This package provides a method to detect and enable removal of doublets from single-cell RNA-sequencing.

The BIOM file format is designed to be a general-use format for representing counts of observations e.g. operational taxonomic units, KEGG orthology groups or lipid types, in one or more biological samples e.g. microbiome samples, genomes, metagenomes.

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 package is an integrated pipeline for large-scale phylogenetic profiling of genomes and metagenomes. PhyloPhlAn is an accurate, rapid, and easy-to-use method for large-scale microbial genome characterization and phylogenetic analysis at multiple levels of resolution. This software package can assign both genomes and MAGs to SGBs. PhyloPhlAn can reconstruct strain-level phylogenies using clade- specific maximally informative phylogenetic markers, and can also scale to very large phylogenies comprising >17,000 microbial species.

Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis, phylogenetics, molecular evolution and population genetics. It is Object Oriented and is designed to be both easy to use and computer efficient. Bio++ intends to help programmers to write computer expensive programs, by providing them a set of re-usable tools.

This package provides an object for plotting GRanges, RleList, UCSC file formats, and ffTrack objects in multi-track panels.

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.

Scanorama enables batch-correction and integration of heterogeneous scRNA-seq datasets, which is described in the paper "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama" by Brian Hie, Bryan Bryson, and Bonnie Berger.

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

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 adds 3D perspective plotting of points, paths, and line, 3D perspective axes, 3D perspective annotations, and wireframe plots.

inStrain is a Python program for analysis of co-occurring genome populations from metagenomes that allows highly accurate genome comparisons, analysis of coverage, microdiversity, and linkage, and sensitive SNP detection with gene localization and synonymous non-synonymous identification.

PAIRADISE is a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, PAIRADISE formulates ASAS detection as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates.

Forester is a collection of Java libraries for phylogenomics and evolutionary biology research. It includes support for reading, writing, and exporting phylogenetic trees.

MuSiC is a deconvolution method that utilizes cross-subject scRNA-seq to estimate cell type proportions in bulk RNA-seq data.

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.

SnapTools can operate on snap files the following types of operations:

• index the reference genome before alignment;

• align reads to the corresponding reference genome;

• pre-process by convert pair-end reads into fragments, checking the mapping quality score, alignment and filtration;

• create the cell-by-bin matrix.

This package provides a Python environment for phylogenetic tree exploration.

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

RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides a user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. For visualization, it can generate BAM and Wiggle files in both transcript-coordinate and genomic-coordinate.

MAFFT offers a range of multiple alignment methods for nucleotide and protein sequences. For instance, it offers L-INS-i (accurate; for alignment of <~200 sequences) and FFT-NS-2 (fast; for alignment of <~30,000 sequences).

Arriba is a command-line tool for the detection of gene fusions from RNA-Seq data. It was developed for the use in a clinical research setting. Therefore, short runtimes and high sensitivity were important design criteria. It is based on the fast STAR aligner and the post-alignment runtime is typically just around two minutes. In contrast to many other fusion detection tools which build on STAR, Arriba does not require to reduce the alignIntronMax parameter of STAR to detect small deletions.

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.

This package provides a GFF/GTF file parsing utility providing format conversions, region filtering, FASTA sequence extraction and more.