Browsing by Author "Luna, A."

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Open Access
Analyzing causal relationships in proteomic profiles using CausalPath
(Cell Press, 2021-12-17) Luna, A.; Siper, M. C.; Korkut, A.; Durupinar, F.; Aslan, J. E.; Sander, C.; Demir, E.; Babur, O.; Doğrusöz, Uğur
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset.
Open Access
The BioPAX community standard for pathway data sharing
(Nature Publishing Group, 2010-09) Demir, Emek; Cary, M. P.; Paley, S.; Fukuda, K.; Lemer, C.; Vastrik, I.; Wu, G.; D'Eustachio, P.; Schaefer, C.; Luciano, J.; Schacherer, F.; Martinez-Flores, I.; Hu, Z.; Jimenez-Jacinto, V.; Joshi-Tope, G.; Kandasamy, K.; Lopez-Fuentes, A. C.; Mi, H.; Pichler, E.; Rodchenkov, I.; Splendiani, A.; Tkachev, S.; Zucker, J.; Gopinath, G.; Rajasimha, H.; Ramakrishnan, R.; Shah, I.; Syed, M.; Anwar, N.; Babur, Özgün; Blinov, M.; Brauner, E.; Corwin, D.; Donaldson, S.; Gibbons, F.; Goldberg, R.; Hornbeck, P.; Luna, A.; Murray-Rust, P.; Neumann, E.; Reubenacker, O.; Samwald, M.; Iersel, Martijn van; Wimalaratne, S.; Allen, K.; Braun, B.; Whirl-Carrillo, M.; Cheung, Kei-Hoi; Dahlquist, K.; Finney, A.; Gillespie, M.; Glass, E.; Gong, L.; Haw, R.; Honig, M.; Hubaut, O.; Kane, D.; Krupa, S.; Kutmon, M.; Leonard, J.; Marks, D.; Merberg, D.; Petri, V.; Pico, A.; Ravenscroft, D.; Ren, L.; Shah, N.; Sunshine, M.; Tang R.; Whaley, R.; Letovksy, S.; Buetow, K. H.; Rzhetsky, A.; Schachter, V.; Sobral, B. S.; Doğrusöz, Uğur; McWeeney, S.; Aladjem, M.; Birney, E.; Collado-Vides, J.; Goto, S.; Hucka, M.; Novère, Nicolas Le; Maltsev, N.; Pandey, A.; Thomas, P.; Wingender, E.; Karp, P. D.; Sander, C.; Bader, G. D.
Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery. © 2010 Nature America, Inc. All rights reserved.
Open Access
Causal interactions from proteomic profiles: Molecular data meet pathway knowledge
(Cell Press, 2021-06) Babur, Ö.; Luna, A.; Korkut, A.; Durupınar, F.; Siper, M. C.; Doğrusöz, Uğur; Jacome, A. S. V.; Peckner, R.; Christiansen, K. E.; Jaffe, J.D; Spellman, P.T.; Aslan, J. E.; Sander, C.; Demir, E.
We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org.
