Pyani provides a package and script for calculation of genome-scale average nucleotide identity.

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

Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis, phylogenetics, molecular evolution and population genetics. This library provides phylogenetics-related modules.

ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

VSEARCH supports DNA sequence searching, clustering, chimera detection, dereplication, pairwise alignment, shuffling, subsampling, sorting and masking. The tool takes advantage of parallelism in the form of SIMD vectorization as well as multiple threads to perform accurate alignments at high speed. VSEARCH uses an optimal global aligner (full dynamic programming Needleman-Wunsch).

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).

eXpress is a streaming tool for quantifying the abundances of a set of target sequences from sampled subsequences. Example applications include transcript-level RNA-Seq quantification, allele-specific/haplotype expression analysis (from RNA-Seq), transcription factor binding quantification in ChIP-Seq, and analysis of metagenomic data.

PLINK is a whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.

The loom file format is an efficient format for very large omics datasets, consisting of a main matrix, optional additional layers, a variable number of row and column annotations. Loom also supports sparse graphs. This library makes it easy to work with .loom files for single-cell RNA-seq data.

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.

The NCBI-VDB library implements a highly compressed columnar data warehousing engine that is most often used to store genetic information. Databases are stored in a portable image within the file system, and can be accessed/downloaded on demand across HTTP.

PRINSEQ is a bioinformatics tool to help you preprocess your genomic or metagenomic sequence data in FASTA or FASTQ formats. The tool is written in Perl and can be helpful if you want to filter, reformat, or trim your sequence data. It also generates basic statistics for your sequences.

PLINK is a whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.

This package provides tools for handling BAM, SAM, Tabix, bgzf, CRAM, CSIv1, CSIv2 and FAI files.

This package provides a Python environment for phylogenetic tree exploration.

Exonerate is a generic tool for pairwise sequence comparison. It allows the alignment of sequences using a many alignment models, either exhaustive dynamic programming or a variety of heuristics.

This package is a Python-based command line interface for processing .bam files with mitochondrial reads and generating high-quality heteroplasmy estimation from sequencing data. The mgatk package places a special emphasis on mitochondrial genotypes generated from single-cell genomics data, primarily mtscATAC-seq, but is generally applicable across other assays.

DNA Features Viewer is a Python library to visualize DNA features, e.g. from GenBank or Gff files, or Biopython SeqRecords.

SeuratWrappers is a collection of community-provided methods and extensions for Seurat, curated by the Satija Lab at NYGC. These methods comprise functionality not presently found in Seurat, and are able to be updated much more frequently.

This package provides helper functions to detect cross-hybridization on Illumina DNAm arrays.

This package provides a framework to process and analyze data from high-throughput sequencing (HTS) assays

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.

This package provides a collection of useful functions for working with DNA methylation micro-array data.

The goal of bedtorch is to provide a fast BED file manipulation tool suite native in R.