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Organizing Committee
University of Alaska Fairbanks
University of Tasmania
University of Auckland
University of Maryland, College Park
Abstract
Inferring phylogenetic relationships requires complex mathematical models. As advances are made in modeling complex evolutionary processes, we need practical algorithms that translate the mathematical advances into software tools. This translation of theory to usable tools is more challenging than it may appear. Phylogenetic problems are often NP-hard, necessitating heuristic solutions that can compromise accuracy. The accuracy and scalability of such heuristics are often established only empirically, creating a need for careful simulation and testing. Moreover, software tools are used within complicated pipelines, so the input to tools may be impacted by errors from prior data processing steps. In addition, the output has many aspects, from the discrete-spaced topology to continuous-spaced branch lengths and other numeric parameters, and measures of uncertainty and visualizations. Furthermore, software tools need to be evolvable, allowing the incorporation of new features and new models, and optimized, allowing efficient use of vectorization, parallelization, and memory hierarchy.
In this workshop, we will consider challenges and best practices in translating theory to software for phylogenetic inference. We will discuss strategies adopted by the tool development community, their relative advantages, and lessons learned through software development. We will discuss a variety of objective functions (parametric and non-parametric) and algorithmic techniques used by inference methods. We will cover parallelization and how it interacts with the choice of objective function and algorithmic technique. Finally, we will discuss best practices for evaluating new and existing software tools, not just to validate the implementation but also to study accuracy and scalability in relation to theory and usage in realistic bioinformatics pipelines. In addition to discussing how theory influences tools, we will examine how tool development can inform the theoretical questions asked.
In this workshop, we will consider challenges and best practices in translating theory to software for phylogenetic inference. We will discuss strategies adopted by the tool development community, their relative advantages, and lessons learned through software development. We will discuss a variety of objective functions (parametric and non-parametric) and algorithmic techniques used by inference methods. We will cover parallelization and how it interacts with the choice of objective function and algorithmic technique. Finally, we will discuss best practices for evaluating new and existing software tools, not just to validate the implementation but also to study accuracy and scalability in relation to theory and usage in realistic bioinformatics pipelines. In addition to discussing how theory influences tools, we will examine how tool development can inform the theoretical questions asked.
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Workshop Schedule
Monday, November 18, 2024
8:30 - 8:50 AM EST
Check In
11th Floor Collaborative Space
8:50 - 9:00 AM EST
Welcome
11th Floor Lecture Hall
Session Chair
Brendan Hassett, ICERM/Brown University
9:00 - 9:45 AM EST
Practical, Accurate, and Scalable Computational and Statistical Approaches for Phylogenomics
11th Floor Lecture Hall
Speaker
Sudhir Kumar, Temple University
Session Chair
Elizabeth Allman, University of Alaska Fairbanks
Abstract
Molecular evolutionary and phylogenetic analyses are critical to understanding evolutionary biology and functional genomics. However, as sequencing technologies become more affordable and databases expand, the computational demands for analyzing large sequence datasets continue to rise. These challenges—ranging from long computation times to memory constraints—limit the accessibility and scalability of phylogenetic inference, especially for researchers relying on desktop computers. In this presentation, I will discuss algorithmic, statistical, and heuristic approaches my group has developed to address these challenges. We have developed novel approaches that dramatically reduce the computational resources required for key phylogenomic tasks, such as selecting the best-fit substitution model, conducting bootstrap analyses, and estimating evolutionary parameters like substitution rates, branch lengths, and divergence times. By reducing memory footprints, our methods enable greater parallelization, improving performance on both personal computers and high-performance computing systems. We validate new approaches and their implementations through simulations and real-world datasets to ensure high accuracy while gaining dramatic computational efficiencies, bridging the gap between theoretical advances and practical tool development in MEGA. Ultimately, we hope to contribute to more sustainable, accessible, and reproducible phylogenomics, democratizing participation in evolutionary research and accelerating discovery.
