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@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{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{Barardo2017-xk,
  title     = "The {DrugAge} database of aging-related drugs",
  author    = "Barardo, Diogo and Thornton, Daniel and Thoppil, Harikrishnan
               and Walsh, Michael and Sharifi, Samim and Ferreira, Susana and
               An{\v z}i{\v c}, Andreja and Fernandes, Maria and Monteiro,
               Patrick and Grum, Tja{\v s}a and {Others}",
  journal   = "Aging Cell",
  publisher = "Wiley Online Library",
  volume    =  16,
  number    =  3,
  pages     = "594--597",
  year      =  2017,
  url       = "http://onlinelibrary.wiley.com/doi/10.1111/acel.12585/full"
}


@ARTICLE{Gaulton2012-ji,
  title    = "{ChEMBL}: a large-scale bioactivity database for drug discovery",
  author   = "Gaulton, Anna and Bellis, Louisa J and Bento, A Patricia and
              Chambers, Jon and Davies, Mark and Hersey, Anne and Light, Yvonne
              and McGlinchey, Shaun and Michalovich, David and Al-Lazikani,
              Bissan and Overington, John P",
  abstract = "ChEMBL is an Open Data database containing binding, functional
              and ADMET information for a large number of drug-like bioactive
              compounds. These data are manually abstracted from the primary
              published literature on a regular basis, then further curated and
              standardized to maximize their quality and utility across a wide
              range of chemical biology and drug-discovery research problems.
              Currently, the database contains 5.4 million bioactivity
              measurements for more than 1 million compounds and 5200 protein
              targets. Access is available through a web-based interface, data
              downloads and web services at: https://www.ebi.ac.uk/chembldb.",
  journal  = "Nucleic Acids Res.",
  volume   =  40,
  number   = "Database issue",
  pages    = "D1100--7",
  month    =  jan,
  year     =  2012,
  url      = "http://dx.doi.org/10.1093/nar/gkr777",
  language = "en"
}


@ARTICLE{Kim2019-tg,
  title    = "{PubChem} 2019 update: improved access to chemical data",
  author   = "Kim, Sunghwan and Chen, Jie and Cheng, Tiejun and Gindulyte, Asta
              and He, Jia and He, Siqian and Li, Qingliang and Shoemaker,
              Benjamin A and Thiessen, Paul A and Yu, Bo and Zaslavsky, Leonid
              and Zhang, Jian and Bolton, Evan E",
  abstract = "PubChem (https://pubchem.ncbi.nlm.nih.gov) is a key chemical
              information resource for the biomedical research community.
              Substantial improvements were made in the past few years. New
              data content was added, including spectral information,
              scientific articles mentioning chemicals, and information for
              food and agricultural chemicals. PubChem released new web
              interfaces, such as PubChem Target View page, Sources page,
              Bioactivity dyad pages and Patent View page. PubChem also
              released a major update to PubChem Widgets and introduced a new
              programmatic access interface, called PUG-View. This paper
              describes these new developments in PubChem.",
  journal  = "Nucleic Acids Res.",
  volume   =  47,
  number   = "D1",
  pages    = "D1102--D1109",
  month    =  jan,
  year     =  2019,
  url      = "http://dx.doi.org/10.1093/nar/gky1033",
  language = "en"
}


@ARTICLE{The_UniProt_Consortium2017-bx,
  title    = "{UniProt}: the universal protein knowledgebase",
  author   = "{The UniProt Consortium}",
  abstract = "The UniProt knowledgebase is a large resource of protein
              sequences and associated detailed annotation. The database
              contains over 60 million sequences, of which over half a million
              sequences have been curated by experts who critically review
              experimental and predicted data for each protein. The remainder
              are automatically annotated based on rule systems that rely on
              the expert curated knowledge. Since our last update in 2014, we
              have more than doubled the number of reference proteomes to 5631,
              giving a greater coverage of taxonomic diversity. We implemented
              a pipeline to remove redundant highly similar proteomes that were
              causing excessive redundancy in UniProt. The initial run of this
              pipeline reduced the number of sequences in UniProt by 47
              million. For our users interested in the accessory proteomes, we
              have made available sets of pan proteome sequences that cover the
              diversity of sequences for each species that is found in its
              strains and sub-strains. To help interpretation of genomic
              variants, we provide tracks of detailed protein information for
              the major genome browsers. We provide a SPARQL endpoint that
              allows complex queries of the more than 22 billion triples of
              data in UniProt (http://sparql.uniprot.org/). UniProt resources
              can be accessed via the website at http://www.uniprot.org/.",
  journal  = "Nucleic Acids Res.",
  volume   =  45,
  number   = "D1",
  pages    = "D158--D169",
  month    =  jan,
  year     =  2017,
  url      = "http://dx.doi.org/10.1093/nar/gkw1099",
  language = "en"
}


