Transcriptome Assembly and Quantification
from Ion Torrent RNA-Seq Data
Alex Zelikovsky
Department of Computer Science
Georgia State University
Joint work with Serghei Mangul, Sahar Al Seesi, Adrian Caciula, Dumitru Brinza, Ion
Mandoiu
Advances in Next Generation Sequencing
Illumina HiSeq 2000
Up to 6 billion PE reads/run
35-100bp read length
Roche/454 FLX Titanium
400-600 million reads/run
400bp avg. length
http://www.economist.com/node/16349358
Ion Proton Sequencer
SOLiD 4/5500
1.4-2.4 billion PE reads/run
35-50bp read length
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RNA-Seq
Make cDNA & shatter into fragments
Sequence fragment ends
Map reads
A
Gene Expression
B
C
D
Transcriptome Reconstruction
A
B
A
C
D
E
E
Isoform Expression
C
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Transcriptome Assembly
• Given partial or incomplete information about
something, use that information to make an
informed guess about the missing or unknown
data.
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Transcriptome Assembly Types
• Genome-independent reconstruction (de novo)
– de Brujin k-mer graph
• Genome-guided reconstruction (ab initio)
– Spliced read mapping
– Exon identification
– Splice graph
• Annotation-guided reconstruction
– Use existing annotation (known transcripts)
– Focus on discovering novel transcripts
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Previous approaches
• Genome-independent reconstruction
– Trinity(2011), Velvet(2008), TransABySS(2008)
• Genome-guided reconstruction
– Scripture(2010)
• Reports “all” transcripts
– Cufflinks(2010), IsoLasso(2011), SLIDE(2012),
CLIIQ(2012), TRIP(2012), Traph (2013)
• Minimizes set of transcripts explaining reads
• Annotation-guided reconstruction
– RABT(2011), DRUT(2011)
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Gene representation
Tr1:
e1
Tr2:
e1
Tr3:
Pseudoexons:
e5
e3
e2
pse1
Spse1
e4
pse2
Epse1
Spse2
e5
pse3
Epse2
Spse3
pse4
Epse3
Spse4
e6
pse5
Epse4
Spse5
pse6
Epse5
Spse6
pse7
Epse6
Spse7
Epse7
• Pseudo-exons - regions of a gene between
consecutive transcriptional or splicing events
• Gene - set of non-overlapping pseudo-exons
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pseudo-exons
Splice Graph
TSS
TES
Genome
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2
3
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5
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MaLTA
Maximum Likelihood Transcriptome Assembly
• Map the RNA-Seq reads to
genome
• Construct Splice Graph G(V,E)
Genome
– V : exons
– E: splicing events
• Candidate transcripts
– depth-first-search (DFS)
• Select candidate transcripts
– IsoEM
– greedy algorithm
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How to select?
• Select the smallest set of candidate transcripts
• covering all transcript variants
Transcript : set of transcript variants
alternative first exon
alternative last exon
alternative 5' splice junction
exon skipping
alternative 5' splice junction
intron retention
splice junction
Sharmistha Pal, Ravi Gupta, Hyunsoo Kim, et al., Alternative transcription exceeds alternative splicing in generating the transcriptome
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diversity of cerebellar development, Genome Res. 2011 21: 1260-1272
IsoEM: Isoform Expression Level Estimation
• Expectation-Maximization algorithm
• Unified probabilistic model incorporating
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–
–
–
–
Single and/or paired reads
Fragment length distribution
Strand information
Base quality scores
Repeat and hexamer bias correction
Read-isoform compatibility graph
wr ,i
wr ,i   OaQa Fa
a
Fragment length distribution
i
A
B
j
A
C
C
Fa(i)
Fa (j)
Greedy algorithm
1. Sort transcripts by inferred IsoEM expression
levels in decreasing order
2. Traverse transcripts
– Select transcripts if it contains novel transcript
variant
– Continue traversing until all transcript variant
are covered
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
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Greedy algorithm
Transcripts sorted by expression levels
Transcript Variants:
STOP. All transcript variant are covered.
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MaLTA results on GOG-350 dataset
• 4.5M single Ion reads with
average read length 121 bp,
aligned using TopHat2
• Number of assembled transcripts
– MaLTA : 15385
– Cufflinks : 17378
• Number of transcripts matching annotations
– MaLTA : 4555(26%)
– Cufflinks : 2031(13%)
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Expression Estimation on Ion Torrent reads
• Squared correlation
– IsoEM / Cufflinks FPKMs vs qPCR values for 800 genes
– 2 MAQC samples : Human Brain and Universal
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R2 for IsoEM/Cufflinks Estimates vs qPCR
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
IsoEM HBR
Cufflinks HBR
IsoEM UHR
Cufflinks UHR
Conclusions
• Novel method for transcriptome assembly
• Validated on Ion Torrent RNA-Seq Data
• Comparing with Cufflinks:
– similar number of assembled transcripts
– 2x more previously annotated transcripts
• Transcript quantification is useful for
transcript assembly  better quantification?
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