期刊论文详细信息
BMC Bioinformatics
TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens
Markus K Muellner1  Gerhard Duernberger2  Florian Ganglberger1  Claudia Kerzendorfer1  Iris Z Uras1  Andreas Schoenegger1  Klaudia Bagienski1  Jacques Colinge1  Sebastian MB Nijman1 
[1] Research Center for Molecular Medicine of the Austrian Academy of Sciences (CeMM), Vienna, Austria
[2] Current address: The Research Institute of Molecular Pathology (IMP), Vienna, Austria
关键词: Epistasis;    Synergy;    Drug screens;    Luminex xMAP;    Massive parallel sequencing;    Functional genetics;    Synthetic lethality;    Double perturbation screens;   
Others  :  1087575
DOI  :  10.1186/1471-2105-15-98
 received in 2013-05-17, accepted in 2014-03-28,  发布年份 2014
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【 摘 要 】

Background

Measuring 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.

Results

We 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).

Conclusions

TOPS 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.

【 授权许可】

   
2014 Muellner et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Hillenmeyer ME, Ericson E, Davis RW, Nislow C, Koller D, Giaever G: Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action. Genome Biol 2010, 11(3):R30. BioMed Central Full Text
  • [2]Hillenmeyer ME, Fung E, Wildenhain J, Pierce SE, Hoon S, Lee W, Proctor M, St Onge RP, Tyers M, Koller D, Altman RB, Davis RW, Nislow C, Giaever G: The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 2008, 320(5874):362-365.
  • [3]Nijman SM: Synthetic lethality: general principles, utility and detection using genetic screens in human cells. FEBS Lett 2010, 585(1):1-6.
  • [4]Kessler JD, Kahle KT, Sun T, Meerbrey KL, Schlabach MR, Schmitt EM, Skinner SO, Xu Q, Li MZ, Hartman ZC, Rao M, Yu P, Dominguez-Vidana R, Liang AC, Solimini NL, Bernardi RJ, Yu B, Hsu T, Golding I, Luo J, Osborne CK, Creighton CJ, Hilsenbeck SG, Schiff R, Shaw CA, Elledge SJ, Westbrook TF: A SUMOylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis. Science 2011, 335(6066):348-353.
  • [5]Scholl C, Fröhling S, Dunn IF, Schinzel AC, Barbie DA, Kim SY, Silver SJ, Tamayo P, Wadlow RC, Ramaswamy S, Döhner K, Bullinger L, Sandy P, Boehm JS, Root DE, Jacks T, Hahn WC, Gilliland DG: Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. Cell 2009, 137(5):821-834.
  • [6]Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, Schinzel AC, Sandy P, Meylan E, Scholl C, Fröhling S, Chan EM, Sos ML, Michel K, Mermel C, Silver SJ, Weir BA, Reiling JH, Sheng Q, Gupta PB, Wadlow RC, Le H, Hoersch S, Wittner BS, Ramaswamy S, Livingston DM, Sabatini DM, Meyerson M, Thomas RK, Lander ES, et al.: Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 2009, 462(7269):108-112.
  • [7]Muellner MK, Uras IZ, Gapp BV, Kerzendorfer C, Smida M, Lechtermann H, Craig-Mueller N, Colinge J, Duernberger G, Nijman SM: A chemical-genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer. Nat Chem Biol 2011, 7(11):787-793.
  • [8]Brummelkamp TR, Bernards R: New tools for functional mammalian cancer genetics. Nat Rev Cancer 2003, 3(10):781-789.
  • [9]Marcotte R, Brown KR, Suarez F, Sayad A, Karamboulas K, Krzyzanowski PM, Sircoulomb F, Medrano M, Fedyshyn Y, Koh JL, van Dyk D, Fedyshyn B, Luhova M, Brito GC, Vizeacoumar FJ, Vizeacoumar FS, Datti A, Kasimer D, Buzina A, Mero P, Misquitta C, Normand J, Haider M, Ketela T, Wrana JL, Rottapel R, Neel BG, Moffat J: Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer Discov 2012, 2(2):172-189.
  • [10]Sims D, Mendes-Pereira AM, Frankum J, Burgess D, Cerone MA, Lombardelli C, Mitsopoulos C, Hakas J, Murugaesu N, Isacke CM, Fenwick K, Assiotis I, Kozarewa I, Zvelebil M, Ashworth A, Lord CJ: High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing. Genome Biol 2011, 12(10):R104. BioMed Central Full Text
  • [11]Tischler J, Lehner B, Fraser AG: Evolutionary plasticity of genetic interaction networks. Nat Genet 2008, 40(4):390-391.
  • [12]Fang Y, Brass A, Hoyle DC, Hayes A, Bashein A, Oliver SG, Waddington D, Rattray M: A model-based analysis of microarray experimental error and normalisation. Nucleic Acids Res 2003, 31(16):e96.
  • [13]Schadt EE, Li C, Ellis B, Wong WH: Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J Cell Biochem Suppl 2001, Suppl 37:120-125.
  • [14]Huber P: Robust Statistics. Hoboken, NJ, USA: Wiley-Interscience; 2003.
  • [15]Marazzi A: Algorithms, Routines, and S-Functions for Robust Statistics. London, UK: Taylor & Francis; 1993.
  • [16]Axelsson E, Sandmann T, Horn T, Boutros M, Huber W, Fischer B: Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi. BMC Bioinforma 2011, 12:342. BioMed Central Full Text
  • [17]Mani R, St Onge RP, Hartman JL, Giaever G, Roth FP: Defining genetic interaction. Proc Natl Acad Sci U S A 2008, 105(9):3461-3466.
  • [18]Laufer C, Fischer B, Billmann M, Huber W, Boutros M: Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat Methods 2013, 10(5):427-431.
  • [19]Brown M: A method for combining non-independent, one-sided tests of significance. Biometrics 1975, 31(4):987-992.
  • [20]Ilouga PE, Hesterkamp T: On the prediction of statistical parameters in high-throughput screening using resampling techniques. J Biomol Screen 2012, 17(6):705-712.
  • [21]Boutros M, Bras LP, Huber W: Analysis of cell-based RNAi screens. Genome Biol 2006, 7(7):R66. BioMed Central Full Text
  • [22]Luo B, Cheung HW, Subramanian A, Sharifnia T, Okamoto M, Yang X, Hinkle G, Boehm JS, Beroukhim R, Weir BA, Mermel C, Barbie DA, Awad T, Zhou X, Nguyen T, Piqani B, Li C, Golub TR, Meyerson M, Hacohen N, Hahn WC, Lander ES, Sabatini DM, Root DE: Highly parallel identification of essential genes in cancer cells. Proc Natl Acad Sci U S A 2008, 105(51):20380-20385.
  • [23]Collins SR, Schuldiner M, Krogan NJ, Weissman JS: A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol 2006, 7(7):R63. BioMed Central Full Text
  • [24]Baryshnikova A, Costanzo M, Kim Y, Ding H, Koh J, Toufighi K, Youn JY, Ou J, San Luis BJ, Bandyopadhyay S, Hibbs M, Hess D, Gingras AC, Bader GD, Troyanskaya OG, Brown GW, Andrews B, Boone C, Myers CL: Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat Methods 2010, 7(12):1017-1024.
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