Mudata is a Python package for multi-omics data analysis. It is designed to provide functionality to load, process, and store multimodal omics data.

This package provides data for the SeuratExtend tool.

The metacells package implements the improved metacell algorithm for single-cell RNA sequencing (scRNA-seq) data analysis within the scipy framework, and projection algorithm based on it. The original metacell algorithm was implemented in R. The Python package contains various algorithmic improvements and is scalable for larger data sets (millions of cells).

Bíogo is a bioinformatics library for the Go language.

This package provides Python bindings to the bwa mem aligner.

BioJava is a project dedicated to providing a Java framework for processing biological data. It provides analytical and statistical routines, parsers for common file formats, reference implementations of popular algorithms, and allows the manipulation of sequences and 3D structures. The goal of the biojava project is to facilitate rapid application development for bioinformatics.

This package provides the core libraries.

dnaio is a Python library for fast parsing of FASTQ and also FASTA files. The code was previously part of the cutadapt tool.

This package implements scalable gene regulatory network inference using tree-based ensemble regressors.

This package builds on Seurat's Doheatmap function code to produce a heatmap from a Seurat object with multiple annotation bars.

Bioawk is an extension to Brian Kernighan's awk, adding the support of several common biological data formats, including optionally gzip'ed BED, GFF, SAM, VCF, FASTA/Q and TAB-delimited formats with column names. It also adds a few built-in functions and a command line option to use TAB as the input/output delimiter. When the new functionality is not used, bioawk is intended to behave exactly the same as the original BWK awk.

Bismark is a program to map bisulfite treated sequencing reads to a genome of interest and perform methylation calls in a single step. The output can be easily imported into a genome viewer, such as SeqMonk, and enables a researcher to analyse the methylation levels of their samples straight away. Its main features are:

• Bisulfite mapping and methylation calling in one single step

• Supports single-end and paired-end read alignments

• Supports ungapped and gapped alignments

• Alignment seed length, number of mismatches etc are adjustable

• Output discriminates between cytosine methylation in CpG, CHG and CHH context

StringTie is a fast and efficient assembler of RNA-Seq sequence alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only the alignments of raw reads used by other transcript assemblers, but also alignments of longer sequences that have been assembled from those reads. To identify differentially expressed genes between experiments, StringTie's output can be processed either by the Cuffdiff or Ballgown programs.

Infernal ("INFERence of RNA ALignment") is a tool for searching DNA sequence databases for RNA structure and sequence similarities. It is an implementation of a special case of profile stochastic context-free grammars called covariance models (CMs). A CM is like a sequence profile, but it scores a combination of sequence consensus and RNA secondary structure consensus, so in many cases, it is more capable of identifying RNA homologs that conserve their secondary structure more than their primary sequence.

dRep is a Python program for rapidly comparing large numbers of genomes. dRep can also "de-replicate" a genome set by identifying groups of highly similar genomes and choosing the best representative genome for each genome set.

This is a Ligand-Receptor inference framework. The framework enables the use of any LR method with any resources.

HMMER is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).

This package is a rasterization preprocessing framework that aggregates cellular information into spatial pixels to reduce resource requirements for spatial omics data analysis. SEraster reduces the number of points in spatial omics datasets for downstream analysis through a process of rasterization where single cells gene expression or cell-type labels are aggregated into equally sized pixels based on a user-defined resolution. SEraster can be incorporated with other packages to conduct downstream analyses for spatial omics datasets, such as detecting spatially variable genes.

t-Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization of high dimensional datasets. A popular implementation of t-SNE uses the Barnes-Hut algorithm to approximate the gradient at each iteration of gradient descent. This implementation differs in these ways:

• Instead of approximating the N-body simulation using Barnes-Hut, we interpolate onto an equispaced grid and use FFT to perform the convolution.

• Instead of computing nearest neighbors using vantage-point trees, we approximate nearest neighbors using the Annoy library. The neighbor lookups are multithreaded to take advantage of machines with multiple cores.

JAMM is a peak finder for next generation sequencing datasets (ChIP-Seq, ATAC-Seq, DNase-Seq, etc.) that can integrate replicates and assign peak boundaries accurately. JAMM is applicable to both broad and narrow datasets.

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 package provides a framework for the analysis and exploration of single-cell chromatin data. The Signac package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis.

This helper package implements the HiCMatrix class for the HiCExplorer and pyGenomeTracks packages.

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.

Pando leverages multi-modal single-cell measurements to infer gene regulatory networks using a flexible linear model-based framework. By modeling the relationship between TF-binding site pairs with the expression of target genes, Pando simultaneously infers gene modules and sets of regulatory regions for each transcription factor.