense mutations will drive growth of human cancers

probabilistic2020

Simulates somatic mutations, and calls statistically significant oncogenes and tumor suppressor genes based on a randomization-based test

pictograph

A Bayesian hierarchical model to build tumor evolutionary trees

CLUMP

CLUstering by Mutation Position

Tokheim_PNAS_2016

Evaluation protocol for computational methods predicting cancer driver genes

Jupyter Notebook

cancerSeqStudy

Analysis of statistical power and false positives for cancer exome sequencing studies

HotMAPS_2016

Detects hotspot regions for somatic mutations in 3D protein structures

Tokheim_Cell_Systems_2019

Data analysis for the CHASMplus paper

Jupyter Notebook

opencravat-cwl

Common Workflow Language

NAPA

open-cravat-modules-karchinlab

open-cravat-multiuser

open-cravat-readthedocs