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).

Presto is a python toolkit for processing raw reads from high-throughput sequencing of B cell and T cell repertoires.

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.

Bio::Kseq provides ruby bindings to the kseq.h FASTA and FASTQ parsing code. It provides a fast iterator over sequences and their quality scores.

JAMM is a peak finder for next generation sequencing datasets (ChIP-Seq, ATAC-Seq, DNase-Seq, etc.) that can integrate replicates and assign peak boundaries accurately. JAMM is applicable to both broad and narrow datasets.

Seqtk is a fast and lightweight tool for processing sequences in the FASTA or FASTQ format. It parses both FASTA and FASTQ files which can be optionally compressed by gzip.

This package provides several programs that perform operations on SAM/BAM files. All of these programs are built into a single executable called bam.

This package is designed to pileup the expressed alleles in single-cell or bulk RNA-seq data, which can be directly used for donor deconvolution in multiplexed single-cell RNA-seq data, particularly with other packages, which assigns cells to donors and detects doublets as vireo, even without genotyping reference.

This package is the C version of the deprecated cellSNP implemented in Python. Compared to cellSNP, this package is more efficient with higher speed and less memory usage.

This is a Python package for the interactive visualization of bulk RNA-seq data. It provides a range of plotting functions and interactive tools to explore and analyze bulk RNA-seq data.

Several studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. CRISPR has also been used to reconstruct lineage trees by inserting random mutations. The tbsp package implements an alternative method to detect significant, cell type specific sequence mutations from scRNA-Seq data.

This package provides Shiny apps for interactive exploration of single-cell data.

This package provides Python bindings to the bwa mem aligner.

Mudata is a Python package for multi-omics data analysis. It is designed to provide functionality to load, process, and store multimodal omics data.

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).

This is an R package to build generic .loom files aligning with the default naming convention of the .loom format and to integrate other data types e.g.: regulons (SCENIC), clusters from Seurat, trajectory information... The package can also be used to extract data from .loom files.

Hclust2 is a handy tool for plotting heat-maps with several useful options to produce high quality figures that can be used in publications.

CPAT is a method to distinguish coding and noncoding RNA by using a logistic regression model based on four pure sequence-based, linguistic features: ORF size, ORF coverage, Ficket TESTCODE, and Hexamer usage bias. Linguistic features based method does not require other genomes or protein databases to perform alignment and is more robust. Because it is alignment-free, it runs much faster and also easier to use.

Sambamba is a high performance modern robust and fast tool (and library), written in the D programming language, for working with SAM and BAM files. Current parallelised functionality is an important subset of samtools functionality, including view, index, sort, markdup, and depth.

This package provides a fast and accurate analysis toolkit for single cell ATAC-seq (Assay for transposase-accessible chromatin using sequencing). Single cell ATAC-seq can resolve the heterogeneity of a complex tissue and reveal cell-type specific regulatory landscapes. However, the exceeding data sparsity has posed unique challenges for the data analysis. This package r-snapatac is an end-to-end bioinformatics pipeline for analyzing large- scale single cell ATAC-seq data which includes quality control, normalization, clustering analysis, differential analysis, motif inference and exploration of single cell ATAC-seq sequencing data.

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 framework to process and analyze data from high-throughput sequencing (HTS) assays

This package provides a simple web interface for the RNA-centric annotation system (RCAS).

This package implements scalable gene regulatory network inference using tree-based ensemble regressors.

gkm-SVM, a sequence-based method for predicting regulatory DNA elements, is a useful tool for studying gene regulatory mechanisms. LS-GKM is an effort to improve the method. It offers much better scalability and provides further advanced gapped k-mer based kernel functions. As a result, LS-GKM achieves considerably higher accuracy than the original gkm-SVM.