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1. GA4GH: International policies and standards for data sharing across genomic research and healthcare

Author

Rehm, Heidi L., Page, Angela J.H., Smith, Lindsay, Adams, Jeremy B., Alterovitz, Gil, Babb, Lawrence J., Barkley, Maxmillian P., Baudis, Michael, Beauvais, Michael J.S., Beck, Tim, Beckmann, Jacques S., Beltran, Sergi, Bernick, David, Bernier, Alexander, Bonfield, James K., Boughtwood, Tiffany F., Bourque, Guillaume, Bowers, Sarion R., Brookes, Anthony J., Brudno, Michael, Brush, Matthew H., Bujold, David, Burdett, Tony, Buske, Orion J., Cabili, Moran N., Cameron, Daniel L., Carroll, Robert J., Casas-Silva, Esmeralda, Chakravarty, Debyani, Chaudhari, Bimal P., Chen, Shu Hui, Cherry, J. Michael, Chung, Justina, Cline, Melissa, Clissold, Hayley L., Cook-Deegan, Robert M., Courtot, Mélanie, Cunningham, Fiona, Cupak, Miro, Davies, Robert M., Denisko, Danielle, Doerr, Megan J., Dolman, Lena I., Dove, Edward S., Dursi, L. Jonathan, Dyke, Stephanie O.M., Eddy, James A., Eilbeck, Karen, Ellrott, Kyle P., Fairley, Susan, Fakhro, Khalid A., Firth, Helen V., Fitzsimons, Michael S., Fiume, Marc, Flicek, Paul, Fore, Ian M., Freeberg, Mallory A., Freimuth, Robert R., Fromont, Lauren A., Fuerth, Jonathan, Gaff, Clara L., Gan, Weiniu, Ghanaim, Elena M., Glazer, David, Green, Robert C., Griffith, Malachi, Griffith, Obi L., Grossman, Robert L., Groza, Tudor, Guidry Auvil, Jaime M., Guigó, Roderic, Gupta, Dipayan, Haendel, Melissa A., Hamosh, Ada, Hansen, David P., Hart, Reece K., Hartley, Dean Mitchell, Haussler, David, Hendricks-Sturrup, Rachele M., Ho, Calvin W.L., Hobb, Ashley E., Hoffman, Michael M., Hofmann, Oliver M., Holub, Petr, Hsu, Jacob Shujui, Hubaux, Jean-Pierre, Hunt, Sarah E., Husami, Ammar, Jacobsen, Julius O., Jamuar, Saumya S., Janes, Elizabeth L., Jeanson, Francis, Jené, Aina, Johns, Amber L., Joly, Yann, Jones, Steven J.M., Kanitz, Alexander, Kato, Kazuto, Keane, Thomas M., Kekesi-Lafrance, Kristina, Kelleher, Jerome, Kerry, Giselle, Khor, Seik-Soon, Knoppers, Bartha M., Konopko, Melissa A., Kosaki, Kenjiro, Kuba, Martin, Lawson, Jonathan, Leinonen, Rasko, Li, Stephanie, Lin, Michael F., Linden, Mikael, Liu, Xianglin, Liyanage, Isuru Udara, Lopez, Javier, Lucassen, Anneke M., Lukowski, Michael, Mann, Alice L., Marshall, John, Mattioni, Michele, Metke-Jimenez, Alejandro, Middleton, Anna, Milne, Richard J., Molnár-Gábor, Fruzsina, Mulder, Nicola, Munoz-Torres, Monica C., Nag, Rishi, Nakagawa, Hidewaki, Nasir, Jamal, Navarro, Arcadi, Nelson, Tristan H., Niewielska, Ania, Nisselle, Amy, Niu, Jeffrey, Nyrönen, Tommi H., O’Connor, Brian D., Oesterle, Sabine, Ogishima, Soichi, Ota Wang, Vivian, Paglione, Laura A.D., Palumbo, Emilio, Parkinson, Helen E., Philippakis, Anthony A., Pizarro, Angel D., Prlic, Andreas, Rambla, Jordi, Rendon, Augusto, Rider, Renee A., Robinson, Peter N., Rodarmer, Kurt W., Rodriguez, Laura Lyman, Rubin, Alan F., Rueda, Manuel, Rushton, Gregory A., Ryan, Rosalyn S., Saunders, Gary I., Schuilenburg, Helen, Schwede, Torsten, Scollen, Serena, Senf, Alexander, Sheffield, Nathan C., Skantharajah, Neerjah, Smith, Albert V., Sofia, Heidi J., Spalding, Dylan, Spurdle, Amanda B., Stark, Zornitza, Stein, Lincoln D., Suematsu, Makoto, Tan, Patrick, Tedds, Jonathan A., Thomson, Alastair A., Thorogood, Adrian, Tickle, Timothy L., Tokunaga, Katsushi, Törnroos, Juha, Torrents, David, Upchurch, Sean, Valencia, Alfonso, Guimera, Roman Valls, Vamathevan, Jessica, Varma, Susheel, Vears, Danya F., Viner, Coby, Voisin, Craig, Wagner, Alex H., Wallace, Susan E., Walsh, Brian P., Williams, Marc S., Winkler, Eva C., Wold, Barbara J., Wood, Grant M., Woolley, J. Patrick, Yamasaki, Chisato, Yates, Andrew D., Yung, Christina K., Zass, Lyndon J., Zaytseva, Ksenia, Zhang, Junjun, Goodhand, Peter, North, Kathryn, and Birney, Ewan

Published
2021
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2. Integrating genomic information with protein sequence and 3D atomic level structure at the RCSB protein data bank

Author

Prlic, Andreas, Kalro, Tara, Bhattacharya, Roshni, Christie, Cole, Burley, Stephen K, and Rose, Peter W

