Bloom-filter-based error correction solution for high-throughput sequencing reads (BLESS) uses a single minimum-sized bloom filter is a correction tool for genomic reads produced by Next-generation sequencing (NGS). BLESS produces accurate correction results with much less memory compared with previous solutions and is also able to tolerate a higher false-positive rate. BLESS can extend reads like DNA assemblers to correct errors at the end of reads.

This package converts the output of the Sailfish and Salmon RNA-seq quantification tools so that it can be used with the Sleuth differential analysis package.

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).

This package provides a framework to process and analyze data from high-throughput sequencing (HTS) assays

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.

MOSAIK is a program for mapping second and third-generation sequencing reads to a reference genome. MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT.

RSeQC provides a number of modules that can comprehensively evaluate high throughput sequence data, especially RNA-seq data. Some basic modules inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, etc.

The WiggleTools package allows genomewide data files to be manipulated as numerical functions, equipped with all the standard functional analysis operators (sum, product, product by a scalar, comparators), and derived statistics (mean, median, variance, stddev, t-test, Wilcoxon's rank sum test, etc).

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.

This package adds 3D perspective plotting of points, paths, and line, 3D perspective axes, 3D perspective annotations, and wireframe plots.

This package performs a fast Wilcoxon rank sum test and auROC analysis.

This package has been developed under ROpenSci gudelines to integrate conventional and cutting edge cytometry analysis tools under a unified framework. It aims to represent an intuitive and interactive approach to analysing cytometry data in R.

PyLiftover is a library for quick and easy conversion of genomic (point) coordinates between different assemblies.

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

ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

Ngs-bits (Next-Generation Sequencing) is collection of short-read and long-read sequencing tools for diagnostics.

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.

Bioparser is a C++ header only parsing library for several bioinformatics formats (FASTA/Q, MHAP/PAF/SAM), with support for zlib compressed files.

This package provides a method to sample cells from single-cell data. It also generates an aggregate profile on a pruned K-Nearest Neighbor graph. This approach leads to an improved gene expression profile for quantifying gene regulations.

This package provides an object for plotting GRanges, RleList, UCSC file formats, and ffTrack objects in multi-track panels.

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

This package provides utility functions for manipulating BAM files.

wfmash is a DNA sequence read mapper based on mash distances and the wavefront alignment algorithm. It is a fork of MashMap that implements base-level alignment via the wflign tiled wavefront global alignment algorithm. It completes MashMap with a high-performance alignment module capable of computing base-level alignments for very large sequences.

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.