Accurate quantitation of allele-specific expression patterns by analysis of DNA melting

Methylation has previously been associated with allele specific expression in ants. Recently, we found methylation is important in worker reproduction in the bumblebee Bombus terrestris. Here we searched for allele specific expression in twelve genes associated with worker reproduction in bees. We found allele specific expression in Ecdysone 20 monooxygenase and IMP-L2-like. Although we were unable to confirm a genetic or epigenetic cause for this allele specific expression, the expression patterns of the two genes match those predicted for imprinted genes.

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Allele specific expression in worker reproduction genes in the bumblebee Bombus terrestris

10.7287/peerj.preprints.1094 ◽

2015 ◽

Author(s):

Harindra E Amarasinghe ◽

Bradley J Toghill ◽

Despina Nathanael ◽

Eamonn B Mallon

Keyword(s):

Bombus Terrestris ◽

Expression Patterns ◽

Worker Reproduction ◽

Imprinted Genes ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

Methylation has previously been associated with allele specific expression in ants. Recently, we found methylation is important in worker reproduction in the bumblebee Bombus terrestris. Here we searched for allele specific expression in twelve genes associated with worker reproduction in bees. We found allele specific expression in Ecdysone 20 monooxygenase and IMP-L2-like. Although we were unable to confirm a genetic or epigenetic cause for this allele specific expression, the expression patterns of the two genes match those predicted for imprinted genes.

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A Bayesian mixture model for the analysis of allelic expression in single cells

Nature Communications ◽

10.1038/s41467-019-13099-0 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Kwangbom Choi ◽

Narayanan Raghupathy ◽

Gary A. Churchill

Keyword(s):

Gene Expression ◽

Single Cell ◽

Mixture Model ◽

Time Course ◽

Single Cells ◽

Expression Patterns ◽

Allelic Expression ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

AbstractAllele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We demonstrate that discarding multi-mapping reads leads to higher variability in estimates of allelic proportions, an increased frequency of sampling zeros, and can lead to spurious findings of dynamic and monoallelic gene expression. Here, we report a method for ASE analysis from single-cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells. We further demonstrate that combining information across cells using a hierarchical mixture model reduces sampling variability without sacrificing cell-to-cell heterogeneity. We applied our approach to re-evaluate the statistical independence of allelic bursting and track changes in the allele-specific expression patterns of cells sampled over a developmental time course.

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Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

Nature Communications ◽

10.1038/ncomms9687 ◽

2015 ◽

Vol 6 (1) ◽

Cited By ~ 125

Author(s):

Jong Kyoung Kim ◽

Aleksandra A. Kolodziejczyk ◽

Tomislav Ilicic ◽

Sarah A. Teichmann ◽

John C. Marioni

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Large Fraction ◽

Expression Patterns ◽

Cell Types ◽

Specific Expression ◽

Allele Specific Expression ◽

Technical Noise ◽

Allele Specific ◽

High Level

Abstract Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells, we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for lowly and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise.

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Allele-specific expression patterns of interleukin-2 and Pax-5 revealed by a sensitive single-cell RT-PCR analysis

Current Biology ◽

10.1016/s0960-9822(00)00565-0 ◽

2000 ◽

Vol 10 (13) ◽

pp. 789-792 ◽

Cited By ~ 52

Author(s):

Kristina L. Rhoades ◽

Nandita Singh ◽

Itamar Simon ◽

Barbara Glidden ◽

Howard Cedar ◽

...

Keyword(s):

Single Cell ◽

Expression Patterns ◽

Interleukin 2 ◽

Pcr Analysis ◽

Rt Pcr ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

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Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

Scientific Reports ◽

10.1038/s41598-021-83459-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Joseph Tomlinson ◽

Shawn W. Polson ◽

Jing Qiu ◽

Juniper A. Lake ◽

William Lee ◽

...

Keyword(s):

Broiler Chickens ◽

Nucleotide Polymorphisms ◽

Rna Seq ◽

Specific Expression ◽

Single Nucleotide ◽

Allele Specific Expression ◽

Detection Tool ◽

Commercial Broiler ◽

Significant Phenomenon ◽

Allele Specific

AbstractDifferential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

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