SHARC is a pipeline for somatic SV calling and filtering from tumor-only Nanopore sequencing data. It performs mapping, SV calling, SV filtering, random forest classification, blacklist filtering and SV prioritization, followed by automated primer design for PCR amplicons of 80-120 bp that are useful to track cancer ctDNA molecules in liquid biopsies.

MCView creates a Shiny app facilitating interactive exploration and annotation of Metacell models.

Primer3 is a widely used program for designing PCR primers. PCR is an essential and ubiquitous tool in genetics and molecular biology. Primer3 can also design hybridization probes and sequencing primers.

This package provides a tool to extract assembly statistics from FASTA and FASTQ files.

SHARC is a pipeline for somatic SV calling and filtering from tumor-only Nanopore sequencing data. It performs mapping, SV calling, SV filtering, random forest classification, blacklist filtering and SV prioritization, followed by automated primer design for PCR amplicons of 80-120 bp that are useful to track cancer ctDNA molecules in liquid biopsies.

MetaMaps is tool specifically developed for the analysis of long-read (PacBio/Oxford Nanopore) metagenomic datasets.

The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest.

Build contrasts for models defined with formulaic.

This package provides tools for rapid prokaryotic genome annotation.

This package provides a tool to download or view data in the cloud environments of ICGC.

SHARC is a pipeline for somatic SV calling and filtering from tumor-only Nanopore sequencing data. It performs mapping, SV calling, SV filtering, random forest classification, blacklist filtering and SV prioritization, followed by automated primer design for PCR amplicons of 80-120 bp that are useful to track cancer ctDNA molecules in liquid biopsies.

The Spliced Transcripts Alignment to a Reference (STAR) software is based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences.

This package contains a collection of bioinformatics data structures and algorithms. It provides I/O classes, bitio classes, text indexing classes and BAM sequence alignment functionality.

This package provides tools for filtering on quality and/or read length, and optional trimming after passing filters. Reads from stdin, writes to stdout. Optionally reads directly from an uncompressed file specified on the command line. It is intended to be used directly after fastq extraction, prior to mapping, or in a stream between extraction and mapping.

Biosoup is a C++ collection of header-only data structures used for storage and logging in bioinformatics tools.

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies. The core algorithm of both programs involves gene calling, mapping of predicted ORFs against the nr protein database, and voting-based classification of the entire contig / MAG based on classification of the individual ORFs. CAT and BAT can be run from intermediate steps if files are formated appropriately

scvi-tools (single-cell variational inference tools) is a package for probabilistic modeling and analysis of single-cell omics data, built on top of PyTorch and AnnData.

Strelka2 is a fast and accurate small variant caller optimized for analysis of germline variation in small cohorts and somatic variation in tumor/normal sample pairs. The germline caller employs an efficient tiered haplotype model to improve accuracy and provide read-backed phasing, adaptively selecting between assembly and a faster alignment-based haplotyping approach at each variant locus. The germline caller also analyzes input sequencing data using a mixture-model indel error estimation method to improve robustness to indel noise. The somatic calling model improves on the original Strelka method for liquid and late-stage tumor analysis by accounting for possible tumor cell contamination in the normal sample. A final empirical variant re-scoring step using random forest models trained on various call quality features has been added to both callers to further improve precision.