1932

Abstract

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

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2019-07-20
2024-05-21
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Literature Cited

  1. 1. 
    Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R et al. 2000. Functional discovery via a compendium of expression profiles. Cell 102:109–26
    [Google Scholar]
  2. 2. 
    Stoughton RB, Friend SH. 2005. How molecular profiling could revolutionize drug discovery. Nat. Rev. Drug Discov. 4:345–50
    [Google Scholar]
  3. 3. 
    Gunther EC, Stone DJ, Gerwien RW, Bento P, Heyes MP 2003. Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. PNAS 100:9608–13
    [Google Scholar]
  4. 4. 
    Waring JF, Jolly RA, Ciurlionis R, Lum PY, Praestgaard JT et al. 2001. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol. Appl. Pharmacol. 175:28–42
    [Google Scholar]
  5. 5. 
    Steiner G, Suter L, Boess F, Gasser R, de Vera MC et al. 2004. Discriminating different classes of toxicants by transcript profiling. Environ. Health Perspect. 112:1236–48
    [Google Scholar]
  6. 6. 
    Engelberg A. 2004. Iconix Pharmaceuticals, Inc.—removing barriers to efficient drug discovery through chemogenomics. Pharmacogenomics 5:741–44
    [Google Scholar]
  7. 7. 
    Ganter B, Tugendreich S, Pearson CI, Ayanoglu E, Baumhueter S et al. 2005. Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J. Biotechnol. 119:219–44
    [Google Scholar]
  8. 8. 
    Lamb J, Crawford ED, Peck D, Modell JW, Blat IC et al. 2006. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–35
    [Google Scholar]
  9. 9. 
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL et al. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:15545–50
    [Google Scholar]
  10. 10. 
    Liu J, Lee J, Salazar Hernandez MA, Mazitschek R, Ozcan U 2015. Treatment of obesity with celastrol. Cell 161:999–1011
    [Google Scholar]
  11. 11. 
    Raghavan R, Hyter S, Pathak HB, Godwin AK, Konecny G et al. 2016. Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer. BMC Genom 17:811
    [Google Scholar]
  12. 12. 
    Bhat-Nakshatri P, Goswami CP, Badve S, Sledge GW Jr, Nakshatri H 2013. Identification of FDA-approved drugs targeting breast cancer stem cells along with biomarkers of sensitivity. Sci. Rep. 3:2530
    [Google Scholar]
  13. 13. 
    Josset L, Textoris J, Loriod B, Ferraris O, Moules V et al. 2010. Gene expression signature-based screening identifies new broadly effective influenza A antivirals. PLOS ONE 5:e13169
    [Google Scholar]
  14. 14. 
    Vanderstocken G, Dvorkin-Gheva A, Shen P, Brandsma CA, Obeidat M et al. 2018. Identification of drug candidates to suppress cigarette smoke-induced inflammation via Connectivity Map analyses. Am. J. Respir. Cell Mol. Biol. 58:727–35
    [Google Scholar]
  15. 15. 
    Claerhout S, Lim JY, Choi W, Park YY, Kim K et al. 2011. Gene expression signature analysis identifies vorinostat as a candidate therapy for gastric cancer. PLOS ONE 6:e24662
    [Google Scholar]
  16. 16. 
    Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S et al. 2011. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3:96ra76
    [Google Scholar]
  17. 17. 
    Brum AM, van de Peppel J, Nguyen L, Aliev A, Schreuders-Koedam M et al. 2018. Using the Connectivity Map to discover compounds influencing human osteoblast differentiation. J. Cell Physiol. 233:4895–906
    [Google Scholar]
  18. 18. 
    Wang G, Ye Y, Yang X, Liao H, Zhao C, Liang S 2011. Expression-based in silico screening of candidate therapeutic compounds for lung adenocarcinoma. PLOS ONE 6:e14573
    [Google Scholar]
  19. 19. 
    Xu S, Liu R, Da Y 2018. Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma. Thorac. Cancer 9:974–88
    [Google Scholar]
  20. 20. 
