1
|
de Bruijn I, Kundra R, Mastrogiacomo B, Tran TN, Sikina L, Mazor T, Li X, Ochoa A, Zhao G, Lai B, Abeshouse A, Baiceanu D, Ciftci E, Dogrusoz U, Dufilie A, Erkoc Z, Garcia Lara E, Fu Z, Gross B, Haynes C, Heath A, Higgins D, Jagannathan P, Kalletla K, Kumari P, Lindsay J, Lisman A, Leenknegt B, Lukasse P, Madela D, Madupuri R, van Nierop P, Plantalech O, Quach J, Resnick AC, Rodenburg SY, Satravada BA, Schaeffer F, Sheridan R, Singh J, Sirohi R, Sumer SO, van Hagen S, Wang A, Wilson M, Zhang H, Zhu K, Rusk N, Brown S, Lavery JA, Panageas KS, Rudolph JE, LeNoue-Newton ML, Warner JL, Guo X, Hunter-Zinck H, Yu TV, Pilai S, Nichols C, Gardos SM, Philip J, Kehl KL, Riely GJ, Schrag D, Lee J, Fiandalo MV, Sweeney SM, Pugh TJ, Sander C, Cerami E, Gao J, Schultz N. Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res 2023; 83:3861-3867. [PMID: 37668528 PMCID: PMC10690089 DOI: 10.1158/0008-5472.can-23-0816] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/24/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer. SIGNIFICANCE Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.
Collapse
Affiliation(s)
- Ino de Bruijn
- Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ritika Kundra
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | - Luke Sikina
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Tali Mazor
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Xiang Li
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Angelica Ochoa
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gaofei Zhao
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Bryan Lai
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Adam Abeshouse
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Ersin Ciftci
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Ziya Erkoc
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | - Zhaoyuan Fu
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Benjamin Gross
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Charles Haynes
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Allison Heath
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - David Higgins
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | | | - Priti Kumari
- Dana-Farber Cancer Institute, Boston, Massachusetts
- Caris Life Sciences, Irving, Texas
| | | | - Aaron Lisman
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | - Divya Madela
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | | | - Joyce Quach
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Adam C. Resnick
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | | | | | | | | | - Rajat Sirohi
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Avery Wang
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Manda Wilson
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Hongxin Zhang
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kelsey Zhu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Nicole Rusk
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Samantha Brown
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | | | | | | | - Xindi Guo
- Sage Bionetworks, Seattle, Washington
| | | | | | - Shirin Pilai
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | - John Philip
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | | | - Deborah Schrag
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jocelyn Lee
- American Association for Cancer Research: Project GENIE, Philadelphia, Pennsylvania
| | - Michael V. Fiandalo
- American Association for Cancer Research: Project GENIE, Philadelphia, Pennsylvania
| | - Shawn M. Sweeney
- American Association for Cancer Research: Project GENIE, Philadelphia, Pennsylvania
| | - Trevor J. Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | | | - Ethan Cerami
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jianjiong Gao
- Memorial Sloan Kettering Cancer Center, New York, New York
- Caris Life Sciences, Irving, Texas
| | | |
Collapse
|
2
|
Mazein A, Acencio ML, Balaur I, Rougny A, Welter D, Niarakis A, Ramirez Ardila D, Dogrusoz U, Gawron P, Satagopam V, Gu W, Kremer A, Schneider R, Ostaszewski M. A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance. Front Bioinform 2023; 3:1197310. [PMID: 37426048 PMCID: PMC10325725 DOI: 10.3389/fbinf.2023.1197310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
Collapse
Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche Pour la Polyarthrite Rhumatoïde–Genhotel, University Evry, Evry, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Diana Ramirez Ardila
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara, Türkiye
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Andreas Kremer
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| |
Collapse
|
3
|
de Bruijn I, Mazor T, Abeshouse A, Baiceanu D, Carrero S, Garcia Lara E, Gross B, Higgins DM, Jagannathan PK, Kumari P, Kundra R, Lai B, Li X, Lindsay J, Lisman A, Madala D, Madupuri R, Ochoa A, Özgül YZ, Plantalech O, Rodenburg S, Satravada BA, Sheridan R, Sikina L, Singh J, Sumer SO, Sun Y, van Nierop P, Wang A, Wilson M, Zhang H, Zhao G, van Hagen S, Dogrusoz U, Heath A, Resnick A, Pugh TJ, Sander C, Cerami E, Gao J, Schultz N. Abstract 4256: cBioPortal for Cancer Genomics. Cancer Res 2023. [DOI: 10.1158/1538-7445.am2023-4256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Abstract
cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale clinico-genomic data sets. cBioPortal provides a suite of user-friendly visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization.
The public site (https://www.cbioportal.org) is accessed by >35,000 unique visitors each month and hosts data from >350 studies spanning individual labs and large consortia. In addition, at least 74 instances of cBioPortal are installed at academic institutions and companies worldwide. To better support all users, we unified our documentation (https://docs.cbioportal.org) and added a user guide and an ongoing series of ‘how-to’ videos to address common questions.
In 2022 we added 32 studies (>38,000 samples) to the public site. In addition, we added a nonsynonymous tumor mutation burden (TMB) value for all samples and enhanced the TCGA PanCancer Atlas studies with DNA methylation and treatment data. All data is available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub.
We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >165,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). The GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment history. The first BPC cohorts are now available: ~2,000 non-small cell lung cancer samples and ~1,500 colorectal cancer samples.
Support for multimodal data analysis has been a major focus, including several new integrations with external tools. Single cell data is now available in the CPTAC GBM study and can be visualized throughout cBioPortal, and via integration with cellxgene. On the patient page, H&E and mIF images can be visualized via integration with Minerva, and the genomic overview now integrates IGV.
We continue to enhance existing features. In the study view, users can now add charts comparing categorical vs continuous data, and the plots tab includes a heatmap option. We replaced the existing fusion data type with a generalized structural variant data type that supports detailed information including breakpoints and orientation, to enable new visualizations and analyses. Pathway level analysis has been extended with a new integration with NDEx.
cBioPortal is fully open source (https://github.com/cBioPortal/). Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Caris Life Sciences, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community.
Citation Format: Ino de Bruijn, Tali Mazor, Adam Abeshouse, Diana Baiceanu, Stephanie Carrero, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Bryan Lai, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Yusuf Ziya Özgül, Oleguer Plantalech, Sander Rodenburg, Baby Anusha Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Jianjiong Gao, Nikolaus Schultz. cBioPortal for Cancer Genomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4256.
Collapse
Affiliation(s)
- Ino de Bruijn
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Tali Mazor
- 2Dana-Farber Cancer Institute, Boston, MA
| | | | | | | | | | | | | | | | | | - Ritika Kundra
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Bryan Lai
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiang Li
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Aaron Lisman
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Divya Madala
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | | | | | | | | | - S Onur Sumer
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yichao Sun
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Avery Wang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Manda Wilson
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hongxin Zhang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gaofei Zhao
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Allison Heath
- 4Children's Hospital of Philadelphia, Philadelphia, PA
| | - Adam Resnick
- 4Children's Hospital of Philadelphia, Philadelphia, PA
| | - Trevor J. Pugh
- 5Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | | | | |
Collapse
|
4
|
Balci H, Dogrusoz U. fCoSE: A Fast Compound Graph Layout Algorithm with Constraint Support. IEEE Trans Vis Comput Graph 2022; 28:4582-4593. [PMID: 34232882 DOI: 10.1109/tvcg.2021.3095303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Visual analysis of relational information is vital in most real-life analytics applications. Automatic layout is a key requirement for effective visual display of such information. This article introduces a new layout algorithm named fCoSE for compound graphs showing varying levels of groupings or abstractions with support for user-specified placement constraints. fCoSE builds on a previous compound spring embedder layout algorithm and makes use of the spectral graph drawing technique for producing a quick draft layout, followed by phases where constraints are enforced and compound structures are properly shown while polishing the layout with respect to commonly accepted graph layout criteria. Experimental evaluation verifies that fCoSE produces quality layouts and is fast enough for interactive applications with small to medium-sized graphs by combining the speed of spectral graph drawing technique with the quality of force-directed layout algorithms while satisfying specified constraints and properly displaying compound structures. An implementation of fCoSE along with documentation and a demo page is freely available on GitHub at https://github.com/iVis-at-Bilkent/cytoscape.js-fcose.
