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Papers: 10.1186/s12864-021-07686-z

https://doi.org/10.1186/s12864-021-07686-z

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

Cited by: 6
Author(s): Johannes Smolander, Sofia Khan, Kalaimathy Singaravelu, Leni Kauko, Riikka Lund, Asta Laiho, Laura L. Elo
Published: about 4 years ago

Software Mentions 6

bioconductor: CGHcall
Calling aberrations for array CGH tumor profiles.
Papers that mentioned: 37
Very Likely Science (90)
bioconductor: CNAnorm
A normalization method for Copy Number Aberration in cancer samples
Papers that mentioned: 21
Very Likely Science (90)
bioconductor: HMMcopy
Copy number prediction with correction for GC and mappability bias for HTS data
Papers that mentioned: 18
Very Likely Science (90)
bioconductor: QDNAseq
Quantitative DNA Sequencing for Chromosomal Aberrations
Papers that mentioned: 31
Very Likely Science (100)
bioconductor: RSVSim
RSVSim: an R/Bioconductor package for the simulation of structural variations
Papers that mentioned: 13
Very Likely Science (100)
bioconductor: TitanCNA
Subclonal copy number and LOH prediction from whole genome sequencing of tumours
Papers that mentioned: 5
Very Likely Science (100)