muon is a multimodal omics Python framework.

Hotspot is a tool for identifying informative genes (and gene modules) in a single-cell dataset. Importantly, "informative" is decided based on how well a gene's variation agrees with some cell metric---some similarity mapping between cells. Genes which are informative are those whose expression varies in similar way among cells which are nearby in the given metric.

This package provides a new batch effect correction method based on Projection to Latent Structures Discriminant Analysis named “PLSDA-batch” to correct data prior to any downstream analysis. PLSDA-batch estimates latent components related to treatment and batch effects to remove batch variation. The method is multivariate, non-parametric and performs dimension reduction. Combined with centered log ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far.

The Spliced Transcripts Alignment to a Reference (STAR) software is based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences.

This package contains data used by pagoda2. The data within this package are the 3000 bone marrow cells used for vignettes.

MinCED is a program to find Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) in DNA sequences. It can be used for unassembled metagenomic reads, but is mainly designed for full genomes and assembled metagenomic sequence.

SlamDunk is a fully automated tool for automated, robust, scalable and reproducible SLAMseq data analysis. Diagnostic plotting features and a MultiQC plugin will make your SLAMseq data ready for immediate QA and interpretation.

This is package for including transposable elements in differential enrichment analysis of sequencing datasets. TEtranscripts and TEcount take RNA-seq (and similar data) and annotates reads to both genes and transposable elements. TEtranscripts then performs differential analysis using DESeq2. Note that TEtranscripts and TEcount rely on specially curated GTF files, which are not included due to their size.

JAMM is a peak finder for next generation sequencing datasets (ChIP-Seq, ATAC-Seq, DNase-Seq, etc.) that can integrate replicates and assign peak boundaries accurately. JAMM is applicable to both broad and narrow datasets.

This is a Python package for the interactive visualization of bulk RNA-seq data. It provides a range of plotting functions and interactive tools to explore and analyze bulk RNA-seq data.

BayesPrism includes deconvolution and embedding learning modules. The deconvolution module models a prior from cell type-specific expression profiles from scRNA-seq to jointly estimate the posterior distribution of cell type composition and cell type-specific gene expression from bulk RNA-seq expression of tumor samples. The embedding learning module uses Expectation-maximization (EM) to approximate the tumor expression using a linear combination of malignant gene programs while conditional on the inferred expression and fraction of non-malignant cells estimated by the deconvolution module.

BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently with both VCFs and BCFs, both uncompressed and BGZF-compressed.

This package addresses the challenge of handling large amounts of data that are now routinely generated from DNA sequencing centers. deepTools contains useful modules to process the mapped reads data for multiple quality checks, creating normalized coverage files in standard bedGraph and bigWig file formats, that allow comparison between different files. Finally, using such normalized and standardized files, deepTools can create many publication-ready visualizations to identify enrichments and for functional annotations of the genome.

PAML (for Phylogentic Analysis by Maximum Likelihood) contains a few programs for model fitting and phylogenetic tree reconstruction using nucleotide or amino-acid sequence data.

This package provides Python bindings for lib2bit to access 2bit files with Python.

Bioparser is a C++ header only parsing library for several bioinformatics formats (FASTA/Q, MHAP/PAF/SAM), with support for zlib compressed files.

CENTIPEDE applies a hierarchical Bayesian mixture model to infer regions of the genome that are bound by particular transcription factors. It starts by identifying a set of candidate binding sites, and then aims to classify the sites according to whether each site is bound or not bound by a transcription factor. CENTIPEDE is an unsupervised learning algorithm that discriminates between two different types of motif instances using as much relevant information as possible.

NGS is a domain-specific API for accessing reads, alignments and pileups produced from Next Generation Sequencing. The API itself is independent from any particular back-end implementation, and supports use of multiple back-ends simultaneously.

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

Kaiju is a program for sensitive taxonomic classification of high-throughput sequencing reads from metagenomic whole genome sequencing experiments.

Sleuth is a program for differential analysis of RNA-Seq data. It makes use of quantification uncertainty estimates obtained via Kallisto for accurate differential analysis of isoforms or genes, allows testing in the context of experiments with complex designs, and supports interactive exploratory data analysis via sleuth live.

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.

BitMapperBS is memory-efficient aligner that is designed for whole-genome bisulfite sequencing (WGBS) reads from directional protocol.

DoubletFinder identifies doublets by generating artificial doublets from existing scRNA-seq data and defining which real cells preferentially co-localize with artificial doublets in gene expression space. Other DoubletFinder package functions are used for fitting DoubletFinder to different scRNA-seq datasets. For example, ideal DoubletFinder performance in real-world contexts requires optimal pK selection and homotypic doublet proportion estimation. pK selection is achieved using pN-pK parameter sweeps and maxima identification in mean-variance-normalized bimodality coefficient distributions. Homotypic doublet proportion estimation is achieved by finding the sum of squared cell annotation frequencies.