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@ARTICLE{Duan2020-bj,
  title    = "signatureSearch: environment for gene expression signature
              searching and functional interpretation",
  author   = "Duan, Yuzhu and Evans, Daniel S and Miller, Richard A and Schork,
              Nicholas J and Cummings, Steven R and Girke, Thomas",
  abstract = "signatureSearch is an R/Bioconductor package that integrates a
              suite of existing and novel algorithms into an analysis
              environment for gene expression signature (GES) searching
              combined with functional enrichment analysis (FEA) and
              visualization methods to facilitate the interpretation of the
              search results. In a typical GES search (GESS), a query GES is
              searched against a database of GESs obtained from large numbers
              of measurements, such as different genetic backgrounds, disease
              states and drug perturbations. Database matches sharing
              correlated signatures with the query indicate related cellular
              responses frequently governed by connected mechanisms, such as
              drugs mimicking the expression responses of a disease. To
              identify which processes are predominantly modulated in the GESS
              results, we developed specialized FEA methods combined with
              drug-target network visualization tools. The provided analysis
              tools are useful for studying the effects of genetic, chemical
              and environmental perturbations on biological systems, as well as
              searching single cell GES databases to identify novel network
              connections or cell types. The signatureSearch software is unique
              in that it provides access to an integrated environment for
              GESS/FEA routines that includes several novel search and
              enrichment methods, efficient data structures, and access to
              pre-built GES databases, and allowing users to work with custom
              databases.",
  journal  = "Nucleic Acids Res.",
  month    =  oct,
  year     =  2020,
  url      = "http://dx.doi.org/10.1093/nar/gkaa878",
  language = "en"
}

@ARTICLE{Subramanian2017-fu,
  title    = "A Next Generation Connectivity Map: {L1000} Platform and the
              First 1,000,000 Profiles",
  author   = "Subramanian, Aravind and Narayan, Rajiv and Corsello, Steven M
              and Peck, David D and Natoli, Ted E and Lu, Xiaodong and Gould,
              Joshua and Davis, John F and Tubelli, Andrew A and Asiedu, Jacob
              K and Lahr, David L and Hirschman, Jodi E and Liu, Zihan and
              Donahue, Melanie and Julian, Bina and Khan, Mariya and Wadden,
              David and Smith, Ian C and Lam, Daniel and Liberzon, Arthur and
              Toder, Courtney and Bagul, Mukta and Orzechowski, Marek and
              Enache, Oana M and Piccioni, Federica and Johnson, Sarah A and
              Lyons, Nicholas J and Berger, Alice H and Shamji, Alykhan F and
              Brooks, Angela N and Vrcic, Anita and Flynn, Corey and Rosains,
              Jacqueline and Takeda, David Y and Hu, Roger and Davison, Desiree
              and Lamb, Justin and Ardlie, Kristin and Hogstrom, Larson and
              Greenside, Peyton and Gray, Nathanael S and Clemons, Paul A and
              Silver, Serena and Wu, Xiaoyun and Zhao, Wen-Ning and
              Read-Button, Willis and Wu, Xiaohua and Haggarty, Stephen J and
              Ronco, Lucienne V and Boehm, Jesse S and Schreiber, Stuart L and
              Doench, John G and Bittker, Joshua A and Root, David E and Wong,
              Bang and Golub, Todd R",
  abstract = "We previously piloted the concept of a Connectivity Map (CMap),
              whereby genes, drugs, and disease states are connected by virtue
              of common gene-expression signatures. Here, we report more than a
              1,000-fold scale-up of the CMap as part of the NIH LINCS
              Consortium, made possible by a new, low-cost, high-throughput
              reduced representation expression profiling method that we term
              L1000. We show that L1000 is highly reproducible, comparable to
              RNA sequencing, and suitable for computational inference of the
              expression levels of 81\% of non-measured transcripts. We further
              show that the expanded CMap can be used to discover mechanism of
              action of small molecules, functionally annotate genetic variants
              of disease genes, and inform clinical trials. The 1.3 million
              L1000 profiles described here, as well as tools for their
              analysis, are available at https://clue.io.",
  journal  = "Cell",
  volume   =  171,
  number   =  6,
  pages    = "1437--1452.e17",
  month    =  nov,
  year     =  2017,
  url      = "http://dx.doi.org/10.1016/j.cell.2017.10.049",
  keywords = "Functional genomics; chemical biology; gene expression profiling",
  language = "en"
}

