Fully Bayesian analysis of allele-specific RNA-seq data

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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Development of genic KASP SNP markers from RNA-Seq data for map-based cloning and marker-assisted selection in maize

BMC Plant Biology ◽

10.1186/s12870-021-02932-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhengjie Chen ◽

Dengguo Tang ◽

Jixing Ni ◽

Peng Li ◽

Le Wang ◽

...

Keyword(s):

Marker Assisted Selection ◽

Inbred Lines ◽

Average Density ◽

Snp Markers ◽

Data Sets ◽

Rna Seq ◽

Specific Pcr ◽

Maize Inbred Lines ◽

Allele Specific ◽

Allele Specific Pcr

Abstract Background Maize is one of the most important field crops in the world. Most of the key agronomic traits, including yield traits and plant architecture traits, are quantitative. Fine mapping of genes/ quantitative trait loci (QTL) influencing a key trait is essential for marker-assisted selection (MAS) in maize breeding. However, the SNP markers with high density and high polymorphism are lacking, especially kompetitive allele specific PCR (KASP) SNP markers that can be used for automatic genotyping. To date, a large volume of sequencing data has been produced by the next generation sequencing technology, which provides a good pool of SNP loci for development of SNP markers. In this study, we carried out a multi-step screening method to identify kompetitive allele specific PCR (KASP) SNP markers based on the RNA-Seq data sets of 368 maize inbred lines. Results A total of 2,948,985 SNPs were identified in the high-throughput RNA-Seq data sets with the average density of 1.4 SNP/kb. Of these, 71,311 KASP SNP markers (the average density of 34 KASP SNP/Mb) were developed based on the strict criteria: unique genomic region, bi-allelic, polymorphism information content (PIC) value ≥0.4, and conserved primer sequences, and were mapped on 16,161 genes. These 16,161 genes were annotated to 52 gene ontology (GO) terms, including most of primary and secondary metabolic pathways. Subsequently, the 50 KASP SNP markers with the PIC values ranging from 0.14 to 0.5 in 368 RNA-Seq data sets and with polymorphism between the maize inbred lines 1212 and B73 in in silico analysis were selected to experimentally validate the accuracy and polymorphism of SNPs, resulted in 46 SNPs (92.00%) showed polymorphism between the maize inbred lines 1212 and B73. Moreover, these 46 polymorphic SNPs were utilized to genotype the other 20 maize inbred lines, with all 46 SNPs showing polymorphism in the 20 maize inbred lines, and the PIC value of each SNP was 0.11 to 0.50 with an average of 0.35. The results suggested that the KASP SNP markers developed in this study were accurate and polymorphic. Conclusions These high-density polymorphic KASP SNP markers will be a valuable resource for map-based cloning of QTL/genes and marker-assisted selection in maize. Furthermore, the method used to develop SNP markers in maize can also be applied in other species.

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Replicate sequencing libraries are important for quantification of allelic imbalance

Nature Communications ◽

10.1038/s41467-021-23544-8 ◽

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.

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Analysis of Allele-Specific Expression in Mouse Liver by RNA-Seq: A Comparison With Cis-eQTL Identified Using Genetic Linkage

Genetics ◽

10.1534/genetics.113.153882 ◽

2013 ◽

Vol 195 (3) ◽

pp. 1157-1166 ◽

Cited By ~ 34

Author(s):

Sandrine Lagarrigue ◽

Lisa Martin ◽

Farhad Hormozdiari ◽

Pierre-François Roux ◽

Calvin Pan ◽

...

Keyword(s):

Mouse Liver ◽

Genetic Linkage ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

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Discovery of allele-specific protein-RNA interactions in human transcriptomes

10.1101/389205 ◽

2018 ◽

Author(s):

Emad Bahrami-Samani ◽

Yi Xing

Keyword(s):

Genetic Variants ◽

Rna Binding ◽

Rna Binding Proteins ◽

Specific Protein ◽

Computational Method ◽

Joint Analysis ◽

Transcriptional Level ◽

Rna Seq ◽

Allele Specific ◽

Rna Interaction

AbstractGene expression is tightly regulated at the post-transcriptional level through splicing, transport, translation, and decay. RNA-binding proteins (RBPs) play key roles in post-transcriptional gene regulation, and genetic variants that alter RBP-RNA interactions can affect gene products and functions. We developed a computational method ASPRIN (Allele-Specific Protein-RNA Interaction), that uses a joint analysis of CLIP-seq (cross-linking and immunoprecipitation followed by high-throughput sequencing) and RNA-seq data to identify genetic variants that alter RBP-RNA interactions by directly observing the allelic preference of RBP from CLIP-seq experiments as compared to RNA-seq. We used ASPRIN to systematically analyze CLIP-seq and RNA-seq data for 166 RBPs in two ENCODE (Encyclopedia of DNA Elements) cell lines. ASPRIN identified genetic variants that alter RBP-RNA interactions by modifying RBP binding motifs within RNA. Moreover, through an integrative ASPRIN analysis with population-scale RNA-seq data, we showed that ASPRIN can help reveal potential causal variants that affect alternative splicing via allele-specific protein-RNA interactions.

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Detecting Allele-Specific Alternative Splicing from Population-Scale RNA-Seq Data

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2020.07.005 ◽

2020 ◽

Vol 107 (3) ◽

pp. 461-472

Author(s):

Levon Demirdjian ◽

Yungang Xu ◽

Emad Bahrami-Samani ◽

Yang Pan ◽

Shayna Stein ◽

...

Keyword(s):

Alternative Splicing ◽

Rna Seq ◽

Allele Specific ◽

Specific Alternative ◽

Population Scale

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Analysing crack growth

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1243/0954410981532225 ◽

1998 ◽

Vol 212 (3) ◽

pp. 157-166 ◽

Cited By ~ 4

Author(s):

M Newby

Keyword(s):

Bayesian Analysis ◽

Crack Length ◽

Crack Growth ◽

Failure Time ◽

Growth Models ◽

Accelerated Failure Time ◽

Deterministic Models ◽

Stochastic Growth Models ◽

Standard Linear ◽

Fully Bayesian

Deterministic models of crack growth can be fitted to experimental data. This paper shows that stochastic growth models are easy to use and provides a simple framework for data analysis. A simple transformation allows the standard linear regression model to be used and opens the way for a fully Bayesian analysis. The Bayesian analysis allows the incorporation of prior information and coherent predictions of crack length to be made. The parameters of the Paris-Erdogan model are readily evaluated directly from crack length data without the need for intermediate estimates of the crack growth rate. The approach lends itself to the analysis of properly designed experiments to determine the effect of environmental factors on the parameters of the Paris-Erdogan equation through the medium of accelerated failure time models. The paper also emphasizes the need for adequate communication between experimenter and statistician to ensure efficient experimental designs.

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