Stars
ViralCC: leveraging metagenomic proximity-ligation to retrieve complete viral genomes
MIP based joint inference of copy number and rearrangement state in cancer whole genome sequence data.
for visual evaluation of read support for structural variation
Detect location and draw boundary of nuclei from microscopic images
SigProfilerSingleSample allows attributing a known set of mutational signatures to an individual sample. The tool identifies the activity of each signature in the sample and assigns the probability…
SCATE: Single-cell ATAC-seq Signal Extraction and Enhancement
napari: a fast, interactive, multi-dimensional image viewer for python
SigProfilerExtractor allows de novo extraction of mutational signatures from data generated in a matrix format. The tool identifies the number of operative mutational signatures, their activities i…
CoMut is a Python library for visualizing genomic and phenotypic information via comutation plots
FAN-C: Framework for the ANalysis of C-like data
Redbean: A fuzzy Bruijn graph approach to long noisy reads assembly
Plot structural variant signals from many BAMs and CRAMs
Software to assemble contigs/scaffolds into chromosomes using Hi-C data
A versatile tool to perform pile-up analysis on Hi-C data in .cool format.
The software involved in the MetaPhase project, as described in G3 (http://dx.doi.org/10.1534/g3.114.011825)
Extract metagenome-assembled genomes (MAGs) from metagenomic data using Hi-C.
CORGi - COmplex Rearrangement analysis with Graph-search
(check out instaGRAAL for a faster, updated program!) This program is from Marie-Nelly et al., Nature Communications, 2014 (High-quality genome assembly using chromosomal contact data), also Marie-…
🍎Jalpc -- A flexible Jekyll theme, 3 steps to build your website.
code to generate passage of citations through animated reproducibility gates
MS Word templates for a more readable BioRxiv product
Software for comparing contact maps from HiC, CaptureC and other 3D genome data.
Deep neural networks without the learning cliff! Classifiers and regressors compatible with scikit-learn.