10:00 - 10:30 AM EST
Coffee Break
11th Floor Collaborative Space
10:30 - 11:15 AM EST
From species tree identifiability to composite likelihood-based inference: a statistician’s algebraic journey
11th Floor Lecture Hall
Speaker
Laura Kubatko, The Ohio State University
Session Chair
Elizabeth Allman, University of Alaska Fairbanks
Abstract
Species-level phylogenetic inference under the multispecies coalescent model remains challenging in the typical inference frameworks (e.g., the likelihood and Bayesian frameworks) due to the dimensionality of the space of both gene trees and species trees. Algebraic approaches intended to establish identifiability of species tree parameters have suggested computationally efficient inference procedures that have been widely used by empiricists and that have good theoretical properties, such as statistical consistency. However, such approaches are less powerful than approaches based on the full likelihood. Methods based on composite likelihood are a compromise between these two approaches that enable computationally efficient inference while maximizing use of the available sequence data. In this talk, I’ll describe the relationship between these two approaches, highlighting the strengths and weaknesses of each and providing directions for future work.
11:30 AM - 12:15 PM EST
Genome-scale phylogenetics using gene tree parsimony
11th Floor Lecture Hall
Speaker
Mukul Bansal, University of Connecticut
Session Chair
Elizabeth Allman, University of Alaska Fairbanks
Abstract
Gene tree parsimony (GTP) is a popular computational technique for genome-scale species tree reconstruction. This technique is distinguished from other phylogenomic methods by its use of parsimony-based reconciliation models to assess the fit of input gene trees with candidate species trees. In this talk, we will discuss the historical development of GTP-based problem formulations, algorithms, and software, describe some recent methodological advances, review experimental results assessing the performance of GTP-based and other phylogenomic methods on diverse datasets, and discuss promising new directions for future research.
12:30 - 2:30 PM EST
Networking Lunch
Working Lunch - 11th Floor Collaborative Space
2:30 - 3:15 PM EST
The phylogenetic posterior distribution
11th Floor Lecture Hall
Speaker
Frederick Matsen, Fred Hutchinson Cancer Center
Session Chair
Erin Molloy, University of Maryland, College Park
Abstract
For a decade now, we've worked to understand the phylogenetic posterior distribution, as well as to develop non-MCMC approaches to inference. In this talk, I'll survey what we have learned, including our many mis-steps, and speculate about what the future may hold.
3:30 - 4:00 PM EST
Coffee Break
11th Floor Collaborative Space
4:00 - 4:45 PM EST
Spaces of phylogenetic networks: Advances and challenges
11th Floor Lecture Hall
Speaker
Vincent Moulton, University of East Anglia
Session Chair
Erin Molloy, University of Maryland, College Park
Abstract
Spaces of phylogenetic trees have been studied for several years. As well as having mathematical and computational interest, they also have central importance within various tree-building algorithms. Recently, there has been growing interest in defining spaces of phylogenetic networks. Much less is known about such spaces. Indeed, it is often significantly more complex to understand them, as well to develop network building algorithms that exploit their structure. In this talk, we shall review some of the network spaces that have been recently defined, introduce some new ones, and discuss some of the challenges associated with putting them to practical use.
5:00 - 6:30 PM EST
Reception
11th Floor Collaborative Space
Tuesday, November 19, 2024
9:00 - 9:45 AM EST
Site-based quartet-based estimation of species trees
11th Floor Lecture Hall
Speaker
Siavash Mirarab, UCSD
Session Chair
Barbara Holland, University of Tasmania
Abstract
We present a new approach for estimating species trees directly from site patterns. Following similar methods in the literature, our method is based on a statistically consistent score for each quartet, which is then summed over all quartets of the tree. However, unlike similar works, our new score is such that it can be measured for all quartets without enumerating the score for each quartet. This property enables the method, called CASTER, to employ dynamic programming algorithms similar to ASTRAL to infer the species tree in polynomial time. Moreover, the score is defined per site, with the final score being the sum across the genome. This property enables it to reveal interesting patterns of evolution across the genome, including measuring the level of ILS. Simulations show the method to be accurate and much more scalable than alternatives. Applications on real data show its power in finding unexpected patterns. This talk focuses on the theory of the method: how the scores are defined, what is known in theory about their consistency, and what is yet to be understood. We hope the open problems will spur further research on this approach.