Collapse
|
5
|
Gao J, Mazor T, de Bruijn I, Abeshouse A, Baiceanu D, Erkoc Z, Lara EG, Gross B, Higgins DM, Jagannathan PK, Kumari P, Kundra R, Li X, Lindsay J, Lisman A, Madala D, Madupuri R, Ochoa A, Plantalech O, Rodenburg S, Satravada BA, Sheridan R, Sikina L, Singh J, Sumer SO, Sun Y, van Nierop P, Wang A, Wilson M, Zhang H, Zhao G, van Hagen S, van Bochove K, Dogrusoz U, Heath A, Resnick A, Pugh TJ, Sander C, Cerami E, Schultz N. Abstract 1155: cBioPortal for cancer genomics. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-1155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale cancer genomics data sets. cBioPortal provides a user-friendly interface that integrates genomic and clinical data, and provides a suite of visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. cBioPortal also integrates external tools including CIViC, Cancer Digital Slide Archive, Next-Generation Clustered Heat Map, IGV and Bioconductor to facilitate interpretation.
The public site (https://www.cbioportal.org) is accessed by ~35,000 unique visitors each month and hosts data from >325 studies spanning individual labs and large consortia. In addition, >67 instances of cBioPortal are installed at academic institutions and pharmaceutical/biotechnology companies worldwide. In 2021 we added data from 32 studies, totaling >24,000 samples, to the public site. All data is also available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub/.
We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >135,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). In addition, the GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment histories. The first BPC release contains data from >1,800 non-small cell lung cancer samples and will be released in early 2022.
The growing GENIE cohort and the BPC clinical data have driven a number of recent developments, including performance improvements (the load time for the GENIE cohort was reduced from minutes to seconds). To leverage the BPC clinical data, we enabled sample selection based on treatment status, extended support for outcome analysis, and enhanced the patient timeline representation to incorporate response data.
Additional development work has focused on improvements to variant interpretation, enhancements to the Mutations tab, and support for novel molecular assays via the ‘generic assay’ data type. Documentation on these new features and many others is available at https://www.cbioportal.org/news.
cBioPortal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community.
Citation Format: Jianjiong Gao, Tali Mazor, Ino de Bruijn, Adam Abeshouse, Diana Baiceanu, Ziya Erkoc, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Sander Rodenburg, Baby A. Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S. Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. cBioPortal for cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1155.
Collapse
Affiliation(s)
- Jianjiong Gao
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Tali Mazor
- 2Dana-Farber Cancer Institute, Boston, MA
| | - Ino de Bruijn
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | | | | | | | - Ritika Kundra
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiang Li
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Aaron Lisman
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Divya Madala
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | | | | | | | - S. Onur Sumer
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yichao Sun
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Avery Wang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Manda Wilson
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hongxin Zhang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gaofei Zhao
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Allison Heath
- 5Children's Hospital of Philadelphia, Philadelphia, PA
| | - Adam Resnick
- 5Children's Hospital of Philadelphia, Philadelphia, PA
| | - Trevor J. Pugh
- 6Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | | |
Collapse
|
6
|
Abstract
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).
Collapse
Affiliation(s)
- Augustin Luna
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Corresponding author
| | - Metin Can Siper
- Computational Biology Program, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Funda Durupinar
- Computer Science Department, University of Massachusetts Boston, 100 William T. Morrissey Blvd, Boston, MA 02125, USA
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Joseph E. Aslan
- Knight Cardiovascular Institute, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Emek Demir
- Computational Biology Program, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
- Pacific Northwest National Laboratories, 902 Battelle Blvd, Richland, WA 99354, USA
| | - Ozgun Babur
- Computer Science Department, University of Massachusetts Boston, 100 William T. Morrissey Blvd, Boston, MA 02125, USA
- Corresponding author
| |
Collapse
|
7
|
Balci H, Siper MC, Saleh N, Safarli I, Roy L, Kilicarslan M, Ozaydin R, Mazein A, Auffray C, Babur Ö, Demir E, Dogrusoz U. Newt: a comprehensive web-based tool for viewing, constructing and analyzing biological maps. Bioinformatics 2021; 37:1475-1477. [PMID: 33010165 DOI: 10.1093/bioinformatics/btaa850] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/25/2020] [Accepted: 09/18/2020] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. RESULTS We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. AVAILABILITY AND IMPLEMENTATION Newt's source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.
Collapse
Affiliation(s)
- Hasan Balci
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Metin Can Siper
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.,Molecular & Medical Genetics Department, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Nasim Saleh
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Ilkin Safarli
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.,Visualization Design Lab, School of Computing, University of Utah, Salt Lake City, UT 84112, USA
| | - Ludovic Roy
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
| | - Merve Kilicarslan
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Rumeysa Ozaydin
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
| | - Özgün Babur
- Molecular & Medical Genetics Department, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA.,Computer Science Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Emek Demir
- Molecular & Medical Genetics Department, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Ugur Dogrusoz
- i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| |
Collapse
|
8
|
Touré V, Dräger A, Luna A, Dogrusoz U, Rougny A. The Systems Biology Graphical Notation: Current Status and Applications in Systems Medicine. Systems Medicine 2021. [DOI: 10.1016/b978-0-12-801238-3.11515-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
|
9
|
Balaur I, Roy L, Mazein A, Karaca SG, Dogrusoz U, Barillot E, Zinovyev A. cd2sbgnml: bidirectional conversion between CellDesigner and SBGN formats. Bioinformatics 2020; 36:4975. [DOI: 10.1093/bioinformatics/btaa528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
10
|
Gao J, Mazor T, Abeshouse A, de Bruijn I, Gross B, Kalletla K, Kumari P, Kundra R, Li X, Lindsay J, Lisman A, Lukasse P, Madupuri R, Ochoa A, Plantalech O, Rodenburg S, Schaeffer F, Sheridan R, Sikina L, Su J, Sumer SO, Sun Y, van Dijk P, van Hagen S, van Nierop P, Wang A, Wilson M, Zhang H, Zhao G, Zhu K, van Bochove K, Dogrusoz U, Heath A, Resnick A, Pugh TJ, Sander C, Cerami E, Schultz N. Abstract 3209: The cBioPortal for Cancer Genomics. Cancer Res 2020. [DOI: 10.1158/1538-7445.am2020-3209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The cBioPortal for Cancer Genomics is an open-source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets with a biologist-friendly interface. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including OncoPrint, mutation diagram, variant interpretation, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization, among others.
The public site (https://www.cbioportal.org) hosts data from more than 280 studies from diverse sources including individual labs and large consortia. All data is also available in the cBioPortal Datahub (https://github.com/cBioPortal/datahub/). Data from 40 studies, totaling more than 10,000 samples, was added in 2019, including the latest release from the Cancer Cell Line Encyclopedia and the Pediatric Preclinical Testing Consortium. The site is accessed by over 30,000 unique visitors per month. cBioPortal also supports AACR Project GENIE with a dedicated instance hosting the GENIE cohort of 80,000 clinically sequenced samples from 19 institutions worldwide (http://genie.cbioportal.org).
In addition, more than 40 instances are installed locally at academic institutions and pharmaceutical/biotechnology companies. In support of these local installations, cBioPortal now has improved documentation and simplified installation via container technologies such as Docker and Kubernetes.
Building on our successful refactoring of the code base, we have released a variety of new features and enhancements to cBioPortal over the past year. Most notably, we released a group comparison feature, enabling users to define groups of interest based on any clinical or genomic features. User-defined groups can be compared simultaneously across genomic and clinical data, including survival analysis and genomic alteration enrichment analysis. Additional new features include: integration of mutation annotations from dbSNP, ClinVar and gnomAD; support for waterfall plots to enable treatment response analysis; saving user preferences for chart layout on study view.
The cBioPortal remains under active development. The portal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children's Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve. Ongoing and future development is focused on: (1) building the open source community; (2) continued performance improvements; (3) expanding user support, documentation and training resources; (4) supporting longitudinal data analysis and visualization; (5) developing novel features to support immunogenomics and immunotherapy; (6) enhancing individual variants and overall patient interpretation; (7) supporting single cell data visualizations and analysis.