Open Access
Oncogenic signaling pathways in the Cancer Genome Atlas
(Cell Press, 2018) Sanchez-Vega, F.; Mina, M.; Armenia, J.; Chatila, W. K.; Luna, A.; La, K. C.; Dimitriadoy, S.; Liu, D. L.; Kantheti, H. S.; Saghafinia, S.; Chakravarty, D.; Daian, F.; Gao, Q.; Bailey, M. H.; Liang, W. -W.; Foltz, S. M.; Shmulevich, I.; Ding, L.; Heins, Z.; Ochoa, A.; Gross, B.; Gao, J.; Zhang, H.; Kundra, R.; Kandoth, C.; Bahceci, I.; Dervishi, L.; Doğrusöz, Uğur; Zhou, W.; Shen, H.; Laird, P. W.; Way, G. P.; Greene, C. S.; Liang, H.; Xiao, Y.; Wang, C.; Iavarone, A.; Berger, A. H.; Bivona, T. G.; Lazar, A. J.; Hammer, G. D.; Giordano, T.; Kwong, L. N.; McArthur, G.; Huang, C.; Tward, A. D.; Frederick, M. J.; McCormick, F.; Meyerson, M.; Caesar-Johnson, S. J.; Demchok, J. A.; Felau, I.; Kasapi, M.; Ferguson, M. L.; Hutter, C. M.; Sofia, H. J.; Tarnuzzer, R.; Wang, Z.; Yang, L.; Zenklusen, J. C.; Zhang, J. J.; Chudamani, S.; Liu, J.; Lolla, L.; Naresh, R.; Pihl, T.; Sun, Q.; Wan, Y.; Wu, Y.; Cho, J.; DeFreitas, T.; Frazer, S.; Gehlenborg, N.; Getz, G.; Heiman, D. I.; Kim, J.; Lawrence, M. S.; Lin, P.; Meier, S.; Noble, M. S.; Saksena, G.; Voet, D.; Zhang, H.; Bernard, B.; Chambwe, N.; Dhankani, V.; Knijnenburg, T.; Kramer, R.; Leinonen, K.; Liu, Y.; Miller, M.; Reynolds, S.; Shmulevich, I.; Thorsson, V.; Zhang, W.; Akbani, R.; Broom, B. M.; Hegde, A. M.; Ju, Z.; Kanchi, R. S.; Korkut, A.; Li, J.; Liang, H.; Ling, S.; Liu W.; Lu, Y.; Mills, G. B.; Ng, K. -S.; Rao, A.; Ryan, M.; Wang, J.; Weinstein, J. N.; Zhang, J.; Abeshouse, A.; Armenia, J.; Chakravarty, D.; Chatila, W. K.; de, Bruijn, I.; Gao, J.; Gross, B. E.; Heins, Z. J.; Kundra, R.; La, K.; Ladanyi, M.; Luna, A.; Nissan, M. G.; Ochoa, A.; Phillips, S. M.; Reznik, E.; Sanchez-Vega, F.; Sander, C.; Schultz, N.; Sheridan, R.; Sumer, S. O.; Sun, Y.; Taylor, B. S.; Wang, J.; Zhang, H.; Anur, P.; Peto, M.; Spellman, P.; Benz, C.; Stuart, J. M.; Wong, C. K.; Yau, C.; Hayes, D. N.; Parker, J. S.; Wilkerson, M. D.; Ally, A.; Balasundaram, M.; Bowlby, R.; Brooks, D.; Carlsen, R.; Chuah, E.; Dhalla, N.; Holt, R.; Jones, S. J. M.; Kasaian, K.; Lee, D.; Ma, Y.; Marra, M. A.; Mayo, M.; Moore, R. A.; Mungall, A. J.; Mungall, K.; Robertson, A. G.; Sadeghi, S.; Schein, J. E.; Sipahimalani, P.; Tam, A.; Thiessen, N.; Tse, K.; Wong, T.; Berger, A. C.; Beroukhim, R.; Cherniack, A. D.; Cibulskis, C.; Gabriel, S. B.; Gao, G. F.; Ha, G.; Meyerson, M.; Schumacher, S. E.; Shih, J.; Kucherlapati, M. H.; Kucherlapati, R. S.; Baylin, S.; Cope, L.; Danilova, L.; Bootwalla, M. S.; Lai