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7. The Gene Ontology resource: enriching a GOld mine

Author

Carbon, S, Douglass, E, Good, Bm, Unni, Dr, Harris, Nl, Mungall, Cj, Basu, S, Chisholm, Rl, Dodson, Rj, Hartline, E, Fey, P, Thomas, Pd, Albou, Lp, Ebert, D, Kesling, Mj, Mi, Hy, Muruganujan, A, Huang, Xs, Mushayahama, T, Labonte, Sa, Siegele, Da, Antonazzo, G, Attrill, H, Brown, Nh, Garapati, P, Marygold, Sj, Trovisco, V, Dos Santos, G, Falls, K, Tabone, C, Zhou, Pl, Goodman, Jl, Strelets, Vb, Thurmond, J, Garmiri, P, Ishtiaq, R, Rodriguez-Lopez, M, Acencio, Ml, Kuiper, M, Laegreid, A, Logie, C, Lovering, Rc, Kramarz, B, Saverimuttu, Scc, Pinheiro, Sm, Gunn, H, Su, Rz, Thurlow, Ke, Chibucos, M, Giglio, M, Nadendla, S, Munro, J, Jackson, R, Duesbury, Mj, Del-Toro, N, Meldal, Bhm, Paneerselvam, K, Perfetto, L, Porras, P, Orchard, S, Shrivastava, A, Chang, Hy, Finn, Rd, Mitchell, Al, Rawlings, Nd, Richardson, L, Sangrador-Vegas, A, Blake, Ja, Christie, Kr, Dolan, Me, Drabkin, Hj, Hill, Dp, Ni, L, Sitnikov, Dm, Harris, Ma, Oliver, Sg, Rutherford, K, Wood, V, Hayles, J, Bahler, J, Bolton, Er, De Pons JL, Dwinell, Mr, Hayman, Gt, Kaldunski, Ml, Kwitek, Ae, Laulederkind, Sjf, Plasterer, C, Tutaj, Ma, Vedi, M, Wang, Sj, D'Eustachio, P, Matthews, L, Balhoff, Jp, Aleksander, Sa, Alexander, Mj, Cherry, Jm, Engel, Sr, Gondwe, F, Karra, K, Miyasato, Sr, Nash, Rs, Simison, M, Skrzypek, Ms, Weng, S, Wong, Ed, Feuermann, M, Gaudet, P, Morgat, A, Bakker, E, Berardini, Tz, Reiser, L, Subramaniam, S, Huala, E, Arighi, Cn, Auchincloss, A, Axelsen, K, Argoud-Puy, G, Bateman, A, Blatter, Mc, Boutet, E, Bowler, E, Breuza, L, Bridge, A, Britto, R, Bye-A-Jee, H, Casas, Cc, Coudert, E, Denny, P, Estreicher, A, Famiglietti, Ml, Georghiou, G, Gos, A, Gruaz-Gumowski, N, Hatton-Ellis, E, Hulo, C, Ignatchenko, A, Jungo, F, Laiho, K, Le Mercier, P, Lieberherr, D, Lock, A, Lussi, Y, Macdougall, A, Magrane, M, Martin, Mj, Masson, P, Natale, Da, Hyka-Nouspikel, N, Pedruzzi, I, Pourcel, L, Poux, S, Pundir, S, Rivoire, C, Speretta, E, Sundaram, S, Tyagi, N, Warner, K, Zaru, R, Wu, Ch, Diehl, Ad, Chan, Jn, Grove, C, Lee, Ryn, Muller, Hm, Raciti, D, Van Auken, K, Sternberg, Pw, Berriman, M, Paulini, M, Howe, K, Gao, S, Wright, A, Stein, L, Howe, Dg, Toro, S, Westerfield, M, Jaiswal, P, Cooper, L, and Elser, J

Subjects
Traceability, AcademicSubjects/SCI00010, Arabidopsis, Saccharomyces cerevisiae, Biology, Ontology (information science), Gene Ontology, Data curation, GO-CAMs, World Wide Web, Mice, User-Computer Interface, 03 medical and health sciences, Consistency (database systems), Annotation, 0302 clinical medicine, Documentation, Resource (project management), Schema (psychology), Schizosaccharomyces, Escherichia coli, Genetics, Database Issue, Animals, Humans, Dictyostelium, Caenorhabditis elegans, Molecular Biology, Zebrafish, 030304 developmental biology, Internet, 0303 health sciences, Molecular Sequence Annotation, File format, Rats, Drosophila melanogaster, 030217 neurology & neurosurgery
Abstract

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

Published
2021

8. Gene Ontology annotations and resources

Author

Gene Ontology Consortium, Blake, JA, Dolan, M, Drabkin, H, Hill, DP, Li, Ni, Sitnikov, D, Bridges, S, Burgess, S, Buza, T, McCarthy, F, Peddinti, D, Pillai, L, Carbon, S, Dietze, H, Ireland, A, Lewis, SE, Mungall, CJ, Gaudet, P, Chrisholm, RL, Fey, P, Kibbe, WA, Basu, S, Siegele, DA, McIntosh, BK, Renfro, DP, Zweifel, AE, Hu, JC, Brown, NH, Tweedie, S, Alam-Faruque, Y, Apweiler, R, Auchinchloss, A, Axelsen, K, Bely, B, Blatter, M-C, Bonilla, C, Bouguerleret, L, Boutet, E, Breuza, L, Bridge, A, Chan, WM, Chavali, G, Coudert, E, Dimmer, E, Estreicher, A, Famiglietti, L, Feuermann, M, Gos, A, Gruaz-Gumowski, N, Hieta, R, Hinz, C, Hulo, C, Huntley, R, James, J, Jungo, F, Keller, G, Laiho, K, Legge, D, Lemercier, P, Lieberherr, D, Magrane, M, Martin, MJ, Masson, P, Mutowo-Muellenet, P, O'Donovan, C, Pedruzzi, I, Pichler, K, Poggioli, D, Porras Millán, P, Poux, S, Rivoire, C, Roechert, B, Sawford, T, Schneider, M, Stutz, A, Sundaram, S, Tognolli, M, Xenarios, I, Foulgar, R, Lomax, J, Roncaglia, P, Khodiyar, VK, Lovering, RC, Talmud, PJ, Chibucos, M, Giglio, M Gwinn, Chang, H-Y, Hunter, S, McAnulla, C, Mitchell, A, Sangrador, A, Stephan, R, Harris, MA, Oliver, SG, Rutherford, K, Wood, V, Bahler, J, Lock, A, Kersey, PJ, McDowall, DM, Staines, DM, Dwinell, M, Shimoyama, M, Laulederkind, S, Hayman, T, Wang, S-J, Petri, V, Lowry, T, D'Eustachio, P, Matthews, L, Balakrishnan, R, Binkley, G, Cherry, JM, Costanzo, MC, Dwight, SS, Engel, SR, Fisk, DG, Hitz, BC, Hong, EL, Karra, K, Miyasato, SR, Nash, RS, Park, J, Skrzypek, MS, Weng, S, Wong, ED, Berardini, TZ, Huala, E, Mi, H, Thomas, PD, Chan, J, Kishore, R, Sternberg, P, Van Auken, K, Howe, D, Westerfield, M, Brown, Nicholas [0000-0002-8958-7017], Harris, Midori [0000-0003-4148-4606], Oliver, Stephen [0000-0001-6330-7526], Wood, Valerie [0000-0001-6330-7526], and Apollo - University of Cambridge Repository

Subjects
Internet, Genes, Vocabulary, Controlled, Databases, Genetic, Molecular Sequence Annotation, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Phylogeny
Abstract

The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.