@ARTICLE{Seiler2008-dw,
  title    = "{ChemBank}: a small-molecule screening and cheminformatics
              resource database",
  author   = "Seiler, Kathleen Petri and George, Gregory A and Happ, Mary Pat
              and Bodycombe, Nicole E and Carrinski, Hyman A and Norton,
              Stephanie and Brudz, Steve and Sullivan, John P and Muhlich,
              Jeremy and Serrano, Martin and Ferraiolo, Paul and Tolliday,
              Nicola J and Schreiber, Stuart L and Clemons, Paul A",
  abstract = "ChemBank (http://chembank.broad.harvard.edu/) is a public,
              web-based informatics environment developed through a
              collaboration between the Chemical Biology Program and Platform
              at the Broad Institute of Harvard and MIT. This knowledge
              environment includes freely available data derived from small
              molecules and small-molecule screens and resources for studying
              these data. ChemBank is unique among small-molecule databases in
              its dedication to the storage of raw screening data, its rigorous
              definition of screening experiments in terms of statistical
              hypothesis testing, and its metadata-based organization of
              screening experiments into projects involving collections of
              related assays. ChemBank stores an increasingly varied set of
              measurements derived from cells and other biological assay
              systems treated with small molecules. Analysis tools are
              available and are continuously being developed that allow the
              relationships between small molecules, cell measurements, and
              cell states to be studied. Currently, ChemBank stores information
              on hundreds of thousands of small molecules and hundreds of
              biomedically relevant assays that have been performed at the
              Broad Institute by collaborators from the worldwide research
              community. The goal of ChemBank is to provide life scientists
              unfettered access to biomedically relevant data and tools
              heretofore available primarily in the private sector.",
  journal  = "Nucleic Acids Res.",
  volume   =  36,
  number   = "Database issue",
  pages    = "D351--9",
  month    =  jan,
  year     =  2008,
  url      = "http://dx.doi.org/10.1093/nar/gkm843",
  language = "en"
}


@ARTICLE{Cao2008-np,
  title    = "{ChemmineR}: a compound mining framework for {R}",
  author   = "Cao, Yiqun and Charisi, Anna and Cheng, Li-Chang and Jiang, Tao
              and Girke, Thomas",
  abstract = "MOTIVATION: Software applications for structural similarity
              searching and clustering of small molecules play an important
              role in drug discovery and chemical genomics. Here, we present
              the first open-source compound mining framework for the popular
              statistical programming environment R. The integration with a
              powerful statistical environment maximizes the flexibility,
              expandability and programmability of the provided analysis
              functions. RESULTS: We discuss the algorithms and compound mining
              utilities provided by the R package ChemmineR. It contains
              functions for structural similarity searching, clustering of
              compound libraries with a wide spectrum of classification
              algorithms and various utilities for managing complex compound
              data. It also offers a wide range of visualization functions for
              compound clusters and chemical structures. The package is well
              integrated with the online ChemMine environment and allows
              bidirectional communications between the two services.
              AVAILABILITY: ChemmineR is freely available as an R package from
              the ChemMine project site:
              http://bioweb.ucr.edu/ChemMineV2/chemminer",
  journal  = "Bioinformatics",
  volume   =  24,
  number   =  15,
  pages    = "1733--1734",
  month    =  aug,
  year     =  2008,
  url      = "http://dx.doi.org/10.1093/bioinformatics/btn307",
  language = "en"
}
