TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens

BackgroundMeasuring the impact of combinations of genetic or chemical perturbations on cellular fitness, sometimes referred to as synthetic lethal screening, is a powerful method for obtaining novel insights into gene function and drug action. Especially when performed at large scales, gene-gene or gene-drug interaction screens can reveal complex genetic interactions or drug mechanism of action or even identify novel therapeutics for the treatment of diseases.The result of such large-scale screen results can be represented as a matrix with a numeric score indicating the cellular fitness (e.g. viability or doubling time) for each double perturbation. In a typical screen, the majority of combinations do not impact the cellular fitness. Thus, it is critical to first discern true "hits" from noise. Subsequent data exploration and visualization methods can assist to extract meaningful biological information from the data. However, despite the increasing interest in combination perturbation screens, no user friendly open-source program exists that combines statistical analysis, data exploration tools and visualization.ResultsWe developed TOPS (Tool for Combination Perturbation Screen Analysis), a Java and R-based software tool with a simple graphical user interface that allows the user to import, analyze, filter and plot data from double perturbation screens as well as other compatible data. TOPS was designed in a modular fashion to allow the user to add alternative importers for data formats or custom analysis scripts not covered by the original release.We demonstrate the utility of TOPS on two datasets derived from functional genetic screens using different methods. Dataset 1 is a gene-drug interaction screen and is based on Luminex xMAP technology. Dataset 2 is a gene-gene short hairpin (sh)RNAi screen exploring the interactions between deubiquitinating enzymes and a number of prominent oncogenes using massive parallel sequencing (MPS).ConclusionsTOPS provides the benchtop scientist with a free toolset to analyze, filter and visualize data from functional genomic gene-gene and gene-drug interaction screens with a flexible interface to accommodate different technologies and analysis algorithms in addition to those already provided here. TOPS is freely available for academic and non-academic users and is released as open source.

[1]  N. Hacohen,et al.  Highly parallel identification of essential genes in cancer cells , 2008, Proceedings of the National Academy of Sciences.

[2]  Morton B. Brown.  400: A Method for Combining Non-Independent, One-Sided Tests of Significance , 1975 .

[3]  Ben S. Wittner,et al.  Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 , 2009, Nature.

[4]  Yun Tan,et al.  Algorithms, Routines, and S Functions, for Robust Statistics , 1995 .

[5]  BMC Bioinformatics , 2005 .

[6]  Wolfgang Huber,et al.  Analysis of cell-based RNAi screens , 2006, Genome Biology.

[7]  Ji Luo,et al.  A SUMOylation-Dependent Transcriptional Subprogram Is Required for Myc-Driven Tumorigenesis , 2012, Science.

[8]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[9]  Wolfgang Huber,et al.  Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping , 2013, Nature Methods.

[10]  Thomas Hesterkamp,et al.  On the Prediction of Statistical Parameters in High-Throughput Screening Using Resampling Techniques , 2012, Journal of biomolecular screening.

[11]  Daphne Koller,et al.  Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action , 2010, Genome Biology.

[12]  C. Li,et al.  Feature extraction and normalization algorithms for high‐density oligonucleotide gene expression array data , 2001, Journal of cellular biochemistry. Supplement.

[13]  M. Rattray,et al.  A model-based analysis of microarray experimental error and normalisation. , 2003, Nucleic acids research.

[14]  Bernd Fischer,et al.  Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi , 2011, BMC Bioinformatics.

[15]  Ben Lehner,et al.  Evolutionary plasticity of genetic interaction networks , 2008, Nature Genetics.

[16]  Marketa Zvelebil,et al.  High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing , 2011, Genome Biology.

[17]  Alfio Marazzi,et al.  Algorithms, Routines, and s Functions for Robust Statistics: The Fortran Library Robeth With an Interface to S-Plus , 1993 .

[18]  S. Nijman.  Synthetic lethality: General principles, utility and detection using genetic screens in human cells , 2011, FEBS letters.

[19]  René Bernards,et al.  New tools for functional mammalian cancer genetics , 2003, Nature Reviews Cancer.

[20]  Gary D Bader,et al.  Quantitative analysis of fitness and genetic interactions in yeast on a genome scale , 2010, Nature Methods.

[21]  Sean R. Collins,et al.  A strategy for extracting and analyzing large-scale quantitative epistatic interaction data , 2006, Genome Biology.

[22]  Sridhar Ramaswamy,et al.  Synthetic Lethal Interaction between Oncogenic KRAS Dependency and STK33 Suppression in Human Cancer Cells , 2009, Cell.

[23]  Robert P. St.Onge,et al.  The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes , 2008, Science.

[24]  Kevin R Brown,et al.  Essential gene profiles in breast, pancreatic, and ovarian cancer cells. , 2012, Cancer discovery.

[25]  Robert P. St.Onge,et al.  Defining genetic interaction , 2008, Proceedings of the National Academy of Sciences.