Published
2020
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12. The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

Author

Bultet, LA, Aguilar-Rodriguez, J, Ahrens, CH, Ahrne, EL, Ai, N, Aimo, L, Akalin, A, Aleksiev, T, Alocci, D, Altenhoff, A, Alves, I, Ambrosini, G, Pedone, PA, Angelina, P, Anisimova, M, Appel, R, Argoud-Puy, G, Arnold, K, Arpat, B, Artimo, P, Ascencao, K, Auchincloss, A, Axelsen, K, Gerritsen, VB, Bairoch, A, Barisal, P, Baratin, D, Barbato, A, Barbie, V, Barras, D, Barreiro, M, Barret, S, Bastian, F, Batista Neto, TM, Baudis, M, Beaudoing, E, Beckmann, JS, Bekkar, AK, Cammoun, LBH, Benmohammed, S, Bernard, M, Bertelli, C, Bertoni, M, Bienert, S, Bignucolo, O, Bilbao, A, Bilican, A, Blank, D, Blatter, M-C, Blum, L, Bocquet, J, Boeckmann, B, Bolleman, JT, Bordoli, L, Bosshard, L, Boucher, G, Bougueleret, L, Boutet, E, Bovigny, C, Bratulic, S, Breuza, L, Bridge, AJ, Britan, A, Brito, F, Frazao, JB, Bruggmann, R, Bucher, P, Burdet, F, Burger, L, Cabello, EM, Gomez, RMC, Calderon, S, Cannarozzi, G, Carl, S, Casas, CC, Catherinet, S, Perier, RC, Charpilloz, C, Chaskar, PD, Chen, W, Pepe, AC, Chopard, B, Chu, HY, Civic, N, Claassen, M, Clottu, S, Colombo, M, Cosandier, I, Coudert, E, Crespo, I, Creus, M, Cuche, B, Cuendet, MA, Cusin, I, Daga, N, Daina, A, Dauvillier, J, David, F, Davydov, I, Ferreira, MDSRM, de Beer, T, de Castro, E, de Santana, C, Delafontaine, J, Delorenzi, M, Delucinge-Vivier, C, Demirel, O, Derham, R, Dermitzakis, EM, Dib, L, Diene, S, Dilek, N, Dilmi, J, Domagalski, MJ, Dorier, J, Dornevil, D, Dousse, A, Dreos, R, Duchen, P, Roggli, PD, Duperret, ID, Durinx, C, Duvaud, S, Engler, R, Frkek, S, Lopez, PE, Fstreicher, A, Excoffier, L, Fabbretti, R, Falcone, J-L, Falquet, L, Famiglietti, ML, Ferreira, A-M, Feuermann, M, Filliettaz, M, Hegel, V, Foucal, A, Franceschini, A, Fucile, G, Gaidatzis, D, Garcia, V, Gasteiger, E, Gateau, A, Gatti, L, Gaudet, P, Gaudinat, A, Gehant, S, Gfeller, D, Gharib, WH, Ghraichy, M, Gidoin, C, Gil, M, Gleizes, A, Gobeill, J, Gonnet, G, Gos, A, Gotz, L, Gouy, A, Grbic, D, Groux, R, Gruaz-Gumowski, N, Grun, D, Gschwind, A, Guex, N, Gupta, S, Getaz, M, Haake, D, Haas, J, Hatzimanikatis, V, Heckel, G, Gardiol, DFH, Hinard, V, Hinz, U, Homicsko, K, Horlacher, O, Hosseini, S-R, Hotz, H-R, Hulo, C, Hundsrucker, C, Ibberson, M, Ilmjarv, S, Ioannidis, V, Ioannidis, P, Iseli, C, Ivanek, R, Iwaszkiewicz, J, Jacquet, P, Jacquot, M, Jagannathan, V, Jan, M, Jensen, J, Johansson, MU, Johner, N, Jungo, F, Junier, T, Kahraman, A, Katsantoni, M, Keller, G, Kerhornou, A, Khalid, F, Klingbiel, D, Kimljenovic, A, Kriventseva, E, Kryuchkova, N, Kumar, S, Kutalik, Z, Kuznetsov, D, Kuzyakiv, R, Lane, L, Lara, V, Ledesma, L, Leleu, M, Lemercier, P, Lew, D, Lieberherr, D, Liechti, R, Lisacek, F, Fischer, H, Litsios, G, Liu, J, Lombardot, T, Mace, A, Maffioletti, S, Mahi, M-A, Maiolo, M, Majjigapu, SR, Malmstrom, L, Mangold, V, Marek, D, Mariethoz, J, Marin, R, Martin, O, Martin, X, Martin-Campos, T, Mary, C, Masclaux, F, Masson, P, Meier, C, Messina, A, Lenoir, MM, Meyer, X, Michel, P-A, Michielin, O, Milanese, A, Missiaglia, E, Perez, JM, Caria, VM, Moret, P, Moretti, S, Morgat, A, Mottaz, A, Mottin, L, Mouscaz, Y, Mueller, M, Murri, R, Mylonas, R, Neuenschwander, S, Nikitin, F, Niknejad, A, Nouspikel, N, Nso, LN, Okoniewski, M, Omasits, U, Paccaud, B, Pachkov, M, Paesano, SG, Pagni, M, Palagi, PM, Pasche, E, Payne, JL, Pedruzzi, I, Peischl, S, Peitsch, M, Perlini, S, Pilbout, S, Podvinec, M, Pohlmann, R, Polizzi, D, Potter, D, Poux, S, Pozzato, M, Pradervand, S, Praz, V, Pruess, M, Pujadas, E, Racle, J, Raschi, M, Ratib, O, Rausell, A, de Laval, VR, Redaschi, N, Rempfer, C, Ren, G, Vandati, RAR, Rib, L, Grognuz, OR, Altimiras, ER, Rivoire, C, Robin, T, Robinson-Rechavi, M, Rodrigues, J, Roechert, B, Roelli, P, Romano, V, Rossier, G, Roth, A, Rougemont, J, Roux, J, Royo, H, Ruch, P, Ruinelli, M, Rustom, M, Sates, A, Roehrig, UF, Rueeger, S, Salamin, N, Sankar, M, Sarkar, N, Saxenhofer, M, Schaeffer, M, Schaerli, Y, Schaper, E, Schmid, A, Schmid, E, Schmid, C, Schmid, M, Schmidt, S, Schmocker, D, Schneider, M, Schuepbach, T, Schwede, T, Schuetz, F, Sengstag, T, Serrano, M, Sethi, A, Shahmirzadi, O, Sigrist, C, Silvestro, D, Simao Neto, FA, Simillion, C, Simonovic, M, Skunca, N, Sluzek, K, Soneson, C, Sprouffske, K, Stadler, M, Staehli, S, Stevenson, B, Stockinger, H, Straszewski, J, Stricker, T, Studer, G, Stutz, A, Suffiotti, M, Sundaram, S, Szklarczyk, D, Szovenyi, P, Tegenfeldt, F, Teixeira, D, Tellenbach, S, Smith, AAT, Tognolli, M, Topolsky, I, Thuong, VDT, Tsantoulis, P, Tzika, AC, Agote, AU, van Nimwegen, E, von Mering, C, Varadarajan, A, Veranneman, M, Verbregue, L, Veuthey, A-L, Vishnyakova, D, Vyas, R, Wagner, A, Walther, D, Wan, HW, Wang, M, Waterhouse, R, Waterhouse, A, Wicki, A, Wigger, L, Wirapati, P, Witschi, U, Wyder, S, Wyler, K, Wuethrich, D, Xenarios, I, Yamada, K, Yan, Z, Yasrebi, H, Zahn, M, Zangger, N, Zdobnov, E, Zerzion, D, Zoete, V, Zoller, S, Bultet, LA, Aguilar-Rodriguez, J, Ahrens, CH, Ahrne, EL, Ai, N, Aimo, L, Akalin, A, Aleksiev, T, Alocci, D, Altenhoff, A, Alves, I, Ambrosini, G, Pedone, PA, Angelina, P, Anisimova, M, Appel, R, Argoud-Puy, G, Arnold, K, Arpat, B, Artimo, P, Ascencao, K, Auchincloss, A, Axelsen, K, Gerritsen, VB, Bairoch, A, Barisal, P, Baratin, D, Barbato, A, Barbie, V, Barras, D, Barreiro, M, Barret, S, Bastian, F, Batista Neto, TM, Baudis, M, Beaudoing, E, Beckmann, JS, Bekkar, AK, Cammoun, LBH, Benmohammed, S, Bernard, M, Bertelli, C, Bertoni, M, Bienert, S, Bignucolo, O, Bilbao, A, Bilican, A, Blank, D, Blatter, M-C, Blum, L, Bocquet, J, Boeckmann, B, Bolleman, JT, Bordoli, L, Bosshard, L, Boucher, G, Bougueleret, L, Boutet, E, Bovigny, C, Bratulic, S, Breuza, L, Bridge, AJ, Britan, A, Brito, F, Frazao, JB, Bruggmann, R, Bucher, P, Burdet, F, Burger, L, Cabello, EM, Gomez, RMC, Calderon, S, Cannarozzi, G, Carl, S, Casas, CC, Catherinet, S, Perier, RC, Charpilloz, C, Chaskar, PD, Chen, W, Pepe, AC, Chopard, B, Chu, HY, Civic, N, Claassen, M, Clottu, S, Colombo, M, Cosandier, I, Coudert, E, Crespo, I, Creus, M, Cuche, B, Cuendet, MA, Cusin, I, Daga, N, Daina, A, Dauvillier, J, David, F, Davydov, I, Ferreira, MDSRM, de Beer, T, de Castro, E, de Santana, C, Delafontaine, J, Delorenzi, M, Delucinge-Vivier, C, Demirel, O, Derham, R, Dermitzakis, EM, Dib, L, Diene, S, Dilek, N, Dilmi, J, Domagalski, MJ, Dorier, J, Dornevil, D, Dousse, A, Dreos, R, Duchen, P, Roggli, PD, Duperret, ID, Durinx, C, Duvaud, S, Engler, R, Frkek, S, Lopez, PE, Fstreicher, A, Excoffier, L, Fabbretti, R, Falcone, J-L, Falquet, L, Famiglietti, ML, Ferreira, A-M, Feuermann, M, Filliettaz, M, Hegel, V, Foucal, A, Franceschini, A, Fucile, G, Gaidatzis, D, Garcia, V, Gasteiger, E, Gateau, A, Gatti, L, Gaudet, P, Gaudinat, A, Gehant, S, Gfeller, D, Gharib, WH, Ghraichy, M, Gidoin, C, Gil, M, Gleizes, A, Gobeill, J, Gonnet, G, Gos, A, Gotz, L, Gouy, A, Grbic, D, Groux, R, Gruaz-Gumowski, N, Grun, D, Gschwind, A, Guex, N, Gupta, S, Getaz, M, Haake, D, Haas, J, Hatzimanikatis, V, Heckel, G, Gardiol, DFH, Hinard, V, Hinz, U, Homicsko, K, Horlacher, O, Hosseini, S-R, Hotz, H-R, Hulo, C, Hundsrucker, C, Ibberson, M, Ilmjarv, S, Ioannidis, V, Ioannidis, P, Iseli, C, Ivanek, R, Iwaszkiewicz, J, Jacquet, P, Jacquot, M, Jagannathan, V, Jan, M, Jensen, J, Johansson, MU, Johner, N, Jungo, F, Junier, T, Kahraman, A, Katsantoni, M, Keller, G, Kerhornou, A, Khalid, F, Klingbiel, D, Kimljenovic, A, Kriventseva, E, Kryuchkova, N, Kumar, S, Kutalik, Z, Kuznetsov, D, Kuzyakiv, R, Lane, L, Lara, V, Ledesma, L, Leleu, M, Lemercier, P, Lew, D, Lieberherr, D, Liechti, R, Lisacek, F, Fischer, H, Litsios, G, Liu, J, Lombardot, T, Mace, A, Maffioletti, S, Mahi, M-A, Maiolo, M, Majjigapu, SR, Malmstrom, L, Mangold, V, Marek, D, Mariethoz, J, Marin, R, Martin, O, Martin, X, Martin-Campos, T, Mary, C, Masclaux, F, Masson, P, Meier, C, Messina, A, Lenoir, MM, Meyer, X, Michel, P-A, Michielin, O, Milanese, A, Missiaglia, E, Perez, JM, Caria, VM, Moret, P, Moretti, S, Morgat, A, Mottaz, A, Mottin, L, Mouscaz, Y, Mueller, M, Murri, R, Mylonas, R, Neuenschwander, S, Nikitin, F, Niknejad, A, Nouspikel, N, Nso, LN, Okoniewski, M, Omasits, U, Paccaud, B, Pachkov, M, Paesano, SG, Pagni, M, Palagi, PM, Pasche, E, Payne, JL, Pedruzzi, I, Peischl, S, Peitsch, M, Perlini, S, Pilbout, S, Podvinec, M, Pohlmann, R, Polizzi, D, Potter, D, Poux, S, Pozzato, M, Pradervand, S, Praz, V, Pruess, M, Pujadas, E, Racle, J, Raschi, M, Ratib, O, Rausell, A, de Laval, VR, Redaschi, N, Rempfer, C, Ren, G, Vandati, RAR, Rib, L, Grognuz, OR, Altimiras, ER, Rivoire, C, Robin, T, Robinson-Rechavi, M, Rodrigues, J, Roechert, B, Roelli, P, Romano, V, Rossier, G, Roth, A, Rougemont, J, Roux, J, Royo, H, Ruch, P, Ruinelli, M, Rustom, M, Sates, A, Roehrig, UF, Rueeger, S, Salamin, N, Sankar, M, Sarkar, N, Saxenhofer, M, Schaeffer, M, Schaerli, Y, Schaper, E, Schmid, A, Schmid, E, Schmid, C, Schmid, M, Schmidt, S, Schmocker, D, Schneider, M, Schuepbach, T, Schwede, T, Schuetz, F, Sengstag, T, Serrano, M, Sethi, A, Shahmirzadi, O, Sigrist, C, Silvestro, D, Simao Neto, FA, Simillion, C, Simonovic, M, Skunca, N, Sluzek, K, Soneson, C, Sprouffske, K, Stadler, M, Staehli, S, Stevenson, B, Stockinger, H, Straszewski, J, Stricker, T, Studer, G, Stutz, A, Suffiotti, M, Sundaram, S, Szklarczyk, D, Szovenyi, P, Tegenfeldt, F, Teixeira, D, Tellenbach, S, Smith, AAT, Tognolli, M, Topolsky, I, Thuong, VDT, Tsantoulis, P, Tzika, AC, Agote, AU, van Nimwegen, E, von Mering, C, Varadarajan, A, Veranneman, M, Verbregue, L, Veuthey, A-L, Vishnyakova, D, Vyas, R, Wagner, A, Walther, D, Wan, HW, Wang, M, Waterhouse, R, Waterhouse, A, Wicki, A, Wigger, L, Wirapati, P, Witschi, U, Wyder, S, Wyler, K, Wuethrich, D, Xenarios, I, Yamada, K, Yan, Z, Yasrebi, H, Zahn, M, Zangger, N, Zdobnov, E, Zerzion, D, Zoete, V, and Zoller, S

