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Papers: 10.1371/journal.pone.0221235

https://doi.org/10.1371/journal.pone.0221235

Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance

Cited by: 42
Author(s): Hanane Omichessan, Gianluca Severi, Vittorio Perduca
Published: about 7 years ago

Software Mentions 9

bioconductor: maftools
Summarize, Analyze and Visualize MAF Files
Papers that mentioned: 288
Very Likely Science (100)
bioconductor: MutationalPatterns
Comprehensive genome-wide analysis of mutational processes
Papers that mentioned: 32
Very Likely Science (100)
bioconductor: signeR
Empirical Bayesian approach to mutational signature discovery
Papers that mentioned: 9
Very Likely Science (100)
bioconductor: SomaticSignatures
Somatic Signatures
Papers that mentioned: 32
Very Likely Science (67)
bioconductor: SparseSignatures
SparseSignatures
Papers that mentioned: 3
Very Likely Science (100)
bioconductor: YAPSA
Yet Another Package for Signature Analysis
Papers that mentioned: 5
Very Likely Science (100)
cran: coneproj
Primal or Dual Cone Projections with Routines for Constrained Regression
Papers that mentioned: 1
Very Likely Science (93)
cran: deconstructSigs
Identifies Signatures Present in a Tumor Sample
Papers that mentioned: 85
Very Likely Science (100)
cran: mutSignatures
Decipher Mutational Signatures from Somatic Mutational Catalogs
Papers that mentioned: 2
Very Likely Science (75)