Publications by authors named "Özgün Babur"

26 Publications

  • Page 1 of 1

Newt: a comprehensive web-based tool for viewing, constructing, and analyzing biological maps.

Bioinformatics 2020 Oct 3. Epub 2020 Oct 3.

i-Vis Research Lab, Computer Engineering Department, Bilkent University, Ankara, Turkey.

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: 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.
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http://dx.doi.org/10.1093/bioinformatics/btaa850DOI Listing
October 2020

Phosphoproteomic quantitation and causal analysis reveal pathways in GPVI/ITAM-mediated platelet activation programs.

Blood 2020 11;136(20):2346-2358

Knight Cardiovascular Institute.

Platelets engage cues of pending vascular injury through coordinated adhesion, secretion, and aggregation responses. These rapid, progressive changes in platelet form and function are orchestrated downstream of specific receptors on the platelet surface and through intracellular signaling mechanisms that remain systematically undefined. This study brings together cell physiological and phosphoproteomics methods to profile signaling mechanisms downstream of the immunotyrosine activation motif (ITAM) platelet collagen receptor GPVI. Peptide tandem mass tag (TMT) labeling, sample multiplexing, synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) detected >3000 significant (false discovery rate < 0.05) phosphorylation events on >1300 proteins over conditions initiating and progressing GPVI-mediated platelet activation. With literature-guided causal inference tools, >300 site-specific signaling relations were mapped from phosphoproteomics data among key and emerging GPVI effectors (ie, FcRγ, Syk, PLCγ2, PKCδ, DAPP1). Through signaling validation studies and functional screening, other less-characterized targets were also considered within the context of GPVI/ITAM pathways, including Ras/MAPK axis proteins (ie, KSR1, SOS1, STAT1, Hsp27). Highly regulated GPVI/ITAM targets out of context of curated knowledge were also illuminated, including a system of >40 Rab GTPases and associated regulatory proteins, where GPVI-mediated Rab7 S72 phosphorylation and endolysosomal maturation were blocked by TAK1 inhibition. In addition to serving as a model for generating and testing hypotheses from omics datasets, this study puts forth a means to identify hemostatic effectors, biomarkers, and therapeutic targets relevant to thrombosis, vascular inflammation, and other platelet-associated disease states.
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http://dx.doi.org/10.1182/blood.2020005496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702475PMC
November 2020

Pathway Commons 2019 Update: integration, analysis and exploration of pathway data.

Nucleic Acids Res 2020 01;48(D1):D489-D497

cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

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.
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http://dx.doi.org/10.1093/nar/gkz946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145667PMC
January 2020

Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas.

Cell Rep 2019 07;28(5):1370-1384.e5

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.

The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.
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http://dx.doi.org/10.1016/j.celrep.2019.07.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546539PMC
July 2019

Identification, Quantification, and System Analysis of Protein N-ε Lysine Methylation in Anucleate Blood Platelets.

Proteomics 2019 06 9;19(11):e1900001. Epub 2019 May 9.

Department of Biomedical Engineering, Oregon Health & Science University, 97239, Portland, OR, USA.

Protein posttranslational modifications critically regulate a range of physiological and disease processes. In addition to tyrosine, serine, and threonine phosphorylation, reversible N-ε acylation and alkylation of protein lysine residues also modulate diverse aspects of cellular function. Studies of lysine acyl and alkyl modifications have focused on nuclear proteins in epigenetic regulation; however, lysine modifications are also prevalent on cytosolic proteins to serve increasingly apparent, although less understood roles in cell regulation. Here, the methyl-lysine (meK) proteome of anucleate blood platelets is characterized. With high-resolution, multiplex MS methods, 190 mono-, di-, and tri-meK modifications are identified on 150 different platelet proteins-including 28 meK modifications quantified by tandem mass tag (TMT) labeling. In addition to identifying meK modifications on calmodulin (CaM), GRP78 (HSPA5, BiP), and EF1A1 that have been previously characterized in other cell types, more novel modifications are also uncovered on cofilin, drebin-like protein (DBNL, Hip-55), DOCK8, TRIM25, and numerous other cytoplasmic proteins. Together, the results and analyses support roles for lysine methylation in mediating cytoskeletal, translational, secretory, and other cellular processes. MS data for this study have been deposited into the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD012217.
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http://dx.doi.org/10.1002/pmic.201900001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062300PMC
June 2019

Large-scale automated machine reading discovers new cancer-driving mechanisms.