Abstract

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

Published
2016

14. The UniProt-GO Annotation database in 2011

Author

Dimmer, E. C., primary, Huntley, R. P., additional, Alam-Faruque, Y., additional, Sawford, T., additional, O'Donovan, C., additional, Martin, M. J., additional, Bely, B., additional, Browne, P., additional, Mun Chan, W., additional, Eberhardt, R., additional, Gardner, M., additional, Laiho, K., additional, Legge, D., additional, Magrane, M., additional, Pichler, K., additional, Poggioli, D., additional, Sehra, H., additional, Auchincloss, A., additional, Axelsen, K., additional, Blatter, M.-C., additional, Boutet, E., additional, Braconi-Quintaje, S., additional, Breuza, L., additional, Bridge, A., additional, Coudert, E., additional, Estreicher, A., additional, Famiglietti, L., additional, Ferro-Rojas, S., additional, Feuermann, M., additional, Gos, A., additional, Gruaz-Gumowski, N., additional, Hinz, U., additional, Hulo, C., additional, James, J., additional, Jimenez, S., additional, Jungo, F., additional, Keller, G., additional, Lemercier, P., additional, Lieberherr, D., additional, Masson, P., additional, Moinat, M., additional, Pedruzzi, I., additional, Poux, S., additional, Rivoire, C., additional, Roechert, B., additional, Schneider, M., additional, Stutz, A., additional, Sundaram, S., additional, Tognolli, M., additional, Bougueleret, L., additional, Argoud-Puy, G., additional, Cusin, I., additional, Duek- Roggli, P., additional, Xenarios, I., additional, and Apweiler, R., additional

Published
2011
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15. Collaborative annotation of genes and proteins between UniProtKB/Swiss-Prot and dictyBase