    Dyle MC, Ebert SM, Cook DP, Kunkel SD, Fox DK et al. 2014. Systems-based discovery of tomatidine as a natural small molecule inhibitor of skeletal muscle atrophy. J. Biol. Chem. 289:14913–24
    [Google Scholar]
  21. 21. 
    Zerbini LF, Bhasin MK, de Vasconcellos JF, Paccez JD, Gu X et al. 2014. Computational repositioning and preclinical validation of pentamidine for renal cell cancer. Mol. Cancer Ther. 13:1929–41
    [Google Scholar]
  22. 22. 
    Smalley JL, Gant TW, Zhang SD 2010. Application of connectivity mapping in predictive toxicology based on gene-expression similarity. Toxicology 268:143–46
    [Google Scholar]
  23. 23. 
    Brum AM, van de Peppel J, van der Leije CS, Schreuders-Koedam M, Eijken M et al. 2015. Connectivity Map-based discovery of parbendazole reveals targetable human osteogenic pathway. PNAS 112:12711–16
    [Google Scholar]
  24. 24. 
    Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L et al. 2018. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173:338–54.e15
    [Google Scholar]
  25. 25. 
    Jun HY, Kim TH, Choi JW, Lee YH, Lee KK, Yoon KH 2017. Evaluation of connectivity map-discovered celastrol as a radiosensitizing agent in a murine lung carcinoma model: feasibility study of diffusion-weighted magnetic resonance imaging. PLOS ONE 12:e0178204
    [Google Scholar]
  26. 26. 
    Greenhill C. 2015. Celastrol identified as a leptin sensitizer and potential novel treatment for obesity. Nat. Rev. Endocrinol. 11:444
    [Google Scholar]
  27. 27. 
    Chong CR, Sullivan DJ Jr 2007. New uses for old drugs. Nature 448:645–46
    [Google Scholar]
  28. 28. 
    Slonim DK, Koide K, Johnson KL, Tantravahi U, Cowan JM et al. 2009. Functional genomic analysis of amniotic fluid cell-free mRNA suggests that oxidative stress is significant in Down syndrome fetuses. PNAS 106:9425–29
    [Google Scholar]
  29. 29. 
    Flynn C, Zheng S, Yan L, Hedges L, Womack B et al. 2012. Connectivity map analysis of nonsense-mediated decay-positive BMPR2-related hereditary pulmonary arterial hypertension provides insights into disease penetrance. Am. J. Respir. Cell Mol. Biol. 47:20–27
    [Google Scholar]
  30. 30. 
    Toscano MG, Navarro-Montero O, Ayllon V, Ramos-Mejia V, Guerrero-Carreno X et al. 2015. SCL/TAL1-mediated transcriptional network enhances megakaryocytic specification of human embryonic stem cells. Mol. Ther. 23:158–70
    [Google Scholar]
  31. 31. 
    Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P et al. 2010. Discovery of drug mode of action and drug repositioning from transcriptional responses. PNAS 107:14621–26
    [Google Scholar]
  32. 32. 
    Wang K, Sun J, Zhou S, Wan C, Qin S et al. 2013. Prediction of drug-target interactions for drug repositioning only based on genomic expression similarity. PLOS Comput. Biol. 9:e1003315
    [Google Scholar]
  33. 33. 
    Isik Z, Baldow C, Cannistraci CV, Schroeder M 2015. Drug target prioritization by perturbed gene expression and network information. Sci. Rep. 5:17417
    [Google Scholar]
  34. 34. 
    Zhong Y, Chen EY, Liu R, Chuang PY, Mallipattu SK et al. 2013. Renoprotective effect of combined inhibition of angiotensin-converting enzyme and histone deacetylase. J. Am. Soc. Nephrol. 24:801–11
    [Google Scholar]
  35. 35. 
    Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B et al. 2010. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11:733–39
    [Google Scholar]
  36. 36. 
