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Article ; Online: Critical limitations of consensus clustering in class discovery.

Șenbabaoğlu, Yasin / Michailidis, George / Li, Jun Z

2014 Volume 4, Page(s) 6207

Abstract: Consensus clustering (CC) has been adopted for unsupervised class discovery in many genomic studies. It calculates how frequently two samples are grouped together in repeated clustering runs, and uses the resulting pairwise "consensus rates" for visual ... ...

Abstract	Consensus clustering (CC) has been adopted for unsupervised class discovery in many genomic studies. It calculates how frequently two samples are grouped together in repeated clustering runs, and uses the resulting pairwise "consensus rates" for visual demonstration that clusters exist, for comparing cluster stability, and for estimating the optimal cluster number (K). However, the sensitivity and specificity of CC have not been systemically assessed. Through simulations we find that CC is able to divide randomly generated unimodal data into apparently stable clusters for a range of K, essentially reporting chance partitions of cluster-less data. For data with known structure, the common implementations of CC perform poorly in identifying the true K. These results suggest that CC should be applied and interpreted with caution. We found that a new metric based on CC, the proportion of ambiguously clustered pairs (PAC), infers K equally or more reliably than similar methods in simulated data with known K. Our overall approach involves the use of realistic null distributions based on the observed gene-gene correlation structure in a given study, and the implementation of PAC to more accurately estimate K. We discuss the strength of our approach in the context of other ensemble-based methods.
MeSH term(s)	Algorithms ; Cluster Analysis ; Computer Simulation ; Epistasis, Genetic ; Genes ; Models, Genetic
Language	English
Publishing date	2014-08-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/srep06207
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Article ; Online: The neutrophil protein CD177 is a novel PDPN receptor that regulates human cancer-associated fibroblast physiology.

Astarita, Jillian L / Keerthivasan, Shilpa / Husain, Bushra / Şenbabaoğlu, Yasin / Verschueren, Erik / Gierke, Sarah / Pham, Victoria C / Peterson, Sean M / Chalouni, Cecile / Pierce, Andrew A / Lill, Jennie R / Gonzalez, Lino C / Martinez-Martin, Nadia / Turley, Shannon J

PloS one

2021 Volume 16, Issue 12, Page(s) e0260800

Abstract: The cancer-associated fibroblast (CAF) marker podoplanin (PDPN) is generally correlated with poor clinical outcomes in cancer patients and thus represents a promising therapeutic target. Despite its biomedical relevance, basic aspects of PDPN biology ... ...

Abstract	The cancer-associated fibroblast (CAF) marker podoplanin (PDPN) is generally correlated with poor clinical outcomes in cancer patients and thus represents a promising therapeutic target. Despite its biomedical relevance, basic aspects of PDPN biology such as its cellular functions and cell surface ligands remain poorly uncharacterized, thus challenging drug development. Here, we utilize a high throughput platform to elucidate the PDPN cell surface interactome, and uncover the neutrophil protein CD177 as a new binding partner. Quantitative proteomics analysis of the CAF phosphoproteome reveals a role for PDPN in cell signaling, growth and actomyosin contractility, among other processes. Moreover, cellular assays demonstrate that CD177 is a functional antagonist, recapitulating the phenotype observed in PDPN-deficient CAFs. In sum, starting from the unbiased elucidation of the PDPN co-receptome, our work provides insights into PDPN functions and reveals the PDPN/CD177 axis as a possible modulator of fibroblast physiology in the tumor microenvironment.
MeSH term(s)	Apoptosis ; Biomarkers, Tumor/genetics ; Biomarkers, Tumor/metabolism ; Cancer-Associated Fibroblasts/immunology ; Cancer-Associated Fibroblasts/metabolism ; Cancer-Associated Fibroblasts/pathology ; Cell Proliferation ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/immunology ; Colorectal Neoplasms/metabolism ; Colorectal Neoplasms/pathology ; GPI-Linked Proteins/genetics ; GPI-Linked Proteins/metabolism ; Gene Expression Regulation, Neoplastic ; Humans ; Isoantigens/genetics ; Isoantigens/metabolism ; Membrane Glycoproteins/genetics ; Membrane Glycoproteins/metabolism ; Neutrophils/immunology ; Neutrophils/metabolism ; Prognosis ; Receptors, Cell Surface/genetics ; Receptors, Cell Surface/metabolism ; Survival Rate ; Tumor Cells, Cultured ; Tumor Microenvironment
Chemical Substances	Biomarkers, Tumor ; CD177 protein, human ; GPI-Linked Proteins ; Isoantigens ; Membrane Glycoproteins ; PDPN protein, human ; Receptors, Cell Surface
Language	English
Publishing date	2021-12-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0260800
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Article ; Online: OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.

