This package is tools for analysing intercellular and intracellular signaling from single cell RNA-seq (scRNA-seq) data.

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.

This package builds on Seurat's Doheatmap function code to produce a heatmap from a Seurat object with multiple annotation bars.

Pando leverages multi-modal single-cell measurements to infer gene regulatory networks using a flexible linear model-based framework. By modeling the relationship between TF-binding site pairs with the expression of target genes, Pando simultaneously infers gene modules and sets of regulatory regions for each transcription factor.

CodeAndRoll2 is a set of more than 130 productivity functions. These functions are used by MarkdownReports, ggExpress, and SeuratUtils.

This package implements an algorithm which increases the number of simultaneously measurable markers and in this way helps with study of the immune responses. Thus, the present algorithm, named CytoBackBone, allows combining phenotypic information of cells from different cytometric profiles obtained from different cytometry panels. This computational approach is based on the principle that each cell has its own phenotypic and functional characteristics that can be used as an identification card. CytoBackBone uses a set of predefined markers, that we call the backbone, to define this identification card. The phenotypic information of cells with similar identification cards in the different cytometric profiles is then merged.

Python scripts to find enrichment of GO terms. In addition, this package is used for processing the obo-formatted file from Gene Ontology website. The data structure is a directed acyclic graph that allows easy traversal from leaf to root.

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

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

CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts.

An interval tree can be used to efficiently find a set of numeric intervals overlapping or containing another interval. This library provides a basic implementation of an interval tree using C++ templates, allowing the insertion of arbitrary types into the tree.

This package aims to simplify working with genomic region / interval data by providing a common interface that lets you access a wide selection of file types and formats for handling genomic region data---all using the same syntax.

Roary is a high speed stand alone pan genome pipeline, which takes annotated assemblies in GFF3 format (produced by the Prokka program) and calculates the pan genome. Using a standard desktop PC, it can analyse datasets with thousands of samples, without compromising the quality of the results. 128 samples can be analysed in under 1 hour using 1 GB of RAM and a single processor. Roary is not intended for metagenomics or for comparing extremely diverse sets of genomes.

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.

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.

Miniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final unitig sequences. Thus the per-base error rate is similar to the raw input reads.

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

pySCENIC is a Python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq 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.

This is a collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.

Bamnostic is a pure Python Binary Alignment Map (BAM) file parser and random access tool.

Biopython is a set of tools for biological computation including parsers for bioinformatics files into Python data structures; interfaces to common bioinformatics programs; a standard sequence class and tools for performing common operations on them; code to perform data classification; code for dealing with alignments; code making it easy to split up parallelizable tasks into separate processes; and more.

Kallisto is a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate as existing quantification tools.

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.