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Genetics, Biological Sciences, Networking and Information Technology R&D (NITRD), Human Genome, Bioengineering, Biotechnology, Underpinning research, 1.4 Methodologies and measurements, Generic health relevance, Amino Acid Sequence, Computational Biology, Computer Graphics, Databases, Protein, Genomics, Humans, Protein Conformation, Software, User-Computer Interface, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

The Protein Data Bank (PDB) now contains more than 120,000 three-dimensional (3D) structures of biological macromolecules. To allow an interpretation of how PDB data relates to other publicly available annotations, we developed a novel data integration platform that maps 3D structural information across various datasets. This integration bridges from the human genome across protein sequence to 3D structure space. We developed novel software solutions for data management and visualization, while incorporating new libraries for web-based visualization using SVG graphics.Availability and implementationThe new views are available from http://www.rcsb.org and software is available from https://github.com/rcsb/.Contactandreas.prlic@rcsb.orgSupplementary information: Supplementary data are available at Bioinformatics online.

Published
2016

3. Pre-calculated protein structure alignments at the RCSB PDB website.

Author

Prlic, Andreas, Bliven, Spencer, Rose, Peter W, Bluhm, Wolfgang F, Bizon, Chris, Godzik, Adam, and Bourne, Philip E

Subjects
Proteins, Amino Acid Sequence, Structural Homology, Protein, Algorithms, Internet, Software, User-Computer Interface, Databases, Protein, Structural Homology, Protein, Databases, Bioinformatics, Mathematical Sciences, Biological Sciences, Information and Computing Sciences
Abstract

SummaryWith the continuous growth of the RCSB Protein Data Bank (PDB), providing an up-to-date systematic structure comparison of all protein structures poses an ever growing challenge. Here, we present a comparison tool for calculating both 1D protein sequence and 3D protein structure alignments. This tool supports various applications at the RCSB PDB website. First, a structure alignment web service calculates pairwise alignments. Second, a stand-alone application runs alignments locally and visualizes the results. Third, pre-calculated 3D structure comparisons for the whole PDB are provided and updated on a weekly basis. These three applications allow users to discover novel relationships between proteins available either at the RCSB PDB or provided by the user.Availability and implementationA web user interface is available at http://www.rcsb.org/pdb/workbench/workbench.do. The source code is available under the LGPL license from http://www.biojava.org. A source bundle, prepared for local execution, is available from http://source.rcsb.orgContactandreas@sdsc.edu; pbourne@ucsd.edu.

Published
2010

5. The Distributed Annotation System for Integration of Biological Data

Author

Prlić, Andreas, Birney, Ewan, Cox, Tony, Down, Thomas A., Finn, Rob, Gräf, Stefan, Jackson, David, Kähäri, Andreas, Kulesha, Eugene, Pettett, Roger, Smith, James, Stalker, Jim, Hubbard, Tim J. P., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Leser, Ulf, editor, Naumann, Felix, editor, and Eckman, Barbara, editor

Published
2006
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6. Trendspotting in the Protein Data Bank

Author

Berman, Helen M., Coimbatore Narayanan, Buvaneswari, Costanzo, Luigi Di, Dutta, Shuchismita, Ghosh, Sutapa, Hudson, Brian P., Lawson, Catherine L., Peisach, Ezra, Prlić, Andreas, Rose, Peter W., Shao, Chenghua, Yang, Huanwang, Young, Jasmine, and Zardecki, Christine

Published
2013
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7. GA4GH: International policies and standards for data sharing across genomic research and healthcare