Lim, Néhémy / Senbabaoglu, Yasin / Michailidis, George / d'Alché-Buc, Florence

Bioinformatics (Oxford, England)

2013 Volume 29, Issue 11, Page(s) 1416–1423

Abstract: Motivation: Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of ...

Abstract	Motivation: Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time-series data have appeared in the literature addressing this problem, with the latter using linear temporal models. Nonlinear dynamical models are particularly appropriate for this inference task, given the generation mechanism of the time-series data. In this study, we introduce a novel nonlinear autoregressive model based on operator-valued kernels that simultaneously learns the model parameters, as well as the network structure. Results: A flexible boosting algorithm (OKVAR-Boost) that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model. Specifically, at each boosting iteration, a regularized Operator-valued Kernel-based Vector AutoRegressive model (OKVAR) is trained on a random subnetwork. The final model consists of an ensemble of such models. The empirical estimation of the ensemble model's Jacobian matrix provides an estimation of the network structure. The performance of the proposed algorithm is first evaluated on a number of benchmark datasets from the DREAM3 challenge and then on real datasets related to the In vivo Reverse-Engineering and Modeling Assessment (IRMA) and T-cell networks. The high-quality results obtained strongly indicate that it outperforms existing approaches. Availability: The OKVAR-Boost Matlab code is available as the archive: http://amis-group.fr/sourcecode-okvar-boost/OKVARBoost-v1.0.zip. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Computer Simulation ; Gene Regulatory Networks ; Models, Genetic ; Nonlinear Dynamics ; T-Lymphocytes/immunology
Language	English
Publishing date	2013-04-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btt167
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Article ; Online: Graph Curvature for Differentiating Cancer Networks.

Sandhu, Romeil / Georgiou, Tryphon / Reznik, Ed / Zhu, Liangjia / Kolesov, Ivan / Senbabaoglu, Yasin / Tannenbaum, Allen

Scientific reports

2015 Volume 5, Page(s) 12323

Abstract: Cellular interactions can be modeled as complex dynamical systems represented by weighted graphs. The functionality of such networks, including measures of robustness, reliability, performance, and efficiency, are intrinsically tied to the topology and ... ...

Abstract	Cellular interactions can be modeled as complex dynamical systems represented by weighted graphs. The functionality of such networks, including measures of robustness, reliability, performance, and efficiency, are intrinsically tied to the topology and geometry of the underlying graph. Utilizing recently proposed geometric notions of curvature on weighted graphs, we investigate the features of gene co-expression networks derived from large-scale genomic studies of cancer. We find that the curvature of these networks reliably distinguishes between cancer and normal samples, with cancer networks exhibiting higher curvature than their normal counterparts. We establish a quantitative relationship between our findings and prior investigations of network entropy. Furthermore, we demonstrate how our approach yields additional, non-trivial pair-wise (i.e. gene-gene) interactions which may be disrupted in cancer samples. The mathematical formulation of our approach yields an exact solution to calculating pair-wise changes in curvature which was computationally infeasible using prior methods. As such, our findings lay the foundation for an analytical approach to studying complex biological networks.
MeSH term(s)	Algorithms ; Gene Regulatory Networks ; Humans ; Metabolic Networks and Pathways ; Models, Biological ; Neoplasms/etiology ; Neoplasms/metabolism
Language	English
Publishing date	2015-07-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/srep12323
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Article ; Online: Tumor-intrinsic expression of the autophagy gene Atg16l1 suppresses anti-tumor immunity in colorectal cancer.