@ARTICLE{Lamb2006-du,
  title    = "The Connectivity Map: using gene-expression signatures to connect
              small molecules, genes, and disease",
  author   = "Lamb, Justin and Crawford, Emily D and Peck, David and Modell,
              Joshua W and Blat, Irene C and Wrobel, Matthew J and Lerner, Jim
              and Brunet, Jean-Philippe and Subramanian, Aravind and Ross,
              Kenneth N and Reich, Michael and Hieronymus, Haley and Wei, Guo
              and Armstrong, Scott A and Haggarty, Stephen J and Clemons, Paul
              A and Wei, Ru and Carr, Steven A and Lander, Eric S and Golub,
              Todd R",
  abstract = "To pursue a systematic approach to the discovery of functional
              connections among diseases, genetic perturbation, and drug
              action, we have created the first installment of a reference
              collection of gene-expression profiles from cultured human cells
              treated with bioactive small molecules, together with
              pattern-matching software to mine these data. We demonstrate that
              this ``Connectivity Map'' resource can be used to find
              connections among small molecules sharing a mechanism of action,
              chemicals and physiological processes, and diseases and drugs.
              These results indicate the feasibility of the approach and
              suggest the value of a large-scale community Connectivity Map
              project.",
  journal  = "Science",
  volume   =  313,
  number   =  5795,
  pages    = "1929--1935",
  month    =  sep,
  year     =  2006,
  url      = "http://dx.doi.org/10.1126/science.1132939",
  language = "en"
}

@ARTICLE{Peck2006-rf,
  title    = "A method for high-throughput gene expression signature analysis",
  author   = "Peck, David and Crawford, Emily D and Ross, Kenneth N and
              Stegmaier, Kimberly and Golub, Todd R and Lamb, Justin",
  abstract = "Genome-wide transcriptional profiling has shown that different
              biologic states (for instance, disease and response to
              pharmacologic manipulation) can be recognized by the expression
              pattern of relatively small numbers of genes. However, the lack
              of a practical and cost-effective technology for detection of
              these gene expression 'signatures' in large numbers of samples
              has severely limited their exploitation in important medical and
              pharmaceutical discovery applications. Here, we describe a
              solution based on the combination of ligation-mediated
              amplification with an optically addressed microsphere and flow
              cytometric detection system.",
  journal  = "Genome Biol.",
  volume   =  7,
  number   =  7,
  pages    = "R61",
  year     =  2006,
  url      = "http://dx.doi.org/10.1186/gb-2006-7-7-r61",
  language = "en"
}

@ARTICLE{Edgar2002-di,
  title    = "Gene Expression Omnibus: {NCBI} gene expression and hybridization
              array data repository",
  author   = "Edgar, Ron and Domrachev, Michael and Lash, Alex E",
  abstract = "The Gene Expression Omnibus (GEO) project was initiated in
              response to the growing demand for a public repository for
              high-throughput gene expression data. GEO provides a flexible and
              open design that facilitates submission, storage and retrieval of
              heterogeneous data sets from high-throughput gene expression and
              genomic hybridization experiments. GEO is not intended to replace
              in house gene expression databases that benefit from coherent
              data sets, and which are constructed to facilitate a particular
              analytic method, but rather complement these by acting as a
              tertiary, central data distribution hub. The three central data
              entities of GEO are platforms, samples and series, and were
              designed with gene expression and genomic hybridization
              experiments in mind. A platform is, essentially, a list of probes
              that define what set of molecules may be detected. A sample
              describes the set of molecules that are being probed and
              references a single platform used to generate its molecular
              abundance data. A series organizes samples into the meaningful
              data sets which make up an experiment. The GEO repository is
              publicly accessible through the World Wide Web at
              http://www.ncbi.nlm.nih.gov/geo.",
  journal  = "Nucleic Acids Res.",
  volume   =  30,
  number   =  1,
  pages    = "207--210",
  month    =  jan,
  year     =  2002,
  url      = "https://www.ncbi.nlm.nih.gov/pubmed/11752295",
  language = "en"
}





@ARTICLE{Kuhn2010-hz,
  title    = "{STITCH} 2: an interaction network database for small molecules
              and proteins",
  author   = "Kuhn, Michael and Szklarczyk, Damian and Franceschini, Andrea and
              Campillos, Monica and von Mering, Christian and Jensen, Lars Juhl
              and Beyer, Andreas and Bork, Peer",
  abstract = "Over the last years, the publicly available knowledge on
              interactions between small molecules and proteins has been
              steadily increasing. To create a network of interactions, STITCH
              aims to integrate the data dispersed over the literature and
              various databases of biological pathways, drug-target
              relationships and binding affinities. In STITCH 2, the number of
              relevant interactions is increased by incorporation of BindingDB,
              PharmGKB and the Comparative Toxicogenomics Database. The
              resulting network can be explored interactively or used as the
              basis for large-scale analyses. To facilitate links to other
              chemical databases, we adopt InChIKeys that allow identification
              of chemicals with a short, checksum-like string. STITCH 2.0
              connects proteins from 630 organisms to over 74,000 different
              chemicals, including 2200 drugs. STITCH can be accessed at
              http://stitch.embl.de/.",
  journal  = "Nucleic Acids Res.",
  volume   =  38,
  number   = "Database issue",
  pages    = "D552--6",
  month    =  jan,
  year     =  2010,
  url      = "http://dx.doi.org/10.1093/nar/gkp937",
  language = "en"
}