Author

Abigail Wexner Research Institute, Academy of Finland, Medical Research Future Fund, BioBank Japan, Canada Foundation for Innovation, Canadian Institutes of Health Research, European Commission, German Research Foundation, Genome Canada, Google, Howard Hughes Medical Institute, Instituto de Salud Carlos III, Japan Agency for Medical Research and Development, Mayo Clinic, Fundación la Caixa, Ministère de l'Économie et de l'Innovation (Québec), Monarch Initiative, National Human Genome Research Institute (US), National University of Singapore, Agency for Science, Technology and Research A*STAR (Singapore), National Health and Medical Research Council (Australia), National Institutes of Health (US), National Institute of General Medical Sciences (US), Swiss Institute of Bioinformatics, State Secretariat for Education, Research and Innovation (Switzerland), Terry Fox Research Institute, Canada Research Chairs, European Molecular Biology Laboratory, Ministry of Research, Innovation and Science (Ontario), Ontario Genomics Institute, Natural Sciences and Engineering Research Council of Canada, Wellcome Trust, National Taiwan University, Rehm, Heidi L., Page, Angela, Smith, Lindsay, Adams, Jeremy B., Alterovitz, Gil, Babb, Lawrence J., Barkley, Maxmillian P., Baudis, Michael, Beauvais, Michael J. S., Beck, Tim, Beckmann, Jacques S., Varma, Susheel, Vears, Danya F., Viner, Coby, Voisin, Craig, Wagner, Alex H., Wallace, Susan E., Walsh, Brian P., Williams, Marc S., Winkler, Eva C., Brudno, Michael, Kelleher, Jerome, Wold, Barbara J., Wood, Grant M., Woolley, J. Patrick, Yamasaki, Chisato, Yates, Andrew D., Yung, Christina K., Zass, Lyndon J., Zaytseva, Ksenia, Zhang, Junjun, Goodhand, Peter, Kerry, Giselle, Brush,Matthew H., North, Kathryn, Birney, Ewan, Bujold, David, Burdett, Tony, Buske, Orion J., Cabili, Moran N., Cameron, Daniel L., Carroll, Robert J., Casas-Silva, Esmeralda, Khor, Seik-Soon, Chakravarty, Debyani, Chaudhari, Bimal P., Chen, Shu Hui, Cherry, J. Michael, Chung, Justina, Cline, Melissa, Clissold, Hayley L., Cook-Deegan, Robert M., Courtot, Melanie, Cunningham, Fiona, Knoppers, Bartha M., Cupak, Miro, Davies, Robert M., Denisko, Danielle, Doerr, Megan J., Dolman, Lena I., Dove, Edward S., Dursi, Lewis Jonathan, Dyke, Stephanie O. M., Eddy, James A., Eilbeck, Karen, Konopko, Melissa A., Ellrott, Kyle P., Fairley, Susan, Fakhro, Khalid A., Firth, Helen V., Fitzsimons, Michael S., Fiume, Marc, Flicek, Paul, Fore, Ian M., Freeberg, Mallory A., Freimuth, Robert R., Kosaki, Kenjiro, Fromont, Lauren A., Fuerth, Jonathan, Gaff, Clara L., Gan, Weiniu, Ghanaim, Elena M., Glazer, David, Green, Robert C., Griffith, Malachi, Griffith, Obi L., Grossman, Robert L., Kuba, Martin, Groza, Tudor, Guidry Auvil, Jaime M., Guigó, Roderic, Gupta, Dipayan, Haendel, Melissa A., Hamosh, Ada, Hansen, David P., Hart, Reece K., Hartley, Dean Mitchell, Haussler, David, Lawson, Jonathan, Hendricks-Sturrup, Rachele M., Ho, Calvin W. L., Hobb, Ashley E., Hoffman, Michael M., Hofmann, Oliver M., Holub, Petr, Shujui Hsu, Jacob, Hubaux, Jean-Pierre, Hunt, Sarah E., Husami, Ammar, Leinonen, Rasko, Jacobsen, Julius O., Jamuar, Saumya S., Janes, Elizabeth L., Jeanson, Francis, Jene, Aina, Johns, Amber L., Joly, Yann, Jones, Steven J. M., Kanitz, Alexander, Kato, Kazuto, Li, Stephanie, Keane, Thomas M., Kekesi-Lafrance, Kristina, Beltran, Sergi, Lin, Michael F., Linden, Mikael, Liu, Xianglin, Udara Liyanage, Isuru, López, Javier, Lucassen, Anneke M., Lukowski, Michael, Mann, Alice L., Marshall, John, Mattioni, Michele, Bernick, David, Metke-Jiménez, Alejandro, Middleton, Anna, Milne, Richard J., Molnár-Gábor, Fruzsina, Mulder, Nicola, Muñoz-Torres, Mónica C., Nag, Rishi, Nakagawa, Hidewaki, Nasir, Jamal, Navarro, Arcadi, Bernier, Alexander, Nelson, Tristan H., Niewielska, Ania, Nisselle, Amy, Niu, Jeffrey, Nyrönen, Tommi H., O’Connor, Brian D., Oesterle, Sabine, Ogishima, Soichi, Wang, Vivian Ota, Paglione, Laura A. D., Bonfield, James K., Palumbo, Emilio, Parkinson, Helen E., Philippakis, Anthony A., Pizarro, Angel D., Prlic, Andreas, Rambla, Jordi, Rendon, Augusto, Rider, Renee A., Robinson, Peter N., Rodarmer, Kurt W., Boughtwood, Tiffany F., Lyman Rodríguez, Laura, Rubin, Alan F., Rueda, Manuel, Rushton, Gregory A., Ryan, Rosalyn S., Saunders, Gary I., Schuilenburg, Helen, Schwede, Torsten, Scollen, Serena, Senf, Alexander, Bourque, Guillaume, Sheffield, Nathan C., Skantharajah, Neerjah, Smith, Albert V., Sofia, Heidi J., Spalding, Dylan, Spurdle, Amanda B., Stark, Zornitza, Stein, Lincoln D., Suematsu, Makoto, Tan, Patrick, Bowers, Sarion R., Tedds, Jonathan A., Thomson, Alastair A., Thorogood, Adrian, Tickle, Timothy L., Tokunaga, Katsushi, Törnroos,Juha, Torrents, David, Upchurch, Sean, Valencia, Alfonso, Valls Guimera, Roman, Brookes, Anthony J., Vamathevan, Jessica, Abigail