This is a Python package for the interactive visualization of bulk RNA-seq data. It provides a range of plotting functions and interactive tools to explore and analyze bulk RNA-seq data.

The IDR (Irreproducible Discovery Rate) framework is a unified approach to measure the reproducibility of findings identified from replicate experiments and provide highly stable thresholds based on reproducibility.

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.

PLINK is a whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.

TADbit is a complete Python library to deal with all steps to analyze, model, and explore 3C-based data. With TADbit the user can map FASTQ files to obtain raw interaction binned matrices (Hi-C like matrices), normalize and correct interaction matrices, identify and compare the so-called Topologically Associating Domains (TADs), build 3D models from the interaction matrices, and finally, extract structural properties from the models. TADbit is complemented by TADkit for visualizing 3D models.

Samblaster is a fast and flexible program for marking duplicates in read-id grouped paired-end SAM files. It can also optionally output discordant read pairs and/or split read mappings to separate SAM files, and/or unmapped/clipped reads to a separate FASTQ file. When marking duplicates, samblaster will require approximately 20MB of memory per 1M read pairs.

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.

HTSJDK is an implementation of a unified Java library for accessing common file formats, such as SAM and VCF, used for high-throughput sequencing (HTS) data. There are also an number of useful utilities for manipulating HTS data.

This is a package providing efficient operations for single cell ATAC-seq fragments and RNA counts matrices. It is interoperable with standard file formats, and introduces efficient bit-packed formats that allow large storage savings and increased read speeds.

This tool provides a Python framework to streamline genomics operations. It offers a direct interface to Ensembl genome assemblies and annotations, while also accommodating custom genomes via FASTA/GTF inputs. The primary objective of pybio is to simplify genome management. It achieves this by providing automatic download of Ensembl genome assemblies and annotation, provides Python genomic feature search and sequence retrieval from the managed genomes, STAR indexing and mapping and more.

Kaiju is a program for sensitive taxonomic classification of high-throughput sequencing reads from metagenomic whole genome sequencing experiments.

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 is a client implementation of the GA4GH htsget protocol. It provides a simple and reliable way to retrieve genomic data from servers supporting the protocol.

Psupertime is supervised pseudotime for single cell RNAseq data. It uses single cell RNAseq data, where the cells have a known ordering. This ordering helps to identify a small number of genes which place cells in that known order. It can be used for discovery of relevant genes, for identification of subpopulations, and characterization of further unknown or differently labelled data.

The goal of anpan is to consolidate statistical methods for strain analysis. This includes automated filtering of metagenomic functional profiles, testing genetic elements for association with outcomes, phylogenetic association testing, and pathway-level random effects models.

Picard is a set of Java command line tools for manipulating high-throughput sequencing (HTS) data and formats. Picard is implemented using the HTSJDK Java library to support accessing file formats that are commonly used for high-throughput sequencing data such as SAM, BAM, CRAM and VCF.

This is a fast parser for minimap2 PAF (Pairwise mApping Format) files.

MultiQC is a tool to aggregate bioinformatics results across many samples into a single report. It contains modules for a large number of common bioinformatics tools.

This package provides an object for plotting GRanges, RleList, UCSC file formats, and ffTrack objects in multi-track panels.

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

This package generates a Miami plot with centered chromosome labels. The output is a ggplot2 object. Users can specify which data they want plotted on top vs. bottom, whether to display significance line(s), what colors to give chromosomes, and what points to label.

Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis, phylogenetics, molecular evolution and population genetics. This package provides command line tools using the Bio++ library.

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.).

BayesPrism includes deconvolution and embedding learning modules. The deconvolution module models a prior from cell type-specific expression profiles from scRNA-seq to jointly estimate the posterior distribution of cell type composition and cell type-specific gene expression from bulk RNA-seq expression of tumor samples. The embedding learning module uses Expectation-maximization (EM) to approximate the tumor expression using a linear combination of malignant gene programs while conditional on the inferred expression and fraction of non-malignant cells estimated by the deconvolution module.