Dataset Information

Identifying transposon insertions and their effects from RNA-sequencing data.

ABSTRACT: Insertional mutagenesis using engineered transposons is a potent forward genetic screening technique used to identify cancer genes in mouse model systems. In the analysis of these screens, transposon insertion sites are typically identified by targeted DNA-sequencing and subsequently assigned to predicted target genes using heuristics. As such, these approaches provide no direct evidence that insertions actually affect their predicted targets or how transcripts of these genes are affected. To address this, we developed IM-Fusion, an approach that identifies insertion sites from gene-transposon fusions in standard single- and paired-end RNA-sequencing data. We demonstrate IM-Fusion on two separate transposon screens of 123 mammary tumors and 20 B-cell acute lymphoblastic leukemias, respectively. We show that IM-Fusion accurately identifies transposon insertions and their true target genes. Furthermore, by combining the identified insertion sites with expression quantification, we show that we can determine the effect of a transposon insertion on its target gene(s) and prioritize insertions that have a significant effect on expression. We expect that IM-Fusion will significantly enhance the accuracy of cancer gene discovery in forward genetic screens and provide initial insight into the biological effects of insertions on candidate cancer genes.

SUBMITTER: de Ruiter JR

PROVIDER: S-EPMC5499543 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Identifying transposon insertions and their effects from RNA-sequencing data.

de Ruiter Julian R JR Kas Sjors M SM Schut Eva E Adams David J DJ Koudijs Marco J MJ Wessels Lodewyk F A LFA Jonkers Jos J

Nucleic acids research 20170701 12

Insertional mutagenesis using engineered transposons is a potent forward genetic screening technique used to identify cancer genes in mouse model systems. In the analysis of these screens, transposon insertion sites are typically identified by targeted DNA-sequencing and subsequently assigned to predicted target genes using heuristics. As such, these approaches provide no direct evidence that insertions actually affect their predicted targets or how transcripts of these genes are affected. To ad ...[more]

PMID: 28575524

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Project description:BackgroundLarge sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.ResultsWe describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations.ConclusionsResearchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency.

Dataset Information

Identifying transposon insertions and their effects from RNA-sequencing data.

Publications

Identifying transposon insertions and their effects from RNA-sequencing data.

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