This package is intended to help users to efficiently analyze genomic data resulting from various experiments.

This R package lets you estimate signatures of mutational processes and their activities on mutation count data. Starting from a set of single-nucleotide variants (SNVs), it allows both estimation of the exposure of samples to predefined mutational signatures (including whether the signatures are present at all), and identification of signatures de novo from the mutation counts.

The subread package contains the following tools: subread aligner, a general-purpose read aligner; subjunc aligner: detecting exon-exon junctions and mapping RNA-seq reads; featureCounts: counting mapped reads for genomic features; exactSNP: a SNP caller that discovers SNPs by testing signals against local background noises.

LoFreq is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.

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.

SQUID is Sean Eddy's personal library of C functions and utility programs for sequence analysis.

This package infers, visualizes and analyzes the cell-cell communication networks from 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.

The data within this package is a panel of four samples, each with 3000 cells. There are two samples which are bone marrow (BM), and two samples which are cord blood (CB).

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.

Bowtie 2 is a fast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes.

This is an R package to build generic .loom files aligning with the default naming convention of the .loom format and to integrate other data types e.g.: regulons (SCENIC), clusters from Seurat, trajectory information... The package can also be used to extract data from .loom files.

This is a package for the discovery of communities in Pore-C concatemers.

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.

This package aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot: bigwig, bed (many options), bedgraph, links (represented as arcs), and Hi-C matrices. pyGenomeTracks can make plots with or without Hi-C data.

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.

HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). In addition to using one global graph FM (GFM) index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome. These small indexes, combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).

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.

R-scape discovers RNA secondary structure consensus elements. These elements include riboswitches and ribozymes. It utilizes probabilistic modeling of sequence alignments, explicitly considering folding dependencies. The tool enables the de novo search for new structural elements and facilitates comparative analysis of known RNA families.

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.

This package facilitates the analysis of single-cell RNA-seq UMI matrices. It does this by computing partitions of a cell similarity graph into small homogeneous groups of cells, which are defined as metacells (MCs). The derived MCs are then used for building different representations of the data, allowing matrix or 2D graph visualization forming a basis for analysis of cell types, subtypes, transcriptional gradients,cell-cycle variation, gene modules and their regulatory models and more.

This package implements FLAIR (Full-Length Alternative Isoform analysis of RNA) for the correction, isoform definition, and alternative splicing analysis of noisy reads. FLAIR has primarily been used for nanopore cDNA, native RNA, and PacBio sequencing reads.

The R package rareMETALS2 is an extension of the R package rareMETALS. It was designed to meta-analyze gene-level association tests for binary trait. While rareMETALS offers a near-complete solution for meta-analysis of gene-level tests for quantitative trait, it does not offer the optimal solution for binary trait. The package rareMETALS2 offers improved features for analyzing gene-level association tests in meta-analyses for binary trait.

Very fast parallel big-data BLAST XML file parser which can be used as command line utility. Use blastxmlparser to: Parse BLAST XML; filter output; generate FASTA, JSON, YAML, RDF, JSON-LD, HTML, CSV, tabular output etc.