This package provides definitions for the Giotto object and its subobjects. Functionalities relating to data ingestion, basic object creation, data access within the Giotto object, and generics defined for the Giotto classes are all found here. Applications of this framework and convenience functions for loading specific technologies are found in the base Giotto package.

WebLogo is a web based application designed to make the generation of sequence logos as easy and painless as possible.

WebLogo can create output in several common graphics' formats, including the bitmap formats GIF and PNG, suitable for on-screen display, and the vector formats EPS and PDF, more suitable for printing, publication, and further editing. Additional graphics options include bitmap resolution, titles, optional axis, and axis labels, antialiasing, error bars, and alternative symbol formats.

A sequence logo is a graphical representation of an amino acid or nucleic acid multiple sequence alignment. Each logo consists of stacks of symbols, one stack for each position in the sequence. The overall height of the stack indicates the sequence conservation at that position, while the height of symbols within the stack indicates the relative frequency of each amino or nucleic acid at that position. The width of the stack is proportional to the fraction of valid symbols in that position.

Sickle is a tool that trims reads based on quality and length thresholds. It uses sliding windows to detect low-quality bases at the 3'-end and high-quality bases at the 5'-end. Additionally, it discards reads based on the length threshold.

Skewer implements the bit-masked k-difference matching algorithm dedicated to the task of adapter trimming and it is specially designed for processing next-generation sequencing (NGS) paired-end sequences.

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.

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.

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

BamTools provides both a C++ API and a command-line toolkit for handling BAM files.

Pypairix is a Python module for fast querying on a pairix-indexed bgzipped text file that contains a pair of genomic coordinates per line.

bustools is a program for manipulating BUS files for single cell RNA-Seq datasets. It can be used to error correct barcodes, collapse UMIs, produce gene count or transcript compatibility count matrices, and is useful for many other tasks.

BLAST is a popular method of performing a DNA or protein sequence similarity search, using heuristics to produce results quickly. It also calculates an “expect value” that estimates how many matches would have occurred at a given score by chance, which can aid a user in judging how much confidence to have in an alignment.

Jellyfish is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. Jellyfish is a command-line program that reads FASTA and multi-FASTA files containing DNA sequences. It outputs its k-mer counts in a binary format, which can be translated into a human-readable text format using the jellyfish dump command, or queried for specific k-mers with jellyfish query.

LibSBML is a library to help you read, write, manipulate, translate, and validate SBML files and data streams. The Systems Biology Markup Language (SBML) is an interchange format for computer models of biological processes. SBML is useful for models of metabolism, cell signaling, and more. It continues to be evolved and expanded by an international community.

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.

Collectively, the bedtools utilities are a swiss-army knife of tools for a wide-range of genomics analysis tasks. The most widely-used tools enable genome arithmetic: that is, set theory on the genome. For example, bedtools allows one to intersect, merge, count, complement, and shuffle genomic intervals from multiple files in widely-used genomic file formats such as BAM, BED, GFF/GTF, VCF.

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

The goal of bedtorch is to provide a fast BED file manipulation tool suite native in R.

Ngless is a domain-specific language for next-generation sequencing (NGS) data processing.

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.

This package is a library to enable flexible and scalable operations on genomic interval dataframes in Python. Bioframe enables access to a rich set of dataframe operations. Working in Python enables rapid visualization and iteration of genomic analyses. The philosophy underlying bioframe is to enable flexible operations. Instead of creating a function for every possible use-case, we encourage users to compose functions to achieve their goals.

This package has been developed under ROpenSci gudelines to integrate conventional and cutting edge cytometry analysis tools under a unified framework. It aims to represent an intuitive and interactive approach to analysing cytometry data in R.

This package provides an implementation of the BITS (Binary Interval Search) algorithm, an approach to interval set intersection. It is especially suited for the comparison of diverse genomic datasets and the exploration of large datasets of genome intervals (e.g. genes, sequence alignments).

This package provides a Python package for reading raw fcs files

This package provides Python bindings to the bwa mem aligner.