Author

Gaudet, P., primary, Lane, L., additional, Fey, P., additional, Bridge, A., additional, Poux, S., additional, Auchincloss, A., additional, Axelsen, K., additional, Braconi Quintaje, S., additional, Boutet, E., additional, Brown, P., additional, Coudert, E., additional, Datta, R.S., additional, de Lima, W.C., additional, de Oliveira Lima, T., additional, Duvaud, S., additional, Farriol-Mathis, N., additional, Ferro Rojas, S., additional, Feuermann, M., additional, Gateau, A., additional, Hinz, U., additional, Hulo, C., additional, James, J., additional, Jimenez, S., additional, Jungo, F., additional, Keller, G., additional, Lemercier, P., additional, Lieberherr, D., additional, Moinat, M., additional, Nikolskaya, A., additional, Pedruzzi, I., additional, Rivoire, C., additional, Roechert, B., additional, Schneider, M., additional, Stanley, E., additional, Tognolli, M., additional, Sjolander, K., additional, Bougueleret, L., additional, Chisholm, R.L., additional, and Bairoch, A., additional

Published
2009
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26. P-type H(+)- and Ca(2+)-ATPases in plant cells

Author

Stangeland, B., Fuglsang, A. T., Malmström, S., Axelsen, K. B., Baunsgaard, L., Lanfermeijer, F. C., Venema, K., Okkels, F. T., Askerlund, Per, Palmgren, M. G., Stangeland, B., Fuglsang, A. T., Malmström, S., Axelsen, K. B., Baunsgaard, L., Lanfermeijer, F. C., Venema, K., Okkels, F. T., Askerlund, Per, and Palmgren, M. G.

Published
1997

27. Plant H+ and Ca2+ P-type ATPases

Author

Stangeland, B., Fuglsang, A.T., Malmström, S., Axelsen, K., Baunsgaard, Lone, Lanfermeijer, Frank, Venema, F.C., Askerlund, P., Okkels, F., Palmgren, Michael Gjedde, Stangeland, B., Fuglsang, A.T., Malmström, S., Axelsen, K., Baunsgaard, Lone, Lanfermeijer, Frank, Venema, F.C., Askerlund, P., Okkels, F., and Palmgren, Michael Gjedde

Published
1997

30. P-Type H+- and Ca2+-ATPases in Plant Cells

Author

STANGELAND, B., primary, FUGLSANG, A. T., additional, MALMSTRÖM, S., additional, AXELSEN, K. B., additional, BAUNSGAARD, L., additional, LANFERMEIJER, F. C., additional, VENEMA, K., additional, OKKELS, F. T., additional, ASKERLUND, P., additional, and PALMGREN, M. G., additional

Published
1997
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34. CYP83b1 is the oxime-metabolizing enzyme in the glucosinolate pathway in Arabidopsis.

Author

Hansen, C H, Du, L, Naur, P, Olsen, C E, Axelsen, K B, Hick, A J, Pickett, J A, and Halkier, B A

Abstract

CYP83B1 from Arabidopsis thaliana has been identified as the oxime-metabolizing enzyme in the biosynthetic pathway of glucosinolates. Biosynthetically active microsomes isolated from Sinapis alba converted p-hydroxyphenylacetaldoxime and cysteine into S-alkylated p-hydroxyphenylacetothiohydroximate, S-(p-hydroxyphenylacetohydroximoyl)-l-cysteine, the next proposed intermediate in the glucosinolate pathway. The production was shown to be dependent on a cytochrome P450 monooxygenase. We searched the genome of A. thaliana for homologues of CYP71E1 (P450ox), the only known oxime-metabolizing enzyme in the biosynthetic pathway of the evolutionarily related cyanogenic glucosides. By a combined use of bioinformatics, published expression data, and knock-out phenotypes, we identified the cytochrome P450 CYP83B1 as the oxime-metabolizing enzyme in the glucosinolate pathway as evidenced by characterization of the recombinant protein expressed in Escherichia coli. The data are consistent with the hypothesis that the oxime-metabolizing enzyme in the cyanogenic pathway (P450ox) was mutated into a "P450mox" that converted oximes into toxic compounds that the plant detoxified into glucosinolates.

Published
2001
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35. Inventory of the superfamily of P-type ion pumps in Arabidopsis.

Author

Axelsen, K B and Palmgren, M G

Abstract

A total of 45 genes encoding for P-type ATPases have been identified in the complete genome sequence of Arabidopsis. Thus, this plant harbors a primary transport capability not seen in any other eukaryotic organism sequenced so far. The sequences group in all five subfamilies of P-type ATPases. The most prominent subfamilies are P(1B) ATPases (heavy metal pumps; seven members), P(2A) and P(2B) ATPases (Ca(2+) pumps; 14 in total), P(3A) ATPases (plasma membrane H(+) pumps; 12 members including a truncated pump, which might represent a pseudogene or an ATPase-like protein with an alternative function), and P(4) ATPases (12 members). P(4) ATPases have been implicated in aminophosholipid flipping but it is not known whether this is a direct or an indirect effect of pump activity. Despite this apparent plethora of pumps, Arabidopsis appears to be lacking Na(+) pumps and secretory pathway (PMR1-like) Ca(2+)-ATPases. A cluster of Arabidopsis heavy metal pumps resembles bacterial Zn(2+)/Co(2+)/Cd(2+)/Pb(2+) transporters. Two members of the cluster have extended C termini containing putative heavy metal binding motifs. The complete inventory of P-type ATPases in Arabidopsis is an important starting point for reverse genetic and physiological approaches aiming at elucidating the biological significance of these pumps.

Published
2001
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36. A visible marker for antisense mRNA expression in plants: inhibition of chlorophyll synthesis with a glutamate-1-semialdehyde aminotransferase antisense gene.

Author

Höfgen, R, Axelsen, K B, Kannangara, C G, Schüttke, I, Pohlenz, H D, Willmitzer, L, Grimm, B, and von Wettstein, D

Abstract

Glutamate 1-semialdehyde aminotransferase [(S)-4-amino-5-oxopentanoate 4,5-aminomutase, EC 5.4.3.8] catalyzes the last step in the conversion of glutamate to delta-aminolevulinate of which eight molecules are needed to synthesize a chlorophyll molecule. Two full-length cDNA clones that probably represent the homeologous Gsa genes of the two tobacco (Nicotiana tabacum) genomes have been isolated. The deduced amino acid sequences of the 468-residue-long precursor polypeptides differ by 10 amino acids. The cDNA sequence of isoenzyme 2 was inserted in reverse orientation under the control of a cauliflower mosaic virus 35S promoter derivative in an expression vector and was introduced by Agrobacterium-mediated transformation into tobacco plants. Antisense gene expression decreased the steady-state mRNA level of glutamate 1-semialdehyde aminotransferase, the translation of the enzyme, and chlorophyll synthesis. Remarkably, partial or complete suppression of the aminotransferase mimics in tobacco a wide variety of chlorophyll variegation patterns caused by nuclear or organelle gene mutations in different higher plants. The antisense gene is inherited as a dominant marker.

Published
1994
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37. 246P Factors affecting the diagnostic delay of myasthenia gravis.

Author

Marlet, I., Andersen, R., Axelsen, K., Andersen, L., Wissing, J., and Witting, N.

Subjects
*DELAYED diagnosis, *EARLY diagnosis, *DIAGNOSIS methods, *SOCIOECONOMIC status, *SYMPTOMS
Abstract

Early detection and diagnosis of myasthenia gravis (MG) is important in order to quickly initiate treatment, which in turn can enhance the individual's quality of life and prognosis. This study aims to investigate the delay from onset of symptoms to the diagnosis of MG, and the variables that may affect the delay. By doing so, we aim to highlight the potential for improvement and increase awareness about the challenges in the diagnostic process of MG. We studied retrospective data from a cohort of 350 patients treated for MG at the Neuromuscular Unit, Rigshospitalet, Copenhagen, between 1980 and 2022. To identify potential factors, we examined the baseline characteristics and the factors involved in the diagnostic process. We classified individuals into two subgroups based on their time to diagnosis: early diagnosis and delayed diagnosis. Moreover, a separate analysis was conducted for the cases with an extremely delayed time to diagnosis exceeding the upper quartile range of 864 days (n=28). Finally, the change in time to diagnose over the last decades has been identified. The overall mean time to diagnosis was 331 days (range: 5-4492 days), with 64 days for the early group and 600 days for the delayed group. Patients who experienced a delayed diagnosis were significantly younger at onset (50.4 versus 55.4 years, p-value < 0.05), and had higher frequency of asymptomatic periods (47% vs 15%, p-value <0.001). We found a delayed referral to specialists, extended time spent at specialists, and delayed initiation of paraclinical diagnostic tests. Gender, type of onset symptom, final type of MG and socioeconomic status where not found to be factors affecting the time to diagnosis. A trend of decreasing time to diagnosis over the last decades was observed. In conclusion, early onset of symptoms and higher prevalence of asymptomatic periods may be risk factors for a delayed MG diagnosis. [ABSTRACT FROM AUTHOR]

