title: HiTSelect: a comprehensive tool for high-complexity-pooled screen analysis.
abstract: Genetic screens of an unprecedented scale have recently been made possible by the availability of high-complexity libraries of synthetic oligonucleotides designed to mediate either gene knockdown or gene knockout, coupled with next-generation sequencing. However, several sources of random noise and statistical biases complicate the interpretation of the resulting high-throughput data. We developed HiTSelect, a comprehensive analysis pipeline for rigorously selecting screen hits and identifying functionally relevant genes and pathways by addressing off-target effects, controlling for variance in both gene silencing efficiency and sequencing depth of coverage and integrating relevant metadata. We document the superior performance of HiTSelect using data from both genome-wide RNAi and CRISPR/Cas9 screens. HiTSelect is implemented as an open-source package, with a user-friendly interface for data visualization and pathway exploration. Binary executables are available at http://sourceforge.net/projects/hitselect/, and the source code is available at https://github.com/diazlab/HiTSelect.
authors.affiliation: Institute for Human Genetics, University of California, San Francisco, CA, USA The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
authors.forename: Aaron A
authors.initials: AA
authors.lastname: Diaz
authors.affiliation: The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA Departments of Obstetrics and Gynecology and Pathology and Center for Reproductive Sciences, University of California, San Francisco, CA, USA Diabetes Center, University of California, San Francisco, CA, USA.
authors.forename: Han
authors.initials: H
authors.lastname: Qin
authors.affiliation: The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA Departments of Obstetrics and Gynecology and Pathology and Center for Reproductive Sciences, University of California, San Francisco, CA, USA Diabetes Center, University of California, San Francisco, CA, USA songj@illinois.edu.
authors.forename: Miguel
authors.initials: M
authors.lastname: Ramalho-Santos
authors.affiliation: Institute for Human Genetics, University of California, San Francisco, CA, USA The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA songj@illinois.edu.
authors.forename: Jun S
authors.initials: JS
authors.lastname: Song
mesh_terms: D000465:Algorithms; D064112:Clustered Regularly Interspaced Short Palindromic Repeats; D055785:Gene Knockdown Techniques; D059014:High-Throughput Nucleotide Sequencing; D057026:Induced Pluripotent Stem Cells / Q000378:metabolism; D014411:Neoplastic Stem Cells / Q000378:metabolism; D012313:RNA / Q000032:analysis* / Q000235:genetics; D013269:Stochastic Processes
chemical_list: D012313:RNA