Inference of Allele Specific Expression Levels from RNA-Seq Data
Sahar Al Seesi and Ion Măndoiu
Computer Science and Engineering Dept., University of Connecticut
Current Approaches
Allele Specific Gene/Isoform Expression
H0
H1
Make cDNA &
shatter into fragments
Sequence fragment ends
H0
Map reads
H1
A
B
C
D
Allele Specific Gene Expression (GE)
H1
E
A
B
C
D
E
Allele Specific Isoform Expression (IE)
H1
H0
H0
H0
H0
H1
H1
H1
 [Gregg et al., 2010]: parent-of-origin effect in hybrids of inbred mouse
strains
 [McManus et al., 2010]: cis- and trans-regulatory effects in hybrids of
inbred drosophila species
 [Heap et al., 2010]: allelic expression imbalance in human primary cells by
allele coverage analysis for heterozygous SNP sites within transcripts
 [Turro et al., 2011]: allele specific isoform expression through SNP calling
and diploid transcriptome construction
 [Missirian et al. , 2012]: parentally biased gene expression in Arabidopsis
hybrids
RNA-PhASE Analysis Pipeline
Preliminary Results
Methods
 Hybrid Mapping Approach
 Independently map reads onto reference genome and
transcriptome using bowtie (for Illumina or SOLiD reads) or tmap
(for ION Torrent and 454 reads)
 Discard reads with multiple alignments in either genome or
transcriptome, or unique but discordant alignments in both
 Discordance determined at base level to accomodate local
alignments of long reads with indel errors (ION Torrent and 454)
 SNV Calling and Genotyping (SNVQ) [Duitama et al. 2012]
 Bayesian model for SNV discovery and genotype calling from RNASeq reads
 Phasing SNVs
 RefHap [Duitama et al. 2010]
 Based on finding a maximum-weight cut in each connected
component of the read graph with edges between reads with
overlapping alignments ; edge weights given by #mismateches
 Coverage Based Phasing
 Haplotypes in disconnected blocks of SNVs connected based
on allele coverage at the their closest SNV sites
 Inference of Allele Specific Isoform Expression
 Experimental Setup
Whole brain RNA-Seq Data from Sanger Institute Mouse Genomes
Project [Keane et al. 2011]
Synthetic hybrids with different levels of heterozygosity generated by
pooling reads from C57/BL6 and four other strains
Read statistics
Strain variation
Strain
C57BL
BALBc
A/J
CAST
SPRET
SNPs
9,844
3,920,925
4,198,324
17,673,726
35,441,735
Strain/Hybrid
Private SNPs
1,488
29,973
44,837
5,368,019
23,455,525
C57BL
BALBc
A/J
CAST
SPRET
C57BL x BALBc
C57BL x AJ
C57BL x CAST
C57BL x SPRET
# read pairs # mapped pairs % mapped
57,187,342
62,465,347
46,993,887
54,569,423
57,411,555
114,374,684
93,987,774
109,138,846
114,374,684
21,756,070
28,358,653
22,449,227
22,307,194
19,016,949
47,682,108
35,353,398
43,134,951
40,780,806
38.0
45.4
47.8
40.9
33.1
41.7
37.6
39.5
35.7
Inference accuracy
C57BL x Strain
Hybrid
C57BL IE
Strain IE
C57BL GE
Strain GE
C57BL x BALBc
0.705
0.675
0.706
0.675
C57BL x AJ
0.855
0.902
0.856
0.903
C57BL x CAST
0.872
0.824
0.924
0.882
C57BL x SPRET
0.952
0.726
0.951
0.725
Pearson correlation between strainspecific FPKM values inferred from
separate strain RNA-Seq reads vs.
those inferred from pooled reads
 Diploid extension of IsoEM [Nicolae et al. 2011]
 Expectation-Maximization algorithm based on a probabilistic model
that incorporates fragment length distribution, quality scores, read
pairing and, if available, strand information
 Detection of Allelic Expression Imbalance
 Fisher Exact test for isoforms/genes with allelic expression change
fold over a certain threshold
References & Acknowledements
• J. Duitama, et al., ReFHap: A Reliable and fast algorithm for Single Individual Haplotyping, Proc. ACM-BCB, pp. 160-169, 2010
• J. Duitama and P.K. Srivastava and I.I. Mandoiu, Towards accurate detection and genotyping of expressed variants from Whole
Transcriptome Sequencing data, BMC Genomics 13(Suppl 2):S6, 2012
• C. Gregg et al., Sex-specific parent-of-origin allelic expression in the mouse brain, Science 239:682-685, 2010
• G.A. Heap, et al, Genome-wide analysis of allelic expression imbalance in human primary cells by high-throughput transcriptome
resequencing, Human Molecular Genetics, 19(1):122134, 2010
• T.M. Keane, et al., Mouse genomic variation and its efect on phenotypes and gene regulation, Nature 477(7364):289-294, 2011
• C.J. McManus, et, al., Regulatory divergence in Drosophila revealed by mRNA-seq, Genome Research 20:816-825, 2010
• V. Missirian, I. Henry, L. Comai, and Vladimir Filkov, POPE: Pipeline of Parentally-Biased Expression, Proc. ISBRA, LNCS 7292:177-188, 2012
• M. Nicolae, S. Mangul, I.I. Mandoiu, A. Zelikovsky, Estimation of alternative splicing isoform frequencies from RNA-Seq data, Algorithms
for Molecular Biology 6:9,2011
• E. Turro, et al., Haplotype and isoform specific expression estimation using multimapping RNA-Seq reads, Genome Biology 12(2):R13,
2011
ACKNOWLEDGEMENTS: This work is supported in part by awards IIS-0546457 from NSF, Agriculture and Food
Research Initiative Competitive Grant no. 2011-67016-30331 from the USDA NIFA, and a Collaborative Research
Compact award from Life Technologies Corporation.
Conclusions and Ongoing Work
 RNA-PhASE pipeline addresses limitations of existing ASE methods
 Does not require prior availability of diploid genome/transcriptome
 Mapping reads against the diploid transcriptome reconstructed onthe-fly resolves bias towards reference alleles
 EM model improves inference accuracy by using all reads, including
those that map to more than one isoform
 In collaboration with the Michael and Rachel O’Neill labs, RNA-PhASE is
being used to identify parentally imprinted genes associated with autism