Published
2024
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38. The Gene Ontology: enhancements for 2011

Author

P D'Eustachio, Benjamin C. Hitz, Julie Park, Paul Browne, Douglas G. Howe, Cynthia J. Krieger, Kalpana Karra, Stan Laulederkind, Karen R. Christie, Susan Tweedie, Eurie L. Hong, Lydie Bougueleret, Michele Magrane, Cathy R. Gresham, Rolf Apweiler, Lisa Matthews, Dong Li, Philippa J. Talmud, Ioannis Xenarios, J. M. Cherry, Tanya Z. Berardini, Deborah A. Siegele, Rama Balakrishnan, D. Sitnikov, A. Auchinchloss, Selina S. Dwight, Tony Sawford, Paul J. Kersey, Ruth C. Lovering, Ruth Y. Eberhardt, Ursula Hinz, Lakshmi Pillai, Sylvain Poux, Edith D. Wong, Klemens Pichler, Kati Laiho, Malcolm J. Gardner, Stephen G. Oliver, Lionel Breuza, Kara Dolinski, P Lemercier, Kristian B. Axelsen, Midori A. Harris, Adrienne E. Zweifel, H. Drabkin, Guillaume Keller, Marek S. Skrzypek, Daniel M. Staines, Fiona M. McCarthy, Nicholas H. Brown, Mark D. McDowall, Antonia Lock, Mary Shimoyama, Maria C. Costanzo, Teresia Buza, S. Jimenez, Rex L. Chisholm, Paul W. Sternberg, Hui Wang, Nadine Gruaz-Gumowski, Chantal Hulo, Rebecca E. Foulger, Melinda R. Dwinell, Judith A. Blake, Marcus C. Chibucos, B. K. McIntosh, C. D. Amundsen, Jane Lomax, L Famiglietti, Tom Hayman, Michael Tognolli, Eva Huala, James C. Hu, Patrick Masson, Maria Jesus Martin, Benoit Bely, Shuai Weng, Heather C. Wick, E. Dimmer, L. Ni, Catherine Rivoire, Christopher J. Mungall, H. Sehra, P. Duek-Roggli, Maria Victoria Schneider, Dianna G. Fisk, Michael S. Livstone, Ivo Pedruzzi, Shyamala Sundaram, Donna K. Slonim, Isabelle Cusin, Stuart R. Miyasato, Timothy F. Lowry, Varsha K. Khodiyar, Seth Carbon, Elisabeth Coudert, Jürg Bähler, Juancarlos Chan, Evelyn Camon, Daniel P. Renfro, Anne Estreicher, M. C. Blatter, Robert S. Nash, P Gaudet, Sven Heinicke, K. Van Auken, Stacia R. Engel, Alan Bridge, Ralf Stephan, Mary E. Dolan, Shane C. Burgess, Petra Fey, Shur-Jen Wang, Damien Lieberherr, Duncan Legge, P. Porras Millán, Andre Stutz, Yasmin Alam-Faruque, Gail Binkley, Bernd Roechert, S. Branconi-Quintaje, Ghislaine Argoud-Puy, S. Basu, Kim Rutherford, M. Moinat, Monte Westerfield, Arnaud Gos, Eleanor J Stanley, Valerie Wood, Ranjana Kishore, Diego Poggioli, S. Ferro-Rojas, Victoria Petri, Florence Jungo, Suzanna E. Lewis, Emmanuel Boutet, Warren A. Kibbe, M Feuermann, Claire O'Donovan, W. M. Chan, J. James, David P. Hill, Rachael P. Huntley, M. Gwinn Giglio, Paul Thomas, Jodi E. Hirschman, Paola Roncaglia, Gene Ontology Consortium, Blake, JA., Dolan, M., Drabkin, H., Hill, DP., Ni, L., Sitnikov, D., Burgess, S., Buza, T., Gresham, C., McCarthy, F., Pillai, L., Wang, H., Carbon, S., Lewis, SE., Mungall, CJ., Gaudet, P., Chisholm, RL., Fey, P., Kibbe, WA., Basu, S., Siegele, DA., McIntosh, BK., Renfro, DP., Zweifel, AE., Hu, JC., Brown, NH., Tweedie, S., Alam-Faruque, Y., Apweiler, R., Auchinchloss, A., Axelsen, K., Argoud-Puy, G., Bely, B., Blatter, M-., Bougueleret, L., Boutet, E., Branconi, S., Breuza, L., Bridge, A., Browne, P., Chan, WM., Coudert, E., Cusin, I., Dimmer, E., Duek-Roggli, P., Eberhardt, R., Estreicher, A., Famiglietti, L., Ferro-Rojas, S., Feuermann, M., Gardner, M., Gos, A., Gruaz-Gumowski, N., Hinz, U., Hulo, C., Huntley, R., James, J., Jimenez, S., Jungo, F., Keller, G., Laiho, K., Legge, D., Lemercier, P., Lieberherr, D., Magrane, M., Martin, MJ., Masson, P., Moinat, M., O'Donovan, C., Pedruzzi, I., Pichler, K., Poggioli, D., Porras Millán, P., Poux, S., Rivoire, C., Roechert, B., Sawford, T., Schneider, M., Sehra, H., Stanley, E., Stutz, A., Sundaram, S., Tognolli, M., Xenarios, I., Foulger, R., Lomax, J., Roncaglia, P., Camon, E., Khodiyar, VK., Lovering, RC., Talmud, PJ., Chibucos, M., Gwinn Giglio, M., Dolinski, K., Heinicke, S., Livstone, MS., Stephan, R., Harris, MA., Oliver, SG., Rutherford, K., Wood, V., Bahler, J., Lock, A., Kersey, PJ., McDowall, MD., Staines, DM., Dwinell, M., Shimoyama, M., Laulederkind, S., Hayman, T., Wang, S-., Petri, V., Lowry, T., D'Eustachio, P., Matthews, L., Amundsen, CD., Balakrishnan, R., Binkley, G., Cherry, JM., Christie, KR., Costanzo, MC., Dwight, SS., Engel, SR., Fisk, DG., Hirschman, JE., Hitz, BC., Hong, EL., Karra, K., Krieger, CJ., Miyasato, SR., Nash, RS., Park, J., Skrzypek, MS., Weng, S., Wong, ED., Berardini, TZ., Li, D., Huala, E., Slonim, D., Wick, H., Thomas, P., Chan, J., Kishore, R., Sternberg, P., Van Auken, K., Howe, D., and Westerfield, M.

Subjects
Quality Control, 0303 health sciences, media_common.quotation_subject, Databases, Genetic, Molecular Sequence Annotation/standards, Vocabulary, Controlled, Inference, Molecular Sequence Annotation, Articles, Biology, Ontology (information science), World Wide Web, Open Biomedical Ontologies, 03 medical and health sciences, Annotation, 0302 clinical medicine, Resource (project management), Controlled vocabulary, Genetics, Social media, Function (engineering), 030217 neurology & neurosurgery, 030304 developmental biology, media_common
Abstract

The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

Published
2011
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39. The Universal Protein Resource (UniProt) in 2010