    Clark NR, Ma'ayan A. 2011. Introduction to statistical methods to analyze large data sets: principal components analysis. Sci. Signal. 4:tr3
    [Google Scholar]
  37. 37. 
    van der Maaten L, Hinton G 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605
    [Google Scholar]
  38. 38. 
    Iskar M, Campillos M, Kuhn M, Jensen LJ, van Noort V, Bork P 2010. Drug-induced regulation of target expression. PLOS Comput. Biol. 6:e1000925
    [Google Scholar]
  39. 39. 
    Love MI, Huber W, Anders S 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550
    [Google Scholar]
  40. 40. 
    Law CW, Chen Y, Shi W, Smyth GK 2014. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29
    [Google Scholar]
  41. 41. 
    Leek JT, Storey JD. 2007. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLOS Genet 3:1724–35
    [Google Scholar]
  42. 42. 
    Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD 2012. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882–83
    [Google Scholar]
  43. 43. 
    Benito M, Parker J, Du Q, Wu J, Xiang D et al. 2004. Adjustment of systematic microarray data biases. Bioinformatics 20:105–14
    [Google Scholar]
  44. 44. 
    Alter O, Brown PO, Botstein D 2000. Singular value decomposition for genome-wide expression data processing and modeling. PNAS 97:10101–6
    [Google Scholar]
  45. 45. 
    Nygaard V, Rodland EA, Hovig E 2016. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics 17:29–39
    [Google Scholar]
  46. 46. 
    Zhang SD, Gant TW. 2008. A simple and robust method for connecting small-molecule drugs using gene-expression signatures. BMC Bioinform 9:258
    [Google Scholar]
  47. 47. 
    Zhang SD, Gant TW. 2009. sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures. BMC Bioinform 10:236
    [Google Scholar]
  48. 48. 
    McArt DG, Bankhead P, Dunne PD, Salto-Tellez M, Hamilton P, Zhang SD 2013. cudaMap: a GPU accelerated program for gene expression connectivity mapping. BMC Bioinform 14:305
    [Google Scholar]
  49. 49. 
    Tenenbaum JD, Walker MG, Utz PJ, Butte AJ 2008. Expression-based Pathway Signature Analysis (EPSA): mining publicly available microarray data for insight into human disease. BMC Med. Genom. 1:51
    [Google Scholar]
  50. 50. 
    Gower AC, Spira A, Lenburg ME 2011. Discovering biological connections between experimental conditions based on common patterns of differential gene expression. BMC Bioinform 12:381
    [Google Scholar]
  51. 51. 
    Clark NR, Hu KS, Feldmann AS, Kou Y, Chen EY et al. 2014. The characteristic direction: a geometrical approach to identify differentially expressed genes. BMC Bioinform 15:79
    [Google Scholar]
  52. 52. 
    Duan Q, Reid SP, Clark NR, Wang Z, Fernandez NF et al. 2016. L1000CDS2: LINCS L1000 characteristic direction signatures search engine. NPJ Syst. Biol. Appl. 2:16015
    [Google Scholar]
  53. 53. 
    Cheng J, Yang L, Kumar V, Agarwal P 2014. Systematic evaluation of connectivity map for disease indications. Genome Med 6:540
    [Google Scholar]
  54. 54. 
    Iorio F, Tagliaferri R, di Bernardo D 2009. Identifying network of drug mode of action by gene expression profiling. J. Comput. Biol. 16:241–51
    [Google Scholar]
  55. 55. 
    Lee J-Y, Fujimoto GM, Wilson R, Wiley HS, Payne SH 2018. Blazing Signature Filter: a library for fast pairwise similarity comparisons. BMC Bioinform 19:221
    [Google Scholar]
  56. 56. 
    Musa A, Ghoraie LS, Zhang S-D, Glazko G, Yli-Harja O et al. 2017. A review of connectivity map and computational approaches in pharmacogenomics. Brief. Bioinform. 19:506–23
    [Google Scholar]
  57. 57. 
    Keenan AB, Jenkins SL, Jagodnik KM, Koplev S, He E et al. 2017. The Library of Integrated Network-Based Cellular Signatures NIH Program: system-level cataloging of human cells response to perturbations. Cell Syst 6:13–24
    [Google Scholar]
  58. 58. 
    Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE et al. 2017. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171:1437–52.e17
    [Google Scholar]
  59. 59. 
    Edgar R, Domrachev M, Lash AE 2002. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–10
    [Google Scholar]
  60. 60. 
    Lakhani K, Garvin D, Lonstein E 2010. TopCoder (A): developing software through crowdsourcing Harvard Bus. Sch. Case 610-032 Cambridge, MA: https://www.hbs.edu/faculty/Pages/item.aspx?num=38356
  61. 61. 
    Chen Y, Li Y, Narayan R, Subramanian A, Xie X 2016. Gene expression inference with deep learning. Bioinformatics 32:1832–39
    [Google Scholar]
  62. 62. 
    Niepel M, Hafner M, Duan Q, Wang Z, Paull EO et al. 2017. Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling. Nat. Commun 8:1186
    [Google Scholar]
  63. 63. 
    Hsu YC, Chiu YC, Chen Y, Hsiao TH, Chuang EY 2016. A simple gene set-based method accurately predicts the synergy of drug pairs. BMC Syst. Biol. 10:Suppl. 366
    [Google Scholar]
  64. 64. 
    Hassane DC, Sen S, Minhajuddin M, Rossi RM, Corbett CA et al. 2010. Chemical genomic screening reveals synergism between parthenolide and inhibitors of the PI-3 kinase and mTOR pathways. Blood 116:5983–90
    [Google Scholar]
  65. 65. 
    Wang Z, Lachmann A, Keenan AB, Ma'ayan A, Stegle O 2018. L1000FWD: fireworks visualization of drug-induced transcriptomic signatures. Bioinformatics 34:2150–52
    [Google Scholar]
  66. 66. 
    Liu T-P, Hsieh Y-Y, Chou C-J, Yang P-M 2018. Systematic polypharmacology and drug repurposing via an integrated L1000-based Connectivity Map database mining. R. Soc. Open Sci. 5:181321
    [Google Scholar]
  67. 67. 
    Han H-W, Hahn S, Jeong HY, Jee J-H, Nam M-O et al. 2018. LINCS L1000 dataset-based repositioning of CGP-60474 as a highly potent anti-endotoxemic agent. Sci. Rep. 8:14969
    [Google Scholar]
  68. 68. 
    Wang Y, Arora K, Yang F, Shin W-H, Chen J et al. 2018. PP-2, a src-kinase inhibitor, is a potential corrector for F508del-CFTR in cystic fibrosis. bioRxiv 288324. https://doi.org/10.1101/288324
    [Crossref]
  69. 69. 
    Fagone P, Caltabiano R, Russo A, Lupo G, Anfuso CD et al. 2017. Identification of novel chemotherapeutic strategies for metastatic uveal melanoma. Sci. Rep. 7:44564
    [Google Scholar]
  70. 70. 
    Er JL, Goh PN, Lee CY, Tan YJ, Hii L-W et al. 2018. Identification of inhibitors synergizing gemcitabine sensitivity in the squamous subtype of pancreatic ductal adenocarcinoma (PDAC). Apoptosis 23:343–55
    [Google Scholar]
  71. 71. 
    Lachmann A, Giorgi FM, Alvarez MJ, Califano A 2016. Detection and removal of spatial bias in multiwell assays. Bioinformatics 32:1959–65
    [Google Scholar]
  72. 72. 
    Young WC, Raftery AE, Yeung KY 2017. Model-based clustering with data correction for removing artifacts in gene expression data. Ann. Appl. Stat. 11:1998–2026
    [Google Scholar]
  73. 73. 
    Hodos R, Zhang P, Lee H-C, Duan Q, Wang Z et al. 2017. Cell-specific prediction and application of drug-induced gene expression profiles. Pac. Symp. Biocomput. 23:32–43
    [Google Scholar]
  74. 74. 