Wexner Research Institute, Academy of Finland, Medical Research Future Fund, BioBank Japan, Canada Foundation for Innovation, Canadian Institutes of Health Research, European Commission, German Research Foundation, Genome Canada, Google, Howard Hughes Medical Institute, Instituto de Salud Carlos III, Japan Agency for Medical Research and Development, Mayo Clinic, Fundación la Caixa, Ministère de l'Économie et de l'Innovation (Québec), Monarch Initiative, National Human Genome Research Institute (US), National University of Singapore, Agency for Science, Technology and Research A*STAR (Singapore), National Health and Medical Research Council (Australia), National Institutes of Health (US), National Institute of General Medical Sciences (US), Swiss Institute of Bioinformatics, State Secretariat for Education, Research and Innovation (Switzerland), Terry Fox Research Institute, Canada Research Chairs, European Molecular Biology Laboratory, Ministry of Research, Innovation and Science (Ontario), Ontario Genomics Institute, Natural Sciences and Engineering Research Council of Canada, Wellcome Trust, National Taiwan University, Rehm, Heidi L., Page, Angela, Smith, Lindsay, Adams, Jeremy B., Alterovitz, Gil, Babb, Lawrence J., Barkley, Maxmillian P., Baudis, Michael, Beauvais, Michael J. S., Beck, Tim, Beckmann, Jacques S., Varma, Susheel, Vears, Danya F., Viner, Coby, Voisin, Craig, Wagner, Alex H., Wallace, Susan E., Walsh, Brian P., Williams, Marc S., Winkler, Eva C., Brudno, Michael, Kelleher, Jerome, Wold, Barbara J., Wood, Grant M., Woolley, J. Patrick, Yamasaki, Chisato, Yates, Andrew D., Yung, Christina K., Zass, Lyndon J., Zaytseva, Ksenia, Zhang, Junjun, Goodhand, Peter, Kerry, Giselle, Brush,Matthew H., North, Kathryn, Birney, Ewan, Bujold, David, Burdett, Tony, Buske, Orion J., Cabili, Moran N., Cameron, Daniel L., Carroll, Robert J., Casas-Silva, Esmeralda, Khor, Seik-Soon, Chakravarty, Debyani, Chaudhari, Bimal P., Chen, Shu Hui, Cherry, J. Michael, Chung, Justina, Cline, Melissa, Clissold, Hayley L., Cook-Deegan, Robert M., Courtot, Melanie, Cunningham, Fiona, Knoppers, Bartha M., Cupak, Miro, Davies, Robert M., Denisko, Danielle, Doerr, Megan J., Dolman, Lena I., Dove, Edward S., Dursi, Lewis Jonathan, Dyke, Stephanie O. M., Eddy, James A., Eilbeck, Karen, Konopko, Melissa A., Ellrott, Kyle P., Fairley, Susan, Fakhro, Khalid A., Firth, Helen V., Fitzsimons, Michael S., Fiume, Marc, Flicek, Paul, Fore, Ian M., Freeberg, Mallory A., Freimuth, Robert R., Kosaki, Kenjiro, Fromont, Lauren A., Fuerth, Jonathan, Gaff, Clara L., Gan, Weiniu, Ghanaim, Elena M., Glazer, David, Green, Robert C., Griffith, Malachi, Griffith, Obi L., Grossman, Robert L., Kuba, Martin, Groza, Tudor, Guidry Auvil, Jaime M., Guigó, Roderic, Gupta, Dipayan, Haendel, Melissa A., Hamosh, Ada, Hansen, David P., Hart, Reece K., Hartley, Dean Mitchell, Haussler, David, Lawson, Jonathan, Hendricks-Sturrup, Rachele M., Ho, Calvin W. L., Hobb, Ashley E., Hoffman, Michael M., Hofmann, Oliver M., Holub, Petr, Shujui Hsu, Jacob, Hubaux, Jean-Pierre, Hunt, Sarah E., Husami, Ammar, Leinonen, Rasko, Jacobsen, Julius O., Jamuar, Saumya S., Janes, Elizabeth L., Jeanson, Francis, Jene, Aina, Johns, Amber L., Joly, Yann, Jones, Steven J. M., Kanitz, Alexander, Kato, Kazuto, Li, Stephanie, Keane, Thomas M., Kekesi-Lafrance, Kristina, Beltran, Sergi, Lin, Michael F., Linden, Mikael, Liu, Xianglin, Udara Liyanage, Isuru, López, Javier, Lucassen, Anneke M., Lukowski, Michael, Mann, Alice L., Marshall, John, Mattioni, Michele, Bernick, David, Metke-Jiménez, Alejandro, Middleton, Anna, Milne, Richard J., Molnár-Gábor, Fruzsina, Mulder, Nicola, Muñoz-Torres, Mónica C., Nag, Rishi, Nakagawa, Hidewaki, Nasir, Jamal, Navarro, Arcadi, Bernier, Alexander, Nelson, Tristan H., Niewielska, Ania, Nisselle, Amy, Niu, Jeffrey, Nyrönen, Tommi H., O’Connor, Brian D., Oesterle, Sabine, Ogishima, Soichi, Wang, Vivian Ota, Paglione, Laura A. D., Bonfield, James K., Palumbo, Emilio, Parkinson, Helen E., Philippakis, Anthony A., Pizarro, Angel D., Prlic, Andreas, Rambla, Jordi, Rendon, Augusto, Rider, Renee A., Robinson, Peter N., Rodarmer, Kurt W., Boughtwood, Tiffany F., Lyman Rodríguez, Laura, Rubin, Alan F., Rueda, Manuel, Rushton, Gregory A., Ryan, Rosalyn S., Saunders, Gary I., Schuilenburg, Helen, Schwede, Torsten, Scollen, Serena, Senf, Alexander, Bourque, Guillaume, Sheffield, Nathan C., Skantharajah, Neerjah, Smith, Albert V., Sofia, Heidi J., Spalding, Dylan, Spurdle, Amanda B., Stark, Zornitza, Stein, Lincoln D., Suematsu, Makoto, Tan, Patrick, Bowers, Sarion R., Tedds, Jonathan A., Thomson, Alastair A., Thorogood, Adrian, Tickle, Timothy L., Tokunaga, Katsushi, Törnroos,Juha, Torrents, David, Upchurch, Sean, Valencia, Alfonso, Valls Guimera, Roman, Brookes, Anthony J., and Vamathevan, Jessica