Taraborrelli, Lucia / Şenbabaoğlu, Yasin / Wang, Lifen / Lim, Junghyun / Blake, Kerrigan / Kljavin, Noelyn / Gierke, Sarah / Scherl, Alexis / Ziai, James / McNamara, Erin / Owyong, Mark / Rao, Shilpa / Calviello, Aslihan Karabacak / Oreper, Daniel / Jhunjhunwala, Suchit / Argiles, Guillem / Bendell, Johanna / Kim, Tae Won / Ciardiello, Fortunato /

Wongchenko, Matthew J / de Sauvage, Frederic J / de Sousa E Melo, Felipe / Yan, Yibing / West, Nathaniel R / Murthy, Aditya

Nature communications

2023 Volume 14, Issue 1, Page(s) 5945

Abstract: Microsatellite-stable colorectal cancer (MSS-CRC) is highly refractory to immunotherapy. Understanding tumor-intrinsic determinants of immunotherapy resistance is critical to improve MSS-CRC patient outcomes. Here, we demonstrate that high tumor ... ...

Abstract	Microsatellite-stable colorectal cancer (MSS-CRC) is highly refractory to immunotherapy. Understanding tumor-intrinsic determinants of immunotherapy resistance is critical to improve MSS-CRC patient outcomes. Here, we demonstrate that high tumor expression of the core autophagy gene ATG16L1 is associated with poor clinical response to anti-PD-L1 therapy in KRAS-mutant tumors from IMblaze370 (NCT02788279), a large phase III clinical trial of atezolizumab (anti-PD-L1) in advanced metastatic MSS-CRC. Deletion of Atg16l1 in engineered murine colon cancer organoids inhibits tumor growth in primary (colon) and metastatic (liver and lung) niches in syngeneic female hosts, primarily due to increased sensitivity to IFN-γ-mediated immune pressure. ATG16L1 deficiency enhances programmed cell death of colon cancer organoids induced by IFN-γ and TNF, thus increasing their sensitivity to host immunity. In parallel, ATG16L1 deficiency reduces tumor stem-like populations in vivo independently of adaptive immune pressure. This work reveals autophagy as a clinically relevant mechanism of immune evasion and tumor fitness in MSS-CRC and provides a rationale for autophagy inhibition to boost immunotherapy responses in the clinic.
MeSH term(s)	Animals ; Female ; Humans ; Mice ; Autophagy/genetics ; Autophagy-Related Proteins/genetics ; Colonic Neoplasms ; Colorectal Neoplasms/drug therapy ; Colorectal Neoplasms/genetics ; Genes, Regulator ; Liver ; Clinical Trials, Phase III as Topic
Chemical Substances	Atg16l1 protein, mouse ; Autophagy-Related Proteins
Language	English
Publishing date	2023-09-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-41618-7
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Article ; Online: Joint estimation of DNA copy number from multiple platforms.

Zhang, Nancy R / Senbabaoglu, Yasin / Li, Jun Z

Bioinformatics (Oxford, England)

2009 Volume 26, Issue 2, Page(s) 153–160

Abstract: Motivation: DNA copy number variants (CNVs) are gains and losses of segments of chromosomes, and comprise an important class of genetic variation. Recently, various microarray hybridization-based techniques have been developed for high-throughput ... ...

Abstract	Motivation: DNA copy number variants (CNVs) are gains and losses of segments of chromosomes, and comprise an important class of genetic variation. Recently, various microarray hybridization-based techniques have been developed for high-throughput measurement of DNA copy number. In many studies, multiple technical platforms or different versions of the same platform were used to interrogate the same samples; and it became necessary to pool information across these multiple sources to derive a consensus molecular profile for each sample. An integrated analysis is expected to maximize resolution and accuracy, yet currently there is no well-formulated statistical method to address the between-platform differences in probe coverage, assay methods, sensitivity and analytical complexity. Results: The conventional approach is to apply one of the CNV detection ('segmentation') algorithms to search for DNA segments of altered signal intensity. The results from multiple platforms are combined after segmentation. Here we propose a new method, Multi-Platform Circular Binary Segmentation (MPCBS), which pools statistical evidence across platforms during segmentation, and does not require pre-standardization of different data sources. It involves a weighted sum of t-statistics, which arises naturally from the generalized log-likelihood ratio of a multi-platform model. We show by comparing the integrated analysis of Affymetrix and Illumina SNP array data with Agilent and fosmid clone end-sequencing results on eight HapMap samples that MPCBS achieves improved spatial resolution, detection power and provides a natural consensus across platforms. We also apply the new method to analyze multi-platform data for tumor samples. Availability: The R package for MPCBS is registered on R-Forge (http://r-forge.r-project.org/) under project name MPCBS. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Computational Biology/methods ; DNA/chemistry ; DNA Copy Number Variations ; Databases, Genetic ; Gene Expression Profiling/methods ; Genetic Variation
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2009-11-20
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btp653
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Article ; Online: The Immunoglobulin Superfamily Receptome Defines Cancer-Relevant Networks Associated with Clinical Outcome.