Author

Elisabeth Gasteiger, Amos Bairoch, John Garavelli, Julius Jacobsen, Lionel Breuza, Rachael Huntley, Rolf Apweiler, Christian J. A. SIGRIST, Rebecca Foulger, Jerven Bolleman, Raja Mazumder, Ivo Pedruzzi, Florence Jungo, Anaïs Mottaz, Michael Tognolli, Emmanuel Boutet, Claire O'Donovan, Edward Turner, Sandra Orchard, Patrick Masson, Peter McGarvey, Nicole Redaschi, Sébastien Géhant, Michele Magrane, Anne Estreicher, Alan Bridge, Michel Schneider, Daniel Barrell, Benoit Bely, Anne Morgat, Sylvain Poux, Petra Langendijk-Genevaux, Maria-Jesus Martin, Catherine Rivoire, Elisabeth Coudert, Rasko Leinonen, Cecilia Arighi, UniProt Consortium, Apweiler, R., Martin, MJ., O'Donovan, C., Magrane, M., Alam-Faruque, Y., Antunes, R., Barrell, D., Bely, B., Bingley, M., Binns, D., Bower, L., Browne, P., Chan, WM., Dimmer, E., Eberhardt, R., Fedotov, A., Foulger, R., Garavelli, J., Huntley, R., Jacobsen, J., Kleen, M., Laiho, K., Leinonen, R., Legge, D., Lin, Q., Liu, W., Luo, J., Orchard, S., Patient, S., Poggioli, D., Pruess, M., Corbett, M., di Martino, G., Donnelly, M., van Rensburg, P., Bairoch, A., Bougueleret, L., Xenarios, I., Altairac, S., Auchincloss, A., Argoud-Puy, G., Axelsen, K., Baratin, D., Blatter, MC., Boeckmann, B., Bolleman, J., Bollondi, L., Boutet, E., Quintaje, SB., Breuza, L., Bridge, A., deCastro, E., Ciapina, L., Coral, D., Coudert, E., Cusin, I., Delbard, G., Doche, M., Dornevil, D., Roggli, PD., Duvaud, S., Estreicher, A., Famiglietti, L., Feuermann, M., Gehant, S., Farriol-Mathis, N., Ferro, S., Gasteiger, E., Gateau, A., Gerritsen, V., Gos, A., Gruaz-Gumowski, N., Hinz, U., Hulo, C., Hulo, N., James, J., Jimenez, S., Jungo, F., Kappler, T., Keller, G., Lachaize, C., Lane-Guermonprez, L., Langendijk-Genevaux, P., Lara, V., Lemercier, P., Lieberherr, D., de Oliveira Lima, T., Mangold, V., Martin, X., Masson, P., Moinat, M., Morgat, A., Mottaz, A., Paesano, S., Pedruzzi, I., Pilbout, S., Pillet, V., Poux, S., Pozzato, M., Redaschi, N., Rivoire, C., Roechert, B., Schneider, M., Sigrist, C., Sonesson, K., Staehli, S., Stanley, E., Stutz, A., Sundaram, S., Tognolli, M., Verbregue, L., Veuthey, AL., Yip, L., Zuletta, L., Wu, C., Arighi, C., Arminski, L., Barker, W., Chen, C., Chen, Y., Hu, ZZ., Huang, H., Mazumder, R., McGarvey, P., Natale, DA., Nchoutmboube, J., Petrova, N., Subramanian, N., Suzek, BE., Ugochukwu, U., Vasudevan, S., Vinayaka, CR., Yeh, LS., and Zhang, J.

Subjects
Proteomics, Internet, 0303 health sciences, Proteome, 030302 biochemistry & molecular biology, Computational Biology, Information Storage and Retrieval, Genome, Viral, Articles, Europe, 03 medical and health sciences, Algorithms, Animals, Computational Biology/methods, Computational Biology/trends, Databases, Nucleic Acid, Databases, Protein, Genome, Fungal, Humans, Information Storage and Retrieval/methods, Protein Isoforms, Software, Genetics, 030304 developmental biology
Abstract

The primary mission of UniProt is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 3 weeks and can be accessed online for searches or download at http://www.uniprot.org.

Published
2009
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40. Ongoing and future developments at the Universal Protein Resource

Author

Vicente Lara, Arnaud Gos, Nikolas Pontikos, L. Bollondi, L Famiglietti, Barker Wc, Chuming Chen, Michael Tognolli, Patrick Masson, Salvo Paesano, Tony Sawford, Sandrine Pilbout, Jerven Bolleman, Jian Zhang, S Staehli, Elisabeth Gasteiger, David Binns, Ursula Hinz, Nadine Gruaz-Gumowski, Chantal Hulo, Maria Jesus Martin, M. Corbett, Veuthey Al, Paul Browne, Guillaume Keller, S. Jimenez, Roberts Nv, Brigitte Boeckmann, Natale Da, Suzek Be, Edward Turner, T. Kappler, Steven Rosanoff, Leslie Arminski, Florence Jungo, M Donnelly, P. Dubey, Ruth Y. Eberhardt, Lydie Bougueleret, Vivienne Baillie Gerritsen, Maria Victoria Schneider, Lionel Breuza, Isabelle Cusin, Delphine Baratin, E. Dimmer, Rachael P. Huntley, Julius O.B. Jacobsen, Arighi Cn, Quintaje Sb, Alan Bridge, Nicole Redaschi, Shyamala Sundaram, Peter B. McGarvey, Rebecca E. Foulger, Duncan Legge, Andre Stutz, Monica Pozzato, Raja Mazumder, Anne Morgat, M Doche, K Sonesson, Anne Estreicher, Blatter Mc, Manuela Pruess, Serenella Ferro, Séverine Duvaud, F. Fazzini, Rolf Apweiler, Natarajan Tg, E. Stanley, M Feuermann, Claire O'Donovan, J. James, Ivo Pedruzzi, Castro Lg, Wu Ch, M Bingley, W Liu, He Huang, Bernd Roechert, Laure Verbregue, Elisabeth Coudert, Ioannis Xenarios, M. Moinat, Sandra Orchard, P Lemercier, Damien Lieberherr, Ricardo Antunes, Ghislaine Argoud-Puy, Sebastien Gehant, Qiang Wang, Klemens Pichler, S. Patient, Nicolas Hulo, Diego Poggioli, J. Nchoutmboube, Emmanuel Boutet, H. Sehra, Chan Wm, Yasmin Alam-Faruque, M. Kleen, Van Rensburg P, Kati Laiho, Kristian B. Axelsen, John S. Garavelli, Christian J. A. Sigrist, Amos Marc Bairoch, Yongxing Chen, A. Fedotov, Yeh Ls, Sylvain Poux, Lynette Bower, Quan Lin, Daniel Barrell, Benoit Bely, U. Ugochukwu, Xavier D. Martin, Catherine Rivoire, E. Decastro, Jie Luo, Alain Gateau, Michele Magrane, Dolnide Dornevil, Vinayaka Cr, Andrea H. Auchincloss, Yuqi Wang, An algorithmic view on genomes, cells, and environments (BAMBOO), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), Institut de Microélectronique, Electromagnétisme et Photonique - Laboratoire d'Hyperfréquences et Caractérisation (IMEP-LAHC), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Institut de minéralogie et de physique des milieux condensés (IMPMC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lausanne (UNIL), Université Pierre et Marie Curie - Paris 6 (UPMC)-IPG PARIS-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), UniProt Consortium, Apweiler, R., Martin, MJ., O'Donovan, C., Magrane, M., Alam-Faruque, Y., Antunes, R., Barrell, D., Bely, B., Bingley, M., Binns, D., Bower, L., Browne, P., Chan, WM., Dimmer, E., Eberhardt, R., Fazzini, F., Fedotov, A., Foulger, R., Garavelli, J., Castro, LG., Huntley, R., Jacobsen, J., Kleen, M., Laiho, K., Legge, D., Lin, Q., Liu, W., Luo, J., Orchard, S., Patient, S., Pichler, K., Poggioli, D., Pontikos, N., Pruess, M., Rosanoff, S., Sawford, T., Sehra, H., Turner, E., Corbett, M., Donnelly, M., van Rensburg, P., Xenarios, I., Bougueleret, L., Auchincloss, A., Argoud-Puy, G., Axelsen, K., Bairoch, A., Baratin, D., Blatter, MC., Boeckmann, B., Bolleman, J., Bollondi, L., Boutet, E., Quintaje, SB., Breuza, L., Bridge, A., deCastro, E., Coudert, E., Cusin, I., Doche, M., Dornevil, D., Duvaud, S., Estreicher, A., Famiglietti, L., Feuermann, M., Gehant, S., Ferro, S., Gasteiger, E., Gateau, A., Gerritsen, V., Gos, A., Gruaz-Gumowski, N., Hinz, U., Hulo, C., Hulo, N., James, J., Jimenez, S., Jungo, F., Kappler, T., Keller, G., Lara, V., Lemercier, P., Lieberherr, D., Martin, X., Masson, P., Moinat, M., Morgat, A., Paesano, S., Pedruzzi, I., Pilbout, S., Poux, S., Pozzato, M., Redaschi, N., Rivoire, C., Roechert, B., Schneider, M., Sigrist, C., Sonesson, K., Staehli, S., Stanley, E., Stutz, A., Sundaram, S., Tognolli, M., Verbregue, L., Veuthey, AL., Wu, CH., Arighi, CN., Arminski, L., Barker, WC., Chen, C., Chen, Y., Dubey, P., Huang, H., Mazumder, R., McGarvey, P., Natale, DA., Natarajan, TG., Nchoutmboube, J., Roberts, NV., Suzek, BE., Ugochukwu, U., Vinayaka, CR., Wang, Q., Wang, Y., Yeh, LS., and Zhang, J.