Abstract

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Published
2021

9. Analyzing the symmetrical arrangement of structural repeats in proteins with CE-Symm

Author

Bliven, Spencer E, Lafita, Aleix, Rose, Peter W, Capitani, Guido, Prlic, Andreas, Bourne, Philip, Bliven, Spencer E, Lafita, Aleix, Rose, Peter W, Capitani, Guido, Prlic, Andreas, and Bourne, Philip

Abstract

Many proteins fold into highly regular and repetitive three dimensional structures. The analysis of structural patterns and repeated elements is fundamental to understand protein function and evolution. We present recent improvements to the CE-Symm tool for systematically detecting and analyzing the internal symmetry and structural repeats in proteins. In addition to the accurate detection of internal symmetry, the tool is now capable of i) reporting the type of symmetry, ii) identifying the smallest repeating unit, iii) describing the arrangement of repeats with transformation operations and symmetry axes, and iv) comparing the similarity of all the internal repeats at the residue level. CE-Symm 2.0 helps the user investigate proteins with a robust and intuitive sequence-to-structure analysis, with many applications in protein classification, functional annotation and evolutionary studies. We describe the algorithmic extensions of the method and demonstrate its applications to the study of interesting cases of protein evolution.

Published
2018

10. Recon3D enables a three-dimensional view of gene variation in human metabolism.

Author

Brunk, Elizabeth, Sahoo, Swagatika, Zielinski, Daniel C., Altunkaya, Ali, Drager, Andreas, Mih, Nathan, Gatto, Francesco, Nilsson, Avlant, Preciat Gonzalez, German Andres, Aurich, Maike Kathrin, Prlic, Andreas, Sastry, Anand, Danielsdottir, Anna D., Heinken, Almut Katrin, Noronha, Alberto, Rose, Peter W., Burley, Stephen K., Fleming, Ronan MT, Nielsen, Jens, Thiele, Ines, Palsson, Bernhard O., Brunk, Elizabeth, Sahoo, Swagatika, Zielinski, Daniel C., Altunkaya, Ali, Drager, Andreas, Mih, Nathan, Gatto, Francesco, Nilsson, Avlant, Preciat Gonzalez, German Andres, Aurich, Maike Kathrin, Prlic, Andreas, Sastry, Anand, Danielsdottir, Anna D., Heinken, Almut Katrin, Noronha, Alberto, Rose, Peter W., Burley, Stephen K., Fleming, Ronan MT, Nielsen, Jens, Thiele, Ines, and Palsson, Bernhard O.

Abstract

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

Published
2018
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14. Ten Simple Rules for the Open Development of Scientific Software

Author

Prlic, Andreas and Procter, James B.

Abstract

pen-source software development has had significant impact, not only on society, but also on scientific research. Papers describing software published as open source are amongst the most widely cited publications (e.g., BLAST and Clustal-W), suggesting many scientific studies may not have been possible without some kind of open software to collect observations, analyze data, or present results. It is surprising, therefore, that so few papers are accompanied by open software, given the benefits that this may bring.

Published
2012

19. Integration of open access literature into the RCSBProtein Data Bank using BioLit.

Author

Prlic, Andreas, Martinez, Marco A., Dimitropoulos, Dimitris, Beran, Bojan, Yukich, Benjamin T., Rose, Peter W., Bourne, Philip E., and Fink, J. Lynn

Subjects
*PROTEINS, *BIOMOLECULES, *ONLINE databases, *ELECTRONIC directories, *BIOINFORMATICS
Abstract

Background: Biological data have traditionally been stored and made publicly available through a variety of on-line databases, whereas biological knowledge has traditionally been found in the printed literature. With journals now online and providing an increasing amount of open access content, often free of copyright restriction, this distinction between database and literature is blurring. To exploit this opportunity we present the integration of open access literature with the RCSB Protein Data Bank (PDB). Results: BioLit provides an enhanced view of articles with markup of semantic data and links to biological databases, based on the content of the article. For example, words matching to existing biological ontologies are highlighted and database identifiers are linked to their database of origin. Among other functions, it identifies PDB IDs that are mentioned in the open access literature, by parsing the full text for all research articles in PubMed Central (PMC) and exposing the results as simple XML Web Services. Here, we integrate BioLit results with the RCSB PDB website by using these services to find PDB IDs that are mentioned in research articles and subsequently retrieving abstract, figures, and text excerpts for those articles. A new RCSB PDB literature view permits browsing through the figures and abstracts of the articles that mention a given structure. The BioLit Web Services that are providing the underlying data are publicly accessible. A client library is provided that supports querying these services (Java). Conclusions: The integration between literature and websites, as demonstrated here with the RCSB PDB, provides a broader view for how a given structure has been analyzed and used. This approach detects the mention of a PDB structure even if it is not formally cited in the paper. Other structures related through the same literature references can also be identified, possibly providing new scientific insight. To our knowledge this is the first time that database and literature have been integrated in this way and it speaks to the opportunities afforded by open and free access to both database and literature content. [ABSTRACT FROM AUTHOR]

Published
2010
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20. Detection of circular permutations within protein structures using CE-CP.

Author

Bliven, Spencer E., Bourne, Philip E., and Prlic, Andreas

Subjects
CYCLIC permutations, PERMUTATIONS, PROTEIN structure, STRUCTURAL proteomics, TERTIARY structure
Abstract

Motivation: Circular permutation is an important type of protein rearrangement. Natural circular permutations have implications for protein function, stability and evolution. Artificial circular permutations have also been used for protein studies. However, such relationships are difficult to detect for many sequence and structure comparison algorithms and require special consideration. Results: We developed a new algorithm, called Combinatorial Extension for Circular Permutations (CE-CP), which allows the structural comparison of circularly permuted proteins. CE-CP was designed to be user friendly and is integrated into the RCSB Protein Data Bank. It was tested on two collections of circularly permuted proteins. Pairwise alignments can be visualized both in a desktop application or on the web using Jmol and exported to other programs in a variety of formats. [ABSTRACT FROM AUTHOR]

Published
2015
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21. WILMA--automated annotation of protein sequences

Author

Prlic, Andreas, Domingues, Francisco S., Lackner, Peter, and Sippl, Manfred J.

