scholarly journals Allele expression biases in mixed-ploid sugarcane accessions

2021 ◽  
Author(s):  
Fernando Henrique Correr ◽  
Agnelo Furtado ◽  
Antonio Augusto Franco Garcia ◽  
Robert James Henry ◽  
Gabriel Rodrigues Alves Margarido
Keyword(s):  
Allelic Imbalance ◽  
Accurate Estimation ◽  
Binomial Model ◽  
Specific Expression ◽  
General Metabolism ◽  
Allele Dosage ◽  
Allele Expression ◽  
Allele Specific ◽  
Sugarcane Hybrids ◽  
High Level

Allele-specific expression (ASE) represents differences in the magnitude of expression between alleles of the same gene. This is not straightforward for polyploids, especially autopolyploids, as knowledge about the dose of each allele is required for accurate estimation of ASE. This is the case for the genomically complex Saccharum species, characterized by high levels of ploidy and aneuploidy. We used a Beta-Binomial model to test for allelic imbalance in Saccharum, with adaptations for mixed-ploid organisms. The hierarchical Beta-Binomial model was used to test if allele expression followed the expectation based on genomic allele dosage. The highest frequencies of ASE occurred in sugarcane hybrids, suggesting a possible influence of interspecific hybridization in these genotypes. For all accessions, ASEGs were less frequent than those with balanced allelic expression. These genes were related to a broad range of processes, mostly associated with general metabolism, organelles, responses to stress and responses to stimuli. In addition, the frequency of ASEGs in high-level functional terms was similar among the genotypes, with a few genes associated with more specific biological processes. We hypothesize that ASE in Saccharum is largely a genotype-specific phenomenon, as a large number of ASEGs were exclusive to individual accessions.

scholarly journals Replicate sequencing libraries are important for quantification of allelic imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Asia Mendelevich ◽  
Svetlana Vinogradova ◽  
Saumya Gupta ◽  
Andrey A. Mironov ◽  
Shamil R. Sunyaev ◽  
...  
Keyword(s):  
Allelic Imbalance ◽  
False Positive Rate ◽  
Error Rates ◽  
Differential Analysis ◽  
Rna Seq ◽  
Specific Expression ◽  
Technical Noise ◽  
Specific Analysis ◽  
Positive Rate ◽  
Allele Specific

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

scholarly journals Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Prashant N. M. ◽  
Hongyu Liu ◽  
Pavlos Bousounis ◽  
Liam Spurr ◽  
Nawaf Alomran ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Sequencing Data ◽  
Specific Expression ◽  
Single Nucleotide ◽  
Healthy Donors ◽  
Allele Expression ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

APC germ-line allele-specific expression in familial adenomatous polyposis

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22037-e22037
Author(s):  
E. Castellsague ◽  
S. González ◽  
I. Blanco ◽  
E. Guinó ◽  
C. Lázaro ◽  
...  
Keyword(s):  
Familial Adenomatous Polyposis ◽  
Germ Line ◽  
Significant Proportion ◽  
Adenomatous Polyposis ◽  
Specific Expression ◽  
Nonsense Mediated Decay ◽  
Allele Expression ◽  
Fresh Frozen ◽  
Specific Allele ◽  
Allele Specific

e22037 Background: About 13% of Familial Adenomatous Polyposis (FAP) families and 70% of Attenuated FAP families remain with unknown molecular pathogenic cause after APC and MYH mutational analyses. Also, mutations can affect specific allele expression (ASE) at the germline level. The aim of the study was to determine the presence of germline ASE in the APC gene in FAP and AFP with and without detectable APC or MYH mutations. Methods: Germline RNA from fresh frozen and/or cultured lymphocytes of 17 APC/MYH-negative Polyposis (7 FAP, 10 AFAP) families (21 individuals) and 35 APC-mutated Polyposis (30 FAP, 5 AFAP) families (60 individuals) was analyzed. Fourteen controls were also studied. ASE was investigated by single nucleotide primer extension (SNuPE) of rs2229992 APC coding SNP. Results: In controls ASE was 1.04± 0.3. We found that 17% (3 of 17) APC/MYH(-) FAP and AFAP families showed ASE (range=1.17–1.39) and ASE co-segregated with disease. ASE was more intense in short-cultured lymphocytes except for two cases and completely reversed by puromycin treatment. Eleven of 35 (31%) APC-FAP/AFAP harbored ASE (range=1.20–7.76), and the mutant allele was underexpressed in each case. ASE was restricted to splicing (4 families), nonsense (3 families) and frameshift (3 families) mutations outside of exon 15. Puromycin reversed ASE in all cases analyzed. Conclusions: APC ASE is present in a significant proportion (17%) of APC/MYH(-) FAP or AFAP. ASE, due to nonsense-mediated decay (NMD), is present in APC-FAP and is associated with specific mutation location, similar to reports for other hereditary syndromes. No significant financial relationships to disclose.

scholarly journals RNA sequencing-based screen for reactivation of silenced alleles of autosomal genes

2021 ◽  
Author(s):  
Saumya Gupta ◽  
Denis L Lafontaine ◽  
Sebastien Vigneau ◽  
Asia Mendelevich ◽  
Svetlana Vinogradova ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Rna Sequencing ◽  
Mammalian Cells ◽  
Allelic Imbalance ◽  
Dna Demethylation ◽  
Monoallelic Expression ◽  
Specific Expression ◽  
Allele Specific ◽  
Mechanistic Basis

Abstract In mammalian cells, maternal and paternal alleles usually have similar transcriptional activity. Epigenetic mechanisms such as X-chromosome inactivation (XCI) and imprinting were historically viewed as rare exceptions to this rule. Discovery of autosomal monoallelic expression (MAE) a decade ago revealed an additional allele-specific mode regulating thousands of mammalian genes. Despite MAE prevalence, its mechanistic basis remains unknown. Using an RNA sequencing-based screen for reactivation of silenced alleles, we identified DNA methylation as key mechanism of MAE mitotic maintenance. In contrast with the all-or-nothing allelic choice in XCI, allele-specific expression in MAE loci is tunable, with exact allelic imbalance dependent on the extent of DNA methylation. In a subset of MAE genes, allelic imbalance was insensitive to DNA demethylation, implicating additional mechanisms in MAE maintenance in these loci. Our findings identify a key mechanism of MAE maintenance and provide basis for understanding the biological role of MAE.