10:00 - 10:30 AM EST
Coffee Break
11th Floor Collaborative Space
10:30 - 11:15 AM EST
Accurate, scalable, and fully automated inference of species trees from raw genome assemblies
11th Floor Lecture Hall
Speaker
Yatish Turakhia, University of California San Diego
Session Chair
Barbara Holland, University of Tasmania
Abstract
Inference of species trees plays a crucial role in advancing our understanding of evolutionary relationships and has immense significance for diverse biological and medical applications. Extensive genome sequencing efforts are currently in progress across a broad spectrum of life forms, holding the potential to unravel the intricate branching patterns within the tree of life. However, estimating species trees starting from raw genome sequences is quite challenging, and the current cutting-edge methodologies require a series of error-prone steps that are neither entirely automated nor standardized. In this talk, I will present ROADIES, a novel pipeline for species tree inference from raw genome assemblies that is fully automated, easy to use, scalable, free from reference bias, and provides flexibility to adjust the tradeoff between accuracy and runtime. The ROADIES pipeline eliminates the need to align whole genomes, choose a single reference species, or pre-select loci such as functional genes found using cumbersome annotation steps. Moreover, it leverages recent advances in phylogenetic inference to allow multi-copy genes, eliminating the need to detect orthology. Using the genomic datasets released from large-scale sequencing consortia across three diverse life forms (placental mammals, pomace flies, and birds), we show that ROADIES infers species trees that are comparable in quality with the state-of-the-art approaches but in a fraction of the time. By incorporating optimal approaches and automating all steps from assembled genomes to species and gene trees, ROADIES is poised to improve the accuracy, scalability, and reproducibility of phylogenomic analyses.
11:30 AM - 12:15 PM EST
Transcript phylogeny inference
11th Floor Lecture Hall
Speaker
Aïda Ouangraoua, Université de Sherbrooke
Session Chair
Barbara Holland, University of Tasmania
Abstract
Alternative splicing is a ubiquitous process in eukaryotes, which allows the production of distinct transcripts from the same gene by joining different combinations of exons. It has raised interest in the last few decades because of its significant role in gene functional diversification, as well as in several diseases such as cancers. Reconstructing transcript phylogenies across a set of homologous genes is challenging, but it provides additional information on gene evolution, such as the identification of conserved isoforms, the history of isoform gains and losses along gene phylogenies, and the evolution of alternative splicing. We will present a unified view of transcript phylogeny reconstruction problems, as well as current challenges and existing practical solutions to study transcript evolution.
12:30 - 2:30 PM EST
Lunch/Free Time
2:30 - 3:15 PM EST
Poster Blitz
Other - 11th Floor Lecture Hall
Session Chair
Barbara Holland, University of Tasmania
3:30 - 4:00 PM EST
Coffee Break
11th Floor Collaborative Space
4:00 - 5:00 PM EST
Wednesday, November 20, 2024
9:00 - 9:45 AM EST
Visualizing phylogenetic data using ggtree
11th Floor Lecture Hall
Virtual Speaker
Guangchuang Yu, Southern Medical University.
Session Chair
Simone Linz, University of Auckland
Abstract
This introduces the functionalities of the ggtree suite of packages for data integration, manipulation, and visualization, as well as the convenience of using these packages together. The focus is on ggtree's feature of mapping data onto the tree topology for visualization and the alignment visualization of multiple datasets. Integrated data visualization helps us uncover patterns and trends. Finally, several extension packages developed on top of ggtree are introduced.
10:00 - 10:30 AM EST
Coffee Break
11th Floor Collaborative Space
10:30 - 11:15 AM EST
Benefits and Challenges of Data-Motivated Phylogenetic Inference
11th Floor Lecture Hall
Speaker
Katherine St. John, City University of New York
Session Chair
Simone Linz, University of Auckland
Abstract
Many evolutionary scenarios require complex mathematical models to capture the underlying processes. Evaluating these complex models is often computationally hard, motivating the use of heuristics to estimate the optimal phylogenies. Instead of focusing on the general case, this talk highlights the benefits and challenges of focusing on individual scenarios. In some scenarios, simpler models can efficiently capture the relationships and yield rich insights that often improve, both in accuracy and running time, the analysis and visualization of the results. These simpler models hold promise for data sets with high consistency index as well as inferring the mutation history of tumors. As a challenge of data-motivated inference, when data sets increase in taxonomic scope, some characters may describe traits not found across all the taxa. It is common practice to treat such inapplicable characters as missing data. But missing some data, due to poor sequencing or broken fossil, is very different from a character that does not apply to some taxa, such as wing color for taxa with no wings. We explore how the treatment of missing and inapplicable characters can greatly affect which phylogenies are inferred.
