RNAseq analysis notes from Ming Tang

🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment

Tools to process and analyze deep sequencing data.

Near-optimal RNA-Seq quantification

RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

An overview of algorithms for estimating pseudotime in single-cell RNA-seq data

Customizable workflows based on snakemake and python for the analysis of NGS data

Differential analysis of RNA-Seq

Table of software for the analysis of single-cell RNA-seq data.

zUMIs: A fast and flexible pipeline to process RNA sequencing data with UMIs

A repository for setting up a RNAseq workflow

RNA-seq workflow using STAR and DESeq2

NicheNet: predict active ligand-target links between interacting cells

Light-weight Snakemake workflow for preprocessing and statistical analysis of RNA-seq data

RNA velocity estimation in Python

Sequana: a set of Snakemake NGS pipelines

Finder of Somatic Fusion Genes in RNA-seq data

Python library to access Gene Expression Omnibus Database (GEO)

Automated generation of tailored bioinformatics Jupyter Notebooks via a user interface.

An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study

Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

Statistical Analysis of RNA-Seq Tools

A software package for analyzing snapshots of developmental processes

TPMCalculator quantifies mRNA abundance directly from the alignments by parsing BAM files

The START App: R Shiny Transcriptome Analysis Resource Tool

Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

🐍 Snakefiles for common RNA-seq data analysis workflows.

Lightweight and Fast; RNA-seq quantification at the event-level