This package provides the kentUtils, a selection of bioinformatics utilities used in combination with the UCSC genome browser.

This package implements a bioinformatics algorithm for demultiplexing multiplexed single cell datasets. It is built on a statistical model of tag read counts derived from the physical mechanism of tag cross-contamination.

This framework facilitates the execution of differential junction usage (DJU) methods. Additionally, it enables the integration of results from multiple DJU methods.

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

Trinity assembles transcript sequences from Illumina RNA-Seq data. Trinity represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes.

This package provides graphical user interfaces to organize and visualize Nanopore sequencing data.

Bowtie 2 is a fast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes.

This package provides Python bindings to the UCSC Big Binary (bigWig/bigBed) file library. This provides read-level access to local and remote bigWig and bigBed files but no write capabilitites. The main feature is fast retrieval of range queries into numpy arrays.

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

This package is a client implementation of the GA4GH htsget protocol. It provides a simple and reliable way to retrieve genomic data from servers supporting the protocol.

This package generates a Miami plot with centered chromosome labels. The output is a ggplot2 object. Users can specify which data they want plotted on top vs. bottom, whether to display significance line(s), what colors to give chromosomes, and what points to label.

FAN-C provides a pipeline for analysing Hi-C data starting at mapped paired-end sequencing reads.

This package is a set of R functions for generating precise figures. This tool helps you to create clean markdown reports about what you just discovered with your analysis script.

This package provides an R API and htmlwidget facilitating interactive visualization of spatial single-cell data with Vitessce. The R API contains classes and functions for loading single-cell data stored in compatible on-disk formats. The htmlwidget is a wrapper around the Vitessce JavaScript library and can be used in the Viewer tab of RStudio or Shiny apps.

This is a package for fast Non-negative Matrix Factorization (NMF) with automatic rank-determination for dimension reduction of single-cell data using Seurat, RcppML nmf, SingleCellExperiments and similar.

ReadWriter is a set of R functions to read and write files conveniently.

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

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.

The alignment module of BioJava provides an API that contains

• implementations of dynamic programming algorithms for sequence alignment;

• reading and writing of popular alignment file formats;

• a single-, or multi- threaded multiple sequence alignment algorithm.

WhatsHap is software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads.

Bismark is a program to map bisulfite treated sequencing reads to a genome of interest and perform methylation calls in a single step. The output can be easily imported into a genome viewer, such as SeqMonk, and enables a researcher to analyse the methylation levels of their samples straight away. Its main features are:

• Bisulfite mapping and methylation calling in one single step

• Supports single-end and paired-end read alignments

• Supports ungapped and gapped alignments

• Alignment seed length, number of mismatches etc are adjustable

• Output discriminates between cytosine methylation in CpG, CHG and CHH context

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.

MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion. Intuitively, MOFA can be viewed as a versatile and statistically rigorous generalization of principal component analysis to multi-omics data. Given several data matrices with measurements of multiple -omics data types on the same or on overlapping sets of samples, MOFA infers an interpretable low-dimensional representation in terms of a few latent factors. These learnt factors represent the driving sources of variation across data modalities, thus facilitating the identification of cellular states or disease subgroups.

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.