Verschueren, Erik / Husain, Bushra / Yuen, Kobe / Sun, Yi / Paduchuri, Sairupa / Senbabaoglu, Yasin / Lehoux, Isabelle / Arena, Tia A / Wilson, Blair / Lianoglou, Steve / Bakalarski, Corey / Franke, Yvonne / Chan, Pamela / Wong, Athena W / Gonzalez, Lino C / Mariathasan, Sanjeev / Turley, Shannon J / Lill, Jennie R / Martinez-Martin, Nadia

Cell

2020 Volume 182, Issue 2, Page(s) 329–344.e19

Abstract: Cell surface receptors and their interactions play a central role in physiological and pathological signaling. Despite its clinical relevance, the immunoglobulin superfamily (IgSF) remains uncharacterized and underrepresented in databases. Here, we ... ...

Abstract	Cell surface receptors and their interactions play a central role in physiological and pathological signaling. Despite its clinical relevance, the immunoglobulin superfamily (IgSF) remains uncharacterized and underrepresented in databases. Here, we present a systematic extracellular protein map, the IgSF interactome. Using a high-throughput technology to interrogate most single transmembrane receptors for binding to 445 IgSF proteins, we identify over 500 interactions, 82% previously undocumented, and confirm more than 60 receptor-ligand pairs using orthogonal assays. Our study reveals a map of cell-type-specific interactions and the landscape of dysregulated receptor-ligand crosstalk in cancer, including selective loss of function for tumor-associated mutations. Furthermore, investigation of the IgSF interactome in a large cohort of cancer patients identifies interacting protein signatures associated with clinical outcome. The IgSF interactome represents an important resource to fuel biological discoveries and a framework for understanding the functional organization of the surfaceome during homeostasis and disease, ultimately informing therapeutic development.
MeSH term(s)	B7-H1 Antigen/metabolism ; Carcinoembryonic Antigen/metabolism ; Cell Communication ; Cluster Analysis ; Culture Media, Conditioned/chemistry ; HEK293 Cells ; Humans ; Immunoglobulins/chemistry ; Immunoglobulins/genetics ; Immunoglobulins/metabolism ; Ligands ; Mutation ; Neoplasms/genetics ; Neoplasms/metabolism ; Neoplasms/pathology ; Protein Binding ; Protein Interaction Maps ; Receptors, Cell Surface/chemistry ; Receptors, Cell Surface/genetics ; Receptors, Cell Surface/metabolism ; T-Lymphocytes/cytology ; T-Lymphocytes/immunology ; T-Lymphocytes/metabolism
Chemical Substances	B7-H1 Antigen ; CEACAM4 protein, human ; Carcinoembryonic Antigen ; Culture Media, Conditioned ; Immunoglobulins ; Ligands ; Receptors, Cell Surface
Language	English
Publishing date	2020-06-25
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	187009-9
ISSN	1097-4172 ; 0092-8674
ISSN (online)	1097-4172
ISSN	0092-8674
DOI	10.1016/j.cell.2020.06.007
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Article ; Online: Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival.