Abstract

Summary: Large-scale annotation of sets of proteins is a frequently occurring task in association with genome sequencing projects. Here, we present an automated platform for the functional annotation of large sets of protein sequences. Various bioinformatics tools are used to achieve a comprehensive description of protein sequences and to link these results to standard Gene Ontology descriptors for molecular function, biological processes and cellular components. Access to the annotation is provided via a web-interface and database queries. These interfaces allow to formulate proteome wide queries as well as the investigation of details of individual results. WILMA annotations of the proteomes of Homo sapiens, Mus musculus, Arabidopsis thaliana and Caenorhabditis elegans are accessible at http://www.came.sbg.ac.at/wilma/

Published
2004
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22. Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

Author

Christopher B. Horuk, Aviral Takkar, Andreas Hellander, Stefan Hellander, Bernie J. Daigle, Giovanni Bellesia, Anand Gupta, Brian Drawert, Benjamin B. Bales, Linda R. Petzold, Debjani Banerjee, Mengyuan Gu, Chandra Krintz, Per Lötstedt, Dibyendu Nath, Geoffrey Douglas, Sheng Wu, and Prlic, Andreas

Subjects
0301 basic medicine, Service (systems architecture), business.product_category, Theoretical computer science, Computer science, Distributed computing, Gene Expression, Cloud computing, 010103 numerical & computational mathematics, 01 natural sciences, Biochemistry, Mathematical Sciences, Modeling and simulation, Software, Stochastic simulation, Biochemical Simulations, lcsh:QH301-705.5, Graphical user interface, Ecology, Simulation and Modeling, Applied Mathematics, Physics, Messenger RNA, Software Engineering, Biological Sciences, Nucleic acids, Networking and Information Technology R&D, Computational Theory and Mathematics, Modeling and Simulation, Laptop, Physical Sciences, Engineering and Technology, Algorithms, Research Article, Computer and Information Sciences, Biophysical Simulations, Bioinformatics, Biophysics, Bioengineering, Research and Analysis Methods, 03 medical and health sciences, Cellular and Molecular Neuroscience, Information and Computing Sciences, DNA-binding proteins, Genetics, Gene Regulation, Computer Simulation, 0101 mathematics, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Stochastic Processes, business.industry, Software Tools, Biology and Life Sciences, Proteins, Computational Biology, Usability, Cloud Computing, Computing Methods, Regulatory Proteins, 030104 developmental biology, lcsh:Biology (General), RNA, business, Mathematics, Transcription Factors
Abstract

© 2016 Drawert et al. We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

Published
2016

23. rasbhari: Optimizing Spaced Seeds for Database Searching, Read Mapping and Alignment-Free Sequence Comparison

Author

Hahn, Lars, Leimeister, Chris-André, Ounit, Rachid, Lonardi, Stefano, Morgenstern, Burkhard, and Prlic, Andreas

Subjects
FOS: Computer and information sciences, q-bio.PE, DNA Mutational Analysis, Mathematical Sciences, Pattern Recognition, Automated, Machine Learning, Database and Informatics Methods, Databases, Genetic, Data Mining, Data Structures and Algorithms (cs.DS), Biology (General), Database Searching, Data Management, Applied Mathematics, Simulation and Modeling, Biological Sciences, Phylogenetics, Physical Sciences, q-bio.GN, Sequence Analysis, Algorithms, Research Article, Optimization, Automated, Computer and Information Sciences, Multiple Alignment Calculation, QH301-705.5, Bioinformatics, Sequence Databases, Pattern Recognition, Research and Analysis Methods, Databases, Genetic, Information and Computing Sciences, Computational Techniques, Computer Science - Data Structures and Algorithms, Evolutionary Systematics, Quantitative Biology - Genomics, Molecular Biology Techniques, Sequencing Techniques, Sequence Similarity Searching, Quantitative Biology - Populations and Evolution, Molecular Biology, Sequence alignment, Database searching, Sequence databases, Multiple alignment calculation, Sequence similarity searching, Taxonomy, Genomics (q-bio.GN), Evolutionary Biology, Populations and Evolution (q-bio.PE), Biology and Life Sciences, Sequence Analysis, DNA, DNA, Split-Decomposition Method, Biological Databases, cs.DS, FOS: Biological sciences, Database Management Systems, Sequence Alignment, Mathematics, Software
Abstract

Many algorithms for sequence analysis rely on word matching or word statistics. Often, these approaches can be improved if binary patterns representing match and don’t-care positions are used as a filter, such that only those positions of words are considered that correspond to the match positions of the patterns. The performance of these approaches, however, depends on the underlying patterns. Herein, we show that the overlap complexity of a pattern set that was introduced by Ilie and Ilie is closely related to the variance of the number of matches between two evolutionarily related sequences with respect to this pattern set. We propose a modified hill-climbing algorithm to optimize pattern sets for database searching, read mapping and alignment-free sequence comparison of nucleic-acid sequences; our implementation of this algorithm is called rasbhari. Depending on the application at hand, rasbhari can either minimize the overlap complexity of pattern sets, maximize their sensitivity in database searching or minimize the variance of the number of pattern-based matches in alignment-free sequence comparison. We show that, for database searching, rasbhari generates pattern sets with slightly higher sensitivity than existing approaches. In our Spaced Words approach to alignment-free sequence comparison, pattern sets calculated with rasbhari led to more accurate estimates of phylogenetic distances than the randomly generated pattern sets that we previously used. Finally, we used rasbhari to generate patterns for short read classification with CLARK-S. Here too, the sensitivity of the results could be improved, compared to the default patterns of the program. We integrated rasbhari into Spaced Words; the source code of rasbhari is freely available at http://rasbhari.gobics.de/, Author Summary We propose a fast algorithm to generate spaced seeds for database searching, read mapping and alignment-free sequence comparison. Spaced seeds—i.e. patterns of match and don’t-care positions—are used by many algorithms for sequence analysis; designing optimal seeds is therefore an active field of research. In sequence-database searching, one wants to optimize sensitivity, i.e. the probability of finding a region of homology; this can be done by minimizing the so-called overlap complexity of pattern sets. In alignment-free DNA sequence comparison, the number N of pattern-based matches is used to estimate phylogenetic distances. Here, one wants to minimize the variance of N in order to obtain stable phylogenies. We show that for spaced seeds, the overlap complexity—and therefore the sensitivity in database searching—is closely related to the variance of N. Our algorithm can optimize the sensitivity, overlap complexity or the variance of N, depending on the application at hand.

