ArticlePLoS computational biology2018
HaSAPPy: A tool for candidate identification in pooled forward genetic screens of haploid mammalian cells.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Applying Spinal Cord Organoids as a quantitative approach to study the mammalian Hedgehog pathway.PloS one · 2024Article
- Haploid-genetic screening of trophectoderm specification identifiesScience advances · 2023Article
- Genome-scale screening in a rat haploid system identifies Thop1 as a modulator of pluripotency exit.Cell proliferation · 2022Article
- Genome-wide screening in the haploid system reveals Slc25a43 as a target gene of oxidative toxicity.Cell death & disease · 2022Article
- TMED2 binding restricts SMO to the ER and Golgi compartments.PLoS biology · 2022Article
- A Two-Color Haploid Genetic Screen Identifies Novel Host Factors Involved in HIV-1 Latency.mBio · 2021Article
- High-throughput screening in postimplantation haploid epiblast stem cells reveals Hs3st3b1 as a modulator for reprogramming.Stem cells translational medicine · 2021Article
- Mycolactone induces cell death by SETD1B-dependent degradation of glutathione.PLoS neglected tropical diseases · 2020Article
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3 authors.
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Abstract
Haploid cells are increasingly used for screening of complex pathways in animal genomes. Hemizygous mutations introduced through viral insertional mutagenesis can be directly selected for phenotypic changes. Here we present HaSAPPy a tool for analysing sequencing datasets of screens using insertional mutations in large pools of haploid cells. Candidate gene prediction is implemented through identification of enrichment of insertional mutations after selection by simultaneously evaluating several parameters. We have developed HaSAPPy for analysis of genetic screens for silencing factors of X chromosome inactivation in haploid mouse embryonic stem cells. To benchmark the performance, we further analyse several datasets of genetic screens in human haploid cells for which candidates have been validated. Our results support the effective candidate prediction strategy of HaSAPPy. HaSAPPy is implemented in Python, licensed under the MIT license, and is available from https://github.com/gdiminin/HaSAPPy.
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