Maxent is a stand-alone Java application for modelling species geographic distributions.

This package provides a Python environment for phylogenetic tree exploration.

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 package has been developed under ROpenSci gudelines to integrate conventional and cutting edge cytometry analysis tools under a unified framework. It aims to represent an intuitive and interactive approach to analysing cytometry data in R.

Scanorama enables batch-correction and integration of heterogeneous scRNA-seq datasets, which is described in the paper "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama" by Brian Hie, Bryan Bryson, and Bonnie Berger.

NGS is a domain-specific API for accessing reads, alignments and pileups produced from Next Generation Sequencing. The API itself is independent from any particular back-end implementation, and supports use of multiple back-ends simultaneously.

This package provides basic routines for estimation of gene-specific transcriptional derivatives and visualization of the resulting velocity patterns.

HTSlib is a C library for reading/writing high-throughput sequencing data. It also provides the bgzip, htsfile, and tabix utilities.

This package conducts batch effects removal from a taxa read count table by a conditional quantile regression method. The distributional attributes of microbiome data - zero-inflation and over-dispersion, are simultaneously considered.

CellTypist is an automated cell type annotation tool for scRNA-seq datasets on the basis of logistic regression classifiers optimised by the stochastic gradient descent algorithm. CellTypist allows for cell prediction using either built-in (with a current focus on immune sub-populations) or custom models, in order to assist in the accurate classification of different cell types and subtypes.

The package reads phylogenetic data in the phyloXML format. It also includes functions for writing data in this format.

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.

The porechop package is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity. Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

This is an R package providing additional capabilities and speed for GenomicRanges operations.

PAIRADISE is a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, PAIRADISE formulates ASAS detection as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates.

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 is a drop-in replacement for the IlluminaHumanMethylationEPIC package. It utilizes a Manifest based on 1.0B5 annotation. As of version 0.3.0, the IlluminaHumanMethylationEPIC package still employs the 1.0B2 annotation manifest. A corresponding annotation package, IlluminaHumanMethylationEPICanno.ilm10b5.hg38, is available to ensure proper annotation. The decision to maintain the same name is due to complications in downstream processing caused by array name lookup in certain preprocessing options.

This package is used for cell type identification in spatial transcriptomics. It also handles cell type-specific differential expression.

LIANA provides a number of methods and resource for ligand-receptor interaction inference from scRNA-seq data.

Ngesh is a Python library and CLI tool for simulating phylogenetic trees and data. It is intended for benchmarking phylogenetic methods, especially in historical linguistics andstemmatology. The generation of stochastic phylogenetic trees also goes by the name simulationmethods for phylogenetic trees, synthetic data generation, or just phylogenetic tree simulation.

This package provides a companion annotation file to the IlluminaHumanMethylationEPICmanifest package based on the same annotation 1.0B5.

This package provides a a transcriptomic-based framework to dissect cell communication in a global manner. It integrates an original expert-curated database of ligand-receptor interactions taking into account multiple subunits expression. Based on transcriptomic profiles (gene expression), this package computes communication scores between cells and provides several visualization modes that can be helpful to dig into cell-cell interaction mechanism and extend biological knowledge.

An interval tree can be used to efficiently find a set of numeric intervals overlapping or containing another interval. This library provides a basic implementation of an interval tree using C++ templates, allowing the insertion of arbitrary types into the tree.

MACS is an implementation of a ChIP-Seq analysis algorithm for identifying transcript factor binding sites named Model-based Analysis of ChIP-Seq (MACS). MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions and it improves the spatial resolution of binding sites through combining the information of both sequencing tag position and orientation.