Published
2016

24. BEAST 2: a software platform for Bayesian evolutionary analysis

Author

Alexei J. Drummond, Chieh-Hsi Wu, Timothy G. Vaughan, Andrew Rambaut, Denise Kühnert, Joseph Heled, Marc A. Suchard, Remco R. Bouckaert, Dong Xie, and Prlic, Andreas

Subjects
0106 biological sciences, Theoretical computer science, Bioinformatics, Computer science, computer.internet_protocol, 010603 evolutionary biology, 01 natural sciences, Extensibility, Mathematical Sciences, GeneralLiterature_MISCELLANEOUS, Software release life cycle, 03 medical and health sciences, Cellular and Molecular Neuroscience, Software, Information and Computing Sciences, Evolutionary Modeling, Genetics, Molecular Biology Techniques, Sequencing Techniques, Molecular Biology, lcsh:QH301-705.5, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, 0303 health sciences, Ecology, business.industry, Software development, Biology and Life Sciences, Computational Biology, Bayes Theorem, Biological Sciences, Biological Evolution, Tree (data structure), Computational Theory and Mathematics, lcsh:Biology (General), Modeling and Simulation, Key (cryptography), Programming Languages, Generic health relevance, User interface, business, Software engineering, computer, Sequence Analysis, XML, Research Article
Abstract

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format., PLoS Computational Biology, 10 (4), ISSN:1553-734X, ISSN:1553-7358

Published
2014

25. ProteinHistorian: Tools for the Comparative Analysis of Eukaryote Protein Origin

Author

John A. Capra, Alexander G. Williams, Katherine S. Pollard, and Prlic, Andreas

Subjects
Time Factors, Source code, Gene Expression, computer.software_genre, Genome, Mathematical Sciences, 0302 clinical medicine, Models, Biology (General), Databases, Protein, Genome Evolution, Phylogeny, media_common, 2. Zero hunger, Genetics, 0303 health sciences, Ecology, Phylogenetic tree, Genomics, Biological Sciences, Computational Theory and Mathematics, Modeling and Simulation, Sequence Analysis, Algorithms, Research Article, Web server, QH301-705.5, Evolution, Bioinformatics, media_common.quotation_subject, Protein domain, Bioengineering, Computational biology, Biology, Evolution, Molecular, Databases, 03 medical and health sciences, Cellular and Molecular Neuroscience, Genetic, Species Specificity, Genome Analysis Tools, Phylogenetics, Molecular evolution, Information and Computing Sciences, Animals, Humans, Computer Simulation, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Evolutionary Biology, Models, Genetic, Protein, Molecular, Computational Biology, Proteins, Genomic Evolution, Comparative Genomics, Expression (mathematics), Generic health relevance, computer, Software, 030217 neurology & neurosurgery
Abstract

The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed ProteinHistorian to make tools for performing analyses of protein origins widely available. Given a list of proteins of interest, ProteinHistorian estimates the phylogenetic age of each protein, quantifies enrichment for proteins of specific ages, and compares variation in protein age with other protein attributes. ProteinHistorian allows flexibility in the definition of protein age by including several algorithms for estimating ages from different databases of evolutionary relationships. We illustrate the use of ProteinHistorian with three example analyses. First, we demonstrate that proteins with high expression in human, compared to chimpanzee and rhesus macaque, are significantly younger than those with human-specific low expression. Next, we show that human proteins with annotated regulatory functions are significantly younger than proteins with catalytic functions. Finally, we compare protein length and age in many eukaryotic species and, as expected from previous studies, find a positive, though often weak, correlation between protein age and length. ProteinHistorian is available through a web server with an intuitive interface and as a set of command line tools; this allows biologists and bioinformaticians alike to integrate these approaches into their analysis pipelines. ProteinHistorian's modular, extensible design facilitates the integration of new datasets and algorithms. The ProteinHistorian web server, source code, and pre-computed ages for 32 eukaryotic genomes are freely available under the GNU public license at http://lighthouse.ucsf.edu/ProteinHistorian/.

Published
2012

26. RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy.

Author

Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Di Costanzo L, Christie C, Dalenberg K, Duarte JM, Dutta S, Feng Z, Ghosh S, Goodsell DS, Green RK, Guranovic V, Guzenko D, Hudson BP, Kalro T, Liang Y, Lowe R, Namkoong H, Peisach E, Periskova I, Prlic A, Randle C, Rose A, Rose P, Sala R, Sekharan M, Shao C, Tan L, Tao YP, Valasatava Y, Voigt M, Westbrook J, Woo J, Yang H, Young J, Zhuravleva M, and Zardecki C

Subjects
Biomedical Research education, Biotechnology education, Data Curation, Software, Databases, Protein, Protein Conformation
Abstract

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, rcsb.org), the US data center for the global PDB archive, serves thousands of Data Depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without usage restrictions to more than 1 million rcsb.org Users worldwide and 600 000 pdb101.rcsb.org education-focused Users around the globe. PDB Data Depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy and 3D electron microscopy. PDB Data Consumers include researchers, educators and students studying Fundamental Biology, Biomedicine, Biotechnology and Energy. Recent reorganization of RCSB PDB activities into four integrated, interdependent services is described in detail, together with tools and resources added over the past 2 years to RCSB PDB web portals in support of a 'Structural View of Biology.'

Published
2019
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27. NGL viewer: web-based molecular graphics for large complexes.

