Dataset Information

Tools and best practices for retrotransposon analysis using high-throughput sequencing data.

ABSTRACT: Background:Sequencing technologies give access to a precise picture of the molecular mechanisms acting upon genome regulation. One of the biggest technical challenges with sequencing data is to map millions of reads to a reference genome. This problem is exacerbated when dealing with repetitive sequences such as transposable elements that occupy half of the mammalian genome mass. Sequenced reads coming from these regions introduce ambiguities in the mapping step. Therefore, applying dedicated parameters and algorithms has to be taken into consideration when transposable elements regulation is investigated with sequencing datasets. Results:Here, we used simulated reads on the mouse and human genomes to define the best parameters for aligning transposable element-derived reads on a reference genome. The efficiency of the most commonly used aligners was compared and we further evaluated how transposable element representation should be estimated using available methods. The mappability of the different transposon families in the mouse and the human genomes was calculated giving an overview into their evolution. Conclusions:Based on simulated data, we provided recommendations on the alignment and the quantification steps to be performed when transposon expression or regulation is studied, and identified the limits in detecting specific young transposon families of the mouse and human genomes. These principles may help the community to adopt standard procedures and raise awareness of the difficulties encountered in the study of transposable elements.

SUBMITTER: Teissandier A

PROVIDER: S-EPMC6935493 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Tools and best practices for retrotransposon analysis using high-throughput sequencing data.

Teissandier Aurélie A Servant Nicolas N Barillot Emmanuel E Bourc'his Deborah D

Mobile DNA 20191229

<h4>Background</h4>Sequencing technologies give access to a precise picture of the molecular mechanisms acting upon genome regulation. One of the biggest technical challenges with sequencing data is to map millions of reads to a reference genome. This problem is exacerbated when dealing with repetitive sequences such as transposable elements that occupy half of the mammalian genome mass. Sequenced reads coming from these regions introduce ambiguities in the mapping step. Therefore, applying dedi ...[more]

PMID: 31890048

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Project description:BackgroundIdentification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data.Materials and methodsWe have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure.ResultsHere, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes.ConclusionWe show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.

Dataset Information

Tools and best practices for retrotransposon analysis using high-throughput sequencing data.

Publications

Tools and best practices for retrotransposon analysis using high-throughput sequencing data.

Similar Datasets

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