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

This package provides a tool for creating alignment plots from bam files.

MUSIC is an algorithm for identification of enriched regions at multiple scales in the read depth signals from ChIP-Seq experiments.

Millefy is a tool for visualizing read coverage of scRNA-seq(single-cell RNA sequencing) datasets in genomic contexts. By dynamically and automatically reorder single cells based on locus-specific pseudo time, Millefy highlights cell-to-cell heterogeneity in read coverage of scRNA-seq data.

Change-O is a collection of tools for processing the output of V(D)J alignment tools, assigning clonal clusters to immunoglobulin (Ig) sequences, and reconstructing germline sequences.

This package provides Python bindings for spoa, a C++ implementation of the partial order alignment (POA) algorithm (as described in 10.1093/bioinformatics/18.3.452) which is used to generate consensus sequences

GSEApy is a Python/Rust implementation for GSEA and wrapper for Enrichr. GSEApy can be used for RNA-seq, ChIP-seq, Microarray data. It can be used for convenient GO enrichment and to produce publication quality figures in Python.

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 Python bindings for lib2bit to access 2bit files with Python.

DoubletFinder identifies doublets by generating artificial doublets from existing scRNA-seq data and defining which real cells preferentially co-localize with artificial doublets in gene expression space. Other DoubletFinder package functions are used for fitting DoubletFinder to different scRNA-seq datasets. For example, ideal DoubletFinder performance in real-world contexts requires optimal pK selection and homotypic doublet proportion estimation. pK selection is achieved using pN-pK parameter sweeps and maxima identification in mean-variance-normalized bimodality coefficient distributions. Homotypic doublet proportion estimation is achieved by finding the sum of squared cell annotation frequencies.

This package implements parallel block gzip. For many formats, in particular genomics data formats, data are compressed in fixed-length blocks such that they can be easily indexed based on a (genomic) coordinate order, since typically each block is sorted according to this order. This allows for each block to be individually compressed (deflated), or more importantly, decompressed (inflated), with the latter enabling random retrieval of data in large files (gigabytes to terabytes). pbgzip is not limited to any particular format, but certain features are tailored to genomics data formats when enabled. Parallel decompression is somewhat faster, but the true speedup comes during compression.

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 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 R package lets you estimate signatures of mutational processes and their activities on mutation count data. Starting from a set of single-nucleotide variants (SNVs), it allows both estimation of the exposure of samples to predefined mutational signatures (including whether the signatures are present at all), and identification of signatures de novo from the mutation counts.

This package provides a converter between .hic files (from juicer) and single-resolution or multi-resolution .cool files (for cooler). Both hic and cool files describe Hi-C contact matrices.

CrossMap is a program for conversion of genome coordinates or annotation files between different genome assemblies. It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.

This is a simple "libraryfication" of the GFF/GTF parsing code that is used in the Cufflinks codebase. The goal of this library is to provide this functionality without the necessity of drawing in a heavy-weight dependency like SeqAn.

Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis, phylogenetics, molecular evolution and population genetics. This library provides phylogenetics-related modules.

This package contains a multicore Barnes-Hut implementation of the t-SNE algorithm. The implementation is described here: http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf.

Pysam is a Python module for reading and manipulating files in the SAM/BAM format. Pysam is a lightweight wrapper of the SAMtools C API. It also includes an interface for tabix.

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

This package provides data for the book "Computational Genomics with R".

This package addresses the challenge of handling large amounts of data that are now routinely generated from DNA sequencing centers. deepTools contains useful modules to process the mapped reads data for multiple quality checks, creating normalized coverage files in standard bedGraph and bigWig file formats, that allow comparison between different files. Finally, using such normalized and standardized files, deepTools can create many publication-ready visualizations to identify enrichments and for functional annotations of the genome.

This package provides a Variant Effect Predictor, which predicts the functional effects of genomic variants. It also provides Haplosaurus, which uses phased genotype data to predict whole-transcript haplotype sequences, and Variant Recoder, which translates between different variant encodings.