Author

Rose AS, Bradley AR, Valasatava Y, Duarte JM, Prlic A, and Rose PW

Subjects
Computer Graphics, Internet, Macromolecular Substances, Software
Abstract

Motivation: The interactive visualization of very large macromolecular complexes on the web is becoming a challenging problem as experimental techniques advance at an unprecedented rate and deliver structures of increasing size., Results: We have tackled this problem by developing highly memory-efficient and scalable extensions for the NGL WebGL-based molecular viewer and by using Macromolecular Transmission Format (MMTF), a binary and compressed MMTF. These enable NGL to download and render molecular complexes with millions of atoms interactively on desktop computers and smartphones alike, making it a tool of choice for web-based molecular visualization in research and education., Availability and Implementation: The source code is freely available under the MIT license at github.com/arose/ngl and distributed on NPM (npmjs.com/package/ngl). MMTF-JavaScript encoders and decoders are available at github.com/rcsb/mmtf-javascript.

Published
2018
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28. BioJava-ModFinder: identification of protein modifications in 3D structures from the Protein Data Bank.

Author

Gao J, Prlic A, Bi C, Bluhm WF, Dimitropoulos D, Xu D, Bourne PE, and Rose PW

Subjects
Internet, Computational Biology methods, Databases, Protein, Protein Conformation, Software
Abstract

Summary: We developed a new software tool, BioJava-ModFinder, for identifying protein modifications observed in 3D structures archived in the Protein Data Bank (PDB). Information on more than 400 types of protein modifications were collected and curated from annotations in PDB, RESID, and PSI-MOD. We divided these modifications into three categories: modified residues, attachment modifications, and cross-links. We have developed a systematic method to identify these modifications in 3D protein structures. We have integrated this package with the RCSB PDB web application and added protein modification annotations to the sequence diagram and structure display. By scanning all 3D structures in the PDB using BioJava-ModFinder, we identified more than 30 000 structures with protein modifications, which can be searched, browsed, and visualized on the RCSB PDB website., Availability and Implementation: BioJava-ModFinder is available as open source (LGPL license) at ( https://github.com/biojava/biojava/tree/master/biojava-modfinder ). The RCSB PDB can be accessed at http://www.rcsb.org ., Contact: pwrose@ucsd.edu., (© The Author 2017. Published by Oxford University Press.)

Published
2017
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29. The RCSB Protein Data Bank: new resources for research and education.

Author

Rose PW, Bi C, Bluhm WF, Christie CH, Dimitropoulos D, Dutta S, Green RK, Goodsell DS, Prlic A, Quesada M, Quinn GB, Ramos AG, Westbrook JD, Young J, Zardecki C, Berman HM, and Bourne PE

Subjects
Biochemistry education, Computer Graphics, Internet, Ligands, Protein Structure, Tertiary, Research, Structural Homology, Protein, Databases, Protein, Protein Conformation
Abstract

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) develops tools and resources that provide a structural view of biology for research and education. The RCSB PDB web site (http://www.rcsb.org) uses the curated 3D macromolecular data contained in the PDB archive to offer unique methods to access, report and visualize data. Recent activities have focused on improving methods for simple and complex searches of PDB data, creating specialized access to chemical component data and providing domain-based structural alignments. New educational resources are offered at the PDB-101 educational view of the main web site such as Author Profiles that display a researcher's PDB entries in a timeline. To promote different kinds of access to the RCSB PDB, Web Services have been expanded, and an RCSB PDB Mobile application for the iPhone/iPad has been released. These improvements enable new opportunities for analyzing and understanding structure data.

Published
2013
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30. Quality assurance for the query and distribution systems of the RCSB Protein Data Bank.

Author

Bluhm WF, Beran B, Bi C, Dimitropoulos D, Prlic A, Quinn GB, Rose PW, Shah C, Young J, Yukich B, Berman HM, and Bourne PE

Subjects
Computers, Quality Control, Software, User-Computer Interface, Databases, Protein standards, Internet standards
Abstract

The RCSB Protein Data Bank (RCSB PDB, www.pdb.org) is a key online resource for structural biology and related scientific disciplines. The website is used on average by 165,000 unique visitors per month, and more than 2000 other websites link to it. The amount and complexity of PDB data as well as the expectations on its usage are growing rapidly. Therefore, ensuring the reliability and robustness of the RCSB PDB query and distribution systems are crucially important and increasingly challenging. This article describes quality assurance for the RCSB PDB website at several distinct levels, including: (i) hardware redundancy and failover, (ii) testing protocols for weekly database updates, (iii) testing and release procedures for major software updates and (iv) miscellaneous monitoring and troubleshooting tools and practices. As such it provides suggestions for how other websites might be operated.

Published
2011
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31. The RCSB Protein Data Bank: redesigned web site and web services.

Author

Rose PW, Beran B, Bi C, Bluhm WF, Dimitropoulos D, Goodsell DS, Prlic A, Quesada M, Quinn GB, Westbrook JD, Young J, Yukich B, Zardecki C, Berman HM, and Bourne PE

Subjects
Animals, Computer Graphics, Humans, Internet, Ligands, Mice, Protein Conformation, Systems Integration, User-Computer Interface, Databases, Protein, Proteins chemistry
Abstract

The RCSB Protein Data Bank (RCSB PDB) web site (http://www.pdb.org) has been redesigned to increase usability and to cater to a larger and more diverse user base. This article describes key enhancements and new features that fall into the following categories: (i) query and analysis tools for chemical structure searching, query refinement, tabulation and export of query results; (ii) web site customization and new structure alerts; (iii) pair-wise and representative protein structure alignments; (iv) visualization of large assemblies; (v) integration of structural data with the open access literature and binding affinity data; and (vi) web services and web widgets to facilitate integration of PDB data and tools with other resources. These improvements enable a range of new possibilities to analyze and understand structure data. The next generation of the RCSB PDB web site, as described here, provides a rich resource for research and education.

Published
2011
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