    Xiao J, Blatti C, Sinha S 2018. SigMat: a classification scheme for gene signature matching. Bioinformatics 34:i547–54
    [Google Scholar]
  75. 75. 
    Wang Z, Clark NR, Ma'ayan A 2016. Drug-induced adverse events prediction with the LINCS L1000 data. Bioinformatics 32:2338–45
    [Google Scholar]
  76. 76. 
    Bray M-A, Gustafsdottir SM, Rohban MH, Singh S, Ljosa V et al. 2017. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. GigaScience 6:giw014
    [Google Scholar]
  77. 77. 
    Nagiec MM, Skepner AP, Negri J, Eichhorn M, Kuperwasser N et al. 2015. Modulators of hepatic lipoprotein metabolism identified in a search for small-molecule inducers of tribbles pseudokinase 1 expression. PLOS ONE 10:e0120295
    [Google Scholar]
  78. 78. 
    De Wolf H, Cougnaud L, Van Hoorde K, De Bondt A, Wegner JK et al. 2018. High-throughput gene expression profiles to define drug similarity and predict compound activity. Assay Drug Dev. Technol. 16:162–76
    [Google Scholar]
  79. 79. 
    Mav D, Shah RR, Howard BE, Auerbach SS, Bushel PR et al. 2018. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLOS ONE 13:e0191105
    [Google Scholar]
  80. 80. 
    Liberzon A. 2014. A description of the Molecular Signatures Database (MSigDB) website. Stem Cell Transcriptional Networks BL Kidder153–60 New York: Springer
    [Google Scholar]
  81. 81. 
    Kleensang A, Maertens A, Rosenberg M, Fitzpatrick S, Lamb J et al. 2014. t4 workshop report: pathways of toxicity. Altex 31:53
    [Google Scholar]
  82. 82. 
    Li H, Qiu J, Fu XD 2012. RASL-seq for massively parallel and quantitative analysis of gene expression. Curr. Protoc. Mol. Biol. 98:4.13.1–4.13.9
    [Google Scholar]
  83. 83. 
    Yeakley JM, Shepard PJ, Goyena DE, VanSteenhouse HC, McComb JD, Seligmann BE 2017. A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling. PLOS ONE 12:e0178302
    [Google Scholar]
  84. 84. 
    Bush EC, Ray F, Alvarez MJ, Realubit R, Li H et al. 2017. PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens. Nat. Commun. 8:105
    [Google Scholar]
  85. 85. 
    Ye C, Ho DJ, Neri M, Yang C, Kulkarni T et al. 2018. DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery. Nat. Commun. 9:4307
    [Google Scholar]
  86. 86. 
    Bushel PR, Paules RS, Auerbach SS 2018. A comparison of the TempO-Seq S1500+ platform to RNA-Seq and microarray using rat liver mode of action samples. Front. Genet. 9:485
    [Google Scholar]
  87. 87. 
    Brazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J et al. 2003. ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 31:68–71
    [Google Scholar]
  88. 88. 
    Yi Y, Li C, Miller C, George AL Jr 2007. Strategy for encoding and comparison of gene expression signatures. Genome Biol 8:R133
    [Google Scholar]
  89. 89. 
    Hu G, Agarwal P. 2009. Human disease-drug network based on genomic expression profiles. PLOS ONE 4:e6536
    [Google Scholar]
  90. 90. 
    Huang H, Liu CC, Zhou XJ 2010. Bayesian approach to transforming public gene expression repositories into disease diagnosis databases. PNAS 107:6823–28
    [Google Scholar]
  91. 91. 
    Liu CC, Hu J, Kalakrishnan M, Huang H, Zhou XJ 2009. Integrative disease classification based on cross-platform microarray data. BMC Bioinform 10:Suppl. 1S25
    [Google Scholar]
  92. 92. 
    Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA et al. 2011. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med. 3:96ra77
    [Google Scholar]
  93. 93. 
    Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A-L 2007. The human disease network. PNAS 104:8685–90
    [Google Scholar]
  94. 94. 
