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Open Access

Analysis of Allele Specific Expression - A Survey

Feng Gu†(), Xue Wang†
Department of Computer Science, The College of Staten Island, The City University of New York, Staten Island, NY 10314, USA.
Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA.

† Both authors contribute equally to the work.

Show Author Information

Abstract

Allele specific expression is essential for cellular programming and development and the diversity of cellular phenotypes. Traditional analysis methods utilize RNA and depend on single nucleotide polymorphisms, thus to suffer from limited amount of materials for analysis. The rapid development of next-generation sequencing technologies provides more comprehensive and powerful approaches to analyze the genomic, epigenetic, and transcriptomic data, and further to detect and measure allele specific expressions. It will potentially enhance the understanding of the allele specific expressions, their complexities, and the effect on biological processes. In this paper, we extensively review the state-of-art enabling technologies and tools to analyze, detect, and measure allele specific expressions, compare their features, and point out the future trend of the methods.

Keywords

allele specific expressioncellular phenotypessequencing technologiesSingle Nucleotide Polymorphism (SNP)Single Nucleotide Variant (SNV)allele specific expressionstatistical methodstranscripts

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Tsinghua Science and Technology
Volume 20 Issue 5,
October 2015
Pages 513-529
DOI: 10.1109/TST.2015.7297750
Cite this article:
Gu F, Wang X. Analysis of Allele Specific Expression - A Survey. Tsinghua Science and Technology, 2015, 20(5): 513-529. https://doi.org/10.1109/TST.2015.7297750
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10.1109/TST.2015.7297750.T1Methods and applications of allele specific expression.

MethodsAssociated applications
SNP levelImpact of SNP variation on the reliability of read-mapping in the ASE detection[49];
RNA allelotyping methods to quantitatively interrogate allele specific gene expression[50];
Solexa-based method to perform large number of ASE assay using only a single lane of a Solexa flowcell[51];
Measure allele specific expression to identify allelic difference in transcript structure[52];
Construct a diploid personal genome sequence and identify allele specific events[34];
Access allele specific transcription of about 4000 human genes and find the widespread monoallelic expression on human autosomes[6];
Identify the cis-acting components of expression variation by the mapping of differences in allelic expression[28].
Gene or transcript levelMeta-analysis based allele specific expression detection using RNA-seq data that aggregates information across multiple SNV variation loci to obtain a gene level measure of ASE[32];
Evaluate allelic imbalance at the SNP transcript level and flags transcripts with significant opposite directional allele specific expression[53];
Use RNA-seq to explore allele specific expression in adipose tissue of male and female F1 mice[8];
Investigate the transcript abundance of six individual, highly homologous allele of a ploy polygalacturonase-inhibiting protein gene from octoploid strawberry[54];
Detect paternal or maternal allelic bias in the expression of several genes and transcripts[55];
Identify specific genes that influence Marek’s disease in order to optimally implement the control strategy[56];
Understand the mechanism of Bt resistance for pest control to identify related allele specific expression involved in insect resistance to Bt toxins[57].
Reduce reading mapping biasInvestigate the impact of SNP variation on the reliability of read-mapping in the context of detecting allele specific expression[58];
Compare two different methods to reduce the effect of mapping bias on allele specific expression identification[31];
Construct an enhanced reference genome to reduce the reading-mapping biases[59];
Use a personalized diploid reference genome to improve mapping accuracy and reduce the bias toward reference allele[60];
Identify properties of differentiating sites for biased measures of relative allele specific expression to improve accuracy of measured ASE[61];
Combine two strategies to account for the mapping biases and the simulation tool is used to estimate the residual mapping bias[37];
Provide guidelines for correcting and filtering mapping biases and develop the framework to reduce the bias[62].

10.1109/TST.2015.7297750.T2

Homoscedastic t test
Group 1 Xmaternal,RNAi/Xpaternal,RNAi,1⩽i⩽3
Group 2 Xmaternal,DNAi/Xpaternal,DNAi,1⩽i⩽3
𝑯𝟎Mean of group 1 equals mean of group 2
𝑯𝟏Mean of group 1 does not equal mean of group 2

10.1109/TST.2015.7297750.T3

Confidence interval method
Observed allele ratio f=Intensityref/Intensityalt
Confidence interval of f[0.5, 2.0]
ASE f>2.0 or f<0.5

10.1109/TST.2015.7297750.T4

2×2 contingency table and chi-square test
RNADNATotal
Ref allele Xref,RNA (a) Xref,DNA (b) a+b
Alt allele Xalt,RNA (c) Xalt,DNA (d) c+d
Total a+c b+d a+b+c+d
Test statistics χ2⁢(1)=(a⁢d-b⁢c)2⁢(a+b+c+d)(a+b)⁢(c+d)⁢(b+d)⁢(a+c)

10.1109/TST.2015.7297750.T5

Binomial test
Number of of trials Xref,RNA+Xalt,RNA
Number of success Xref,RNA
𝑯𝟎Success rate is 0.5
𝑯𝟏Success rate ≠ 0.5

10.1109/TST.2015.7297750.T6

Chi-square goodness-of-fit test
Observed allele ratio Xref,RNA/(Xref,RNA+Xalt,RNA)
Expectation 0.5
𝑯𝟎True allele ratio is 0.5
𝑯𝟏True allele ratio is not 0.5
Test statistics χ2⁢(1)=(Xref,RNA-0.5)20.5

10.1109/TST.2015.7297750.T7The pseudocode of the algorithm.

