BEDOPS is a suite of tools to address common questions raised in genomic studies---mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.

BEDOPS provides tools that perform highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale. Tasks can be easily split by chromosome for distributing whole-genome analyses across a computational cluster.

RSeQC provides a number of modules that can comprehensively evaluate high throughput sequence data, especially RNA-seq data. Some basic modules inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, etc.

This package provides data for the book "Computational Genomics with R".

ChIPKernels is an R package for building different string kernels used for DNA Sequence analysis. A dictionary of the desired kernel must be built and this dictionary can be used for determining kernels for DNA Sequences.

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

python-gffutils is a Python package for working with and manipulating the GFF and GTF format files typically used for genomic annotations. The files are loaded into a SQLite database, allowing much more complex manipulation of hierarchical features (e.g., genes, transcripts, and exons) than is possible with plain-text methods alone.

This package provides a computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

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.

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 Python bindings to the libBigWig library for accessing bigWig files.

This package provides a Variant Effect Predictor, which predicts the functional effects of genomic variants. It also provides Haplosaurus, which uses phased genotype data to predict whole-transcript haplotype sequences, and Variant Recoder, which translates between different variant encodings.

This package stores motif collections as lists of position frequency matrix (PWMatrixList) objects provided by the TFBSTools package for use in R with packages like motifmatchr or chromVAR.

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.

This package can be used to normalize cytometry samples when a control sample is taken along in each of the batches. This is done by first identifying multiple clusters/cell types, learning the batch effects from the control samples and applying quantile normalization on all markers of interest.

MyGene.Info provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. Mygene is a Python wrapper to access MyGene.Info services.

SNAP is a fast and accurate aligner for short DNA reads. It is optimized for modern read lengths of 100 bases or higher, and takes advantage of these reads to align data quickly through a hash-based indexing scheme.

CMSeq is a set of commands to provide an interface to .bam files for coverage and sequence consensus.

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

HTSJDK is an implementation of a unified Java library for accessing common file formats, such as SAM and VCF, used for high-throughput sequencing (HTS) data. There are also an number of useful utilities for manipulating HTS data.

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 extra utility functions to perform common tasks in the analysis of omics data, leveraging and enhancing features provided by Bioconductor packages.

This package analyses the Oxford Nanopore sequencing data at signal-level. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs (Single nucleotide polymorphisms) and indels with respect to a reference genome and more.

Circe is a Python package for inferring co-accessibility networks from single-cell ATAC-seq data, using skggm for the graphical lasso and python-scanpy for data processing.

Chromap is a fast method for aligning and preprocessing high throughput chromatin profiles. Typical use cases include:

• trimming sequencing adapters, mapping bulk ATAC-seq or ChIP-seq genomic reads to the human genome and removing duplicates;

• trimming sequencing adapters, mapping single cell ATAC-seq genomic reads to the human genome, correcting barcodes, removing duplicates and performing Tn5 shift;

• split alignment of Hi-C reads against a reference genome.