    Yıldırım MA, Goh K-I, Cusick ME, Barabási A-L, Vidal M 2007. Drug−target network. Nat. Biotechnol. 25:1119–26
    [Google Scholar]
  95. 95. 
    Ma'ayan A, Jenkins SL, Goldfarb J, Iyengar R 2007. Network analysis of FDA approved drugs and their targets. Mt. Sinai J. Med. 74:27–32
    [Google Scholar]
  96. 96. 
    Xiao Y, Gong Y, Lv Y, Lan Y, Hu J et al. 2015. Gene Perturbation Atlas (GPA): a single-gene perturbation repository for characterizing functional mechanisms of coding and non-coding genes. Sci. Rep. 5:10889
    [Google Scholar]
  97. 97. 
    ENCODE Proj. Consort 2004. The ENCODE (encyclopedia of DNA elements) project. Science 306:636–40
    [Google Scholar]
  98. 98. 
    Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM et al. 2017. The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res 46:D794–801
    [Google Scholar]
  99. 99. 
    Axelsson AS, Tubbs E, Mecham B, Chacko S, Nenonen HA et al. 2017. Sulforaphane reduces hepatic glucose production and improves glucose control in patients with type 2 diabetes. Sci. Transl. Med. 9:eaah4477
    [Google Scholar]
  100. 100. 
    Wu H, Huang J, Zhong Y, Huang Q 2017. DrugSig: a resource for computational drug repositioning utilizing gene expression signatures. PLOS ONE 12:e0177743
    [Google Scholar]
  101. 101. 
    Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW et al. 2016. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nat. Commun 7:12846
    [Google Scholar]
  102. 102. 
    Gundersen GW, Jones MR, Rouillard AD, Kou Y, Monteiro CD et al. 2015. GEO2Enrichr: browser extension and server app to extract gene sets from GEO and analyze them for biological functions. Bioinformatics 31:3060–62
    [Google Scholar]
  103. 103. 
    Hadley D, Pan J, El-Sayed O, Aljabban J, Aljabban I et al. 2017. Precision annotation of digital samples in NCBI's gene expression omnibus. Sci. Data 4:170125
    [Google Scholar]
  104. 104. 
    Djordjevic D, Tang JYS, Chen YX, Shannon SL, Ling RWK et al. 2019. Discovery of pertubation gene targets via free text metadata mining in Gene Expression Omnibus. Comput. Biol. Chem 80152–58
  105. 105. 
    Bernstein MN, Doan A, Dewey CN 2017. MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive. Bioinformatics 33:2914–23
    [Google Scholar]
  106. 106. 
    Vivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J et al. 2017. Toil enables reproducible, open source, big biomedical data analyses. Nat. Biotechnol. 35:314–16
    [Google Scholar]
  107. 107. 
    Petryszak R, Keays M, Tang YA, Fonseca NA, Barrera E et al. 2015. Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res 44:D746–52
    [Google Scholar]
  108. 108. 
    Fonseca NA, Petryszak R, Marioni J, Brazma A 2014. iRAP-an integrated RNA-seq analysis pipeline. bioRxiv 005991. https://doi.org/10.1101/005991
    [Crossref]
  109. 109. 
    Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA et al. 2017. Reproducible RNA-seq analysis using recount2. Nat. Biotechnol 35:319–21
    [Google Scholar]
  110. 110. 
    Wang Q, Armenia J, Zhang C, Penson AV, Reznik E et al. 2017. Enabling cross-study analysis of RNA-sequencing data. bioRxiv 110734. https://doi.org/10.1101/110734
    [Crossref]
  111. 111. 
    Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ et al. 2018. Massive mining of publicly available RNA-seq data from human and mouse. Nat. Commun 9:1366
    [Google Scholar]
  112. 112. 
    Al Mahi N, Najafabadi MF, Pilarczyk M, Kouril M, Medvedovic M 2018. GREIN: an interactive web platform for re-analyzing GEO RNA-seq data. bioRxiv 326223. https://doi.org/10.1101/326223
    [Crossref]
  113. 113. 
