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Journal article

HaSAPPy: A tool for candidate identification in pooled forward genetic screens of haploid mammalian cells.

  • Di Minin G Institute of Molecular Health Sciences, Department of Biology, Swiss Federal Institute of Technology ETH Hönggerberg, Zurich, Switzerland.
  • Postlmayr A Institute of Molecular Health Sciences, Department of Biology, Swiss Federal Institute of Technology ETH Hönggerberg, Zurich, Switzerland.
  • Wutz A Institute of Molecular Health Sciences, Department of Biology, Swiss Federal Institute of Technology ETH Hönggerberg, Zurich, Switzerland.
  • 2018-01-17
Published in:
  • PLoS computational biology. - 2018
Algorithms
Alleles
Animals
Computational Biology
Computer Graphics
Gene Library
Genome, Human
Haploidy
Humans
Mice
Mouse Embryonic Stem Cells
Mutagenesis, Insertional
Mutation
Phenotype
Picornaviridae
Programming Languages
Sequence Analysis, DNA
Software
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/52311
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