This package provides a Variant Effect Predictor, which predicts the functional effects of genomic variants. It also provides Haplosaurus, which uses phased genotype data to predict whole-transcript haplotype sequences, and Variant Recoder, which translates between different variant encodings.

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.

libmaus2 is a collection of data structures and algorithms. It contains:

• I/O classes (single byte and UTF-8);

• bitio classes (input, output and various forms of bit level manipulation);

• text indexing classes (suffix and LCP array, fulltext and minute (FM), etc.);

• BAM sequence alignment files input/output (simple and collating); and many lower level support classes.

Isolator analyzes RNA-Seq experiments. Isolator has a particular focus on producing stable, consistent estimates. It implements a full hierarchical Bayesian model of an entire RNA-Seq experiment. It saves all the samples generated by the sampler, which can be processed to compute posterior probabilities for arbitrarily complex questions, far beyond the confines of pairwise tests. It aggressively corrects for technical effects, such as random priming bias, GC-bias, 3' bias, and fragmentation effects. Compared to other MCMC approaches, it is exceedingly efficient, though generally slower than modern maximum likelihood approaches.

This package generates a Miami plot with centered chromosome labels. The output is a ggplot2 object. Users can specify which data they want plotted on top vs. bottom, whether to display significance line(s), what colors to give chromosomes, and what points to label.

The package is ideal for analyzing RNA structure and chemical probing data.

This package is analyzing TCR and BCR sequences using unselected RNA sequencing data, profiled from fluid and solid tissues, including tumors. TRUST4 performs de novo assembly on V, J, C genes including the hypervariable CDR3 and reports consensus contigs of BCR/TCR sequences. TRUST4 then realigns the contigs to IMGT reference gene sequences to identify the corresponding gene and CDR3 details. TRUST4 supports both single-end and paired-end bulk or single-cell sequencing data with any read length.

Infernal ("INFERence of RNA ALignment") is a tool for searching DNA sequence databases for RNA structure and sequence similarities. It is an implementation of a special case of profile stochastic context-free grammars called covariance models (CMs). A CM is like a sequence profile, but it scores a combination of sequence consensus and RNA secondary structure consensus, so in many cases, it is more capable of identifying RNA homologs that conserve their secondary structure more than their primary sequence.

Smithlab CPP is a C++ library that includes functions used in many of the Smith lab bioinformatics projects, such as a wrapper around Samtools data structures, classes for genomic regions, mapped sequencing reads, etc.

Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis, phylogenetics, molecular evolution and population genetics. This library provides sequence-related modules.

This is an R package for pre-processing of flow and mass cytometry data. This package includes panel editing or renaming for FCS files, bead-based normalization and debarcoding.

SeqAn is a C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. It contains algorithms and data structures for string representation and their manipulation, online and indexed string search, efficient I/O of bioinformatics file formats, sequence alignment, and more.

This package lets you read and write the PLINK BED format, simply and efficiently.

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.

SeuratWrappers is a collection of community-provided methods and extensions for Seurat, curated by the Satija Lab at NYGC. These methods comprise functionality not presently found in Seurat, and are able to be updated much more frequently.

Megadepth is an efficient tool for extracting coverage related information from RNA and DNA-seq BAM and BigWig files. It supports reading whole-genome coverage from BAM files and writing either indexed TSV or BigWig files, as well as efficient region coverage summary over intervals from both types of files.

METAL is a tool for meta-analysis genomewide association scans. METAL can combine either test statistics and standard errors or p-values across studies (taking sample size and direction of effect into account). METAL analysis is a convenient alternative to a direct analysis of merged data from multiple studies. It is especially appropriate when data from the individual studies cannot be analyzed together because of differences in ethnicity, phenotype distribution, gender or constraints in sharing of individual level data imposed. Meta-analysis results in little or no loss of efficiency compared to analysis of a combined dataset including data from all individual studies.

Pybiomart provides a simple pythonic interface to biomart.

This package offers a set of functions to use in order to compute communities on graphs weighted or unweighted.

This package provides accelerated functions for the CIRI toolkit. It also provides the ccs executable to scan for circular consensus sequences.

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.

Bowtie is a fast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of over 25 million 35-bp reads per hour. Bowtie indexes the genome with a Burrows-Wheeler index to keep its memory footprint small: typically about 2.2 GB for the human genome (2.9 GB for paired-end).

PyEGA3 is a tool for viewing and downloading files from authorized EGA datasets. It uses the EGA data API and has several key features:

• Files are transferred over secure https connections and received unencrypted, so no need for decryption after download.

• Downloads resume from where they left off in the event that the connection is interrupted.

• Supports file segmenting and parallelized download of segments, improving overall performance.

• After download completes, file integrity is verified using checksums.

• Implements the GA4GH-compliant htsget protocol for download of genomic ranges for data files with accompanying index files.

Genrich is a peak-caller for genomic enrichment assays (e.g. ChIP-seq, ATAC-seq). It analyzes alignment files generated following the assay and produces a file detailing peaks of significant enrichment.