Subjects
Proteomics, 0303 health sciences, Sequence database, 030302 biochemistry & molecular biology, Proteins, Articles, Computational biology, Biology, Bioinformatics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Systems Integration, Universal Protein Resource, 03 medical and health sciences, Information resource, Protein sequencing, Sequence Analysis, Protein, Metagenomics, UniProt Knowledgebase, Genetics, Databases, Protein/trends, Proteins/chemistry, Proteins/genetics, UniProt, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Databases, Protein, 030304 developmental biology
Abstract

International audience; The primary mission of Universal Protein Resource (UniProt) is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org.

Published
2011
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41. From protein sequences to 3D-structures and beyond: the example of the UniProt knowledgebase

Author

Elisabeth Gasteiger, Amos Bairoch, John Garavelli, Julius Jacobsen, Lionel Breuza, Rachael Huntley, Rolf Apweiler, Christian J. A. SIGRIST, Rebecca Foulger, Raja Mazumder, Ivo Pedruzzi, Florence Jungo, Anaïs Mottaz, Michael Tognolli, Emmanuel Boutet, Claire O'Donovan, Edward Turner, Sandra Orchard, Patrick Masson, Peter McGarvey, Nicole Redaschi, Sébastien Géhant, Michele Magrane, Anne Estreicher, Alan Bridge, Daniel Barrell, Benoit Bely, Anne Morgat, Sylvain Poux, Petra Langendijk-Genevaux, Maria-Jesus Martin, Catherine Rivoire, Elisabeth Coudert, Rasko Leinonen, Cecilia Arighi, UniProt Consortium, Apweiler, R., Martin, MJ., O'Donovan, C., Magrane, M., Alam-Faruque, Y., Antunes, R., Barrell, D., Bely, B., Bingley, M., Binns, D., Bower, L., Browne, P., Chan, WM., Dimmer, E., Eberhardt, R., Fedotov, A., Foulger, R., Garavelli, J., Huntley, R., Jacobsen, J., Kleen, M., Laiho, K., Leinonen, R., Legge, D., Lin, Q., Liu, W., Luo, J., Orchard, S., Patient, S., Poggioli, D., Pruess, M., Corbett, M., di Martino, G., Donnelly, M., van Rensburg, P., Bairoch, A., Bougueleret, L., Xenarios, I., Altairac, S., Auchincloss, A., Argoud-Puy, G., Axelsen, K., Baratin, D., Blatter, MC., Boeckmann, B., Bolleman, J., Bollondi, L., Boutet, E., Quintaje, SB., Breuza, L., Bridge, A., de Castro, E., Ciapina, L., Coral, D., Coudert, E., Cusin, I., David, F., Delbard, G., Doche, M., Dornevil, D., Roggli, PD., Duvaud, S., Estreicher, A., Famiglietti, L., Feuermann, M., Gehant, S., Farriol-Mathis, N., Ferro, S., Gasteiger, E., Gateau, A., Gerritsen, V., Gos, A., Gruaz-Gumowski, N., Hinz, U., Hulo, C., Hulo, N., James, J., Jimenez, S., Jungo, F., Kappler, T., Keller, G., Lachaize, C., Lane-Guermonprez, L., Langendijk-Genevaux, P., Lara, V., Lemercier, P., Lieberherr, D., Lima Tde, O., Mangold, V., Martin, X., Masson, P., Moinat, M., Morgat, A., Mottaz, A., Paesano, S., Pedruzzi, I., Pilbout, S., Pillet, V., Poux, S., Pozzato, M., Redaschi, N., Rivoire, C., Roechert, B., Schneider, M., Sigrist, C., Sonesson, K., Staehli, S., Stanley, E., Stutz, A., Sundaram, S., Tognolli, M., Verbregue, L., Veuthey, AL., Yip, L., Zuletta, L., Wu, C., Arighi, C., Arminski, L., Barker, W., Chen, C., Chen, Y., Hu, ZZ., Huang, H., Mazumder, R., McGarvey, P., Natale, DA., Nchoutmboube, J., Petrova, N., Subramanian, N., Suzek, BE., Ugochukwu, U., Vasudevan, S., Vinayaka, CR., Yeh, LS., and Zhang, J.

Subjects
Proteomics, Binding Sites, Catalytic Domain, Databases, Protein, Knowledge Bases, Protein Conformation, Proteins/chemistry, Proteins/genetics, Sequence Analysis, Protein, Sequence analysis, Computer science, Annotation, Structural genomics, Context (language use), Review, Computational biology, Bioinformatics, 03 medical and health sciences, Cellular and Molecular Neuroscience, Protein 3D-structure, Swiss-Prot, Molecular Biology, UniProtKB, 030304 developmental biology, Pharmacology, 0303 health sciences, 030302 biochemistry & molecular biology, Proteins, Experimental data, Data flood, Cell Biology, Knowledgebase, Data access, Molecular Medicine, UniProt
Abstract

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website ( http://www.uniprot.org/ ). It also evokes precautions that are necessary for successful predictions and extrapolations.

Published
2010

43. Evidence to inform biopharmaceutical policy: call for research on the impact of public policies on investment in drug development.

Author

Barbosu S, Axelsen K, and Ezell S

Abstract

This paper highlights the pressing need for updated, robust evidence to inform biopharmaceutical policy, particularly in light of recent initiatives such as the Inflation Reduction Act. Current estimates that inform such policies, including those from the Congressional Budget Office, rely on outdated data and models that fail to fully capture the complexities of modern investment decisions or the broader impact of policies on drug development in areas like oncology, rare diseases, and vaccines. Understanding how expectations of financial returns influence investment in all stages of drug development is critical for evaluating these policies' long-term effects on innovation. This piece reviews the current evidence on the relationship between financial returns and research and development investment and considers how this evidence is being used to shape biopharmaceutical policy. It also highlights gaps in data and methodology, emphasizing the need for better models that reflect real-world trade-offs, investment risks, and therapeutic area-specific impacts. Finally, this paper calls for improved access to federal and private data to better inform evidence-based policymaking and to study policy impact on investments in the next generation of medicines, particularly in emerging fields like gene and cell therapies, where the implications of policy decisions are not yet fully understood., Competing Interests: Conflicts of interest Please see ICMJE form(s) for author conflicts of interest. These have been provided as supplementary materials., (© The Author(s) 2024. Published by Oxford University Press on behalf of Project HOPE - The People-To-People Health Foundation, Inc.)

Published
2024
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44. EnzChemRED, a rich enzyme chemistry relation extraction dataset.

Author

Lai PT, Coudert E, Aimo L, Axelsen K, Breuza L, de Castro E, Feuermann M, Morgat A, Pourcel L, Pedruzzi I, Poux S, Redaschi N, Rivoire C, Sveshnikova A, Wei CH, Leaman R, Luo L, Lu Z, and Bridge A

Subjects
PubMed, Databases, Protein, Knowledge Bases, Enzymes chemistry, Natural Language Processing
Abstract

Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as (large) language models that can assist enzyme curation. EnzChemRED consists of 1,210 expert curated PubMed abstracts where enzymes and the chemical reactions they catalyze are annotated using identifiers from the protein knowledgebase UniProtKB and the chemical ontology ChEBI. We show that fine-tuning language models with EnzChemRED significantly boosts their ability to identify proteins and chemicals in text (86.30% F 1 score) and to extract the chemical conversions (86.66% F 1 score) and the enzymes that catalyze those conversions (83.79% F 1 score). We apply our methods to abstracts at PubMed scale to create a draft map of enzyme functions in literature to guide curation efforts in UniProtKB and the reaction knowledgebase Rhea., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Published
2024
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45. The Gene Ontology knowledgebase in 2023.