@ARTICLE{Wishart2018-ap,
  title    = "{DrugBank} 5.0: a major update to the {DrugBank} database for
              2018",
  author   = "Wishart, David S and Feunang, Yannick D and Guo, An C and Lo,
              Elvis J and Marcu, Ana and Grant, Jason R and Sajed, Tanvir and
              Johnson, Daniel and Li, Carin and Sayeeda, Zinat and Assempour,
              Nazanin and Iynkkaran, Ithayavani and Liu, Yifeng and
              Maciejewski, Adam and Gale, Nicola and Wilson, Alex and Chin,
              Lucy and Cummings, Ryan and Le, Diana and Pon, Allison and Knox,
              Craig and Wilson, Michael",
  abstract = "DrugBank (www.drugbank.ca) is a web-enabled database containing
              comprehensive molecular information about drugs, their
              mechanisms, their interactions and their targets. First described
              in 2006, DrugBank has continued to evolve over the past 12 years
              in response to marked improvements to web standards and changing
              needs for drug research and development. This year's update,
              DrugBank 5.0, represents the most significant upgrade to the
              database in more than 10 years. In many cases, existing data
              content has grown by 100\% or more over the last update. For
              instance, the total number of investigational drugs in the
              database has grown by almost 300\%, the number of drug-drug
              interactions has grown by nearly 600\% and the number of
              SNP-associated drug effects has grown more than 3000\%.
              Significant improvements have been made to the quantity, quality
              and consistency of drug indications, drug binding data as well as
              drug-drug and drug-food interactions. A great deal of brand new
              data have also been added to DrugBank 5.0. This includes
              information on the influence of hundreds of drugs on metabolite
              levels (pharmacometabolomics), gene expression levels
              (pharmacotranscriptomics) and protein expression levels
              (pharmacoprotoemics). New data have also been added on the status
              of hundreds of new drug clinical trials and existing drug
              repurposing trials. Many other important improvements in the
              content, interface and performance of the DrugBank website have
              been made and these should greatly enhance its ease of use,
              utility and potential applications in many areas of
              pharmacological research, pharmaceutical science and drug
              education.",
  journal  = "Nucleic Acids Res.",
  volume   =  46,
  number   = "D1",
  pages    = "D1074--D1082",
  month    =  jan,
  year     =  2018,
  url      = "http://dx.doi.org/10.1093/nar/gkx1037",
  language = "en"
}



@ARTICLE{Sandmann2014-qm,
  title    = "{gCMAP}: user-friendly connectivity mapping with {R}",
  author   = "Sandmann, Thomas and Kummerfeld, Sarah K and Gentleman, Robert
              and Bourgon, Richard",
  abstract = "UNLABELLED: Connections between disease phenotypes and drug
              effects can be made by identifying commonalities in the
              associated patterns of differential gene expression. Searchable
              databases that record the impacts of chemical or genetic
              perturbations on the transcriptome--here referred to as
              'connectivity maps'--permit discovery of such commonalities. We
              describe two R packages, gCMAP and gCMAPWeb, which provide a
              complete framework to construct and query connectivity maps
              assembled from user-defined collections of differential gene
              expression data. Microarray or RNAseq data are processed in a
              standardized way, and results can be interrogated using various
              well-established gene set enrichment methods. The packages also
              feature an easy-to-deploy web application that facilitates
              reproducible research through automatic generation of graphical
              and tabular reports. AVAILABILITY AND IMPLEMENTATION: The gCMAP
              and gCMAPWeb R packages are freely available for UNIX, Windows
              and Mac OS X operating systems at Bioconductor
              (http://www.bioconductor.org).",
  journal  = "Bioinformatics",
  volume   =  30,
  number   =  1,
  pages    = "127--128",
  month    =  jan,
  year     =  2014,
  url      = "http://dx.doi.org/10.1093/bioinformatics/btt592",
  language = "en"
}

@ARTICLE{Subramanian2005-ro,
  title    = "Gene set enrichment analysis: a knowledge-based approach for
              interpreting genome-wide expression profiles",
  author   = "Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K and
              Mukherjee, Sayan and Ebert, Benjamin L and Gillette, Michael A
              and Paulovich, Amanda and Pomeroy, Scott L and Golub, Todd R and
              Lander, Eric S and Mesirov, Jill P",
  abstract = "Although genomewide RNA expression analysis has become a routine
              tool in biomedical research, extracting biological insight from
              such information remains a major challenge. Here, we describe a
              powerful analytical method called Gene Set Enrichment Analysis
              (GSEA) for interpreting gene expression data. The method derives
              its power by focusing on gene sets, that is, groups of genes that
              share common biological function, chromosomal location, or
              regulation. We demonstrate how GSEA yields insights into several
              cancer-related data sets, including leukemia and lung cancer.
              Notably, where single-gene analysis finds little similarity
              between two independent studies of patient survival in lung
              cancer, GSEA reveals many biological pathways in common. The GSEA
              method is embodied in a freely available software package,
              together with an initial database of 1,325 biologically defined
              gene sets.",
  journal  = "Proc. Natl. Acad. Sci. U. S. A.",
  volume   =  102,
  number   =  43,
  pages    = "15545--15550",
  month    =  oct,
  year     =  2005,
  url      = "http://dx.doi.org/10.1073/pnas.0506580102",
  language = "en"
}