Database (Oxford) 2018 01 1;2018. Epub 2018 Jan 1.

School of Information, University of Arizona, Tucson, AZ, USA.

PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput. We demonstrate that combining the extracted pathway fragments with existing biological data analysis algorithms that rely on curated models helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types. This work shows that combining human-curated 'big mechanisms' with extracted 'big data' can lead to a causal, predictive understanding of cellular processes and unlock important downstream applications.
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http://dx.doi.org/10.1093/database/bay098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156821PMC
January 2018

Platelet procoagulant phenotype is modulated by a p38-MK2 axis that regulates RTN4/Nogo proximal to the endoplasmic reticulum: utility of pathway analysis.

Am J Physiol Cell Physiol 2018 05 7;314(5):C603-C615. Epub 2018 Feb 7.

Department of Biochemistry and Molecular Biology, Oregon Health & Science University , Portland, Oregon.

Upon encountering physiological cues associated with damaged or inflamed endothelium, blood platelets set forth intracellular responses to ultimately support hemostatic plug formation and vascular repair. To gain insights into the molecular events underlying platelet function, we used a combination of interactome, pathway analysis, and other systems biology tools to analyze associations among proteins functionally modified by reversible phosphorylation upon platelet activation. While an interaction analysis mapped out a relative organization of intracellular mediators in platelet signaling, pathway analysis revealed directional signaling relations around protein kinase C (PKC) isoforms and mitogen-activated protein kinases (MAPKs) associated with platelet cytoskeletal dynamics, inflammatory responses, and hemostatic function. Pathway and causality analysis further suggested that platelets activate a specific p38-MK2 axis to phosphorylate RTN4 (reticulon-4, also known as Nogo), a Bcl-xl sequestration protein and critical regulator of endoplasmic reticulum (ER) physiology. In vitro, we find that platelets drive a p38-MK2-RTN4-Bcl-xl pathway associated with the regulation of the ER and platelet phosphatidylserine exposure. Together, our results support the use of pathway tools in the analysis of omics data sets as a means to help generate novel, mechanistic, and testable hypotheses for platelet studies while uncovering RTN4 as a putative regulator of platelet cell physiological responses.
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http://dx.doi.org/10.1152/ajpcell.00177.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6008067PMC
May 2018

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma.

Cancer Cell 2017 08;32(2):204-220.e15

Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address:

Comprehensive multiplatform analysis of 80 uveal melanomas (UM) identifies four molecularly distinct, clinically relevant subtypes: two associated with poor-prognosis monosomy 3 (M3) and two with better-prognosis disomy 3 (D3). We show that BAP1 loss follows M3 occurrence and correlates with a global DNA methylation state that is distinct from D3-UM. Poor-prognosis M3-UM divide into subsets with divergent genomic aberrations, transcriptional features, and clinical outcomes. We report change-of-function SRSF2 mutations. Within D3-UM, EIF1AX- and SRSF2/SF3B1-mutant tumors have distinct somatic copy number alterations and DNA methylation profiles, providing insight into the biology of these low- versus intermediate-risk clinical mutation subtypes.
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http://dx.doi.org/10.1016/j.ccell.2017.07.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619925PMC
August 2017

PathwayMapper: a collaborative visual web editor for cancer pathways and genomic data.

Bioinformatics 2017 Jul;33(14):2238-2240

Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

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.
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http://dx.doi.org/10.1093/bioinformatics/btx149DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859976PMC
July 2017

Assessment of roles for the Rho-specific guanine nucleotide dissociation inhibitor Ly-GDI in platelet function: a spatial systems approach.

Am J Physiol Cell Physiol 2017 Apr 1;312(4):C527-C536. Epub 2017 Feb 1.

Knight Cardiovascular Institute, School of Medicine, Oregon Health & Science University, Portland, Oregon;