Citation Format: Jianjiong Gao, Tali Mazor, Adam Abeshouse, Ino de Bruijn, Benjamin Gross, Karthik Kalletla, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Pieter Lukasse, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Sander Rodenburg, Fedde Schaeffer, Robert Sheridan, Lucas Sikina, Jing Su, S. Onur Sumer, Yichao Sun, Paul van Dijk, Sjoerd van Hagen, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Kelsey Zhu, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for Cancer Genomics [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3209.
Collapse
Affiliation(s)
- Jianjiong Gao
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Tali Mazor
- 2Dana-Farber Cancer Institute, Boston, MA
| | - Adam Abeshouse
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Ino de Bruijn
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Benjamin Gross
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | | | - Ritika Kundra
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Xiang Li
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | - Aaron Lisman
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | | | - Angelica Ochoa
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | | | | | | | | | - Jing Su
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - S. Onur Sumer
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Yichao Sun
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | | | | | - Avery Wang
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Manda Wilson
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Hongxin Zhang
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Gaofei Zhao
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Kelsey Zhu
- 5Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | - Allison Heath
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | - Adam Resnick
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | - Trevor J. Pugh
- 5Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | | |
Collapse
|
11
|
Bergmann FT, Czauderna T, Dogrusoz U, Rougny A, Dräger A, Touré V, Mazein A, Blinov ML, Luna A. Systems biology graphical notation markup language (SBGNML) version 0.3. J Integr Bioinform 2020; 17:jib-2020-0016. [PMID: 32568733 PMCID: PMC7756621 DOI: 10.1515/jib-2020-0016] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/16/2020] [Indexed: 11/15/2022] Open
Abstract
This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.
Collapse
Affiliation(s)
- Frank T Bergmann
- BioQUANT/COS, Heidelberg University, INF 267, Heidelberg, 69120, Germany
| | - Tobias Czauderna
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara, 06800, Turkey.,i-Vis Research Lab, Bilkent University, Ankara, 06800, Turkey
| | - Adrien Rougny
- Biotechnology Research Institute for Drug Discovery, AIST, Tokyo, 135-0064, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST, Tokyo, 169-8555, Japan
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Tübingen, 72076, Germany.,Department of Computer Science, University of Tübingen, Tübingen, 72076, Germany.,German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, 72076, Germany
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg.,European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, Lyon, 69007, France
| | - Michael L Blinov
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, 06030, USA
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.,Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| |
Collapse
|
12
|
Rodchenkov I, Babur O, Luna A, Aksoy BA, Wong JV, Fong D, Franz M, Siper MC, Cheung M, Wrana M, Mistry H, Mosier L, Dlin J, Wen Q, O’Callaghan C, Li W, Elder G, Smith PT, Dallago C, Cerami E, Gross B, Dogrusoz U, Demir E, Bader GD, Sander C. Pathway Commons 2019 Update: integration, analysis and exploration of pathway data. Nucleic Acids Res 2020; 48:D489-D497. [PMID: 31647099 PMCID: PMC7145667 DOI: 10.1093/nar/gkz946] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/07/2019] [Accepted: 10/10/2019] [Indexed: 12/14/2022] Open
Abstract
Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.
Collapse
Affiliation(s)
- Igor Rodchenkov
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Ozgun Babur
- Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Bulent Arman Aksoy
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
| | - Jeffrey V Wong
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Dylan Fong
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Max Franz
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Metin Can Siper
- Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Manfred Cheung
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Wrana
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Harsh Mistry
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Logan Mosier
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Jonah Dlin
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Qizhi Wen
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Caitlin O’Callaghan
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Wanxin Li
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Geoffrey Elder
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Peter T Smith
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Christian Dallago
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA
- Department of Informatics, Technische Universität München, 85748 Garching, Germany
| | - Ethan Cerami
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Benjamin Gross
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ugur Dogrusoz
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey
| | - Emek Demir
- Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Chris Sander
- cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| |
Collapse
|
13
|
Balaur I, Roy L, Mazein A, Karaca SG, Dogrusoz U, Barillot E, Zinovyev A. cd2sbgnml: bidirectional conversion between CellDesigner and SBGN formats. Bioinformatics 2020; 36:2620-2622. [DOI: 10.1093/bioinformatics/btz969] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 11/20/2019] [Accepted: 01/01/2020] [Indexed: 12/27/2022] Open
Abstract
Abstract
Motivation
CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGN-ML) and the CellDesigner’s proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files.
Results
We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner’s proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML.
Availability and implementation
The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively).
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Irina Balaur
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
| | - Ludovic Roy
- Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
- Institut Curie, PSL Research University, F-75005 Paris, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
- Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Moscow Region, Pushchino 142290, Russia
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - S Gökberk Karaca
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Emmanuel Barillot
- Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
- Institut Curie, PSL Research University, F-75005 Paris, France
| | - Andrei Zinovyev
- Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
- Institut Curie, PSL Research University, F-75005 Paris, France
| |
Collapse
|
14
|
Gao J, Mazor T, Abeshouse A, Ciftci E, Bruijn ID, Gross B, Kalletla K, Kumari P, Kundra R, Lindsay J, Lisman A, Lukasse P, Madupuri R, Ochoa A, Plantalech O, Raman P, Schaeffer F, Sheridan R, Su J, Sumer SO, Sun Y, Tan S, Hagen SV, Wang A, Wilson M, Zhang H, Zhao G, Zhu K, Bochove KV, Dogrusoz U, Pugh TJ, Resnick A, Sander C, Cerami E, Schultz N. Abstract 910: The cBioPortal for cancer genomics. Cancer Res 2019. [DOI: 10.1158/1538-7445.am2019-910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The cBioPortal for Cancer Genomics is an open-source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets with a biologist-friendly interface. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including OncoPrint, mutation diagram, variant interpretation, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization, among others.
The public site (http://www.cbioportal.org) hosts data from more than 200 studies from individual labs and large consortia, including the newly added TCGA Pan-Cancer Atlas data and the Count Me In project. These studies can be explored and queried individually or combined together into “virtual studies”. Users are now allowed to login and save virtual studies for query and analysis. The site is currently accessed by approximately 30,000 unique visitors per month. The software is also installed locally at dozens of academic institutions and pharmaceutical/biotechnology companies. A notable instance is the cBioPortal for AACR GENIE (http://www.cbioportal.org/genie/) hosting 60,000 clinically sequenced samples from multiple institutions.
Over the past year, the code base has been fully refactored, resulting in a more responsive and interactive website. A new web API is in beta facilitating easier programmatic access to data. In addition, all public studies are available for download from the new datahub (https://github.com/cBioPortal/datahub/).
The cBioPortal remains under active development. The portal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve. Ongoing and future development is focused on: (1) building the open source community; (2) continued performance improvements; (3) expanding user support, documentation and training resources; (4) developing novel features to support immunogenomics and immunotherapy; (5) enhancing individual variants and overall patient interpretation; (6) creating a simplified query interface; and (7) enabling comparative analysis of user-defined patient cohorts.
Citation Format: Jianjiong Gao, Tali Mazor, Adam Abeshouse, Ersin Ciftci, Ino de Bruijn, Benjamin Gross, Karthik Kalletla, Priti Kumari, Ritika Kundra, James Lindsay, Aaron Lisman, Pieter Lukasse, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Pichai Raman, Fedde Schaeffer, Robert Sheridan, Jing Su, S. Onur Sumer, Yichao Sun, Sander Tan, Sjoerd van Hagen, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Kelsey Zhu, Kees van Bochove, Ugur Dogrusoz, Trevor J. Pugh, Adam Resnick, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 910.
Collapse
Affiliation(s)
- Jianjiong Gao
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Tali Mazor
- 2Dana-Farber Cancer Institute, Boston, MA
| | | | | | - Ino de Bruijn
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Ritika Kundra
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Aaron Lisman
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | - Pichai Raman
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | - Jing Su
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - S. Onur Sumer
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yichao Sun
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Avery Wang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Manda Wilson
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hongxin Zhang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gaofei Zhao
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kelsey Zhu
- 5Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | - Trevor J. Pugh
- 5Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Adam Resnick
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | | |
Collapse
|
15
|
Rougny A, Touré V, Moodie S, Balaur I, Czauderna T, Borlinghaus H, Dogrusoz U, Mazein A, Dräger A, Blinov ML, Villéger A, Haw R, Demir E, Mi H, Sorokin A, Schreiber F, Luna A. Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2019-0022/jib-2019-0022.xml. [PMID: 31199769 PMCID: PMC6798820 DOI: 10.1515/jib-2019-0022] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 05/21/2019] [Indexed: 12/01/2022] Open
Abstract
The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).