Procedure build_enhanced_reference
Inputs: Reference genome G, sorted list of polymorphic loci S, maximum read length r
Output: Enhanced reference genome G*
G* ←G
for i from 1 to |S|
pos ←S⁢(i)
K← number of SNPs in the window G[pos, pos +r-1]
V← list of all possible 2 k-1 binary vectors of length k
such that for each v∈V, v⁢[0]=1
for each v∈V
start ←S⁢(i+max⁡{j|v⁢[j]=1})-r+1
end ←pos + r - 1
E←G[start, end]
for each j that v[ j] = 1
E[ S⁢(i+j)-start] ←NonRef( S(i+j))
end for
G*←G*∪E
end for
end for

10.1109/TST.2015.7297750.T8Tools and software packages for allele specific expression.

Software packageDetailed information
ALEAUsage: Identification of significant associates between epigenetic modifications and specific allelic variants in human and mouse cells
Environment: Java and Python
Available at http://www.bcgsc.ca/platform/bioinfo/software/alea
Reference: see Ref. [67]
AlleleSeqUsage: Allele specific expression and allele specific binding and their integration and coordination
Environment: Python
Available at http://alleleseq.gersteinlab.org/home.html
Reference: see Ref. [34]
Allele workbenchUsage: Compute heterozygous SNPs, build mySQL database, compute allelic imbalance, query, and display
Environment: Java
Available at https://code.google.com/p/allele-workbench/
Reference: see Ref. [53]
AllelicImbalanceUsage: Provide a framework to investigate allele specific expression using RNA-seq data.
Environment: R
Available at https://github.com/pappewaio/AllelicImbalance
Reference: see Ref. [71]
AllimUsage: Measure allele specific gene expression within species
Environment: Python
Available at http://sourceforge.net/projects/allim/
Reference: see Ref. [37]
ASARPUsage: Discover ASE/ASARP genes/SNVs and provide comprehensive analysis for ASARP with support to different RNA-seq data
Environment: Perl
Available at https://www.ibp.ucla.edu/research/xiao/Software_files/ASARP/doc/index.html
Reference: see Ref. [63]
asSeqUsage: Perform eQTL mapping using total expression and/or allele specific expression
Environment: R and C
Available at http://www.bios.unc.edu/∼weisun/software/asSeq.htm
Reference: see Ref. [72]
EMASEUsage: Quantify allele specific expression and gene expression simultaneously from RNA-seq data
Environment: Python
Available at https://github.com/jax-cgd/emase
Reference: see Ref. [72]
eXpressUsage: Quantify a set of targeted sequences from sampled subsequences including allele specific expression analysis
Environment: C++, Cloud version is available
Available at http://bio.math.berkeley.edu/eXpress/index.html
Reference: see Ref. [73]
iASeqUsage: Detect allele specific events and describe correlation patters
Environment: R
Available at http://www.bioconductor.org/packages/release/bioc/html/iASeq.html
Reference: see Ref. [74]
MAMBAUsage: Analyze and interpret genomics data to understand the genetic basis of biomedical traits
Environment: Python
Available at http://www.well.ox.ac.uk/∼rivas/mamba/
Reference: see Ref. [75]
MBASEDUsage: Detect allele specific gene expression from RNA count data and use simulations to access the statistical significance of observed ASE
Environment: R
Available at http://www.bioconductor.org/packages/devel/bioc/html/MBASED.html
Reference: see Ref. [32]
PanGEAUsage: use next generation sequencing technologies to analyze gene expression and investigate allele specific gene expression.
Environment: mainly .NET framework
Available at http://www.kofler.or.at/bioinformatics/PanGEA/Manual/index.html
Reference: see Ref. [76]
QuASARUsage: Genotype from next generation sequence read and conduct inference on allelic imbalance at heterozygous sites.
Environment: R
Available at https://github.com/piquelab/QuASAR
Reference: see Ref. [77]
WASPUsage: Remove allelic bias in mapped sequencing reads and identify molecular quantitative trait loci using next generation sequencing data
Environment: Python
Available at https://github.com/bmvdgeijn/WASP/
Reference: see Ref. [69]
WaSPUsage: Design allele specific primers to detect SNPs and mutations
Environment: Web-based online
Available at http://bioinfo.biotec.or.th/WASP
Reference: see Ref. [70]