Author

Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, Feuermann M, Gaudet P, Harris NL, Hill DP, Lee R, Mi H, Moxon S, Mungall CJ, Muruganugan A, Mushayahama T, Sternberg PW, Thomas PD, Van Auken K, Ramsey J, Siegele DA, Chisholm RL, Fey P, Aspromonte MC, Nugnes MV, Quaglia F, Tosatto S, Giglio M, Nadendla S, Antonazzo G, Attrill H, Dos Santos G, Marygold S, Strelets V, Tabone CJ, Thurmond J, Zhou P, Ahmed SH, Asanitthong P, Luna Buitrago D, Erdol MN, Gage MC, Ali Kadhum M, Li KYC, Long M, Michalak A, Pesala A, Pritazahra A, Saverimuttu SCC, Su R, Thurlow KE, Lovering RC, Logie C, Oliferenko S, Blake J, Christie K, Corbani L, Dolan ME, Drabkin HJ, Hill DP, Ni L, Sitnikov D, Smith C, Cuzick A, Seager J, Cooper L, Elser J, Jaiswal P, Gupta P, Jaiswal P, Naithani S, Lera-Ramirez M, Rutherford K, Wood V, De Pons JL, Dwinell MR, Hayman GT, Kaldunski ML, Kwitek AE, Laulederkind SJF, Tutaj MA, Vedi M, Wang SJ, D'Eustachio P, Aimo L, Axelsen K, Bridge A, Hyka-Nouspikel N, Morgat A, Aleksander SA, Cherry JM, Engel SR, Karra K, Miyasato SR, Nash RS, Skrzypek MS, Weng S, Wong ED, Bakker E, Berardini TZ, Reiser L, Auchincloss A, Axelsen K, Argoud-Puy G, Blatter MC, Boutet E, Breuza L, Bridge A, Casals-Casas C, Coudert E, Estreicher A, Livia Famiglietti M, Feuermann M, Gos A, Gruaz-Gumowski N, Hulo C, Hyka-Nouspikel N, Jungo F, Le Mercier P, Lieberherr D, Masson P, Morgat A, Pedruzzi I, Pourcel L, Poux S, Rivoire C, Sundaram S, Bateman A, Bowler-Barnett E, Bye-A-Jee H, Denny P, Ignatchenko A, Ishtiaq R, Lock A, Lussi Y, Magrane M, Martin MJ, Orchard S, Raposo P, Speretta E, Tyagi N, Warner K, Zaru R, Diehl AD, Lee R, Chan J, Diamantakis S, Raciti D, Zarowiecki M, Fisher M, James-Zorn C, Ponferrada V, Zorn A, Ramachandran S, Ruzicka L, and Westerfield M

Subjects
Gene Ontology, Molecular Sequence Annotation, Computational Biology, Proteins genetics, Databases, Genetic
Abstract

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

Published
2023
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46. Enzyme annotation in UniProtKB using Rhea.

Author

Morgat A, Lombardot T, Coudert E, Axelsen K, Neto TB, Gehant S, Bansal P, Bolleman J, Gasteiger E, de Castro E, Baratin D, Pozzato M, Xenarios I, Poux S, Redaschi N, and Bridge A

Subjects
Animals, Databases, Protein, Knowledge Bases, Rheiformes
Abstract

Motivation: To provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ChEBI (Chemical Entities of Biological Interest) ontology., Results: We replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that Rhea and ChEBI provide., Availability and Implementation: UniProtKB at https://www.uniprot.org; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/; Rhea at https://www.rhea-db.org., (© The Author(s) 2019. Published by Oxford University Press.)

Published
2020
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47. Payer and Pharmaceutical Manufacturer Considerations for Outcomes-Based Agreements in the United States.

Author

Brown JD, Sheer R, Pasquale M, Sudharshan L, Axelsen K, Subedi P, Wiederkehr D, Brownfield F, and Kamal-Bahl S

Subjects
Cost-Benefit Analysis, Evidence-Based Medicine, Humans, Marketing of Health Services economics, Outcome Assessment, Health Care, United States, Anticholesteremic Agents economics, Drug Industry economics, Hypercholesterolemia drug therapy, Models, Economic, Risk Sharing, Financial economics
Abstract

Background: Considerable interest exists among health care payers and pharmaceutical manufacturers in designing outcomes-based agreements (OBAs) for medications for which evidence on real-world effectiveness is limited at product launch., Objectives: To build hypothetical OBA models in which both payer and manufacturer can benefit., Methods: Models were developed for a hypothetical hypercholesterolemia OBA, in which the OBA was assumed to increase market access for a newly marketed medication. Fixed inputs were drug and outcome event costs from the literature over a 1-year OBA period. Model estimates were developed using a range of inputs for medication effectiveness, medical cost offsets, and the treated population size. Positive or negative feedback to the manufacturer was incorporated on the basis of expectations of drug performance through changes in the reimbursement level. Model simulations demonstrated that parameters had the greatest impact on payer cost and manufacturer reimbursement., Results: Models suggested that changes in the size of the population treated and drug effectiveness had the largest influence on reimbursement and costs. Despite sharing risk for potential product underperformance, manufacturer reimbursement increased relative to having no OBA, if the OBA improved market access for the new product. Although reduction in medical costs did not fully offset the cost of the medication, the payer could still save on net costs per patient relative to having no OBA by tying reimbursement to drug effectiveness., Conclusions: Pharmaceutical manufacturers and health care payers have demonstrated interest in OBAs, and under a certain set of assumptions both may benefit., (Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)

Published
2018
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49. Real-World Data: Policy Issues Regarding their Access and Use.

Author

Basu A, Axelsen K, Grabowski DC, Meltzer DO, Polsky D, Ridley DB, Wiederkehr D, and Philipson TJ

Subjects
Access to Information legislation & jurisprudence, Confidentiality legislation & jurisprudence, Data Accuracy, Decision Making, Organizational, Humans, Public Health statistics & numerical data, Randomized Controlled Trials as Topic statistics & numerical data, Delivery of Health Care statistics & numerical data, Health Policy, Information Dissemination legislation & jurisprudence
Abstract

As real-world data (RWD) in health care begin to cross over to the Big Data realms, a panel of health economists was gathered to establish how well the current US policy environment further the goals of RWD and, if not, what can be done to improve matters. This report summarizes these discussions spanning the current US landscape of RWD availability and usefulness, private versus public development of RWD assets, the current inherent bias in terms of access to RWD, and guiding principles in providing quality assessments of new RWD studies. Three main conclusions emerge: (1) a business case is often required to incentivize investments in RWD assets. However, access restrictions for public data assets have failed to generate a proper market for these data and hence may have led to an underinvestment of public RWDs; (2) Very weak empirical evidence exist on for-profit entities misusing public RWD data entities to further their own agendas, which is the basis for supporting access restrictions of public RWD data; and (3) perhaps developing standardized metrics that could flag misuse of RWDs in an efficient way could help quell some of the fear of sharing public RWD assets with for-profit entities. It is hoped that these discussions and conclusions would pave the way for more rigorous and timely debates on the greater availability and accessibility of RWD assets.

Published
2016
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50. Optimizing the leveraging of real-world data to improve the development and use of medicines.

Author

Berger ML, Lipset C, Gutteridge A, Axelsen K, Subedi P, and Madigan D

Subjects
Comparative Effectiveness Research trends, Delivery of Health Care trends, Humans, Comparative Effectiveness Research methods, Delivery of Health Care methods, Health Policy trends, Information Dissemination methods, Pharmaceutical Preparations administration & dosage, Research Personnel trends
Abstract

Health research, including health outcomes and comparative effectiveness research, is on the cusp of a golden era of access to digitized real-world data, catalyzed by the adoption of electronic health records and the integration of clinical and biological information with other data. This era promises more robust insights into what works in health care. Several barriers, however, will need to be addressed if the full potential of these new data are fully realized; these will involve both policy solutions and stakeholder cooperation. Although a number of these issues have been widely discussed, we focus on the one we believe is the most important-the facilitation of greater openness among public and private stakeholders to collaboration, connecting information and data sharing, with the goal of making robust and complete data accessible to all researchers. In this way, we can better understand the consequences of health care delivery, improve the effectiveness and efficiency of health care systems, and develop advancements in health technologies. Early real-world data initiatives illustrate both potential and the need for future progress, as well as the essential role of collaboration and data sharing. Health policies critical to progress will include those that promote open source data standards, expand access to the data, increase data capture and connectivity, and facilitate communication of findings., (Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)

Published
2015
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