11:30 AM - 12:15 PM EST
The good, the bad and the ugly of deep learning in phylogenetics
11th Floor Lecture Hall
Speaker
Claudia Solis-Lemus, University of Wisconsin-Madison
Session Chair
Simone Linz, University of Auckland
Abstract
In this talk, I will illustrate the advantages and challenges of deep learning models in phylogenetics. Phylogenetic trees and networks differ from other graphical structures in which data are only available on the external nodes of the graph, and thus, many models that originated in other graph domains are not readily applicable.
12:25 - 12:28 PM EST
Group Photo (Immediately After Talk)
11th Floor Lecture Hall
12:28 - 12:30 PM EST
Semester Program long-term researchers group photo
Group Photo (Immediately After Talk) - 11th Floor Lecture Hall
12:30 - 2:30 PM EST
Lunch/Free Time
2:30 - 3:15 PM EST
Addressing some computational and modeling challenges in phylogenomic analysis
11th Floor Collaborative Space
Speaker
Minh Bui, Australian National University
Session Chair
Mareike Fischer, University of Greifswald
Abstract
Phylogenetic studies now routinely use genome-scale data of many loci to resolve evolutionary relationships. As the data have grown in size, there are not only increasing computational challenges to handle a massive amount of data but also increasing gaps in modeling sequence evolution to capture biological complexities. In this talk I will first exemplify our collaborative efforts with Nick Goldman’s group in solving substantial bottlenecks with inferring trees of a million SARS-CoV-2 genomes. Secondly, I will show another effort in relaxing the assumption of reversibility and its utility to root a tree in the absence of an outgroup. If time permits I will touch on a pilot project on the application of machine learning and its challenges.
3:30 - 4:00 PM EST
Coffee Break
11th Floor Collaborative Space
4:00 - 4:45 PM EST
Mixture models for phylogenetic analysis in IQTREE2
11th Floor Lecture Hall
Speaker
Thomas Wong, Australian National University
Session Chair
Mareike Fischer, University of Greifswald
Abstract
Modern phylogenomic studies now commonly analyse hundreds or thousands of loci, yet traditional tree inference methods assume that each locus or partition evolves under a single model of sequence evolution and shares the same tree topology. However, this is far removed from reality, especially as different loci often exhibit distinct evolutionary histories due to processes such as incomplete lineage sorting (ILS), introgression, or horizontal gene transfer. To address these limitations, mixture models have been introduced in IQ-TREE2, an efficient software for phylogenomic inference based on maximum likelihood.
We present three contributions: (1) an algorithm called MixtureFinder, which selects the optimal number of Q-matrix classes for modelling DNA sequence evolution; (2) a mixture across sites and trees (MAST) model that allows more than one tree along an alignment; and (3) a Hidden Markov Model (HMM) that consider intercorrelations between neighboring sites of an alignment. Simulations and empirical data analyses demonstrate that these approaches can capture and identify biological processes, suggesting that they will be useful for future empirical studies.
Thursday, November 21, 2024
9:00 - 9:45 AM EST
Distance-based modeling and inference in phylogenetics
11th Floor Lecture Hall
Speaker
Julia Palacios, Stanford University
Session Chair
Vincent Moulton, University of East Anglia
Abstract
Bayesian phylogenetic inference usually explore the posterior distribution of trees via Markov Chain Monte Carlo methods, however assessing uncertainty and summarizing distributions remains challenging for these types of structures. In this talk I will first introduce a distance metric on the space of unlabeled ranked tree shapes and genealogies. I will then use it to define several summary statistics such as the Fréchet mean, variance, and interquartile sets. I will then provide an efficient combinatorial optimization algorithm for computation and show the applicability of our summaries for studying popular tree distributions and for comparing the SARS-CoV-2 evolutionary trees across different locations during the COVID-19 epidemic in 2020. I will extend the distance definition to the space of labeled ranked phylogenies and unlabeled phylogenetic networks and discuss potential applications of the distances for inference.