Morris, Luc G T / Riaz, Nadeem / Desrichard, Alexis / Şenbabaoğlu, Yasin / Hakimi, A Ari / Makarov, Vladimir / Reis-Filho, Jorge S / Chan, Timothy A

Oncotarget

2016 Volume 7, Issue 9, Page(s) 10051–10063

Abstract: As tumors accumulate genetic alterations, an evolutionary process occurs in which genetically distinct subclonal populations of cells co-exist, resulting in intratumor genetic heterogeneity (ITH). The clinical implications of ITH remain poorly defined. ... ...

Abstract	As tumors accumulate genetic alterations, an evolutionary process occurs in which genetically distinct subclonal populations of cells co-exist, resulting in intratumor genetic heterogeneity (ITH). The clinical implications of ITH remain poorly defined. Data are limited with respect to whether ITH is an independent determinant of patient survival outcomes, across different cancer types. Here, we report the results of a pan-cancer analysis of over 3300 tumors, showing a varied landscape of ITH across 9 cancer types. While some gene mutations are subclonal, the majority of driver gene mutations are clonal events, present in nearly all cancer cells. Strikingly, high levels of ITH are associated with poorer survival across diverse types of cancer. The adverse impact of high ITH is independent of other clinical, pathologic and molecular factors. High ITH tends to be associated with lower levels of tumor-infiltrating immune cells, but this association is not able to explain the observed survival differences. Together, these data show that ITH is a prognostic marker in multiple cancers. These results illuminate the natural history of cancer evolution, indicating that tumor heterogeneity represents a significant obstacle to cancer control.
MeSH term(s)	Clone Cells/metabolism ; Clone Cells/pathology ; Genetic Heterogeneity ; Genetic Predisposition to Disease/genetics ; Humans ; Multivariate Analysis ; Mutation ; Neoplasms/classification ; Neoplasms/genetics ; Neoplasms/pathology ; Polymorphism, Single Nucleotide ; Prognosis ; Survival Analysis
Language	English
Publishing date	2016-03-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2560162-3
ISSN	1949-2553 ; 1949-2553
ISSN (online)	1949-2553
ISSN	1949-2553
DOI	10.18632/oncotarget.7067
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Article ; Online: A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.

Şenbabaoğlu, Yasin / Sümer, Selçuk Onur / Sánchez-Vega, Francisco / Bemis, Debra / Ciriello, Giovanni / Schultz, Nikolaus / Sander, Chris

PLoS computational biology

2016 Volume 12, Issue 2, Page(s) e1004765

Abstract: Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has ... ...

Abstract	Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.
MeSH term(s)	Cluster Analysis ; Databases, Protein ; Gene Expression Profiling ; Humans ; Neoplasm Proteins/analysis ; Neoplasm Proteins/genetics ; Neoplasm Proteins/metabolism ; Neoplasms/genetics ; Neoplasms/metabolism ; Principal Component Analysis ; Protein Interaction Maps/physiology ; Proteomics/methods ; Software
Chemical Substances	Neoplasm Proteins
Language	English
Publishing date	2016-02-29
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1004765
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Article ; Online: Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response.

eLife

2021 Volume 10

Abstract: Defective autophagy is strongly associated with chronic inflammation. Loss-of-function of the core autophagy ... ...

Abstract	Defective autophagy is strongly associated with chronic inflammation. Loss-of-function of the core autophagy gene
MeSH term(s)	Animals ; Autophagy ; Autophagy-Related Proteins/deficiency ; Autophagy-Related Proteins/genetics ; Cells, Cultured ; Disease Models, Animal ; Dysentery, Bacillary/immunology ; Dysentery, Bacillary/metabolism ; Dysentery, Bacillary/microbiology ; Host-Pathogen Interactions ; Immunity, Innate ; Inflammation Mediators/metabolism ; Macrophages/immunology ; Macrophages/metabolism ; Macrophages/microbiology ; Mice, Inbred C57BL ; Mice, Knockout ; Microbial Viability ; Oxidative Stress ; Proteome ; Proteomics ; Shigella flexneri/immunology ; Shigella flexneri/metabolism ; Shigella flexneri/pathogenicity ; Virulence ; Mice
Chemical Substances	Atg16l1 protein, mouse ; Autophagy-Related Proteins ; Inflammation Mediators ; Proteome
Language	English
Publishing date	2021-06-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.62320
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