The porechop package is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity. Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

This is a package that lets you process UMI-4C data from scratch to produce nice plots.

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.

HISAT is a fast and sensitive spliced alignment program for mapping RNA-seq reads. In addition to one global FM index that represents a whole genome, HISAT uses a large set of small FM indexes that collectively cover the whole genome. These small indexes (called local indexes) combined with several alignment strategies enable effective alignment of RNA-seq reads, in particular, reads spanning multiple exons.

This package provides graphical user interfaces to organize and visualize Nanopore sequencing data.

BSeq-sc is a bioinformatics analysis pipeline that leverages single-cell sequencing data to estimate cell type proportion and cell type-specific gene expression differences from RNA-seq data from bulk tissue samples. This is a companion package to the publication "A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure." Baron et al. Cell Systems (2016) https://www.ncbi.nlm.nih.gov/pubmed/27667365.

python-cwl-upgrader is a standalone upgrader for CWL documents from version draft-3, v1.0, and v1.1 to v1.2.

BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.

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

The NCBI-VDB library implements a highly compressed columnar data warehousing engine that is most often used to store genetic information. Databases are stored in a portable image within the file system, and can be accessed/downloaded on demand across HTTP.

Flexbar preprocesses high-throughput nucleotide sequencing data efficiently. It demultiplexes barcoded runs and removes adapter sequences. Moreover, trimming and filtering features are provided. Flexbar increases read mapping rates and improves genome and transcriptome assemblies. It supports next-generation sequencing data in fasta/q and csfasta/q format from Illumina, Roche 454, and the SOLiD platform.

SAIGE is a package for efficiently controlling for case-control imbalance and sample relatedness in single-variant assoc tests (SAIGE) and controlling for sample relatedness in region-based assoc tests in large cohorts and biobanks (SAIGE-GENE+).

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.

This package provides tools for dealing with Unique Molecular Identifiers (UMIs) and Random Molecular Tags (RMTs) in genetic sequences. There are six tools: the extract and whitelist commands are used to prepare a fastq containing UMIs +/- cell barcodes for alignment. The remaining commands, group, dedup, and count/count_tab, are used to identify PCR duplicates using the UMIs and perform different levels of analysis depending on the needs of the user.

This framework facilitates the execution of differential junction usage (DJU) methods. Additionally, it enables the integration of results from multiple DJU methods.

This package contains functions for the SCENT algorithm. SCENT uses single-cell multimodal data and links ATAC-seq peaks to their target genes by modeling association between chromatin accessibility and gene expression across individual single cells.

This is a set of functions for processing raw scDam&T-seq data. scDam&T-seq is a method to simultaneously measure protein-DNA interactions and transcription from single cells (Rooijers et al., 2019). It combines a DamID-based method to measure protein-DNA interactions and an adaptation of CEL-Seq to measure transcription. The starting point of the workflow is raw sequencing data and the end result are tables of UMI-unique DamID and CEL-Seq counts.

Mosaicatcher counts Strand-seq reads and classifies strand states of each chromosome in each cell using a Hidden Markov Model.

This package is designed to streamline scATAC analyses in R.

CENTIPEDE applies a hierarchical Bayesian mixture model to infer regions of the genome that are bound by particular transcription factors. It starts by identifying a set of candidate binding sites, and then aims to classify the sites according to whether each site is bound or not bound by a transcription factor. CENTIPEDE is an unsupervised learning algorithm that discriminates between two different types of motif instances using as much relevant information as possible.

This package provides the kentUtils, a selection of bioinformatics utilities used in combination with the UCSC genome browser.

SCENIC (Single-cell regulatory network inference and clustering) is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

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.

The preseq package is aimed at predicting and estimating the complexity of a genomic sequencing library, equivalent to predicting and estimating the number of redundant reads from a given sequencing depth and how many will be expected from additional sequencing using an initial sequencing experiment. The estimates can then be used to examine the utility of further sequencing, optimize the sequencing depth, or to screen multiple libraries to avoid low complexity samples.