This package provides different statistical methods to extract biological activities from omics data within a unified framework.

SortMeRNA is a biological sequence analysis tool for filtering, mapping and OTU picking of NGS reads. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering rRNA from metatranscriptomic data.

Picard is a set of Java command line tools for manipulating high-throughput sequencing (HTS) data and formats. Picard is implemented using the HTSJDK Java library to support accessing file formats that are commonly used for high-throughput sequencing data such as SAM, BAM, CRAM and VCF.

PhyML is a software package that uses modern statistical approaches to analyse alignments of nucleotide or amino acid sequences in a phylogenetic framework. The main tool in this package builds phylogenies under the maximum likelihood criterion. It implements a large number of substitution models coupled with efficient options to search the space of phylogenetic tree topologies. codePhyREX fits the spatial-Lambda-Fleming-Viot model to geo-referenced genetic data. This model is similar to the structured coalescent but assumes that individuals are distributed along a spatial continuum rather than discrete demes. PhyREX can be used to estimate population densities and rates of dispersal. Its output can be processed by treeannotator (from the BEAST package) as well as SPREAD.

Anglemania extracts genes from multi-batch scRNA-seq experiments for downstream dataset integration. It improves conventional usage of highly-variable genes for integration tasks.

python-scanrbp is a Python package that provides the scanRBP tool that loads RNA-protein binding motif PWM and computes the log-odds scores for all the loaded RBPs across a given genomic sequence and draws a heatmap of the scores.

The WiggleTools package allows genomewide data files to be manipulated as numerical functions, equipped with all the standard functional analysis operators (sum, product, product by a scalar, comparators), and derived statistics (mean, median, variance, stddev, t-test, Wilcoxon's rank sum test, etc).

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.

Piranha is a peak-caller for genomic data produced by CLIP-seq and RIP-seq experiments. It takes input in BED or BAM format and identifies regions of statistically significant read enrichment. Additional covariates may optionally be provided to further inform the peak-calling process.

This is a set of R functions that allows you to generate precise figures. This tool will create clean markdown reports about what you just discovered.

This package detects naive associations between omics features and metadata in cross-sectional data-sets using non-parametric tests. In a second step, confounding effects between metadata associated to the same omics feature are detected and labeled using nested post-hoc model comparison tests. The generated output can be graphically summarized using the built-in plotting function.

SAIGE is a package for efficiently controlling for case-control imbalance and sample relatedness in single-variant assoc tests (SAIGE) and controlling for sample relatedness in region-based assoc tests in large cohorts and biobanks (SAIGE-GENE+).

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.

This package provides R miscellaneous utilities for basic data manipulation, debugging, visualization, lsf management, and common mskilab tasks.

The package graph implements graph manipulation functions.

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

MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of gene regulation, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes.

This package provides a set of functions to parse and open (search query) links to genomics related and other websites for R. Useful when you want to explore e.g.: the function of a set of differentially expressed genes.

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.

The IDR (Irreproducible Discovery Rate) framework is a unified approach to measure the reproducibility of findings identified from replicate experiments and provide highly stable thresholds based on reproducibility.

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.

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 library contains the genomics components of the Bio++ phylogenetics library. It is part of the Bio++ project.

HOMER (Hypergeometric Optimization of Motif EnRichment) is a suite of tools for Motif Discovery and next-gen sequencing analysis. It is a collection of command line programs written in Perl and C++. HOMER was primarily written as a de novo motif discovery algorithm and is well suited for finding 8-20 bp motifs in large scale genomics data. HOMER contains many useful tools for analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and numerous other types of functional genomics sequencing data sets.