1. The iPlant Collaborative
Community Cyberinfrastructure for Life Science
Tools and Services Workshop
Transcriptome assembly with SOAPdenovo Trans

2. RNA-Seq without a reference genome
Overview of work
Transcriptome Assembly
Characterize
Transcript abundance
Visualization
Generate Sequence
QC and Processing
We will focus mostly on the transcriptome assembly itself as this is the
greatest challenge to working without a reference genome and involves
several steps.

3. Transcriptome assembly
Reminder: you’ll need to do some thinking and reading here
Assemble Transcriptome
Evaluate and refine
On to mapping
Good read data
The most costly (time, compute) process is empirically determining
what parameters yield the best assembly given the data and organism. We
can suggest some good directions, but we can’t promise “right” answers
Get familiar with examples of work and reasoning
on approaches relevant to your organism

4. Transcriptome assembly from RNA-Seq
Challenges (just a few of them)
DNA assembly assumes even sequencing depth, not true in RNA-Seq (e.g. repetitive regions = more reads for DNA, more expression for RNA), also higher coverage is needed for de novo assembly from RNA-Seq (30X or more)
Sequencing error correction, esp. in highly expressed transcripts
Multiple transcript variants (splicing) can confound assembly

5. Transcriptome assembly from RNA-Seq
Some reminders
Practically the only agreement on which software is the right one to use is that there is no “right” one (we’ll need to experiment*)
Don't: use less than 200 to 500 Million RNA reads, mate-paired, of 100 bp or better length, high quality, and expect to get a complete transcriptome.(1)
Despite the challenges, making your own transcriptome is still very very useful and perhaps more practical than assembling your own genome.
1. How to get Best mRNA Transcript assemblies.
by Don Gilbert, 2013 Jan

6. Transcriptome assembly from RNA-Seq
Overview
Pre-analysis: Data generation

7. Experimental Consideration – Library prep
Generate the best data you can!
Remove ribosomal RNAs
Two options with some pros/cons
Poly(A) selection – effective, but will miss ncRNAs and non polyadenlyated transcripts
rRNA depletion – hybridize rRNAs and remove, but may introduce different biases (e.g. against highly expressed transcripts)

8. Experimental Consideration – Library prep
Generate the best data you can!
PCR Amplification
Most protocols have a PCR amplification step – this of course introduces bias (e.g. against high GC content). Some alternative protocols or technologies (PacBio) can avoid amplification but again have their own issues.

9. Experimental Consideration – Library prep
Generate the best data you can!
Strand specificity
If possible, doing a strand-specific protocol can simplify future analyses

10. Experimental Consideration – Library prep
Generate the best data you can!
No orientation
Strandedness preserved

11. Trinity
Overview
Analysis

12. Trinity
Overview
Trinity: a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data.
Trinity combines three independent software modules:
Inchworm
Chrysalis
Butterfly
Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data : Manfred G. Grabherr, et al; Nat Biotechnol May 15; 29(7): 644–652.

13. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data : Manfred G. Grabherr, et al; Nat Biotechnol May 15; 29(7): 644–652.

14. Trinity Overview Trinity aggregates isolated transcript graphs
ftp://ftp.broad.mit.edu/pub/users/bhaas/rnaseq_workshop/rnaseq_workshop_2014/rnaseq_workshop_slides.pdf

15. Why Trinity? Some comparisons
Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data : Manfred G. Grabherr, et al; Nat Biotechnol May 15; 29(7): 644–652.

16. Trinity
Overview
Inchworm first finds the node with the highest number of reads and extends on both sides by picking the highest intensity nodes. Inchworm will find three segments – A, C and the blue part of B
Chrysalis takes the three segments determined by inchworm and clusters them into two groups that are related to two genes
Butterfly reconstructs the full gene structures including alternate splice forms A and B.

17. Trinity Reads split into k-mers De Brujin graph constructed from kmers
Kmers and De Brujin graphs
Reads split into k-mers
De Brujin graph constructed from kmers
Next-generation transcriptome assembly, Jeffrey A. Martin and Zhong Wang – Nat.Reviw.Gen doi: /nrg3068
Published online 7 September 2011

18. Trinity Redundancies are collapsed
Kmers and De Brujin graphs
Redundancies are collapsed
Paths through the graph that explained the observed sequence generate the alignments
Next-generation transcriptome assembly, Jeffrey A. Martin and Zhong Wang – Nat.Reviw.Gen doi: /nrg3068
Published online 7 September 2011

19. Trinity Trinity.fasta : file containing assembled trancritps
Key Results
Trinity.fasta : file containing assembled trancritps
Trinity groups transcripts into clusters based on shared sequence content
Such a transcript cluster is very loosely referred to as a gene
Information is encoded in the Trinity fasta accession eg:

20. Trinity Examine the quality of the assembly N50 statistic
Where to go from here
Examine the quality of the assembly
N50 statistic
Core gene representation
Transcript annotation- Trinonate

21. Assembly quality
N50

22. CEGMA How good is assembly coverage?
CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes
Bioinformatics (2007) 23 (9): doi: /bioinformatics/btm071 First published online: March 1, 2007

23. Keep asking: ask.iplantcollabortive.org

24. The iPlant Collaborative is funded by a grant from the National Science Foundation Plant Cyberinfrastructure Program (#DBI ).