ArticleScientific reports2017
Vector Integration Sites Identification for Gene-Trap Screening in Mammalian Haploid Cells.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Mammalian haploid stem cells: establishment, engineering and applications.Cellular and molecular life sciences : CMLS · 2019Review
- HaSAPPy: A tool for candidate identification in pooled forward genetic screens of haploid mammalian cells.PLoS computational biology · 2018Article
- New tools for old drugs: Functional genetic screens to optimize current chemotherapy.Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy · 2018Review
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forward genetic screens using retroviral (or transposon) gene-trap vectors in a haploid genome revolutionized the investigation of molecular networks in mammals. However, the sequencing data generated by Phenotypic interrogation followed by Tag sequencing (PhiT-seq) were not well characterized. The analysis of human and mouse haploid screens allowed us to describe PhiT-seq data and to define quality control steps. Moreover, we identified several blind spots in both haploid genomes where gene-trap vectors can hardly integrate. Integration of transcriptomic data improved the performance of candidate gene identification. Furthermore, we experimented with various statistical tests to account for biological replicates in PhiT-seq and investigated the effect of normalization methods and other parameters on the performance. Finally, we developed: VISITs, a dedicated pipeline for analyzing PhiT-seq data (https://sourceforge.net/projects/visits/).
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