, P. H.; Maglinte, D. T.; Van, Den, Berg, D. J.; Weisenberger, D. J.; Auman, J. T.; Balu, S.; Bodenheimer, T.; Fan, C.; Hoadley, K. A.; Hoyle, A. P.; Jefferys, S. R.; Jones, C. D.; Meng, S.; Mieczkowski, P. A.; Mose, L. E.; Perou, A. H.; Perou, C. M.; Roach, J.; Shi, Y.; Simons, J. V.; Skelly, T.; Soloway, M. G.; Tan, D.; Veluvolu, U.; Fan, H.; Hinoue, T.; Laird, P. W.; Shen, H.; Zhou, W.; Bellair, M.; Chang, K.; Covington, K.; Creighton, C. J.; Dinh, H.; Doddapaneni, H.; Donehower, L. A.; Drummond, J.; Gibbs, R. A.; Glenn, R.; Hale, W.; Han, Y.; Hu, J.; Korchina, V.; Lee, S.; Lewis, L.; Li, W.; Liu, X.; Morgan, M.; Morton, D.; Muzny, D.; Santibanez, J.; Sheth, M.; Shinbrot, E.; Wang, L.; Wang, M.; Wheeler, D. A.; Xi, L.; Zhao, F.; Hess, J.; Appelbaum, E. L.; Bailey, M.; Cordes, M. G.; Ding, L.; Fronick, C. C.; Fulton, L. A.; Fulton, R. S.; Kandoth, C.; Mardis, E. R.; McLellan, M. D.; Miller, C. A.; Schmidt, H. K.; Wilson, R. K.; Crain, D.; Curley, E.; Gardner, J.; Lau, K.; Mallery, D.; Morris, S.; Paulauskis, J.; Penny, R.; Shelton, C.; Shelton, T.; Sherman, M.; Thompson, E.; Yena, P.; Bowen, J.; Gastier-Foster, J. M.; Gerken, M.; Leraas, K. M.; Lichtenberg, T. M.; Ramirez, N. C.; Wise, L.; Zmuda, E.; Corcoran, N.; Costello, T.; Hovens, C.; Carvalho, A. L.; de, Carvalho, A. C.; Fregnani, J. H.; Longatto-Filho, A.; Reis, R. M.; Scapulatempo-Neto, C.; Silveira, H. C. S.; Vidal, D. O.; Burnette, A.; Eschbacher, J.; Hermes, B.; Noss, A.; Singh, R.; Anderson, M. L.; Castro, P. D.; Ittmann, M.; Huntsman, D.; Kohl, B.; Le, X.; Thorp, R.; Andry, C.; Duffy, E. R.; Lyadov, V.; Paklina, O.; Setdikova, G.; Shabunin, A.; Tavobilov, M.; McPherson, C.; Warnick, R.; Berkowitz, R.; Cramer, D.; Feltmate, C.; Horowitz, N.; Kibel, A.; Muto, M.; Raut, C. P.; Malykh, A.; Barnholtz-Sloan, J. S.; Barrett, W.; Devine, K.; Fulop, J.; Ostrom, Q. T.; Shimmel, K.; Wolinsky, Y.; Sloan, A. E.; De, Rose, A.; Giuliante, F.; Goodman, M.; Karlan, B. Y.; Hagedorn, C. H.; Eckman, J.; Harr, J.; Myers, J.; Tucker, K.; Zach, L. A.; Deyarmin, B.; Hu, H.; Kvecher, L.; Larson, C.; Mural, R. J.; Somiari, S.; Vicha, A.; Zelinka, T.; Bennett, J.; Iacocca, M.; Rabeno, B.; Swanson, P.; Latour, M.; Lacombe, L.; Têtu, B.; Bergeron, A.; McGraw, M.; Staugaitis, S. M.; Chabot, J.; Hibshoosh, H.; Sepulveda, A.; Su, T.; Wang, T.; Potapova, O.; Voronina, O.; Desjardins, L.; Mariani, O.; Roman-Roman, S.; Sastre, X.; Stern, M. -H.; Cheng, F.; Signoretti, S.; Berchuck, A.; Bigner, D.; Lipp, E.; Marks, J.; McCall, S.; McLendon, R.; Secord, A.; Sharp, A.