    Bray NL, Pimentel H, Melsted P, Pachter L 2016. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34:525–27
    [Google Scholar]
  114. 114. 
    Torre D, Lachmann A, Ma'ayan A 2018. BioJupies: automated generation of interactive notebooks for RNA-seq data analysis in the cloud. Cell Syst 7:556–61
    [Google Scholar]
  115. 115. 
    World Health Organ. (WHO) 2019. Anatomical Therapeutic Chemical (ATC) Classification Index with Defined Daily Doses (DDDs) Oslo, Nor.: WHO Collab. Centre Drug Stat. Method.
  116. 116. 
    Law V, Knox C, Djoumbou Y, Jewison T, Guo AC et al. 2013. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–97
    [Google Scholar]
  117. 117. 
    Seiler KP, George GA, Happ MP, Bodycombe NE, Carrinski HA et al. 2007. ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Res 36:D351–59
    [Google Scholar]
  118. 118. 
    Food Drug Admin 2015. FDA adverse event reporting system (FAERS) Public Database, U.S. Food Drug Admin Silver Spring, MD:
  119. 119. 
    Iorio F, Shrestha RL, Levin N, Boilot V, Garnett MJ et al. 2015. A semi-supervised approach for refining transcriptional signatures of drug response and repositioning predictions. PLOS ONE 10:e0139446
    [Google Scholar]
  120. 120. 
    Napolitano F, Sirci F, Carrella D, di Bernardo D 2016. Drug-set enrichment analysis: a novel tool to investigate drug mode of action. Bioinformatics 32:235–41
    [Google Scholar]
  121. 121. 
    Kanehisa M, Goto S. 2000. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:27–30
    [Google Scholar]
  122. 122. 
    Joshi-Tope G. 2005. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:D428–32
    [Google Scholar]
  123. 123. 
    Wang Z, He E, Sani K, Jagodnik KM, Silverstein M, Ma'ayan A 2018. Drug Gene Budger (DGB): an application for ranking drugs to modulate a specific gene based on transcriptomic signatures. Bioinformatics. In press
    [Google Scholar]
  124. 124. 
    Li J, Zhao W, Akbani R, Liu W, Ju Z et al. 2017. Characterization of human cancer cell lines by reverse-phase protein arrays. Cancer Cell 31:225–39
    [Google Scholar]
  125. 125. 
    Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR et al. 2016. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534:55–62
    [Google Scholar]
  126. 126. 
    Litichevskiy L, Peckner R, Abelin JG, Asiedu JK, Creech AL et al. 2018. A library of phosphoproteomic and chromatin signatures for characterizing cellular responses to drug perturbations. Cell Syst 6:424–43.e7
    [Google Scholar]
  127. 127. 
    Koch RJ, Barrette AM, Stern AD, Hu B, Bouhaddou M et al. 2018. Validating antibodies for quantitative western blot measurements with microwestern array. Sci. Rep. 8:11329
    [Google Scholar]
  128. 128. 
    Abelin JG, Patel J, Lu X, Feeney CM, Fagbami L et al. 2016. Reduced-representation phosphosignatures measured by quantitative targeted MS capture cellular states and enable large-scale comparison of drug-induced phenotypes. Mol. Cell Proteom. 15:1622–41
    [Google Scholar]
  129. 129. 
    Creech AL, Taylor JE, Maier VK, Wu X, Feeney CM et al. 2015. Building the Connectivity Map of epigenetics: chromatin profiling by quantitative targeted mass spectrometry. Methods 72:57–64
    [Google Scholar]
  130. 130. 
    Yu C, Mannan AM, Yvone GM, Ross KN, Zhang Y-L et al. 2016. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34:419–23
    [Google Scholar]
  131. 131. 
    Bray MA, Singh S, Han H, Davis CT, Borgeson B et al. 2016. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc 11:1757–74
    [Google Scholar]
  132. 132. 
    Lin JR, Fallahi-Sichani M, Chen JY, Sorger PK 2016. Cyclic Immunofluorescence (CycIF), a highly multiplexed method for single-cell imaging. Curr. Protoc. Chem. Biol. 8:251–64
    [Google Scholar]
  133. 133. 
