The document outlines the workflow and experiences of a bioinformatics core facility using Nextflow for genomic analyses, highlighting the importance of automation, parallelization, and reproducibility. The facility has developed several pipelines for RNA-seq, SNP calling, and single-cell transcriptomes while discussing the benefits of using containers to address compatibility issues. Future ideas include creating semi-automatic reports and a content management system to enhance data handling.

1. FROM ZERO TO NEXTFLOW
Luca Cozzuto
Bioinformatics Core Facility

2. Bioinformatics Unit@CRG
• Julia Ponomarenko
• Luca Cozzuto
• Toni Hermoso
• Sarah Bonnin

3. Core facility typical workflow
User
Standardised
analysis
Non standard
analysis
Building a database
Reproducing an analysis…
Chipseq
RNAseq
SNP calling …

4. Core facility typical workflow
User
Standardised
analysis
Non standard
analysis
Semi-automatic
pipelines
Chipseq
RNAseq
SNP calling …
Building a database
Reproducing an analysis…

5. Core facility typical workflow
User
Standardised
analysis
Non standard
analysis
Bunch of tools,
custom scripts, R
magics etc..
Semi-automatic
pipelines
Chipseq
RNAseq
SNP calling …
Building a database
Reproducing an analysis…

6. Core facility typical workflow
User
Standardised
analysis
Non standard
analysis
Bunch of tools,
custom scripts, R
magics etc..
Semi-automatic
pipelines
50%50%

7. Core facility typical workflow
Genomics
39%
Database
15%
Microbiome
12%
RNA-seq
18%
ChIP-seq
13%
Microarray &
HTqPCR
3%
Hours by type of projects (2015 & 2016 )

8. Core facility typical workflow
Genomics
39%
Database
15%
Microbiome
12%
RNA-seq
18%
ChIP-seq
13%
Microarray &
HTqPCR
3%
Hours by type of projects (2015 & 2016 )

9. Why nextflow?
• Standard analysis:
• Automation, parallelization, portability, reproducibility
(together with containers).
• NF allows adding new steps without pain (thanks to isolation
of processes) in a collaborative way
[After 2 years] Can you redo the SAME analysis with new samples?

10. Why nextflow?
• Standard analysis:
• Automation, parallelization, portability, reproducibility
(together with containers)
• NF allows adding new steps without pain (thanks to isolation
of processes) in a collaborative way
• Non standard analysis can benefit too:
• NF code is easy to reuse / modify. It is polyglot!
• Using containers prevent several problems like portability, OS
upgrade, libraries / version mismatch, etc.

11. Our experience
First day with NextFlow

12. Our experience
Progressionincoding
Time
Documentation / examples

13. Our experience
Progressionincoding
Time

14. Our experience
Progressionincoding
Time
Invite Paolo for a coffee

15. Our experience
Progressionincoding
Time

16. Our experience
Progressionincoding
Time
Second coffee

17. Our experience
Progressionincoding
Time
Start using the Gitter Chat

18. Our experience
Progressionincoding
Time
Asking to the Singularity Google Group

19. RNAseq pipeline ver 0.1

20. RNAseq pipeline ver 0.2

21. Our experience
Now we are developing / developed:
• ChIPseq pipeline
• RNAseq pipeline
• small RNAseq pipeline
• SNP calling procedures (based on GATK)
Standard analysis

22. Our experience
Now we are developing / developed:
• ChIPseq pipeline
• RNAseq pipeline
• small RNAseq pipeline
• SNP calling procedures (based on GATK)
• Pipeline for analysis or single cell transcriptome
• Detection of plant resistant genes …
NON standard
Standard analysis

23. Future ideas
• Semi-automatic reports
• A CMS able to mine the NextFlow logfile and store both
metadata and logs
• Maybe a simple graphical interface to compete / complement
with Galaxy?

24. Thanks!
Bioinformatics Unit@CRG
• Julia Ponomarenko
• Luca Cozzuto
• Toni Hermoso
• Sarah Bonnin

25. Our experience
We started developing a pipeline for single cell sequencing.

26. Analysis of single cell sequencing
A concrete case: analysis of single cell sequencing
Pair 1 (cell)
Chunks
Chunks
Chunks
Split
Filtering
/alignment
Parallel
Mapped tags
Mapped tags
Mapped tags
Joining the
results
Genome file
Indexing
Index
Pair 2 (gene)
Expression
per cell