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Master Thesis

Potentiels du séquençage des ARN pour explorer les micro-variations du génome

Abstract : In this internship, we finalized a pipeline for the detection of reliable mononucleotide variants (SNP) from RNA sequencing data (RNA-seq) and applied it to RNA-seq data from 10 commercial and experimental broiler and egg-laying hen populations. A concordance analysis of the variants obtained in RNA-seq and DNA-seq 20X (genomic DNA sequencing of the same tissues) was carried out, for the first time, on the same individuals and in significant numbers (15 hens). We thus show that RNA-seq data are an interesting polymorphism resource to exploit because, at equal expressed regions, RNA-seq allows the detection of more than 85% of the SNP captured in DNA-seq - even more with several tissues analysed - and this with a DNA match of more than 90%. On the other hand, the number of SNP detected is consistent: 9.9M SNP detected for all 10 populations with an average of 1.8M SNP detected per population, of which ~0.5M with informed genotypes. Finally, 250,000 SNP with informed genotypes are common to the 10 populations studied. This list of SNP allowed a first characterization of the genetic links between these 10 populations, which is coherent with their phylogenetic background. We also analyzed the functional impact of these 9.9M SNP on transcripts and associated proteins and identified 1590 stop_gained SNP that remain to be analyzed. We have also developed a program to predict the functional impact of double or triple phased SNP within the same codon, data not yet taken into account in current programs. Although rare (concerning 0.2% of variants), we show that about 70% of the functional predictions on these double or triple phased SNP codons are erroneous.
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Master Thesis
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Submitted on : Friday, December 4, 2020 - 10:47:59 AM
Last modification on : Monday, April 12, 2021 - 3:26:09 AM


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  • HAL Id : dumas-03040111, version 1



Fabien Degalez. Potentiels du séquençage des ARN pour explorer les micro-variations du génome. Sciences du Vivant [q-bio]. 2020. ⟨dumas-03040111⟩



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