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Protocols

CHASM specifications

Information


Unique identifier OMICS_00127
Name CHASM
Alternative names Cancer-specific High-throughput Annotation of Somatic Mutations, CHASM/SNV-Box, CHASMplus
Software type Toolkit/Suite
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages C++, Python, R, Shell (Bash)
Database management system MySQL
Computer skills Advanced
Version 3.0
Stability Stable
Maintained Yes

Taxon


  • Primates
    • Homo sapiens

Versioning


No version available

Documentation


Maintainers


  • person_outline Rachel Karchin
  • person_outline Rachel Karchin

Publications for Cancer-specific High-throughput Annotation of Somatic Mutations

CHASM citations

 (15)
library_books

NIPS, a 3D network integrated predictor of deleterious protein SAPs, and its application in cancer prognosis

2018
Sci Rep
PMCID: 5902451
PMID: 29662108
DOI: 10.1038/s41598-018-24286-2

[…] rotein structural information is also helpful. PolyPhen-2 is a prominent tool that uses both sequence- and structure-based features in a naïve Bayes classification,. As a cancer-specific tool, CHASM (cancer-specific high-throughput annotation of somatic mutations) is a major machine-learning approach employing a random forest algorithm and was trained using 49 predictive features, including conser […]

call_split

Novel putative drivers revealed by targeted exome sequencing of advanced solid tumors

2018
PLoS One
PMCID: 5865730
PMID: 29570743
DOI: 10.1371/journal.pone.0194790
call_split See protocol

[…] nfidentiality. Variants were manually re-validated individually against HG-19 and then evaluated for a potential resulting cancer driver phenotype. Missense mutations were scored with two algorithms, CHASM and FATHMM, which are considered reliable predictors [, ]. Cancer-specific High-throughput Annotation of Somatic Mutations (CHASM) is a computational method based on a Random Forest classifier t […]

library_books

Dualistic Role of BARD1 in Cancer

2017
Genes
PMCID: 5748693
PMID: 29292755
DOI: 10.3390/genes8120375

[…] been included in the list of Cancer Gene Census in COSMIC database (http://cancer.sanger.ac.uk/census.). Here we have analyzed all somatic mutations of BARD1 deposited in COSMIC database by using the Cancer-specific High-throughput Annotation of Somatic Mutations (CHASM) [] tool to distinguish passenger variation events from driver ones across a cohort of tumors and the Variant Effect Scoring Tool […]

library_books

High confidence assessment of functional impact of human mitochondrial non synonymous genome variations by APOGEE

2017
PLoS Comput Biol
PMCID: 5501658
PMID: 28640805
DOI: 10.1371/journal.pcbi.1005628

[…] shold of 12 as harmful, as suggested by the authors. Variants were submitted to CADD in VCF-like data format. We further retrieved predictions from CRAVAT [], both for mendelian (VEST) [] and cancer (CHASM) [] diseases. Input variants were specified as Ensembl Transcript IDs and amino acid substitutions, using the one-letter encoding. It responded to our query with pairs of p-values and FDRs, one […]

library_books

iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes

2016
Genome Med
PMCID: 5180414
PMID: 28007024
DOI: 10.1186/s13073-016-0390-0

[…] analysis. Some batch analysis tools prioritize genes, such as MutSigCV [], MuSiC [], and Youn-Simon [], while others prioritize different kinds of mutations. For example, computational tools such as CHASM [], Mutation Assessor [], and FATHMM (for cancer) [] prioritize point-coding mutations, whereas FunSeq2 [] prioritizes non-coding mutations. While these tools paved the way for cancer driver pri […]

library_books

Genomic characterization of pediatric T cell acute lymphoblastic leukemia reveals novel recurrent driver mutations

2016
Oncotarget
PMCID: 5323170
PMID: 27602765
DOI: 10.18632/oncotarget.11796

[…] en 0.447 and 0.909 were predicted as possibly damaging (P) while a score > 0.909 was considered as damaging. Mutations with a MutationTaster score > 0.9 were also considered as damaging. Finally, for CHASM classification we used the Blood-Lymphocyte training set and a Benjamini and Hochberg's adjusted false discovery rate (FDR) ≤ 0.20, to prioritize mutations based on their predicted driver potent […]

Citations

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CHASM institution(s)
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Oncology, Johns Hopkins University, Baltimore, MD, USA
CHASM funding source(s)
Supported by the National Cancer Institute (NCI) Grant F31CA200266 and NCI Grant U24CA204817.

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