Hi all,

I am a PhD student with biology background. I recently inherit a RNA Sequencing project from another PhD student in my lab. We already have paired-ended RNA-Seq data generated from Illumina HiSeq but haven't started analysis yet. I have basic Linux command line training but have no idea about how to analyze RNA-Seq data. I did a little bit of research and started to take free online courses and read papers.

Could anybody recommend me more up-to-date online trainings or courses?

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Here are the resources that I found so far:

[On-site Course]

On-site and online courses provided by Iowa Institute of Human Genetics (updated on April 27, 2016)

microRNA Analysis Using Next-Generation Sequencing (Deadline: 9/1/2016)

MSU: NGS Summer 2016 - Analyzing Next-Generation Sequencing Data (Deadline: 3/1/2016)

[Youtube videos]

How to analyze RNA-Seq data? Find differentially expressed genes in your research (updated on Oct 6, 2016)

*And the resulted publication: RNA-seq of serial kidney biopsies obtained during progression of chronic kidney disease from dogs with X-linked hereditary nephropathy (updated on Dec 7, 2016)

Informatics for RNA-Seq Analysis 2016 by Bioinformatics.ca (updated on Oct 4, 2016)

Introduction to RNA Sequencing (updated on Sep 20, 2016)

Intro to Illumina Sequencing (updated on July 25, 2016)

Illumina TruSeq RNA libraries: Part I (updated on July 25, 2016)

Illumina TruSeq RNA libraries: Part II (updated on July 25, 2016)

RNA-SEQ: Mapping to a Reference Genome (updated on July 25, 2016)

RPKM, FPKM and TPM(updated on July 25, 2016)

Principle Component Analysis (PCA) clearly explained (updated on July 25, 2016)

Heatmaps - considerations for drawing and interpreting them (updated on July 25, 2016)

[GitHub tutorial / webpage]

RNA-Seq Analysis Tutorial (my personal wiki page. updated July 25, 2016)

RNA-seqlopedia suggested by aleimba (updated on Feb 3, 2016)

RNA-seq Tutorial suggested by Chris Miller

RNA-Seq De novo Assembly Using Trinity suggested by Dave Carlson

[Online Course] (I recommend to study by order)

(1) Basic/essential biology knowledge:

Introduction to Genomic Technologies (updated on March 14, 2016)

(2) RNA-seq intro:

Introduction to Genomic Technologies-Next Generation Sequencing (updated on March 20, 2016)

Bioinformatic Methods I: Next Gen Sequence Analysis (RNA-Seq) / Metagenomics (updated on March 20, 2016)

Bioinformatic Methods II: Gene Expression Analysis I&II (updated on March 21, 2016)

Experimental Methods in Systems Biology: Deep mRNA Sequencing (updated on March 20, 2016)

(3) Tuxedo suite tools / Command line:

Command Line Tools for Genomic Data Science (Learn from basic Linux commands to Cuffdiff! Highly recommended! updated on March 14, 2016)

Introduction to Linux (updated on October 7, 2016)

Network Analysis in Systems Biology-Deep Sequencing Data Processing and Analysis (updated on April 27, 2016)

(4) Kallisto and Sleuth:

Intro to Kallisto for RNA-Seq (updated on April 27, 2016)

Intro to Sleuth for RNA-Seq (updated on April 27, 2016)

(5) R/Bioconductor:

Beginners Introduction to R Statistical Software (updated on April 27, 2016)

Try R (updated on April 27, 2016)

Introduction to R (updated on April 27, 2016)

Bioconductor for Genomic Data Science (updated on April 27, 2016)

R Programming (updated on March 20, 2016)

Data Analysis for Life Sciences 1: Statistics and R (updated on April 3, 2016)

Data Analysis for Life Sciences 5: Introduction to Bioconductor: Annotation and Analysis of Genomes and Genomic Assays (Optional, updated on March 20, 2016)

(6) Python:

Try Python (updated on April 27, 2016)

Intro to Python for Data Science (updated on April 27, 2016)

Python for Genomic Data Science (updated on April 27, 2016)

CodeAcademy-Python (updated on April 28, 2016)

(7) Advanced courses:

Network Analysis in Systems Biology: Deep Sequencing Data Processing and Analysis (updated on March 20, 2016)

Bioinformatics: Introduction and Methods (updated on March 14, 2016)

Data Analysis for Life Sciences 7: Case Studies in Functional Genomics suggested by Radek

Foundations of Computational and Systems Biology (updated on March 20, 2016)

(8) Certification programs:

Become a next generation sequencing data scientist $392

RNA-Seq Short Term Bioinformatics - Certification Program $? suggested by info (updated on Feb 5, 2016)

[Articles]

Thinking About RNA Seq Experimental Design for Measuring Differential Gene Expression: The Basics (updated on April 3, 2016)

[Experimental design]

RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods (updated on Dec 7, 2017)

Calculating Sample Size Estimates for RNA Sequencing Data (updated on July 26, 2016)

Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data (updated on July 26, 2016)

[Papers-work flow]

RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (updated on July 26, 2016)

A Quick Guide to Organizing Computational Biology Projects (updated on Feb 9)

An Online Bioinformatics Curriculum

A Quick Introduction to Version Control with Git and GitHub

[Papers-concepts]

Differential analysis of RNA-seq incorporating quantification uncertainty (updated on Dec 7, 2017)

Trimming of sequence reads alters RNA-Seq gene expression estimates (updated on Dec 7, 2017)

A comparison of methods for differential expression analysis of RNA-seq data (updated on Feb 9)

Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples (updated on Feb 9)

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments (updated on Feb 9)

Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments (updated on Feb 9)

Statistical design and analysis of RNA sequencing data (updated on Feb 9)

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (updated on Feb 9)

Quick RNA-seq Data Analysis Tutorial suggested by BioRyder

A survey of best practices for RNA-seq data analysis suggested by Dan Gaston

An useful post: Review papers on the topic of RNA-seq

A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

Near-optimal RNA-Seq quantification

Differential analyses for RNA-seq- transcript-level estimates improve gene-level inferences

[Papers-specific tools]

SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data (updated one December 7, 2017)

TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions (updated one March 14, 2016)

TopHat: discovering splice junctions with RNA-Seq (updated one March 14, 2016)

Accurate, fast, and model-aware transcript expression quantification with Salmon suggested by Rob (updated on Feb 5, 2016)

RapMap: A Rapid, Sensitive and Accurate Tool for Mapping RNA-seq Reads to Transcriptomes suggested by Rob (updated on Feb 5, 2016)

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I hope that can help other new faces in this forum just like me :)

Thanks!