Collapse
Affiliation(s)
- Adrien Rougny
- Biotechnology Research Institute for Drug Discovery, AIST, Tokyo135-0064, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST, Tokyo 169-8555, Japan
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Stuart Moodie
- Eight Pillars Ltd, 19 Redford Walk, EdinburghEH13 0AG,UK
| | - Irina Balaur
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Tobias Czauderna
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Hanna Borlinghaus
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.,i-Vis Research Lab, Bilkent University, Ankara 06800, Turkey
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg.,Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Center for Bioinformatics Tübingen (ZBIT), 72076 Tübingen, Germany.,Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany.,German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
| | - Michael L Blinov
- Center for Cell Analysis and Modeling, UConn Health, Farmington CT 06030, USA
| | | | - Robin Haw
- Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada
| | - Emek Demir
- Computational Biology Program, Oregon Health and Science University, Portland, Oregon, USA.,Oregon Health and Science University, Department of Molecular and Medical Genetics, Portland, Oregon, USA
| | - Huaiyu Mi
- Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Anatoly Sorokin
- Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia
| | - Falk Schreiber
- Faculty of Information Technology, Monash University, Melbourne, Australia.,Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
16
|
Ostaszewski M, Gebel S, Kuperstein I, Mazein A, Zinovyev A, Dogrusoz U, Hasenauer J, Fleming RMT, Le Novère N, Gawron P, Ligon T, Niarakis A, Nickerson D, Weindl D, Balling R, Barillot E, Auffray C, Schneider R. Community-driven roadmap for integrated disease maps. Brief Bioinform 2019; 20:659-670. [PMID: 29688273 PMCID: PMC6556900 DOI: 10.1093/bib/bby024] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 03/02/2018] [Indexed: 01/07/2023] Open
Abstract
The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
Collapse
Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Ugur Dogrusoz
- Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Ronan M T Fleming
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, Netherlands
| | - Nicolas Le Novère
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, United Kingdom
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Thomas Ligon
- Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany
| | - Anna Niarakis
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, Evry 91025, France
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| |
Collapse
|
17
|
Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La K, Dimitriadoy S, Liu DL, Kantheti HS, Heins Z, Ochoa A, Gross B, Gao J, Zhang H, Kundra R, Kandoth C, Bahceci I, Dervishi L, Dogrusoz U, Zhou W, Shen H, Laird PW, Berger AH, Bivona TG, Lazar AJ, Hammer G, Giordano T, Kwong L, McArthur G, Huang C, Frederick MJ, McCormick F, Meyerson M, Network TCGAR, Allen EV, Cherniack AD, Ciriello G, Sander C, Schultz N. Abstract 3302: The molecular landscape of oncogenic signaling pathways in The Cancer Genome Atlas. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-3302] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Over the past decade, The Cancer Genome Atlas (TCGA) has profiled more than 11,000 tumors spanning 33 distinct cancer types. The TCGA PanCanAtlas is a collaborative project by the TCGA Research Network that aims to address relevant overarching questions in oncology based on a cross-cancer analysis of the full, uniformly reprocessed TCGA data set. Here, we present results from our analysis of genetic alterations in mitogenic signaling pathways across cancer.
Genetic alterations in signaling pathways that control cell cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations and copy-number changes in 9,125 tumor samples profiled by TCGA, we analyzed the mechanisms and patterns of alterations in 10 canonical pathways: cell cycle, Hippo, Myc, Notch, beta-catenin / WNT, PI-3-Kinase / Akt, receptor-tyrosine kinase / RAS / MAP-kinase signaling, TP53, and TGF-beta signaling, as well as oxidative stress response. For each of these pathways, we propose an expert-curated description (or “template”) that includes the relevant (altered) genes and the connections between them, as well as a detailed catalogue of the driver mutations and copy number changes with known oncogenic relevance. We provide a high-level map of pathway alteration frequencies across tissues and relevant cancer subtypes as well as detailed frequencies of alteration at the gene level for each individual pathway. We also investigate relationships of co-occurrence and mutual exclusivity across pathways and evaluate therapeutic implications, including drug combinations. Forty-nine percent of tumors had at least one potentially targetable alteration in the evaluated pathways, and 31% of tumors had multiple targetable alterations, making them candidates for combination therapy.
Our work delineates the full landscape of oncogenic alterations in mitogenic signaling pathways across cancer, and the pathway templates as well as the richly annotated data set that we provide will constitute an invaluable public resource for future use by the cancer genomics and precision oncology communities.
Citation Format: Francisco Sanchez-Vega, Marco Mina, Joshua Armenia, Walid K. Chatila, Augustin Luna, Konnor La, Sofia Dimitriadoy, David L. Liu, Havish S. Kantheti, Zachary Heins, Angelica Ochoa, Benjamin Gross, Jianjiong Gao, Hongxin Zhang, Ritika Kundra, Cyriac Kandoth, Istemi Bahceci, Leonard Dervishi, Ugur Dogrusoz, Wanding Zhou, Hui Shen, Peter W. Laird, Alice H. Berger, Trever G. Bivona, Alexander J. Lazar, Gary Hammer, Thomas Giordano, Lawrence Kwong, Grant McArthur, Chenfei Huang, Mitchell J. Frederick, Frank McCormick, Matthew Meyerson, The Cancer Genome Atlas Research Network, Eliezer Van Allen, Andrew D. Cherniack, Giovanni Ciriello, Chris Sander, Nikolaus Schultz. The molecular landscape of oncogenic signaling pathways in The Cancer Genome Atlas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3302.
Collapse
Affiliation(s)
| | - Marco Mina
- 2University of Lausanne, Lausanne, Switzerland
| | | | | | | | - Konnor La
- 1Memorial Sloan Kettering, New York, NY
| | | | - David L. Liu
- 5Broad Institute of Harvard and MIT, Cambridge, MA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Hui Shen
- 8Van Andel Research Institute, Grand Rapids, MI
| | | | | | | | | | | | | | - Lawrence Kwong
- 11The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
18
|
Gao J, Mazor T, Ciftci E, Raman P, Lukasse P, Bahceci I, Sigaras A, Abeshouse A, Bruijn ID, Gross B, Kundra R, Lisman A, Ochoa A, Sheridan R, Su J, Sumer SO, Sun Y, Wang A, Wang J, Wilson M, Zhang H, Kumari P, Lindsay J, Kalletla K, Zhu K, Plantalech O, Schaeffer F, Tan S, Zaal D, Hagen SV, Bochove KV, Dogrusoz U, Pugh TJ, Resnick A, Sander C, Schultz N, Cerami E. Abstract 923: The cBioPortal for Cancer Genomics: An intuitive open-source platform for exploration, analysis and visualization of cancer genomics data. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-923] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The cBioPortal for Cancer Genomics is an open source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including cohort and patient-level visualization, mutation visualization, survival analysis, alteration enrichment analysis, and network analysis. The user interface is user-friendly, responsive, and makes genomic data easily accessible to scientists and clinicians. The public site (http://www.cbioportal.org) hosts data from more than 165 studies, including data from large consortia (TCGA and ICGC) and individual labs. With newly released functionality, users can now explore and query these studies individually or can combine multiple studies into new “virtual studies”. The main features of the portal include OncoPrints, a compact graphical representation of alterations in multiple genes across a cohort, mutational diagrams that show locations and frequencies of mutations in a single gene, Kaplan-Meier survival curves, plots that allow the visualization of correlation between different data types for a single or multiple genes (e.g. the correlation between DNA copy number and mRNA expression), among others. To facilitate interpretation of genomic data, the cBioPortal also now integrates annotations from several leading knowledgebases (OncoKB, CIViC, MyCancerGenome and COSMIC), as well as other resources that can guide variant interpretation (CancerHotspots, MutationAssessor, SIFT and PolyPhen).