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

2015 ◽  
Vol 6 (1) ◽  
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.

scholarly journals Inferring the genetic architecture of expression variation from replicated high throughput allele-specific expression experiments

2019 ◽  
Author(s):  
Xinwen Zhang ◽  
J.J. Emerson
Keyword(s):  
False Positive ◽  
Statistical Significance ◽  
False Positive Rate ◽  
Regulatory Sequences ◽  
Binomial Model ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Expression Variation ◽  
Positive Rate ◽  
Allele Specific

AbstractGene expression variation between alleles in a diploid cell is mediated by variation in cis regulatory sequences, which usually refers to the differences in DNA sequence between two alleles near the gene of interest. Expression differences caused by cis variation has been estimated by the ratio of the expression level of the two alleles under a binomial model. However, the binomial model underestimates the variance among replicated experiments resulting in the exaggerated statistical significance of estimated cis effects and thus many false discoveries of cis-affected genes. Here we describe a beta-binomial model that estimates the cis-effect for each gene while permitting overdispersion of variance among replicates. We demonstrated with simulated null data (data without true cis-effect) that the new model fits the true distribution better, resulting in approximately 5% false positive rate under 5% significance level in all null datasets, considerably better than the 6%-40% false positive rate of the binomial model. Additional replicates increase the performance of the beta-binomial model but not of the binomial model. We also collected new allele-specific expression data from an experiment comprised of 20 replicates of a yeast hybrid (YPS128/RM11-1a). We eliminated the mapping bias problem with de novo assemblies of the two parental genomes. By applying the beta-binomial model to this dataset, we found that cis effects are ubiquitous, affecting around 70% of genes. However, most of these changes are small in magnitude. The high number of replicates enabled us a better approximation of cis landscape within species and also provides a resource for future exploration for better models.

scholarly journals Nonfluorescent Denaturing HPLC–Based Primer-Extension Method for Allele-Specific Expression: Application to Analysis of Mismatch Repair Genes

2009 ◽  
Vol 55 (9) ◽  
pp. 1711-1718 ◽  
Author(s):  
Gitana M Aceto ◽  
Laura De Lellis ◽  
Teresa Catalano ◽  
Serena Veschi ◽  
Paolo Radice ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Mismatch Repair ◽  
Primer Extension ◽  
Mismatch Repair Genes ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
E Coli ◽  
Allele Expression ◽  
Allele Specific ◽  
Repair Genes

Abstract Background: Altered germline expression of genes may represent a powerful marker of genetic or epigenetic predisposition to cancer or other diseases. Methods: We developed and validated a method of nonfluorescent primer extension that uses a single dideoxynucleotide and denaturing HPLC (DHPLC) to analyze the relative allele expression. We devised 5 independent assays for measuring allele-specific expression (ASE) to exploit different markers of mismatch repair genes MLH1 [mutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli)] and MSH2 [mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli)]. We initially confirmed method reproducibility with genomic DNA (gDNA) from individuals heterozygous for a frequent single-nucleotide polymorphism in the MLH1 gene. After this preliminary validation with gDNA, we confirmed assay reproducibility with cDNA templates from control individuals. Relative allele expression was estimated by comparing the heights of the peaks corresponding to the 2 alleles. Results obtained with gDNA templates were used to normalize cDNA results. Results: With these DHPLC-based primer-extension assays, we detected and confirmed a 5-fold imbalance in MLH1 allele expression in a mutation-negative patient with hereditary nonpolyposis colorectal cancer and in another patient with a modest degree of imbalance in MLH1 expression. Among control individuals, the relative expression of MLH1 alleles displayed a narrow range of variation. Conclusions: Independent DHPLC-based primer-extension assays for measuring and confirming ASE can be developed for different sequence variants of interest. This DHPLC application provides a cost-effective method for detecting ASE in cases for which conventional screening fails to detect pathogenic mutations in candidate genes and may be applicable for confirming ASE revealed by other methods, such as those used for transcriptome-wide analyses. .

scholarly journals Assessment of kinship detection using RNA-seq data

2019 ◽  
Vol 47 (21) ◽  
pp. e136-e136
Author(s):  
Natalia Blay ◽  
Eduard Casas ◽  
Iván Galván-Femenía ◽  
Jan Graffelman ◽  
Rafael de Cid ◽  
...  
Keyword(s):  
Allelic Imbalance ◽  
Population Based ◽  
Rna Seq ◽  
Pairwise Identity ◽  
Specific Expression ◽  
Whole Exome ◽  
High Quality Snps ◽  
Allele Specific ◽  
Related Individuals ◽  
Family Based

Abstract Analysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Prediction of kinship from RNA-seq data would be useful for confirming the expected relationships in family based studies and for highlighting samples from related individuals in case-control or population based studies. Currently, reconstruction of pedigrees is largely based on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression. In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity by descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth.

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