Getting Started with RNA-Seq Data Analysis

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RNA-Seq Data Analysis talk given by Andreas Wilm (iGap & GIS) during the joint Illumina and Genome Institute of Singapore workshop on 24th May 2018

Science

Getting Started With RNA-Seq Data Analysis
Genome Institute of Singapore
Andreas Wilm, PhD :: Team Lead, Bioinformatics Core
24th May 2018

2
Sequencing, Data Management & Genome Analytics Services
http://www.igap.io | igap@gis.a-star.edu.sg

3
This is not an RNASeq tutorial
Disclaimer

4
• Bulk vs. Single Cell
• Transcript discovery
• Differential gene expression
• Allele-specific expression
• Detection of RNA editing
• Viral detection
• Gene fusion detection
• Alternative splicing
• De novo transcript assembly
• …
“The analysis goals of RNA-Seq experiments are diverse. Each of
these analysis goals has distinct requirements and challenges”
Source: Griffith et al. (2015)

5
Outstanding Training Resource
Griffith et al. (PLoS Comp Biol, 2015)
Informatics for RNA Sequencing: A Web Resource for
Analysis on the Cloud

6
Companion Web Resource: www.rnaseq.wiki

7
Short Read Mapping
• Naïve mapping to genome won’t
work: exon-exon junctions
• Use splice-aware mappers (e.g.
STAR). Require annotation
• Recent (fast) alternative: pseudo-
aligners (e.g. Kallisto), which
map to transcriptome
Source: Wikipedia

8
File Formats Plain Reads: FastQ
(usually >= 20M per sample)
Aligned: BAM Courtesy: Jonathan Göke

9
Common Analysis Flowchart
• Example: Tuxedo suite
• Large number of tools
• Number of representative tools
listed in Griffith et al. (2015): >100
• Multiple versions available
Source: Griffith et al. 2015

10
Tools Evolve
Source: Twitter
• Keep upgrading !
• Try different programs

11
Installation: Experts Only?
• Often requires Unix/Linux knowledge
• Install package dependencies first
• Not being root/admin can complicate things
• How to install different version of the same program?
Source: XKCD

12
Package Managers
Homebrew-Bio
Bioinformatics formulae
for Linuxbrew and Homebrew
More than 3000 bioinformatics
packages ready to use with conda
• Precompiled packages
• Installation through single command
• Multiple versions available
• Support for isolated environments

13
RNACocktail Protocol Dependencies
…
…
…
…
…
…
...
…
…
…
…
…
…
…
Sahraeianet al. 2017

14
Containers
Singularity
• Containers package software, dependencies etc.
• No installation needed at all
• Think: entire operating system in a file
• Extreme Portability

15
Example: New Tuxedo Protocol (Pertea et al., 2016)
Source: https://ccb.jhu.edu/software/stringtie/index.shtml?t=manual
• Replaces TopHat, Cuffdiff etc.
• How to scale to many samples?
• What if a step fails?
• How to make useable for others?

16
Workflow Managers
• Automation
• Scalability
• Robustness
• Reentrancy
• Reproducibility
• Sharing
Di Tommaso et al. (NBT, 2017) Köster & Rahmann (Bioinformatics, 2012)
snakemake

17
Putting it all Together
• Portable
• Validated
• Well documented
• Community maintained
Courtesy: Paolo Di Tommaso
Presented at Bio-IT World 2018

18
RNASeq Pipelines, Written in Nextflow and Using Containers
• NF-Core RNASeq: expression analysis and extensive QC
• Tuxedo: Transcript-level expression analysis and DE
• CalliNGS-NF: Variant Calling with GATK, incl. ASE
• …

19
Demo
• Example: GATK best practices for
variant calling for RNAseq
• Plus SNVs post-processing and
quantification for allele specific
expression
• Note: multiple samples, no software
installation, fully orchestrated etc.
Source: https://github.com/CRG-CNAG/CalliNGS-NF

20
Honorable Mentions
Cloud based analytics portals:
And others…
Software for Single Cell Gene Expression:
Cell Ranger Pipelines and Loupe Cell Browser