The cBioPortal has been widely adopted by the cancer community, with dozens of private instances at academic institutions and pharmaceutical/biotechnology companies. The public portal is currently accessed by approximately 25K unique visitors per month. Another notable instance is the cBioPortal for AACR GENIE (http://www.cbioportal.org/genie/), which hosts 31,706 samples from AACR Project GENIE. The cBioPortal is fully open source and all code is available on GitHub (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children's Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve, an open source bioinformatics company based in the Netherlands. Ongoing development efforts are focused on (1) building the open source community; (2) implementing architectural and performance improvements; (3) expanding user support, documentation and training resources; (4) developing novel features to support immunogenomics and immunotherapy; (5) enhancing visualization of patient timelines, multiple tumor profiles, and cohort response; and (6) releasing a new public Application Programming Interface (API).
Citation Format: Jianjiong Gao, Tali Mazor, Ersin Ciftci, Pichai Raman, Pieter Lukasse, Istemi Bahceci, Alexandros Sigaras, Adam Abeshouse, Ino de Bruijn, Benjamin Gross, Ritika Kundra, Aaron Lisman, Angelica Ochoa, Robert Sheridan, Jing Su, Selcuk O. Sumer, Yichao Sun, Avery Wang, Jiaojiao Wang, Manda Wilson, Hongxin Zhang, Priti Kumari, James Lindsay, Karthik Kalletla, Kelsey Zhu, Oleguer Plantalech, Fedde Schaeffer, Sander Tan, Dionne Zaal, Sjoerd van Hagen, Kees van Bochove, Ugur Dogrusoz, Trevor J. Pugh, Adam Resnick, Chris Sander, Nikolaus Schultz, Ethan Cerami. The cBioPortal for Cancer Genomics: An intuitive open-source platform for exploration, analysis and visualization of cancer genomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 923.
Collapse
Affiliation(s)
- Jianjiong Gao
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Tali Mazor
- 2Dana-Farber Cancer Institute, Boston, MA
| | | | - Pichai Raman
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | | | - Adam Abeshouse
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Ino de Bruijn
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Benjamin Gross
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Ritika Kundra
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Aaron Lisman
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Angelica Ochoa
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | - Jing Su
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | - Yichao Sun
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Avery Wang
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Jiaojiao Wang
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Manda Wilson
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | - Hongxin Zhang
- 1Memorial Sloan Kettering Cancer Center, New York City, NY
| | | | | | | | - Kelsey Zhu
- 7Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | | | | | | | | | | | - Trevor J. Pugh
- 7Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Adam Resnick
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | | |
Collapse
|
19
|
Mazein A, Ostaszewski M, Kuperstein I, Watterson S, Le Novère N, Lefaudeux D, De Meulder B, Pellet J, Balaur I, Saqi M, Nogueira MM, He F, Parton A, Lemonnier N, Gawron P, Gebel S, Hainaut P, Ollert M, Dogrusoz U, Barillot E, Zinovyev A, Schneider R, Balling R, Auffray C. Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms. NPJ Syst Biol Appl 2018; 4:21. [PMID: 29872544 PMCID: PMC5984630 DOI: 10.1038/s41540-018-0059-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 04/26/2018] [Accepted: 05/04/2018] [Indexed: 12/18/2022] Open
Abstract
The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.
Collapse
Affiliation(s)
- Alexander Mazein
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Marek Ostaszewski
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Steven Watterson
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nicolas Le Novère
- 8The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT UK
| | - Diane Lefaudeux
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Bertrand De Meulder
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Johann Pellet
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Irina Balaur
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Mansoor Saqi
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Maria Manuela Nogueira
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Feng He
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg
| | - Andrew Parton
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nathanaël Lemonnier
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Piotr Gawron
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Pierre Hainaut
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg.,11Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis, University of Southern Denmark, Odense, Denmark
| | - Ugur Dogrusoz
- 12Faculty of Engineering, Computer Engineering Department, Bilkent University, Ankara, 06800 Turkey
| | - Emmanuel Barillot
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Andrei Zinovyev
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Reinhard Schneider
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Rudi Balling
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Charles Auffray
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| |
Collapse
|
20
|
Bahceci I, Dogrusoz U, La KC, Babur Ö, Gao J, Schultz N. PathwayMapper: a collaborative visual web editor for cancer pathways and genomic data. Bioinformatics 2018; 33:2238-2240. [PMID: 28334343 DOI: 10.1093/bioinformatics/btx149] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 03/14/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation While existing network visualization tools enable the exploration of cancer genomics data, most biologists prefer simplified, curated pathway diagrams, such as those featured in many manuscripts from The Cancer Genome Atlas (TCGA). These pathway diagrams typically summarize how a pathway is altered in individual cancer types, including alteration frequencies for each gene. Results To address this need, we developed the web-based tool PathwayMapper, which runs in most common web browsers. It can be used for viewing pre-curated cancer pathways, or as a graphical editor for creating new pathways, with the ability to overlay genomic alteration data from cBioPortal. In addition, a collaborative mode is available that allows scientists to co-operate interactively on constructing pathways, with support for concurrent modifications and built-in conflict resolution. Availability and Implementation The PathwayMapper tool is accessible at http://pathwaymapper.org and the code is available on Github ( https://github.com/iVis-at-Bilkent/pathway-mapper ). Contact ivis@cs.bilkent.edu.tr. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Istemi Bahceci
- Department of Computer Engineering, Bilkent University, Ankara, Turkey
| | - Ugur Dogrusoz
- Department of Computer Engineering, Bilkent University, Ankara, Turkey
| | - Konnor C La
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA
| | - Özgün Babur
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Jianjiong Gao
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
21
|
Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S, Chakravarty D, Daian F, Gao Q, Bailey MH, Liang WW, Foltz SM, Shmulevich I, Ding L, Heins Z, Ochoa A, Gross B, Gao J, Zhang H, Kundra R, Kandoth C, Bahceci I, Dervishi L, Dogrusoz U, Zhou W, Shen H, Laird PW, Way GP, Greene CS, Liang H, Xiao Y, Wang C, Iavarone A, Berger AH, Bivona TG, Lazar AJ, Hammer GD, Giordano T, Kwong LN, McArthur G, Huang C, Tward AD, Frederick MJ, McCormick F, Meyerson M, Van Allen EM, Cherniack AD, Ciriello G, Sander C, Schultz N. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell 2018; 173:321-337.e10. [PMID: 29625050 PMCID: PMC6070353 DOI: 10.1016/j.cell.2018.03.035] [Citation(s) in RCA: 1709] [Impact Index Per Article: 284.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 02/28/2018] [Accepted: 03/15/2018] [Indexed: 02/08/2023]
Abstract
Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.
Collapse
Affiliation(s)
- Francisco Sanchez-Vega
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Marco Mina
- Department of Computational Biology, University of Lausanne (UNIL), 1011 Lausanne, Vaud, Switzerland and Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Joshua Armenia
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Walid K Chatila
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Augustin Luna
- cBio Center, Dana-Farber Cancer Institute, Boston, MA; Department of Cell Biology, Harvard Medical School, Boston, MA
| | - Konnor C La
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - David L Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, US
| | | | - Sadegh Saghafinia
- Department of Computational Biology, University of Lausanne (UNIL), 1011 Lausanne, Vaud, Switzerland and Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Debyani Chakravarty
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Foysal Daian
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Qingsong Gao
- Department of Medicine and McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - Matthew H Bailey
- Department of Medicine and McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - Wen-Wei Liang
- Department of Medicine and McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - Steven M Foltz
- Department of Medicine and McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | | | - Li Ding
- Department of Medicine and McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri, 63110, USA; Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Zachary Heins
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Angelica Ochoa
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Benjamin Gross
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jianjiong Gao
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Hongxin Zhang
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ritika Kundra
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Cyriac Kandoth
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Istemi Bahceci
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Leonard Dervishi
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Wanding Zhou
- Van Andel Research Institute, 333 Bostwick Ave NE, Grand Rapids Michigan, 49503, USA
| | - Hui Shen
- Van Andel Research Institute, 333 Bostwick Ave NE, Grand Rapids Michigan, 49503, USA
| | - Peter W Laird
- Van Andel Research Institute, 333 Bostwick Ave NE, Grand Rapids Michigan, 49503, USA
| | - Gregory P Way
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Han Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | | | - Chen Wang
- Department of Health Sciences Research and Department of Obstetrics and Gynecology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - Antonio Iavarone
- Institute for Cancer Genetics, Department of Neurology and Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, 10032, USA
| | - Alice H Berger
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Trever G Bivona
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, 1450 3rd Street, San Francisco, California 94143, USA
| | - Alexander J Lazar
- Departments of Pathology, Genomic Medicine & Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd-Unit 85, Houston, Texas 77030, USA
| | - Gary D Hammer
- Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, Endocrine Oncology Program, University of Michigan, Ann Arbor, Michigan, MI 48105, USA
| | - Thomas Giordano
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI; Department of Internal Medicine, Division of Metabolism, Endocrinology & Diabetes, University of Michigan Medical School, Ann Arbor, MI; Comprehensive Cancer Center, Michigan Medicine, Ann Arbor, MI, USA
| | - Lawrence N Kwong
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Grant McArthur
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; University of Melbourne, Melbourne, VIC, Australia
| | - Chenfei Huang
- Dept. of Otolaryngology, Baylor College of Medicine, USA
| | - Aaron D Tward
- University of California, San Francisco Department of Otolaryngology-Head and Neck Surgery. 2233 Post Street, San Francisco, CA, 94143, USA
| | | | - Frank McCormick
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, 1450 3rd Street, San Francisco, CA 94143, USA
| | - Matthew Meyerson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, US
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, US
| | - Andrew D Cherniack
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, US
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne (UNIL), 1011 Lausanne, Vaud, Switzerland and Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
| | - Chris Sander
- cBio Center, Dana-Farber Cancer Institute, Boston, MA; Department of Cell Biology, Harvard Medical School, Boston, MA.
