BnpC – Bayesian non-parametric Clustering of Single-cell Mutation Profiles

BnpC

:: DESCRIPTION

BnpC is a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles.

::DEVELOPER

Computational Biology Group (CBG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

BnpC

:: MORE INFORMATION

Citation:

Borgsmüller N, Bonet J, Marass F, Gonzalez-Perez A, Lopez-Bigas N, Beerenwinkel N.
BnpC: Bayesian non-parametric clustering of single-cell mutation profiles.
Bioinformatics. 2020 Dec 8;36(19):4854-4859. doi: 10.1093/bioinformatics/btaa599. PMID: 32592465; PMCID: PMC7750970.

nucleR 2.24.0 – Non-parametric Nucleosome Positioning

nucleR 2.24.0

:: DESCRIPTION

nucleR is an R/Bioconductor package for a flexible and fast recognition of nucleosome positioning from next generation sequencing and tiling arrays experiments.

::DEVELOPER

Molecular Modeling and Bioinformatics Unit , IRB Barcelona

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
R
BioConductor

:: DOWNLOAD

nucleR

:: MORE INFORMATION

Citation

Oscar Flores and Modesto Orozco (2011).
nucleR: a package for non-parametric nucleosome positioning.
Bioinformatics (2011) 27 (15): 2149-2150., doi:10.1093/bioinformatics/btr345

MotifCut 0.1 beta – Non-parametric graph-based Motif Finding algorithm

MotifCut 0.1 beta

:: DESCRIPTION

MotifCut is a DNA motif-finding algorithm using a Maximum Density Subgraph.

::DEVELOPER

Serafim Batzoglou

:: SCREENSHOTS

:: REQUIREMENTS

Linux/Windows/MacOsX
C++ Compiler

:: DOWNLOAD

MotifCut

:: MORE INFORMATION

Citation

Bioinformatics. 2006 Jul 15;22(14):e150-7.
MotifCut: regulatory motifs finding with maximum density subgraphs.
Fratkin E, Naughton BT, Brutlag DL, Batzoglou S.