On activation at sites of vascular injury, platelets undergo morphological alterations essential to hemostasis via cytoskeletal reorganizations driven by the Rho GTPases Rac1, Cdc42, and RhoA. Here we investigate roles for Rho-specific guanine nucleotide dissociation inhibitor proteins (RhoGDIs) in platelet function. We find that platelets express two RhoGDI family members, RhoGDI and Ly-GDI. Whereas RhoGDI localizes throughout platelets in a granule-like manner, Ly-GDI shows an asymmetric, polarized localization that largely overlaps with Rac1 and Cdc42 as well as microtubules and protein kinase C (PKC) in platelets adherent to fibrinogen. Antibody interference and platelet spreading experiments suggest a specific role for Ly-GDI in platelet function. Intracellular signaling studies based on interactome and pathways analyses also support a regulatory role for Ly-GDI, which is phosphorylated at PKC substrate motifs in a PKC-dependent manner in response to the platelet collagen receptor glycoprotein (GP) VI-specific agonist collagen-related peptide. Additionally, PKC inhibition diffuses the polarized organization of Ly-GDI in spread platelets relative to its colocalization with Rac1 and Cdc42. Together, our results suggest a role for Ly-GDI in the localized regulation of Rho GTPases in platelets and hypothesize a link between the PKC and Rho GTPase signaling systems in platelet function.
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http://dx.doi.org/10.1152/ajpcell.00274.2016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407014PMC
April 2017

PaxtoolsR: pathway analysis in R using Pathway Commons.

Bioinformatics 2016 04 18;32(8):1262-4. Epub 2015 Dec 18.

Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

Purpose: PaxtoolsR package enables access to pathway data represented in the BioPAX format and made available through the Pathway Commons webservice for users of the R language to aid in advanced pathway analyses. Features include the extraction, merging and validation of pathway data represented in the BioPAX format. This package also provides novel pathway datasets and advanced querying features for R users through the Pathway Commons webservice allowing users to query, extract and retrieve data and integrate these data with local BioPAX datasets.

Availability And Implementation: The PaxtoolsR package is compatible with versions of R 3.1.1 (and higher) on Windows, Mac OS X and Linux using Bioconductor 3.0 and is available through the Bioconductor R package repository along with source code and a tutorial vignette describing common tasks, such as data visualization and gene set enrichment analysis. Source code and documentation are at http://www.bioconductor.org/packages/paxtoolsr This plugin is free, open-source and licensed under the LGPL-3.

Contact: paxtools@cbio.mskcc.org or lunaa@cbio.mskcc.org.
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http://dx.doi.org/10.1093/bioinformatics/btv733DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824129PMC
April 2016

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

Elife 2015 Aug 18;4. Epub 2015 Aug 18.

Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.
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http://dx.doi.org/10.7554/eLife.04640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539601PMC
August 2015

SBGNViz: A Tool for Visualization and Complexity Management of SBGN Process Description Maps.

PLoS One 2015 1;10(6):e0128985. Epub 2015 Jun 1.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.

Background: Information about cellular processes and pathways is becoming increasingly available in detailed, computable standard formats such as BioPAX and SBGN. Effective visualization of this information is a key recurring requirement for biological data analysis, especially for -omic data. Biological data analysis is rapidly migrating to web based platforms; thus there is a substantial need for sophisticated web based pathway viewers that support these platforms and other use cases.

Results: Towards this goal, we developed a web based viewer named SBGNViz for process description maps in SBGN (SBGN-PD). SBGNViz can visualize both BioPAX and SBGN formats. Unique features of SBGNViz include the ability to nest nodes to arbitrary depths to represent molecular complexes and cellular locations, automatic pathway layout, editing and highlighting facilities to enable focus on sub-maps, and the ability to inspect pathway members for detailed information from EntrezGene. SBGNViz can be used within a web browser without any installation and can be readily embedded into web pages. SBGNViz has two editions built with ActionScript and JavaScript. The JavaScript edition, which also works on touch enabled devices, introduces novel methods for managing and reducing complexity of large SBGN-PD maps for more effective analysis.

Conclusion: SBGNViz fills an important gap by making the large and fast-growing corpus of rich pathway information accessible to web based platforms. SBGNViz can be used in a variety of contexts and in multiple scenarios ranging from visualization of the results of a single study in a web page to building data analysis platforms.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128985PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4451519PMC
April 2016

Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations.

Genome Biol 2015 Feb 26;16:45. Epub 2015 Feb 26.

Computational Biology Center, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box 460, New York, 10065, USA.

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.
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http://dx.doi.org/10.1186/s13059-015-0612-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4381444PMC
February 2015

Integrating biological pathways and genomic profiles with ChiBE 2.

BMC Genomics 2014 Aug 3;15:642. Epub 2014 Aug 3.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10065, USA.

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.
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http://dx.doi.org/10.1186/1471-2164-15-642DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131037PMC
August 2014

Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles.

Bioinformatics 2014 Jul 24;30(14):2051-9. Epub 2014 Mar 24.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065 and Tri-Institutional Training Program in Computational Biology & Medicine, New York, NY 10065, USA.

