This package implements the method csSAM that computes cell-specific differential expression from measured cell proportions using SAM.

CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts.

Aragorn identifies transfer RNA, mitochondrial RNA and transfer-messenger RNA from nucleotide sequences, based on homology to known tRNA consensus sequences and RNA structure. It also outputs the secondary structure of the predicted RNA.

The package is ideal for analyzing RNA structure and chemical probing data.

This package provides a method to detect and enable removal of doublets from single-cell RNA-sequencing.

MyGene.Info provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. Mygene is a Python wrapper to access MyGene.Info services.

NanoSV is a software package that can be used to identify structural genomic variations in long-read sequencing data, such as data produced by Oxford Nanopore Technologies’ MinION, GridION or PromethION instruments, or Pacific Biosciences RSII or Sequel sequencers.

The alignment module of BioJava provides an API that contains

• implementations of dynamic programming algorithms for sequence alignment;

• reading and writing of popular alignment file formats;

• a single-, or multi- threaded multiple sequence alignment algorithm.

This package provides a package that makes it easy to implement sankey, alluvial and sankey bump plots in ggplot2.

This package aims to bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyze your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects.

An ORF caller finds stretches of DNA that, when translated, are not interrupted by stop codons. OrfM finds and prints these ORFs.

randfold computes the probability that, for a given sequence, the Minimum Free Energy (MFE) of the secondary structure is different from MFE computed with random sequences.

This program searches for and removes remnant adapter sequences from High-Throughput Sequencing (HTS) data and (optionally) trims low quality bases from the 3' end of reads following adapter removal. AdapterRemoval can analyze both single end and paired end data, and can be used to merge overlapping paired-ended reads into (longer) consensus sequences. Additionally, the AdapterRemoval may be used to recover a consensus adapter sequence for paired-ended data, for which this information is not available.

FreeBayes is a Bayesian genetic variant detector designed to find small polymorphisms, specifically SNPs (single-nucleotide polymorphisms), indels (insertions and deletions), MNPs (multi-nucleotide polymorphisms), and complex events (composite insertion and substitution events) smaller than the length of a short-read sequencing alignment.

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.

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.

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 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.

This package is a set of R functions for generating precise figures. This tool helps you to create clean markdown reports about what you just discovered with your analysis script.

This is a set of functions for processing raw scDam&T-seq data. scDam&T-seq is a method to simultaneously measure protein-DNA interactions and transcription from single cells (Rooijers et al., 2019). It combines a DamID-based method to measure protein-DNA interactions and an adaptation of CEL-Seq to measure transcription. The starting point of the workflow is raw sequencing data and the end result are tables of UMI-unique DamID and CEL-Seq counts.

Mudskipper is a tool for projecting genomic alignments to transcriptomic coordinates.

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).

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.

This package implements an algorithm which increases the number of simultaneously measurable markers and in this way helps with study of the immune responses. Thus, the present algorithm, named CytoBackBone, allows combining phenotypic information of cells from different cytometric profiles obtained from different cytometry panels. This computational approach is based on the principle that each cell has its own phenotypic and functional characteristics that can be used as an identification card. CytoBackBone uses a set of predefined markers, that we call the backbone, to define this identification card. The phenotypic information of cells with similar identification cards in the different cytometric profiles is then merged.