RNA-Seq Tools

Alevin-fry-atac is a tool for rapid and memory-frugal mapping of single-cell ATAC-seq data utilizing virtual colors for accurate genomic pseudoalignment. It is freely available under a BSD 3-clause license and integrated into piscem and simpleaf.

The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

A tool to annotate long non-coding RNAs from RNA-seq assembled transcripts.

Method that extracts and annotates the locations of sncRNA-derived RNAs (sncdRNAs).

GenomicSuperSignature is a package for the interpretation of RNA-seq experiments through robust, efficient comparison to public databases.

Detect Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data.

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).

Jointly defining cell types from multiple single-cell datasets using LIGER. LIGER (Linked Inference of Genomic Experimental Relationships). LIGER (liger) is a package for integrating and analyzing multiple single-cell datasets, developed by the Macosko lab and maintained/extended by the Welch lab. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors.

Peak-caller for CLIP- and RIP-Seq high-throughput protein-RNA interaction data. It accepts input in BED or BAM format and identifies regions of significant enrichment for reads. It can also optionally incorporate additional external covariates into the peak-calling process, and identify sites of differential binding occupancy between cell types, conditions or development stages.

We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNASeq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels.

This package implements the remove unwanted variation (RUV) methods for the normalization of RNA-Seq read counts between samples.

Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Ultrafast universal RNA-seq data aligner.

A software tool that implements a novel, is an alignment-free algorithm for the estimation of isoform abundances directly from a set of reference sequences and RNA-seq reads.

A tool for transcript expression quantification from RNA-seq data

Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.

Fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus.

Program that aligns RNA-Seq reads to a genome in order to identify exon-exon splice junctions. It is built on the ultrafast short read mapping program Bowtie. A stable SAMtools version is now packaged with the program.

TransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks.

AGFusion (pronounced 'A G Fusion') is a python package for annotating gene fusions from the human or mouse genomes.

The baredSC (Bayesian Approach to Retreive Expression Distribution of Single Cell) is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence interval on the probability density function (PDF) of expression of one or two genes from single-cell RNA-seq data.

Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.

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.

Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data.

Implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.

A statistical model and software application for RNA-seq differential expression analysis.