SortMeRNA is a biological sequence analysis tool for filtering, mapping and OTU picking of NGS reads. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering rRNA from metatranscriptomic data.

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.

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.

DIAMOND is a BLAST-compatible local aligner for mapping protein and translated DNA query sequences against a protein reference database (BLASTP and BLASTX alignment mode). The speedup over BLAST is up to 20,000 on short reads at a typical sensitivity of 90-99% relative to BLAST depending on the data and settings.

This package provides TagGD barcode demultiplexing utilities for Spatial Transcriptomics data.

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.

This package provides a lightweight interface to Stan <https://mc-stan.org>. The CmdStanR interface is an alternative to RStan that calls the command line interface for compilation and running algorithms instead of interfacing with C++ via Rcpp'.

Discrover is a motif discovery method to find binding sites of nucleic acid binding proteins.

Fastp is a tool designed to provide fast all-in-one preprocessing for FastQ files. This tool has multi-threading support to afford high performance.

NanoSV is a software package that can be used to identify structural genomic variations in long-read sequencing data, such as data produced by Oxford Nanopore Technologies’ MinION, GridION or PromethION instruments, or Pacific Biosciences RSII or Sequel sequencers.

This program compares version strings. It intends to be a replacement for strverscmp.

This package provides an implementation of chunked, compressed, N-dimensional arrays for R, Zarr specification version 2 (2024) <doi:10.5281/zenodo.11320255>.

Telomerecat is a tool for estimating the average telomere length (TL) for a paired end, whole genome sequencing (WGS) sample.

Telomerecat is adaptable, accurate and fast. The algorithm accounts for sequencing amplification artifacts, anneouploidy (common in cancer samples) and noise generated by WGS. For a high coverage WGS BAM file of around 100GB telomerecat can produce an estimate in ~1 hour.

The package reads phylogenetic data in the phyloXML format. It also includes functions for writing data in this format.

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

Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454. Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired read information, if available, to retrieve the repeated areas between contigs.

Seqtk is a fast and lightweight tool for processing sequences in the FASTA or FASTQ format. It parses both FASTA and FASTQ files which can be optionally compressed by gzip.

Sylamer is a system for finding significantly over or under-represented words in sequences according to a sorted gene list. Typically it is used to find significant enrichment or depletion of microRNA or siRNA seed sequences from microarray expression data. Sylamer is extremely fast and can be applied to genome-wide datasets with ease. Results are plotted in terms of a significance landscape plot. These plots show significance profiles for each word studied across the sorted genelist.

MyGene.Info provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. Mygene is a Python wrapper to access MyGene.Info services.

This is a package providing efficient operations for single cell ATAC-seq fragments and RNA counts matrices. It is interoperable with standard file formats, and introduces efficient bit-packed formats that allow large storage savings and increased read speeds.

Scregseg (Single-Cell REGulatory landscape SEGmentation) is a tool that facilitates the analysis of single cell ATAC-seq data by an HMM-based segmentation algorithm. Scregseg uses an HMM with Dirichlet-Multinomial emission probabilities to segment the genome either according to distinct relative cross-cell accessibility profiles or (after collapsing the single-cell tracks to pseudo-bulk tracks) to capture distinct cross-cluster accessibility profiles.

muon is a multimodal omics Python framework.

This package offers a flexible statistical simulator for scRNA-seq data. It can generate data that captures gene correlation. Additionally, it allows for varying the number of cells and sequencing depth.

This is a drop-in replacement for the IlluminaHumanMethylationEPIC package. It utilizes a Manifest based on 1.0B5 annotation. As of version 0.3.0, the IlluminaHumanMethylationEPIC package still employs the 1.0B2 annotation manifest. A corresponding annotation package, IlluminaHumanMethylationEPICanno.ilm10b5.hg38, is available to ensure proper annotation. The decision to maintain the same name is due to complications in downstream processing caused by array name lookup in certain preprocessing options.