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Departments of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| |
Collapse
|
22
|
Gao J, Ciftci E, Raman P, Lukasse P, Bahceci I, Abeshouse A, Chen HW, Bruijn ID, Gross B, Heins Z, Kundra R, Lisman A, Ochoa A, Sheridan R, Sumer O, Sun Y, Wang J, Wilson M, Zhang H, Xu J, Dufilie A, Kumari P, Lindsay J, Cros A, Kalletla K, Schaeffer F, Tan S, Hagen SV, Reis-Filho J, Bochove KV, Dogrusoz U, Pugh T, Resnick A, Sander C, Cerami E, Schultz N. Abstract 2607: The cBioPortal for Cancer Genomics: an open source platform for accessing and interpreting complex cancer genomics data in the era of precision medicine. Cancer Res 2017. [DOI: 10.1158/1538-7445.am2017-2607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The cBioPortal for Cancer Genomics is an open-access portal (http://cbioportal.org) that enables interactive, exploratory analysis of large-scale cancer genomics data. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including cohort and patient-level visualization, mutation visualization, survival analysis, enrichment analysis, and network analysis. The user interface is user-friendly, responsive, and makes genomic data easily accessible to translational scientists, biologists, and clinicians.
The cBioPortal is a fully open source platform. All code is available on GitHub (https://github.com/cBioPortal/) under GNU Affero GPL license. The code base is maintained by multiple groups, including Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve, an open source bioinformatics company based in the Netherlands. More than 30 academic centers as well as multiple pharmaceutical and biotech companies maintain private instances of the cBioPortal. This includes the recently launched cBioPortal instance at the NCI Genomic Data Commons (https://cbioportal.gdc.nci.nih.gov/), and two large cBioPortal instances hosting genomic and clinical data at MSK and DFCI, supporting the MSK-IMPACT and DFCI Profile projects, two of the largest clinical sequencing efforts in the world.
Our multi-institutional software team has accelerated the progress of evolving the core architectural technologies and developing new features to keep pace with the rapidly advancing fields of cancer genomics and precision cancer medicine. For example, we have integrated multi-platform genomics data with extensive clinical data including patient demographics, treatment history, and survival data. We have also developed a patient-centric view that visualizes both clinical and genomic data with annotation from OncoKB knowledge base. In the next few years, the development team will focus on the following areas:
(1) Implementing major architectural changes to ensure future scalability and performance.
(2) New features to support precision medicine, including (i) improved integration of knowledge base annotation, (ii) enhanced visualization of patient timeline, drug response, and tumor evolution, (iii) new patient similarity metrics, (iv) improved support for immunogenomics and immunotherapy, and (v) new visualization and analysis features for understanding response to therapy.
(3) New analysis and target discovery features for large cohorts, including (i) supporting user-defined virtual cohort by selecting samples from multiple studies, and (ii) comparison of genomic or clinical characteristics of two or more selected cohorts.
(4) Expanding community outreach, user support and training, and documentation.
Citation Format: Jianjiong Gao, Ersin Ciftci, Pichai Raman, Pieter Lukasse, Istemi Bahceci, Adam Abeshouse, Hsiao-Wei Chen, Ino de Bruijn, Benjamin Gross, Zachary Heins, Ritika Kundra, Aaron Lisman, Angelica Ochoa, Robert Sheridan, Onur Sumer, Yichao Sun, Jiaojiao Wang, Manda Wilson, Hongxin Zhang, James Xu, Andy Dufilie, Priti Kumari, James Lindsay, Anthony Cros, Karthik Kalletla, Fedde Schaeffer, Sander Tan, Sjoerd van Hagen, Jorge Reis-Filho, Kees van Bochove, Ugur Dogrusoz, Trevor Pugh, Adam Resnick, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for Cancer Genomics: an open source platform for accessing and interpreting complex cancer genomics data in the era of precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2607. doi:10.1158/1538-7445.AM2017-2607
Collapse
Affiliation(s)
- Jianjiong Gao
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Pichai Raman
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | | | | | - Ino de Bruijn
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Zachary Heins
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ritika Kundra
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Aaron Lisman
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Onur Sumer
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yichao Sun
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jiaojiao Wang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Manda Wilson
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hongxin Zhang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - James Xu
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | | | | | | | | | | | | | - Trevor Pugh
- 6Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Adam Resnick
- 3Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | | |
Collapse
|
23
|
Gao J, Lindsay J, Watt S, Bahceci I, Lukasse P, Abeshouse A, Chen HW, de Bruijn I, Gross B, Li D, Kundra R, Heins Z, Reis-Filho J, Sumer O, Sun Y, Wang J, Wang Q, Zhang H, Kumari P, Sahin MF, de Ridder S, Schaeffer F, van Bochove K, Dogrusoz U, Pugh T, Sander C, Cerami E, Schultz N. Abstract 5277: The cBioPortal for cancer genomics and its application in precision oncology. Cancer Res 2016. [DOI: 10.1158/1538-7445.am2016-5277] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The cBioPortal for Cancer Genomics provides intuitive visualization and analysis of complex cancer genomics data. The public site (http://cbioportal.org/) is accessed by more than 1,500 researchers per day, and there are now dozens of local instances of the software that host private data sets at cancer centers around the globe.
We have recently released the software under an open source license, making it free to use and modify by anybody. The software and detailed documentation are available at https://github.com/cBioPortal/cbioportal.
We are now establishing a multi-institutional software development network, which will coordinate and drive the future development of the software and associated data pipelines. This group will focus on four main areas:
1. New analysis and visualization features, including:
a. Improved support for cross-cancer queries and cohort comparisons.
b. Enhanced clinical decision support for precision oncology, including an improved patient view with knowledge base integration, patient timelines and improved tools for visualizing tumor evolution.
2. New data pipelines, including support for new genomic data types and streamlined pipelines for TCGA and the International Cancer Genome Consortium (ICGC).
3. Software architecture and performance improvements.
4. Community engagement: Documentation, user support, and training.
This coordinated effort will help to further establish the cBioPortal as the software of choice in cancer genomics research, both in academia and the pharmaceutical industry. Furthermore, as the sequencing of tumor samples has entered clinical practice, we are expanding the features of the software so that it can be used for precision medicine at cancer centers. In particular, clean, web-accessible, interactive clinical reports integrating multiple sources of genome variation and clinical annotation over time has potential to improve clinical action beyond current text-based molecular reports. By making complex genomic data easily interpretable and linking it to information about drugs and clinical trials, the cBioPortal software has the potential to facilitate the use of genomic data in clinical decision making.
