The R package rareMETALS2 is an extension of the R package rareMETALS. It was designed to meta-analyze gene-level association tests for binary trait. While rareMETALS offers a near-complete solution for meta-analysis of gene-level tests for quantitative trait, it does not offer the optimal solution for binary trait. The package rareMETALS2 offers improved features for analyzing gene-level association tests in meta-analyses for binary trait.

This package provides a concrete implementation of the fast5 file schema using the generic h5py library, plain-named methods to interact with and reflect the fast5 file schema, and tools to convert between multi_read and single_read formats.

The package graph implements graph manipulation functions.

ScVelo is a scalable toolkit for RNA velocity analysis in single cells. RNA velocity enables the recovery of directed dynamic information by leveraging splicing kinetics. scVelo generalizes the concept of RNA velocity by relaxing previously made assumptions with a stochastic and a dynamical model that solves the full transcriptional dynamics. It thereby adapts RNA velocity to widely varying specifications such as non-stationary populations.

FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis.

The main functions of FastQC are:

• Import of data from BAM, SAM or FastQ files (any variant);

• Providing a quick overview to tell you in which areas there may be problems;

• Summary graphs and tables to quickly assess your data;

• Export of results to an HTML based permanent report;

• Offline operation to allow automated generation of reports without running the interactive application.

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

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

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

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

wfmash is a DNA sequence read mapper based on mash distances and the wavefront alignment algorithm. It is a fork of MashMap that implements base-level alignment via the wflign tiled wavefront global alignment algorithm. It completes MashMap with a high-performance alignment module capable of computing base-level alignments for very large sequences.

Ritornello is a ChIP-seq peak calling algorithm based on signal processing that can accurately call binding events without the need to do a pair total DNA input or IgG control sample. It has been tested for use with narrow binding events such as transcription factor ChIP-seq.

R-scape discovers RNA secondary structure consensus elements. These elements include riboswitches and ribozymes. It utilizes probabilistic modeling of sequence alignments, explicitly considering folding dependencies. The tool enables the de novo search for new structural elements and facilitates comparative analysis of known RNA families.

This package infers, visualizes and analyzes the cell-cell communication networks from scRNA-seq data.

This package contains a multicore Barnes-Hut implementation of the t-SNE algorithm. The implementation is described here: http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf.

This package provides a convenient interface to minimap2, a fast and accurate C program to align genomic and transcribe nucleotide sequences.

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.

BWA-PSSM is a probabilistic short genomic sequence read aligner based on the use of position specific scoring matrices (PSSM). Like many of the existing aligners it is fast and sensitive. Unlike most other aligners, however, it is also adaptible in the sense that one can direct the alignment based on known biases within the data set. It is coded as a modification of the original BWA alignment program and shares the genome index structure as well as many of the command line options.

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.

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.

GEMMA provides a standard linear mixed model resolver with application in GWAS.

SQUID is Sean Eddy's personal library of C functions and utility programs for sequence analysis.

Psupertime is supervised pseudotime for single cell RNAseq data. It uses single cell RNAseq data, where the cells have a known ordering. This ordering helps to identify a small number of genes which place cells in that known order. It can be used for discovery of relevant genes, for identification of subpopulations, and characterization of further unknown or differently labelled data.

The Shaman package implements functions for resampling Hi-C matrices in order to generate expected contact distributions given constraints on marginal coverage and contact-distance probability distributions. The package also provides support for visualizing normalized matrices and statistical analysis of contact distributions around selected landmarks.