PiGx is a collection of genomics pipelines. It includes the following pipelines:

• PiGx BSseq for raw fastq read data of bisulfite experiments

• PiGx RNAseq for RNAseq samples

• PiGx scRNAseq for single cell dropseq analysis

• PiGx ChIPseq for reads from ChIPseq experiments

All pipelines are easily configured with a simple sample sheet and a descriptive settings file. The result is a set of comprehensive, interactive HTML reports with interesting findings about your samples.

Savvy is the official C++ interface for the SAV file format and offers seamless support for BCF and VCF files.

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

Bandage is a program for visualising de novo assembly graphs. It allows users to interact with the assembly graphs made by de novo assemblers such as Velvet, SPAdes, MEGAHIT and others. De novo assembly graphs contain not only assembled contigs but also the connections between those contigs, which were previously not easily accessible. Bandage visualises assembly graphs, with connections, using graph layout algorithms. Nodes in the drawn graph, which represent contigs, can be automatically labelled with their ID, length or depth. Users can interact with the graph by moving, labelling and colouring nodes. Sequence information can also be extracted directly from the graph viewer. By displaying connections between contigs, Bandage opens up new possibilities for analysing and improving de novo assemblies that are not possible by looking at contigs alone.

This is the reference implementation of the CWL standards. The CWL open standards are for describing analysis workflows and tools in a way that makes them portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. CWL is designed to meet the needs of data-intensive science, such as Bioinformatics, Medical Imaging, Astronomy, Physics, and Chemistry. The cwltool is intended to be feature complete and to provide comprehensive validation of CWL files as well as provide other tools related to working with CWL descriptions.

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.

The package is ideal for analyzing RNA structure and chemical probing 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.

This R package lets you estimate signatures of mutational processes and their activities on mutation count data. Starting from a set of single-nucleotide variants (SNVs), it allows both estimation of the exposure of samples to predefined mutational signatures (including whether the signatures are present at all), and identification of signatures de novo from the mutation counts.

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.

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.

genomepy is designed to provide a simple and straightforward way to download and use genomic data. This includes

1. searching available data,

2. showing the available metadata,

3. automatically downloading, preprocessing and matching data, and

4. generating optional aligner indexes.

All with sensible, yet controllable defaults.

PiGX scRNAseq is an analysis pipeline for preprocessing and quality control for single cell RNA sequencing experiments. The inputs are read files from the sequencing experiment, and a configuration file which describes the experiment. It produces processed files for downstream analysis and interactive quality reports. The pipeline is designed to work with UMI based methods.

Zarr backend for DelayedArray objects.

FastTree can handle alignments with up to a million of sequences in a reasonable amount of time and memory. For large alignments, FastTree is 100-1,000 times faster than PhyML 3.0 or RAxML 7.

RAxML is a tool for phylogenetic analysis and post-analysis of large phylogenies.

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

This package provides a framework for the analysis and exploration of single-cell chromatin data. The Signac package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis.

This package is analyzing TCR and BCR sequences using unselected RNA sequencing data, profiled from fluid and solid tissues, including tumors. TRUST4 performs de novo assembly on V, J, C genes including the hypervariable CDR3 and reports consensus contigs of BCR/TCR sequences. TRUST4 then realigns the contigs to IMGT reference gene sequences to identify the corresponding gene and CDR3 details. TRUST4 supports both single-end and paired-end bulk or single-cell sequencing data with any read length.

Bio-locus is a tabix-like tool for fast querying of genome locations. Many file formats in bioinformatics contain records that start with a chromosome name and a position for a SNP, or a start-end position for indels. Bio-locus allows users to store this chr+pos or chr+pos+alt information in a database.

This package implements bindings for zarr store that are compatible with Bioconductor S4 data structures, namely the DataFrame and DelayedArray. This allows Zarr-backed data to be easily used as data frames with arbitrary sets of columns.

Pegasusio is a Python package for reading or writing single-cell genomics data.

Python scripts to find enrichment of GO terms. In addition, this package is used for processing the obo-formatted file from Gene Ontology website. The data structure is a directed acyclic graph that allows easy traversal from leaf to root.

This package implements methods for batch correction and integration of scRNA-seq datasets, based on the Seurat anchor-based integration framework. In particular, STACAS is optimized for the integration of heterogeneous datasets with only limited overlap between cell sub-types (e.g. TIL sets of CD8 from tumor with CD8/CD4 T cells from lymphnode), for which the default Seurat alignment methods would tend to over-correct biological differences. The 2.0 version of the package allows the users to incorporate explicit information about cell-types in order to assist the integration process.