Motivation: Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared with normal cells. A recently reported example is the loss of one of the two isoenzymes in glioblastoma cancer cells such that the use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas (TCGA) and of tumor-derived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities.

Results: Based on homozygous deletions affecting metabolic enzymes in 16 TCGA cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass preclinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy.

Availability And Implementation: A web-based tool for exploring all vulnerabilities and their details is available at http://cbio.mskcc.org/cancergenomics/statius/ along with supplemental data files.
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http://dx.doi.org/10.1093/bioinformatics/btu164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4080742PMC
July 2014

Using biological pathway data with paxtools.

PLoS Comput Biol 2013 19;9(9):e1003194. Epub 2013 Sep 19.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.

A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.
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http://dx.doi.org/10.1371/journal.pcbi.1003194DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777916PMC
April 2014

Pattern search in BioPAX models.

Bioinformatics 2014 Jan 16;30(1):139-40. Epub 2013 Sep 16.

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 and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.

Motivation: BioPAX is a standard language for representing complex cellular processes, including metabolic networks, signal transduction and gene regulation. Owing to the inherent complexity of a BioPAX model, searching for a specific type of subnetwork can be non-trivial and difficult.

Results: We developed an open source and extensible framework for defining and searching graph patterns in BioPAX models. We demonstrate its use with a sample pattern that captures directed signaling relations between proteins. We provide search results for the pattern obtained from the Pathway Commons database and compare these results with the current data in signaling databases SPIKE and SignaLink. Results show that a pattern search in public pathway data can identify a substantial amount of signaling relations that do not exist in signaling databases.

Availability: BioPAX-pattern software was developed in Java. Source code and documentation is freely available at http://code.google.com/p/biopax-pattern under Lesser GNU Public License.
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http://dx.doi.org/10.1093/bioinformatics/btt539DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866551PMC
January 2014

Pathway Commons, a web resource for biological pathway data.

Nucleic Acids Res 2011 Jan 10;39(Database issue):D685-90. Epub 2010 Nov 10.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center 1275 York Avenue, Box 460, New York, NY 10065, USA.

Pathway Commons (http://www.pathwaycommons.org) is a collection of publicly available pathway data from multiple organisms. Pathway Commons provides a web-based interface that enables biologists to browse and search a comprehensive collection of pathways from multiple sources represented in a common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats and a web service that software developers can use to conveniently query and access all data. Database providers can share their pathway data via a common repository. Pathways include biochemical reactions, complex assembly, transport and catalysis events and physical interactions involving proteins, DNA, RNA, small molecules and complexes. Pathway Commons aims to collect and integrate all public pathway data available in standard formats. Pathway Commons currently contains data from nine databases with over 1400 pathways and 687,000 interactions and will be continually expanded and updated.
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http://dx.doi.org/10.1093/nar/gkq1039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013659PMC
January 2011

The BioPAX community standard for pathway data sharing.

Nat Biotechnol 2010 Sep 9;28(9):935-42. Epub 2010 Sep 9.

Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
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http://dx.doi.org/10.1038/nbt.1666DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001121PMC
September 2010

Discovering modulators of gene expression.

Nucleic Acids Res 2010 Sep 13;38(17):5648-56. Epub 2010 May 13.

Center for Bioinformatics and Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.

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.
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http://dx.doi.org/10.1093/nar/gkq287DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943625PMC
September 2010

ChiBE: interactive visualization and manipulation of BioPAX pathway models.

Bioinformatics 2010 Feb 9;26(3):429-31. Epub 2009 Dec 9.

Center for Bioinformatics, Bilkent University, Ankara, Turkey.

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.
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http://dx.doi.org/10.1093/bioinformatics/btp665DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815657PMC
February 2010

Algorithms for effective querying of compound graph-based pathway databases.

BMC Bioinformatics 2009 Nov 16;10:376. Epub 2009 Nov 16.

Computer Engineering Dept, Bilkent University, Center for Bioinformatics, Ankara, Turkey.

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.
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http://dx.doi.org/10.1186/1471-2105-10-376DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2784781PMC
November 2009

PATIKAmad: putting microarray data into pathway context.

Proteomics 2008 Jun;8(11):2196-8

Center for Bioinformatics, Bilkent University, Ankara, Turkey.

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.
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http://dx.doi.org/10.1002/pmic.200700769DOI Listing
June 2008