21
We can do the Heavy Lifting for you:
From Off-the-shelf to “Artisan” Analysis
http://www.igap.io | igap@gis.a-star.edu.sg

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Getting Started with RNA-Seq Data Analysis

1. Getting Started With RNA-Seq Data Analysis Genome Institute of Singapore Andreas Wilm, PhD :: Team Lead, Bioinformatics Core 24th May 2018

2. 2 Sequencing, Data Management & Genome Analytics Services http://www.igap.io | igap@gis.a-star.edu.sg

3. 3 This is not an RNASeq tutorial Disclaimer

4. 4 • Bulk vs. Single Cell • Transcript discovery • Differential gene expression • Allele-specific expression • Detection of RNA editing • Viral detection • Gene fusion detection • Alternative splicing • De novo transcript assembly • … “The analysis goals of RNA-Seq experiments are diverse. Each of these analysis goals has distinct requirements and challenges” Source: Griffith et al. (2015)

5. 5 Outstanding Training Resource Griffith et al. (PLoS Comp Biol, 2015) Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

6. 6 Companion Web Resource: www.rnaseq.wiki

7. 7 Short Read Mapping • Naïve mapping to genome won’t work: exon-exon junctions • Use splice-aware mappers (e.g. STAR). Require annotation • Recent (fast) alternative: pseudo- aligners (e.g. Kallisto), which map to transcriptome Source: Wikipedia

8. 8 File Formats Plain Reads: FastQ (usually >= 20M per sample) Aligned: BAM Courtesy: Jonathan Göke

9. 9 Common Analysis Flowchart • Example: Tuxedo suite • Large number of tools • Number of representative tools listed in Griffith et al. (2015): >100 • Multiple versions available Source: Griffith et al. 2015

10. 10 Tools Evolve Source: Twitter • Keep upgrading ! • Try different programs

11. 11 Installation: Experts Only? • Often requires Unix/Linux knowledge • Install package dependencies first • Not being root/admin can complicate things • How to install different version of the same program? Source: XKCD

12. 12 Package Managers Homebrew-Bio Bioinformatics formulae for Linuxbrew and Homebrew More than 3000 bioinformatics packages ready to use with conda • Precompiled packages • Installation through single command • Multiple versions available • Support for isolated environments

13. 13 RNACocktail Protocol Dependencies … … … … … … ... … … … … … … … Sahraeianet al. 2017

14. 14 Containers Singularity • Containers package software, dependencies etc. • No installation needed at all • Think: entire operating system in a file • Extreme Portability

15. 15 Example: New Tuxedo Protocol (Pertea et al., 2016) Source: https://ccb.jhu.edu/software/stringtie/index.shtml?t=manual • Replaces TopHat, Cuffdiff etc. • How to scale to many samples? • What if a step fails? • How to make useable for others?

16. 16 Workflow Managers • Automation • Scalability • Robustness • Reentrancy • Reproducibility • Sharing Di Tommaso et al. (NBT, 2017) Köster & Rahmann (Bioinformatics, 2012) snakemake

17. 17 Putting it all Together • Portable • Validated • Well documented • Community maintained Courtesy: Paolo Di Tommaso Presented at Bio-IT World 2018

18. 18 RNASeq Pipelines, Written in Nextflow and Using Containers • NF-Core RNASeq: expression analysis and extensive QC • Tuxedo: Transcript-level expression analysis and DE • CalliNGS-NF: Variant Calling with GATK, incl. ASE • …

19. 19 Demo • Example: GATK best practices for variant calling for RNAseq • Plus SNVs post-processing and quantification for allele specific expression • Note: multiple samples, no software installation, fully orchestrated etc. Source: https://github.com/CRG-CNAG/CalliNGS-NF

20. 20 Honorable Mentions Cloud based analytics portals: And others… Software for Single Cell Gene Expression: Cell Ranger Pipelines and Loupe Cell Browser

21. 21 We can do the Heavy Lifting for you: From Off-the-shelf to “Artisan” Analysis http://www.igap.io | igap@gis.a-star.edu.sg

22. Thank you

Getting Started with RNA-Seq Data Analysis

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