Citation Format: Jianjiong Gao, James Lindsay, Stuart Watt, Istemi Bahceci, Pieter Lukasse, Adam Abeshouse, Hsiao-Wei Chen, Ino de Bruijn, Benjamin Gross, Dong Li, Ritika Kundra, Zachary Heins, Jorge Reis-Filho, Onur Sumer, Yichao Sun, Jiaojiao Wang, Qingguo Wang, Hongxin Zhang, Priti Kumari, M. Furkan Sahin, Sander de Ridder, Fedde Schaeffer, Kees van Bochove, Ugur Dogrusoz, Trevor Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for cancer genomics and its application in precision oncology. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5277.
Collapse
Affiliation(s)
- Jianjiong Gao
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Stuart Watt
- 3Princess Margaret Cancer Center, Toronto, British Columbia, Canada
| | | | | | | | | | - Ino de Bruijn
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Dong Li
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ritika Kundra
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Zachary Heins
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Onur Sumer
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yichao Sun
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jiaojiao Wang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Qingguo Wang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hongxin Zhang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | | | - Trevor Pugh
- 3Princess Margaret Cancer Center, Toronto, British Columbia, Canada
| | | | | | | |
Collapse
|
24
|
Abstract
MOTIVATION Evolving technology has increased the focus on genomics. The combination of today's advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. RESULTS We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. AVAILABILITY AND IMPLEMENTATION An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). CONTACT ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Begum Genc
- The Insight Centre for Data Analytics, University College Cork, Western Road, Cork, Ireland, Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey and
| | - Ugur Dogrusoz
- Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey and Sander Lab, Memorial Sloan-Kettering Cancer Center, 417 E68th St., New York, NY 10065, USA
| |
Collapse
|
25
|
Babur Ö, Dogrusoz U, Çakır M, Aksoy BA, Schultz N, Sander C, Demir E. Integrating biological pathways and genomic profiles with ChiBE 2. BMC Genomics 2014; 15:642. [PMID: 25086704 PMCID: PMC4131037 DOI: 10.1186/1471-2164-15-642] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 07/24/2014] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Dynamic visual exploration of detailed pathway information can help researchers digest and interpret complex mechanisms and genomic datasets. RESULTS ChiBE is a free, open-source software tool for visualizing, querying, and analyzing human biological pathways in BioPAX format. The recently released version 2 can search for neighborhoods, paths between molecules, and common regulators/targets of molecules, on large integrated cellular networks in the Pathway Commons database as well as in local BioPAX models. Resulting networks can be automatically laid out for visualization using a graphically rich, process-centric notation. Profiling data from the cBioPortal for Cancer Genomics and expression data from the Gene Expression Omnibus can be overlaid on these networks. CONCLUSIONS ChiBE's new capabilities are organized around a genomics-oriented workflow and offer a unique comprehensive pathway analysis solution for genomics researchers. The software is freely available at http://code.google.com/p/chibe.
Collapse
Affiliation(s)
- Özgün Babur
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10065, USA.
| | | | | | | | | | | | | |
Collapse
|
26
|
Abstract
We present a new algorithm for automatic layout of clustered graphs using a circular style. The algorithm tries to determine optimal location and orientation of individual clusters intrinsically within a modified spring embedder. Heuristics such as reversal of the order of nodes in a cluster and swap of neighboring node pairs in the same cluster are employed intermittently to further relax the spring embedder system, resulting in reduced inter-cluster edge crossings. Unlike other algorithms generating circular drawings, our algorithm does not require the quotient graph to be acyclic, nor does it sacrifice the edge crossing number of individual clusters to improve respective positioning of the clusters. Moreover, it reduces the total area required by a cluster by using the space inside the associated circle. Experimental results show that the execution time and quality of the produced drawings with respect to commonly accepted layout criteria are quite satisfactory, surpassing previous algorithms. The algorithm has also been successfully implemented and made publicly available as part of a compound and clustered graph editing and layout tool named CHISIO.
Collapse
Affiliation(s)
- Ugur Dogrusoz
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey.
| | | | | |
Collapse
|
27
|
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013; 6:pl1. [PMID: 23550210 PMCID: PMC4160307 DOI: 10.1126/scisignal.2004088] [Citation(s) in RCA: 9963] [Impact Index Per Article: 905.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
Collapse
Affiliation(s)
- Jianjiong Gao
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
28
|
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012; 2:401-4. [PMID: 22588877 DOI: 10.1158/2159-8290.cd-12-0095] [Citation(s) in RCA: 11054] [Impact Index Per Article: 921.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
Collapse
Affiliation(s)
- Ethan Cerami
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
29
|
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012. [PMID: 22588877 DOI: 10.1158/2159‐8290.cd‐12‐0095] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
Collapse
Affiliation(s)
- Ethan Cerami
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
30
|
van Iersel MP, Villéger AC, Czauderna T, Boyd SE, Bergmann FT, Luna A, Demir E, Sorokin A, Dogrusoz U, Matsuoka Y, Funahashi A, Aladjem MI, Mi H, Moodie SL, Kitano H, Le Novère N, Schreiber F. Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics 2012; 28:2016-21. [PMID: 22581176 PMCID: PMC3400951 DOI: 10.1093/bioinformatics/bts270] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. Availability and implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. Contact:sbgn-libsbgn@lists.sourceforge.net
Collapse
|
31
|
Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I, Wu G, D'Eustachio P, Schaefer C, Luciano J, Schacherer F, Martinez-Flores I, Hu Z, Jimenez-Jacinto V, Joshi-Tope G, Kandasamy K, Lopez-Fuentes AC, Mi H, Pichler E, Rodchenkov I, Splendiani A, Tkachev S, Zucker J, Gopinath G, Rajasimha H, Ramakrishnan R, Shah I, Syed M, Anwar N, Babur Ö, Blinov M, Brauner E, Corwin D, Donaldson S, Gibbons F, Goldberg R, Hornbeck P, Luna A, Murray-Rust P, Neumann E, Reubenacker O, Samwald M, van Iersel M, Wimalaratne S, Allen K, Braun B, Whirl-Carrillo M, Cheung KH, Dahlquist K, Finney A, Gillespie M, Glass E, Gong L, Haw R, Honig M, Hubaut O, Kane D, Krupa S, Kutmon M, Leonard J, Marks D, Merberg D, Petri V, Pico A, Ravenscroft D, Ren L, Shah N, Sunshine M, Tang R, Whaley R, Letovksy S, Buetow KH, Rzhetsky A, Schachter V, Sobral BS, Dogrusoz U, McWeeney S, Aladjem M, Birney E, Collado-Vides J, Goto S, Hucka M, Novère NL, Maltsev N, Pandey A, Thomas P, Wingender E, Karp PD, Sander C, Bader GD. Erratum: Corrigendum: The BioPAX community standard for pathway data sharing. Nat Biotechnol 2010. [DOI: 10.1038/nbt1210-1308c] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
32
|
Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I, Wu G, D'Eustachio P, Schaefer C, Luciano J, Schacherer F, Martinez-Flores I, Hu Z, Jimenez-Jacinto V, Joshi-Tope G, Kandasamy K, Lopez-Fuentes AC, Mi H, Pichler E, Rodchenkov I, Splendiani A, Tkachev S, Zucker J, Gopinath G, Rajasimha H, Ramakrishnan R, Shah I, Syed M, Anwar N, Babur O, Blinov M, Brauner E, Corwin D, Donaldson S, Gibbons F, Goldberg R, Hornbeck P, Luna A, Murray-Rust P, Neumann E, Ruebenacker O, Reubenacker O, Samwald M, van Iersel M, Wimalaratne S, Allen K, Braun B, Whirl-Carrillo M, Cheung KH, Dahlquist K, Finney A, Gillespie M, Glass E, Gong L, Haw R, Honig M, Hubaut O, Kane D, Krupa S, Kutmon M, Leonard J, Marks D, Merberg D, Petri V, Pico A, Ravenscroft D, Ren L, Shah N, Sunshine M, Tang R, Whaley R, Letovksy S, Buetow KH, Rzhetsky A, Schachter V, Sobral BS, Dogrusoz U, McWeeney S, Aladjem M, Birney E, Collado-Vides J, Goto S, Hucka M, Le Novère N, Maltsev N, Pandey A, Thomas P, Wingender E, Karp PD, Sander C, Bader GD. The BioPAX community standard for pathway data sharing. Nat Biotechnol 2010; 28:935-42. [PMID: 20829833 PMCID: PMC3001121 DOI: 10.1038/nbt.1666] [Citation(s) in RCA: 432] [Impact Index Per Article: 30.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.
