This package provides a package that makes it easy to implement sankey, alluvial and sankey bump plots in ggplot2.

Pybiomart provides a simple pythonic interface to biomart.

This package provides a Python environment for phylogenetic tree exploration.

This package is analyzing TCR and BCR sequences using unselected RNA sequencing data, profiled from fluid and solid tissues, including tumors. TRUST4 performs de novo assembly on V, J, C genes including the hypervariable CDR3 and reports consensus contigs of BCR/TCR sequences. TRUST4 then realigns the contigs to IMGT reference gene sequences to identify the corresponding gene and CDR3 details. TRUST4 supports both single-end and paired-end bulk or single-cell sequencing data with any read length.

This package provides a set of functions to parse and open (search query) links to genomics related and other websites for R. Useful when you want to explore e.g.: the function of a set of differentially expressed genes.

CLIPper is a tool to define peaks in CLIP-seq datasets.

Miniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final unitig sequences. Thus the per-base error rate is similar to the raw input reads.

This package computes informative enrichment and quality measures for ChIP-seq/DNase-seq/FAIRE-seq/MNase-seq data. It can also be used to obtain robust estimates of the predominant fragment length or characteristic tag shift values in these assays.

Circe is a Python package for inferring co-accessibility networks from single-cell ATAC-seq data, using skggm for the graphical lasso and python-scanpy for data processing.

RSeQC provides a number of modules that can comprehensively evaluate high throughput sequence data, especially RNA-seq data. Some basic modules inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, etc.

This package is designed to improve and simplify the analysis of scRNA-seq data. It uses the Seurat object for this purpose. It provides an array of enhanced visualization tools, an integrated functional and pathway analysis pipeline, seamless integration with popular Python tools, and a suite of utility functions to aid in data manipulation and presentation.

This package facilitates the analysis of single-cell RNA-seq UMI matrices. It does this by computing partitions of a cell similarity graph into small homogeneous groups of cells, which are defined as metacells (MCs). The derived MCs are then used for building different representations of the data, allowing matrix or 2D graph visualization forming a basis for analysis of cell types, subtypes, transcriptional gradients,cell-cycle variation, gene modules and their regulatory models and more.

This tool offers a pipeline for inferring gene expression programs from scRNA-Seq. It takes a count matrix (N cells X G genes) as input and produces a (K x G) matrix of gene expression programs (GEPs) and a (N x K) matrix specifying the usage of each program for each cell in the data.

MuSiC is a deconvolution method that utilizes cross-subject scRNA-seq to estimate cell type proportions in bulk RNA-seq data.

This package provides an RNA-seq workflow for differential transcript usage (DTU) following Salmon quantification. This workflow performs a DTU analysis on simulated data. It also shows how to use stageR to perform two-stage testing of DTU, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU.

Pypairix is a Python module for fast querying on a pairix-indexed bgzipped text file that contains a pair of genomic coordinates per line.

This is a Python module for analyzing cell-hashing/nucleus-hashing data. It is the demultiplexing module of Pegasus, which is used by Cumulus in the demultiplexing step.

This is a package for normalizing Hi-C contact counts efficiently.

MethylDackel will process a coordinate-sorted and indexed BAM or CRAM file containing some form of BS-seq alignments and extract per-base methylation metrics from them. MethylDackel requires an indexed fasta file containing the reference genome as well.

This package implements an algorithm which increases the number of simultaneously measurable markers and in this way helps with study of the immune responses. Thus, the present algorithm, named CytoBackBone, allows combining phenotypic information of cells from different cytometric profiles obtained from different cytometry panels. This computational approach is based on the principle that each cell has its own phenotypic and functional characteristics that can be used as an identification card. CytoBackBone uses a set of predefined markers, that we call the backbone, to define this identification card. The phenotypic information of cells with similar identification cards in the different cytometric profiles is then merged.

This package provides a method to sample cells from single-cell data. It also generates an aggregate profile on a pruned K-Nearest Neighbor graph. This approach leads to an improved gene expression profile for quantifying gene regulations.

SlamDunk is a fully automated tool for automated, robust, scalable and reproducible SLAMseq data analysis. Diagnostic plotting features and a MultiQC plugin will make your SLAMseq data ready for immediate QA and interpretation.

This package aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot: bigwig, bed (many options), bedgraph, links (represented as arcs), and Hi-C matrices. pyGenomeTracks can make plots with or without Hi-C data.

This package provides a computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.