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.

Morpheus is a modeling and simulation environment for the study of multi-scale and multicellular systems.

PiGx BSseq is a data processing pipeline for raw fastq read data of bisulfite experiments; it produces reports on aggregate methylation and coverage and can be used to produce information on differential methylation and segmentation.

FAN-C provides a pipeline for analysing Hi-C data starting at mapped paired-end sequencing reads.

The loom file format is an efficient format for very large omics datasets, consisting of a main matrix, optional additional layers, a variable number of row and column annotations. Loom also supports sparse graphs. This library makes it easy to work with .loom files for single-cell RNA-seq data.

This package can be used to normalize cytometry samples when a control sample is taken along in each of the batches. This is done by first identifying multiple clusters/cell types, learning the batch effects from the control samples and applying quantile normalization on all markers of interest.

This package provides a collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). GeneNMF contains functions to directly interact with the Seurat toolkit and derive interpretable gene program signatures.

Megadepth is an efficient tool for extracting coverage related information from RNA and DNA-seq BAM and BigWig files. It supports reading whole-genome coverage from BAM files and writing either indexed TSV or BigWig files, as well as efficient region coverage summary over intervals from both types of files.

This package implements methods for batch correction and integration of scRNA-seq datasets, based on the Seurat anchor-based integration framework. In particular, STACAS is optimized for the integration of heterogeneous datasets with only limited overlap between cell sub-types (e.g. TIL sets of CD8 from tumor with CD8/CD4 T cells from lymphnode), for which the default Seurat alignment methods would tend to over-correct biological differences. The 2.0 version of the package allows the users to incorporate explicit information about cell-types in order to assist the integration process.

PiGx is a collection of genomics pipelines. It includes the following pipelines:

• PiGx BSseq for raw fastq read data of bisulfite experiments

• PiGx RNAseq for RNAseq samples

• PiGx scRNAseq for single cell dropseq analysis

• PiGx ChIPseq for reads from ChIPseq experiments

All pipelines are easily configured with a simple sample sheet and a descriptive settings file. The result is a set of comprehensive, interactive HTML reports with interesting findings about your samples.

This package lets you read and write the PLINK BED format, simply and efficiently.

Bamnostic is a pure Python Binary Alignment Map (BAM) file parser and random access tool.

Fastahack is a small application for indexing and extracting sequences and subsequences from FASTA files. The included library provides a FASTA reader and indexer that can be embedded into applications which would benefit from directly reading subsequences from FASTA files. The library automatically handles index file generation and use.

The WiggleTools package allows genomewide data files to be manipulated as numerical functions, equipped with all the standard functional analysis operators (sum, product, product by a scalar, comparators), and derived statistics (mean, median, variance, stddev, t-test, Wilcoxon's rank sum test, etc).

This package implements bindings for h5 files that are compatible with Bioconductor S4 data structures, namely the DataFrame and DelayedArray. This allows HDF5-backed data to be easily used as data frames with arbitrary sets of columns.

This package offers a set of functions to use in order to compute communities on graphs weighted or unweighted.

Grassroots DICOM (GDCM) is an implementation of the DICOM standard designed to be open source so that researchers may access clinical data directly. GDCM includes a file format definition and a network communications protocol, both of which should be extended to provide a full set of tools for a researcher or small medical imaging vendor to interface with an existing medical database.

This package provides a fast and accurate analysis toolkit for single cell ATAC-seq (Assay for transposase-accessible chromatin using sequencing). Single cell ATAC-seq can resolve the heterogeneity of a complex tissue and reveal cell-type specific regulatory landscapes. However, the exceeding data sparsity has posed unique challenges for the data analysis. This package r-snapatac is an end-to-end bioinformatics pipeline for analyzing large- scale single cell ATAC-seq data which includes quality control, normalization, clustering analysis, differential analysis, motif inference and exploration of single cell ATAC-seq sequencing data.

Circus is an R package for annotation, analysis and visualization of circRNA data. Users can annotate their circRNA candidates with host genes, gene features they are spliced from, and discriminate between known and yet unknown splice junctions. Circular-to-linear ratios of circRNAs can be calculated, and a number of descriptive plots easily generated.

MafFilter is a program dedicated to the analysis of genome alignments. It parses and manipulates MAF files as well as more simple fasta files. This package can be used to design a pipeline as a series of consecutive filters, each performing a dedicated analysis. Many of the filters are available, from alignment cleaning to phylogeny reconstruction and population genetics analysis. Despite various filtering options and format conversion tools, MafFilter can compute a wide range of statistics (phylogenetic trees, nucleotide diversity, inference of selection, etc.).

VCFtools is a program package designed for working with VCF files, such as those generated by the 1000 Genomes Project. The aim of VCFtools is to provide easily accessible methods for working with complex genetic variation data in the form of VCF files.

ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

This package provides a Python module creating/accessing GTF-based interval trees with associated meta-data. It is primarily used by the deeptools package.

The SCDE package implements a set of statistical methods for analyzing single-cell RNA-seq data. SCDE fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The SCDE package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify aspects of transcriptional heterogeneity among single cells.