Collapse
Affiliation(s)
- Emek Demir
- Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
33
|
Abstract
Proteins that modulate the activity of transcription factors, often called modulators, play a critical role in creating tissue- and context-specific gene expression responses to the signals cells receive. GEM (Gene Expression Modulation) is a probabilistic framework that predicts modulators, their affected targets and mode of action by combining gene expression profiles, protein–protein interactions and transcription factor–target relationships. Using GEM, we correctly predicted a significant number of androgen receptor modulators and observed that most modulators can both act as co-activators and co-repressors for different target genes.
Collapse
Affiliation(s)
- Ozgün Babur
- Center for Bioinformatics and Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | | | | | | | | |
Collapse
|
34
|
Abstract
With recent advancements in techniques for cellular data acquisition, information on cellular processes has been increasing at a dramatic rate. Visualization is critical to analyzing and interpreting complex information; representing cellular processes or pathways is no exception. VISIBIOweb is a free, open-source, web-based pathway visualization and layout service for pathway models in BioPAX format. With VISIBIOweb, one can obtain well-laid-out views of pathway models using the standard notation of the Systems Biology Graphical Notation (SBGN), and can embed such views within one's web pages as desired. Pathway views may be navigated using zoom and scroll tools; pathway object properties, including any external database references available in the data, may be inspected interactively. The automatic layout component of VISIBIOweb may also be accessed programmatically from other tools using Hypertext Transfer Protocol (HTTP). The web site is free and open to all users and there is no login requirement. It is available at: http://visibioweb.patika.org.
Collapse
Affiliation(s)
- Alptug Dilek
- Center for Bioinformatics and Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | | | | |
Collapse
|
35
|
Abstract
SUMMARY Representing models of cellular processes or pathways in a graphically rich form facilitates interpretation of biological observations and generation of new hypotheses. Solving biological problems using large pathway datasets requires software that can combine data mapping, querying and visualization as well as providing access to diverse data resources on the Internet. ChiBE is an open source software application that features user-friendly multi-view display, navigation and manipulation of pathway models in BioPAX format. Pathway views are rendered in a feature-rich format, and may be laid out and edited with state-of-the-art visualization methods, including compound or nested structures for visualizing cellular compartments and molecular complexes. Users can easily query and visualize pathways through an integrated Pathway Commons query tool and analyze molecular profiles in pathway context. AVAILABILITY http://www.bilkent.edu.tr/%7Ebcbi/chibe.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Ozgun Babur
- Center for Bioinformatics, Bilkent University, Ankara, Turkey
| | | | | | | |
Collapse
|
36
|
Dogrusoz U, Cetintas A, Demir E, Babur O. Algorithms for effective querying of compound graph-based pathway databases. BMC Bioinformatics 2009; 10:376. [PMID: 19917102 PMCID: PMC2784781 DOI: 10.1186/1471-2105-10-376] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2008] [Accepted: 11/16/2009] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools. RESULTS Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases. CONCLUSION The PATIKA Project Web site is http://www.patika.org. PATIKAweb version 2.1 is available at http://web.patika.org.
Collapse
Affiliation(s)
- Ugur Dogrusoz
- Center for Bioinformatics and Computer Engineering Dept., Bilkent University, Ankara, Turkey
| | - Ahmet Cetintas
- Center for Bioinformatics and Computer Engineering Dept., Bilkent University, Ankara, Turkey
| | - Emek Demir
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Ozgun Babur
- Center for Bioinformatics and Computer Engineering Dept., Bilkent University, Ankara, Turkey
| |
Collapse
|
37
|
Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghazal P, Kawaji H, Li L, Matsuoka Y, Villéger A, Boyd SE, Calzone L, Courtot M, Dogrusoz U, Freeman TC, Funahashi A, Ghosh S, Jouraku A, Kim S, Kolpakov F, Luna A, Sahle S, Schmidt E, Watterson S, Wu G, Goryanin I, Kell DB, Sander C, Sauro H, Snoep JL, Kohn K, Kitano H. Erratum: The Systems Biology Graphical Notation. Nat Biotechnol 2009. [DOI: 10.1038/nbt0909-864d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
38
|
Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghazal P, Kawaji H, Li L, Matsuoka Y, Villéger A, Boyd SE, Calzone L, Courtot M, Dogrusoz U, Freeman TC, Funahashi A, Ghosh S, Jouraku A, Kim S, Kolpakov F, Luna A, Sahle S, Schmidt E, Watterson S, Wu G, Goryanin I, Kell DB, Sander C, Sauro H, Snoep JL, Kohn K, Kitano H. The Systems Biology Graphical Notation. Nat Biotechnol 2009; 27:735-41. [PMID: 19668183 DOI: 10.1038/nbt.1558] [Citation(s) in RCA: 534] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
Collapse
|
39
|
|
40
|
Abstract
High-throughput experiments, most significantly DNA microarrays, provide us with system-scale profiles. Connecting these data with existing biological networks poses a formidable challenge to uncover facts about a cell's proteome. Studies and tools with this purpose are limited to networks with simple structure, such as protein-protein interaction graphs, or do not go much beyond than simply displaying values on the network. We have built a microarray data analysis tool, named PATIKAmad, which can be used to associate microarray data with the pathway models in mechanistic detail, and provides facilities for visualization, clustering, querying, and navigation of biological graphs related with loaded microarray experiments. PATIKAmad is freely available to noncommercial users as a new module of PATIKAweb at http://web.patika.org.
Collapse
Affiliation(s)
- Ozgun Babur
- Center for Bioinformatics, Bilkent University, Ankara, Turkey
| | | | | | | |
Collapse
|
41
|
|
42
|
Dogrusoz U, Erson EZ, Giral E, Demir E, Babur O, Cetintas A, Colak R. PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization. Bioinformatics 2005; 22:374-5. [PMID: 16287939 DOI: 10.1093/bioinformatics/bti776] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.
Collapse
Affiliation(s)
- U Dogrusoz
- Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey.
| | | | | | | | | | | | | |
Collapse
|
43
|
Demir E, Babur O, Dogrusoz U, Gursoy A, Ayaz A, Gulesir G, Nisanci G, Cetin-Atalay R. An ontology for collaborative construction and analysis of cellular pathways. Bioinformatics 2004; 20:349-56. [PMID: 14960461 DOI: 10.1093/bioinformatics/btg416] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION As the scientific curiosity in genome studies shifts toward identification of functions of the genomes in large scale, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. In this regard, it is essential to be able to store, integrate, access and analyze this data effectively with the help of software tools. Clearly this requires a strong ontology that is intuitive, comprehensive and uncomplicated. RESULTS We define an ontology for an intuitive, comprehensive and uncomplicated representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information via collaboration, and supports manipulation of the stored data. In addition, it facilitates concurrent modifications to the data while maintaining its validity and consistency. Furthermore, novel structures for representation of multiple levels of abstraction for pathways and homologies is provided. Lastly, our ontology supports efficient querying of large amounts of data. We have also developed a software tool named pathway analysis tool for integration and knowledge acquisition (PATIKA) providing an integrated, multi-user environment for visualizing and manipulating network of cellular events. PATIKA implements the basics of our ontology.
Collapse
Affiliation(s)
- E Demir
- Center for Bioinformatics, Bilkent University, Ankara, Turkey
| | | | | | | | | | | | | | | |
Collapse
|
44
|
Demir E, Babur O, Dogrusoz U, Gursoy A, Nisanci G, Cetin-Atalay R, Ozturk M. PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 2002; 18:996-1003. [PMID: 12117798 DOI: 10.1093/bioinformatics/18.7.996] [Citation(s) in RCA: 90] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. RESULTS We define an ontology for a comprehensive representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporation of the stored data, as well as multiple levels of abstraction. Based on this ontology, we present the architecture of an integrated environment named Patika (Pathway Analysis Tool for Integration and Knowledge Acquisition). Patika is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that Patika will be a valuable tool for rapid knowledge acquisition, microarray generated large-scale data interpretation, disease gene identification, and drug development. AVAILABILITY A prototype of Patika is available upon request from the authors.
Collapse
Affiliation(s)
- E Demir
- Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey
| | | | | | | | | | | | | |
Collapse
|
45
|
|