; Behera, M.; Brat, D. J.; Chen, A.; Delman, K.; Force, S.; Khuri, F.; Magliocca, K.; Maithel, S.; Olson, J. J.; Owonikoko, T.; Pickens, A.; Ramalingam, S.; Shin, D. M.; Sica, G.; Van, Meir, E. G.; Zhang, H.; Eijckenboom, W.; Gillis, A.; Korpershoek, E.; Looijenga, L.; Oosterhuis, W.; Stoop, H.; van, Kessel, K. E.; Zwarthoff, E. C.; Calatozzolo, C.; Cuppini, L.; Cuzzubbo, S.; DiMeco, F.; Finocchiaro, G.; Mattei, L.; Perin, A.; Pollo, B.; Chen, C.; Houck, J.; Lohavanichbutr, P.; Hartmann, A.; Stoehr, C.; Stoehr, R.; Taubert, H.; Wach, S.; Wullich, B.; Kycler, W.; Murawa, D.; Wiznerowicz, M.; Chung, K.; Edenfield, W. J.; Martin, J.; Baudin, E.; Bubley, G.; Bueno, R.; De, Rienzo, A.; Richards, W. G.; Kalkanis, S.; Mikkelsen, T.; Noushmehr, H.; Scarpace, L.; Girard, N.; Aymerich, M.; Campo, E.; Giné, E.; Guillermo, A. L.; Van, Bang, N.; Hanh, P. T.; Phu, B. D.; Tang, Y.; Colman, H.; Evason, K.; Dottino, P. R.; Martignetti, J. A.; Gabra, H.; Juhl, H.; Akeredolu, T.; Stepa, S.; Hoon, D.; Ahn, K.; Kang, K. J.; Beuschlein, F.; Breggia, A.; Birrer, M.; Bell, D.; Borad, M.; Bryce, A. H.; Castle, E.; Chandan, V.; Cheville, J.; Copland, J. A.; Farnell, M.; Flotte, T.; Giama, N.; Ho, T.; Kendrick, M.; Kocher, J. -P.; Kopp, K.; Moser, C.; Nagorney, D.; O'Brien, D.; O'Neill, B. P.; Patel, T.; Petersen, G.; Que, F.; Rivera, M.; Roberts, L.; Smallridge, R.; Smyrk, T.; Stanton, M.; Thompson, R. H.; Torbenson, M.; Yang, J. D.; Zhang, L.; Brimo, F.; Ajani, J. A.; Gonzalez, A. M. A.; Behrens, C.; Bondaruk, J.; Broaddus, R.; Czerniak, B.; Esmaeli, B.; Fujimoto, J.; Gershenwald, J.; Guo, C.; Lazar, A. J.; Logothetis, C.; Meric-Bernstam, F.; Moran, C.; Ramondetta, L.; Rice, D.; Sood, A.; Tamboli, P.; Thompson, T.; Troncoso, P.; Tsao, A.; Wistuba, I.; Carter, C.; Haydu, L.; Hersey, P.; Jakrot, V.; Kakavand, H.; Kefford, R.; Lee, K.; Long, G.; Mann, G.; Quinn, M.; Saw, R.; Scolyer, R.; Shannon, K.; Spillane, A.; Stretch, J.; Synott, M.; Thompson, J.; Wilmott, J.; Al-Ahmadie, H.; Chan, T. A.; Ghossein, R.; Gopalan, A.; Levine, D. A.; Reuter, V.; Singer, S.; Singh, B.; Tien, N. V.; Broudy, T.; Mirsaidi, C.; Nair, P.; Drwiega, P.; Miller, J.; Smith, J.; Zaren, H.; Park, J. -W.; Hung, N. P.; Kebebew, E.; Linehan, W. M.; Metwalli, A. R.; Pacak, K.; Pinto, P. A.; Schiffman, M.; Schmidt, L. S.; Vocke, C. D.; Wentzensen, N.; Worrell, R.; Yang, H.; Moncrieff, M.; Goparaju, C.; Melamed, J.; Pass, H.; Botnariuc, N.; Caraman, I.; Cernat, M.; Chemencedji, I.; Clipca, A.; Doruc, S.; Gorincioi, G.; Mura, S.; Pirtac, M.; Stancul, I.; Tcaciuc, D.; Albert, M.; Alexopoulou, I.; Arnaout, A.; Bartlett, J.; Engel, J.; Gilbert, S.; Parfitt, J.; Sekhon, H.; Thomas, G.; Rassl, D. M.; Rintoul, R. C.; Bifulco, C.; Tamakawa, R.; Urba, W.; Hayward, N.; Timmers, H.; Antenucci, A.; Facciolo, F.; Grazi, G.; Marino, M.; Merola, R.; de, Krijger, R.; Gimenez-Roqueplo, A. -P.; Piché, A.; Chevalier, S.; McKercher, G.; Birsoy, K.; Barnett, G.; Brewer, C.; Farver, C.; Naska, T.; Pennell, N. A.; Raymond, D.; Schilero, C.; Smolenski, K.; Williams, F.; Morrison, C.; Borgia, J. A.; Liptay, M. J.; Pool, M.; Seder, C. W.; Junker, K.; Omberg, L.; Dinkin, M.; Manikhas, G.; Alvaro, D.; Bragazzi, M. C.; Cardinale, V.; Carpino, G.; Gaudio, E.; Chesla, D.; Cottingham, S.; Dubina, M.; Moiseenko, F.; Dhanasekaran, R.; Becker, K. -F.; Janssen, K. -P.; Slotta-Huspenina, J.; Abdel-Rahman, M. H.; Aziz, D.; Bell, S.; Cebulla, C. M.; Davis, A.; Duell, R.; Elder, J. B.; Hilty, J.; Kumar, B.; Lang, J.; Lehman, N. L.; Mandt, R.; Nguyen, P.; Pilarski, R.; Rai, K.; Schoenfield, L.; Senecal, K.; Wakely, P.; Hansen, P.; Lechan, R.; Powers, J.; Tischler, A.; Grizzle, W. E.; Sexton, K. C.; Kastl, A.; Henderson, J.; Porten, S.; Waldmann, J.; Fassnacht, M.; Asa, S. L.; Schadendorf, D.; Couce, M.; Graefen, M.; Huland, H.; Sauter, G.; Schlomm, T.; Simon, R.; Tennstedt, P.; Olabode, O.; Nelson, M.; Bathe, O.; Carroll, P. R.; Chan, J. M.; Disaia, P.; Glenn, P.; Kelley, R. K.; Landen, C. N.; Phillips, J.; Prados, M.; Simko, J.; Smith-McCune, K.; VandenBerg, S.; Roggin, K.; Fehrenbach, A.; Kendler, A.; Sifri, S.; Steele, R.; Jimeno, A.; Carey, F.; Forgie, I.; Mannelli, M.; Carney, M.; Hernandez, B.; Campos, B.; Herold-Mende, C.; Jungk, C.; Unterberg, A.; von, Deimling, A.; Bossler, A.; Galbraith, J.; Jacobus, L.; Knudson, M.; Knutson, T.; Ma, D.; Milhem, M.; Sigmund, R.; Godwin, A. K.; Madan, R.; Rosenthal, H. G.; Adebamowo, C.; Adebamowo, S. N.; Boussioutas, A.; Beer, D.; Giordano, T.; Mes-Masson, A. -M.; Saad, F.; Bocklage, T.; Landrum, L.; Mannel, R.; Moore, K.; Moxley, K.; Postier, R.; Walker, J.; Zuna, R.; Feldman, M.; Valdivieso, F.; Dhir, R.; Luketich, J.; Pinero, E. M. M.; Quintero-Aguilo, M.; Carlotti, C. G.; Jr.; Dos, Santos, J. S.; Kemp, R.; Sankarankuty, A.; Tirapelli, D.; Catto, J.; Agnew, K.; Swisher, E.; Creaney, J.; Robinson, B.; Shelley, C. S.; Godwin, E. M.; Kendall, S.; Shipman, C.; Bradford, C.; Carey, T.; Haddad, A.; Moyer, J.; Peterson, L.; Prince, M.; Rozek, L.; Wolf, G.; Bowman, R.; Fong, K. M.; Yang, I.; Korst, R.; Rathmell, W. K.; Fantacone-Campbell, J. L.; Hooke, J. A.; Kovatich, A. J.; Shriver, C. D.; DiPersio, J.; Drake, B.; Govindan, R.; Heath, S.; Ley, T.; Van, Tine, B.; Westervelt, P.; Rubin, M. A.; Lee, J. I.; Aredes, N. D.; Mariamidze, A.; Van, Allen, E. M.; Cherniack, A. D.; Ciriello, G.; Sander, C.; Schultz, N.; The, Cancer, Genome, Atlas, Research, Network.tif
Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy. An integrated analysis of genetic alterations in 10 signaling pathways in >9,000 tumors profiled by TCGA highlights significant representation of individual and co-occurring actionable alterations in these pathways, suggesting opportunities for targeted and combination therapies.
Open Access
Pathway commons 2019 update: integration, analysis and exploration of pathway data
(Oxford University Press, 2020) Rodchenkov, I.; Babur, Ö.; Luna, A.; Aksoy, B. A.; Wong, J. V.; Fong, D.; Franz, M.; Siper, M. C.; Cheung, M.; Wrana, M.; Mistry, H.; Mosier, L.; Dlin, J.; Wen, Q.; O’Callaghan, C.; Li, W.; Elder, G.; Smith, P. T.; Dallago, C.; Cerami, E.; Gross, B.; Doğrusöz, Uğur; Demir, E.; Bader, G. D.; Sander, C.
Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.
Open Access
Software support for SBGN maps: SBGN-ML and LibSBGN
(Oxford University Press, 2012) Iersel, Martijn P. van; Villéger, A. C.; Czauderna, T.; Boyd, S. E.; Bergmann, F. T.; Luna, A.; Demir, E.; Sorokin, A.; Dogrusoz, U.; Matsuoka, Y.; Funahashi, A.; Aladjem, M. I.; Mi, H.; Moodie, S. L.; Kitano, H.; Le novère, N.; Schreiber, F.
Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. © The Author(s) 2012. Published by Oxford University Press.
Open Access
The systems biology graphical notation
(Nature Publishing Group, 2009-08) Le Novère, N.; Hucka, M.; Mi, H.; Moodie, S.; Schreiber, F.; Sorokin, A.; Demir, Emek; Wegner, K.; Aladjem, M. I.; Wimalaratne, S. M.; Bergman, F. T.; Gauges, R.; Ghazal, P.; Kawaji, H.; Li, L.; Matsuoka, Y.; Villéger, A.; Boyd, S. E.; Calzone, L.; Courtot, M.; Doğrusöz, Uğur; Freeman, T. C.; Funahashi, A.; Ghosh, S.; Jouraku, A.; Kim, S.; Kolpakov, F.; Luna, A.; Sahle, S.; Schmidt, E.; Watterson, S.; Wu, G.; Goryanin, I.; Kell, D. B.; Sander, C.; Sauro, H.; Snoep, J. L.; Kohn, K.; Kitano, H.
Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. © 2009 Nature America, Inc.
Open Access
Systems biology graphical notation markup language (SBGNML) version 0.3
(De Gruyter, 2020) Luna, A.; Bergmann, F. T.; Czauderna, T.; Doğrusöz, Uğur; Rougny, A.; Dräger, A.; Touré, V.; Mazein, A.; Mazein, M. L.
This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.