Literature DB >> 33597613

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

M Joseph Tomlinson1,2, Shawn W Polson3,4,2, Jing Qiu5,2, Juniper A Lake1,2, William Lee6, Behnam Abasht7,8.   

Abstract

Differential 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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Year:  2021        PMID: 33597613      PMCID: PMC7889858          DOI: 10.1038/s41598-021-83459-8

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  49 in total

1.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nat Protoc       Date:  2009       Impact factor: 13.491

2.  Assessing allele-specific expression across multiple tissues from RNA-seq read data.

Authors:  Matti Pirinen; Tuuli Lappalainen; Noah A Zaitlen; Emmanouil T Dermitzakis; Peter Donnelly; Mark I McCarthy; Manuel A Rivas
Journal:  Bioinformatics       Date:  2015-03-27       Impact factor: 6.937

3.  STAR: ultrafast universal RNA-seq aligner.

Authors:  Alexander Dobin; Carrie A Davis; Felix Schlesinger; Jorg Drenkow; Chris Zaleski; Sonali Jha; Philippe Batut; Mark Chaisson; Thomas R Gingeras
Journal:  Bioinformatics       Date:  2012-10-25       Impact factor: 6.937

4.  Allelic Imbalance Is a Prevalent and Tissue-Specific Feature of the Mouse Transcriptome.

Authors:  Stefan F Pinter; David Colognori; Brian J Beliveau; Ruslan I Sadreyev; Bernhard Payer; Eda Yildirim; Chao-Ting Wu; Jeannie T Lee
Journal:  Genetics       Date:  2015-04-09       Impact factor: 4.562

5.  From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.

Authors:  Geraldine A Van der Auwera; Mauricio O Carneiro; Christopher Hartl; Ryan Poplin; Guillermo Del Angel; Ami Levy-Moonshine; Tadeusz Jordan; Khalid Shakir; David Roazen; Joel Thibault; Eric Banks; Kiran V Garimella; David Altshuler; Stacey Gabriel; Mark A DePristo
Journal:  Curr Protoc Bioinformatics       Date:  2013

6.  A framework for variation discovery and genotyping using next-generation DNA sequencing data.

Authors:  Mark A DePristo; Eric Banks; Ryan Poplin; Kiran V Garimella; Jared R Maguire; Christopher Hartl; Anthony A Philippakis; Guillermo del Angel; Manuel A Rivas; Matt Hanna; Aaron McKenna; Tim J Fennell; Andrew M Kernytsky; Andrey Y Sivachenko; Kristian Cibulskis; Stacey B Gabriel; David Altshuler; Mark J Daly
Journal:  Nat Genet       Date:  2011-04-10       Impact factor: 38.330

7.  Genome-wide identification of allele-specific expression (ASE) in response to Marek's disease virus infection using next generation sequencing.

Authors:  Sean Maceachern; William M Muir; Seth Crosby; Hans H Cheng
Journal:  BMC Proc       Date:  2011-06-03

8.  Allele-specific expression and eQTL analysis in mouse adipose tissue.

Authors:  Yehudit Hasin-Brumshtein; Farhad Hormozdiari; Lisa Martin; Atila van Nas; Eleazar Eskin; Aldons J Lusis; Thomas A Drake
Journal:  BMC Genomics       Date:  2014-06-13       Impact factor: 3.969

9.  GeneiASE: Detection of condition-dependent and static allele-specific expression from RNA-seq data without haplotype information.

Authors:  Daniel Edsgärd; Maria Jesus Iglesias; Sarah-Jayne Reilly; Anders Hamsten; Per Tornvall; Jacob Odeberg; Olof Emanuelsson
Journal:  Sci Rep       Date:  2016-02-18       Impact factor: 4.379

10.  Detection of genomic signatures of recent selection in commercial broiler chickens.

Authors:  Weixuan Fu; William R Lee; Behnam Abasht
Journal:  BMC Genet       Date:  2016-08-26       Impact factor: 2.797

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  2 in total

1.  Favoring Expression of Yak Alleles in Interspecies F1 Hybrids of Cattle and Yak Under High-Altitude Environments.

Authors:  Shi-Yi Chen; Cao Li; Zhihao Luo; Xiaowei Li; Xianbo Jia; Song-Jia Lai
Journal:  Front Vet Sci       Date:  2022-06-30

2.  3'UTR-Seq analysis of chicken abdominal adipose tissue reveals widespread intron retention in 3'UTR and provides insight into molecular basis of feed efficiency.

Authors:  Ziqing Wang; Mustafa Özçam; Behnam Abasht
Journal:  PLoS One       Date:  2022-07-01       Impact factor: 3.752

  2 in total

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