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This package infers, visualizes and analyzes the cell-cell communication networks from scRNA-seq data.
Grassroots DICOM (GDCM) is an implementation of the DICOM standard designed to be open source so that researchers may access clinical data directly. GDCM includes a file format definition and a network communications protocol, both of which should be extended to provide a full set of tools for a researcher or small medical imaging vendor to interface with an existing medical database.
Kallisto is a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate as existing quantification tools.
Python-airr provides a library by the AIRR community to for describing, reporting, storing, and sharing adaptive immune receptor repertoire (AIRR) data, such as sequences of antibodies and T cell receptors (TCRs).
EpiScanpy is a toolkit to analyse single-cell open chromatin (scATAC-seq) and single-cell DNA methylation (for example scBS-seq) data. EpiScanpy is the epigenomic extension of the very popular scRNA-seq analysis tool Scanpy (Genome Biology, 2018).
Mash is a fast sequence distance estimator that uses the MinHash algorithm and is designed to work with genomes and metagenomes in the form of assemblies or reads.
METAL is a tool for meta-analysis genomewide association scans. METAL can combine either test statistics and standard errors or p-values across studies (taking sample size and direction of effect into account). METAL analysis is a convenient alternative to a direct analysis of merged data from multiple studies. It is especially appropriate when data from the individual studies cannot be analyzed together because of differences in ethnicity, phenotype distribution, gender or constraints in sharing of individual level data imposed. Meta-analysis results in little or no loss of efficiency compared to analysis of a combined dataset including data from all individual studies.
This is an R package to query and expand DisGeNET data, and to visualize the results within R framework. The disgenet2r package is designed to retrieve data from DisGeNET v6.0 (Jan, 2019).
VoltRon is a novel spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is capable of analyzing multiple types and modalities of spatially-aware datasets. VoltRon visualizes and analyzes regions of interests (ROIs), spots, cells and even molecules.
LAMMPS is a classical molecular dynamics simulator designed to run efficiently on parallel computers. LAMMPS has potentials for solid-state materials (metals, semiconductors), soft matter (biomolecules, polymers), and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale.
PiGx SARS-CoV-2 is a pipeline for analysing data from sequenced wastewater samples and identifying given variants-of-concern of SARS-CoV-2. The pipeline can be used for continuous sampling. The output report will provide an intuitive visual overview about the development of variant abundance over time and location.
CIRI-long is a package for circular RNA identification using long-read sequencing 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.
This package provides string parsing functionalities for generating plotnames, filenames and paths.
CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts.
Logomaker is a Python package for generating publication-quality sequence logos. Logomaker can generate both standard and highly customized logos illustrating the properties of DNA, RNA, or protein sequences. Logos are rendered as vector graphics embedded within native matplotlib Axes objects, making them easy to style and incorporate into multi-panel figures.
ctxcore is part of the SCENIC suite of tools. It provides core functions for pycisTarget and SCENIC.
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.
FAN-C provides a pipeline for analysing Hi-C data starting at mapped paired-end sequencing reads.
This package implements methods for batch correction and integration of scRNA-seq datasets, based on the Seurat anchor-based integration framework. In particular, STACAS is optimized for the integration of heterogeneous datasets with only limited overlap between cell sub-types (e.g. TIL sets of CD8 from tumor with CD8/CD4 T cells from lymphnode), for which the default Seurat alignment methods would tend to over-correct biological differences. The 2.0 version of the package allows the users to incorporate explicit information about cell-types in order to assist the integration process.
Mosaicatcher counts Strand-seq reads and classifies strand states of each chromosome in each cell using a Hidden Markov Model.
MuSiC is a deconvolution method that utilizes cross-subject scRNA-seq to estimate cell type proportions in bulk RNA-seq data.
CNVkit is a Python library and command-line software toolkit to infer and visualize copy number from high-throughput DNA sequencing data. It is designed for use with hybrid capture, including both whole-exome and custom target panels, and short-read sequencing platforms such as Illumina and Ion Torrent.
Megadepth is an efficient tool for extracting coverage related information from RNA and DNA-seq BAM and BigWig files. It supports reading whole-genome coverage from BAM files and writing either indexed TSV or BigWig files, as well as efficient region coverage summary over intervals from both types of files.