1. A Hybrid Approach to Assemble and
Annotate the Brassica rapa
Transcriptome in the Cloud through the
iPlant Collaborative and XSEDE
Upendra Kumar Devisetty
Postdoctoral Researcher
Maloof Lab, UC Davis

2. R500 IMB211
• Reference Transcriptome
• Genome annotation
R500 (oil seed cultivar)
IMB211 (rapid cycling cultivar)
B. rapa mapping population
Research in Maloof Lab

3. Mainly relied on in silico gene models and EST’s data from datasets
(Wang et al. 2011)
– In silico gene models (GENSCAN,
GlimmerHMM, Fgenesh)
• short exons
• very long exons
• non-translated exons
• genes that encode non-coding
RNAs accurately
– EST’s
• miss 20-40% of novel
transcripts
• transcribed only under
highly specific tissue,
environmental or
treatment conditions
• 3’ biased
• short length
Original

4. Why there is a need for accurate genome annotation?
• Accurate and comprehensive genome annotation (e.g. gene
models) is imperative for functional studies
• Useful for accurate mRNA abundance and detection of
eQTLs (expression QTLs) in mapping populations
Objectives
• To detect transcripts that are not present in the existing
genome reference of B. rapa (novel transcripts)
• To update the existing gene models of B. rapa genome

5. UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics
Growth Chamber, Green House, Field
apical
meristem
R500
Library construction
 TRUSEQ RNA-SEQ kit (Illumina)
 High throughput and easy to use
Sequencing
 128 RNA-Seq libraries
 17 lanes
 PE100 sequencing
 Illumina GAIIx
 3,354 million raw paired end reads
Quality control
o Atmosphere and iRODS
o 2,550 million quality controlled
paired end reads (888 GB)

6. Servers
(iPlant Atmosphere)
XX-TB
Storage
(iPlant Data Store and EBS)
Users
Now everyone can share data without sharing resources!

7. B. rapa transcriptome assembly and genome reannotation pipeline
UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics

8. Transcriptome assembly

9. de novo vs Reference based assemblies
Approach Advantages Disadvantages
de novo -no reference needed
-detection of non-collinear
transcripts
-lowly expressed genes
-missassemblies due to repeats
reference -alignment tolerates
sequencing errors
-repeats detected through
alignment
-reference is needed
-assumes transcripts are collinear
with the genome

10. Transcriptome assembly
UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics

11. • XSEDE is the most powerful integrated advanced digital
resources and services in the world funded by NSF
• Scientists and Engineers around the world use XSEDE
resources and services: supercomputers, collections of data,
help services
• Consists of supercomputers, high-end visualization, data
analysis and storage around the country

12. xsede.org
xsede.org

13. Summary statistics for de novo assembly pipeline
UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics
Assembly
type
Number of
transcripts
Average transcript
length (bp)
N50
Velvet-Oases 601,915 1553 2,218
Trinity 158,863 1112 1863

14. Transcriptome assembly
TopHat-Cufflinks-Cuffcompare was run on Atmosphere

15. Summary statistics for Reference assembly pipeline
UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics

16. Do the assembly algorithms differ with respect to
detection of novel transcripts?
UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics
RT-PCR validations of assembled novel transcripts

17. Transcriptome annotation
Entire Transcriptome annotation was run on Atmosphere

18. Problem 1) Cap3 merged transcripts have multiple ORF's
for the same contigs
Challenges and problems during annotation Cap3
transdecoder
cds to bam
Merged final novel transcripts
bam to bed
Use blastx to NCBI nr database and chose appropriate filters

19. Problem 2) Overlapping transcripts in the bed file
Use bedops merge and then select the long transcript
transdecoder
cds to bam
Merged final novel transcripts
bam to bed
Cap3

20. Problem 3) Very long transcripts due to missassembly
transdecoder
cds to bam
Merged final novel transcripts
bam to bed
Filter the transcripts
After filtering
Cap3

21. Problem 4) No lines connecting the exons in bed file
Use a custom script and join the lines. Check UCSC bed file guideline
cds to bam
Merged final novel transcripts
bam to bed
Cap3
transdecode
r

22. UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics
Results for detection of novel transcripts
Number of novel transcripts detected - 3,537 (v1.2) and 2,732 (v1.5)
Original
Novel
Original
Novel

23. o Genome annotation pipeline from TIGR, used
widely elsewhere
o Uses EST spliced alignments to model genes
o Gene structure consistent with experimental
data
o Identifies alternate splicing variations
o Helps to correct gene structure
Program to Assemble Spliced Alignments (PASA)
UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics
PASA was installed and run on Atmosphere

24. Number of gene models updated – 28,139 (v1.2) & 28,112 (v1.5)
UK Devisetty et al. 2014 G3: Genes|Genomes|Genetics
Results for updating Gene models
Original
NovelBra000108
Original
NovelBra022192

25. Genome Browser
(http://tinyurl.com/BrapaGenome)

26. Conclusions
• Deep RNA-Seq provides enough coverage for the detection
of a large number unknown transcripts and genome
improved annotation
• Neither de novo assembly nor reference-based category is
the best choice and hybrid assembly can offer more
accurate assembly and annotation
• Problems during genome re-annotation needs to be
addressed before a fully annotated genome is obtained
• iPlant Collaborative and XSEDE provides the systems and
people to facilitate transcriptome assembly and genome
reannotation

27. ACKNOWLEDGEMENTS
• Julin Maloof
• Mike Covington
• Cody Markelz
• An Tat
• Kazu Nozue
• Saradadevi Lekkala
• Maloof lab
• Harmer lab
• Cynthia Weinig
• Marc T. Brock
• Matthew Rubin
• Brian Haas
• Andy Edmonds
• Edwin Skidmore
• Sangeeta Kuchimanchi
• Matt Vaughan