    Lin JR, Izar B, Wang S, Yapp C, Mei S et al. 2018. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7:e31657
    [Google Scholar]
  134. 134. 
    Rohban MH, Singh S, Wu X, Berthet JB, Bray MA et al. 2017. Systematic morphological profiling of human gene and allele function via Cell Painting. eLife 6:e24060
    [Google Scholar]
  135. 135. 
    Niepel M, Hafner M, Mills CE, Subramanian K, Williams EH et al. 2019. A multi-center study on factors influencing the reproducibility of in vitro drug-response studies. bioRxiv 213553. https://doi.org/10.1101/213553
    [Crossref]
  136. 136. 
    Crockett SD, Schectman R, Stürmer T, Kappelman MD 2014. Topiramate use does not reduce flares of inflammatory bowel disease. Dig. Dis. Sci. 59:1535–43
    [Google Scholar]
  137. 137. 
    OpenStax 2018. Biology Houston, TX: OpenStax CNX
  138. 138. 
    Drenberg CD, Buaboonnam J, Orwick SJ, Hu S, Li L et al. 2016. Evaluation of artemisinins for the treatment of acute myeloid leukemia. Cancer Chemother. Pharmacol 77:1231–43
    [Google Scholar]
  139. 139. 
    Gruber L, Abdelfatah S, Frohlich T, Reiter C, Klein V et al. 2018. Treatment of multidrug-resistant leukemia cells by novel artemisinin-, egonol-, and thymoquinone-derived hybrid compounds. Molecules 23:841
    [Google Scholar]
  140. 140. 
    Wang RL, Biales AD, Garcia-Reyero N, Perkins EJ, Villeneuve DL et al. 2016. Fish connectivity mapping: linking chemical stressors by their mechanisms of action-driven transcriptomic profiles. BMC Genom 17:84
    [Google Scholar]
  141. 141. 
    Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y et al. 2015. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res 43:D921–27
    [Google Scholar]
  142. 142. 
    Senkowski W, Jarvius M, Rubin J, Lengqvist J, Gustafsson MG et al. 2016. Large-scale gene expression profiling platform for identification of context-dependent drug responses in multicellular tumor spheroids. Cell Chem. Biol. 23:1428–38
    [Google Scholar]
  143. 143. 
    Reis SA, Ghosh B, Hendricks JA, Szantai-Kis DM, Tork L et al. 2016. Light-controlled modulation of gene expression by chemical optoepigenetic probes. Nat. Chem. Biol. 12:317–23
    [Google Scholar]
  144. 144. 
    Cusanovich DA, Pavlovic B, Pritchard JK, Gilad Y 2014. The functional consequences of variation in transcription factor binding. PLOS Genet 10:e1004226
    [Google Scholar]
  145. 145. 
    Koleti A, Terryn R, Stathias V, Chung C, Cooper DJ et al. 2018. Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data. Nucleic Acids Res 46:D558–66
    [Google Scholar]
  146. 146. 
    Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q et al. 2016. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90–97
    [Google Scholar]
  147. 147. 
    O'Reilly PG, Wen Q, Bankhead P, Dunne PD, McArt DG et al. 2016. QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics. BMC Bioinform 17:198
    [Google Scholar]
  148. 148. 
    Duan Q, Flynn C, Niepel M, Hafner M, Muhlich JL et al. 2014. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res 42:W449–60
    [Google Scholar]
  149. 149. 
    Gundersen GW, Jagodnik KM, Woodland H, Fernandez NF, Sani K et al. 2016. GEN3VA: aggregation and analysis of gene expression signatures from related studies. BMC Bioinform 17:461
    [Google Scholar]
  150. 150. 
    Becnel LB, Ochsner SA, Darlington YF, McOwiti A, Kankanamge WH et al. 2017. Discovering relationships between nuclear receptor signaling pathways, genes, and tissues in Transcriptomine. Sci. Signal. 10:eaah6275
    [Google Scholar]
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