10:00 - 10:30 AM EST
Coffee Break
11th Floor Collaborative Space
10:30 - 11:15 AM EST
Algebraic and Semi-Algebraic Perspectives on Phylogenetic Reconstruction
11th Floor Lecture Hall
Speaker
Marina Garrote López, Max Planck Institute for Mathematics in the Sciences
Session Chair
Vincent Moulton, University of East Anglia
Abstract
In this talk, we focus on phylogenetic inference using algebraic and semi-algebraic invariants of single-site substitution models used in Markov processes over phylogenetic trees. These models give rise to algebraic varieties, the study of which has proven effective for phylogenetic reconstruction. However, not all points in these varieties correspond to biologically meaningful or probabilistically valid scenarios, so it seems necessary to consider the subsets that do. In this talk, we explore the structure and meaning of these semi-algebraic subsets and demonstrate their important role in phylogenetic inference. We also present two reconstruction methods, Semi-Algebraic Quartet Reconstruction (SAQ) and Algebraic and Semi-Algebraic Quartet Reconstruction (ASAQ), which are consistent with the most general Markov model of nucleotide substitution and allow for rate heterogeneity across lineages. Despite their theoretical foundation, the use of these algebraic and semialgebraic constraints in phylogenetic inference poses significant challenges in practice. We will discuss these challenges and offer ideas for bridging the gap between theoretical results and practical applicability.
11:30 AM - 12:15 PM EST
Distance methods - Theoretical and algorithmic challenges
11th Floor Lecture Hall
Speaker
Daniele Catanzaro, Université Catholique de Louvain
Session Chair
Vincent Moulton, University of East Anglia
Abstract
We provide here a perspective current theoretical and algorithmic challenges in distance methods, with specific focus on Minimum and Balanced Minimum Evolution
12:30 - 2:30 PM EST
Open Problems Lunch
Working Lunch - 11th Floor Collaborative Space
2:30 - 3:15 PM EST
New Software and Algorithms for working with phylogenetic networks
11th Floor Lecture Hall
Speaker
Daniel Huson, University of Tübingen
Session Chair
Claudia Solis-Lemus, University of Wisconsin-Madison
Abstract
In the first part of this talk, I focus on the new PhyloSketch app that allows one to create phylogenetic trees and networks by drawing on an iPad. I will first present the app as currently implemented, then discuss additional future features and will ask for suggestions to make the app more useful.
In the second part of the talk, I will present some recent algorithms that I have been working on in two different projects related to phylogenetic networks.
3:30 - 4:00 PM EST
Coffee Break
11th Floor Collaborative Space
4:00 - 4:45 PM EST
Curbing the complexity of phylogenetic network inference and analysis
11th Floor Lecture Hall
Speaker
Alexey Markin, National Animal Disease Center, USDA-ARS
Session Chair
Claudia Solis-Lemus, University of Wisconsin-Madison
Abstract
I will discuss two approaches to reducing the complexity of phylogenetic network inference and/or analysis.
The first approach tries to reduce the search space for network inference by identifying tree branches of a single gene tree that were likely involved in reticulate evolution. In particular, we consider segmented viruses like influenza that undergo reassortment – a type of recombination where the progeny virus inherits a mixture of complete genes from two distinct parents. Using a fixed gene and the corresponding phylogenetic tree as a reference, the algorithm uses alignments from other query genes to find “breakpoints” in the phylogeny, where the evolution of the query gene does not follow the reference tree. The breakpoints are found using a hypothesis test that assumes a strict molecular clock with certain relaxations. We demonstrated the high accuracy of our algorithm in identifying reassortment events in simulation studies and applied it to large genomic datasets of influenza A viruses to better understand reassortment patterns within the densely sampled lineages.
The second approach limits the complexity of the phylogenetic networks by bounding their treewidth. We show that level-k networks have treewidth at most (k+3)/2 and provide a tighter upper bound for large k. By bounding the treewidth of phylogenetic networks, many computational problems on networks become polynomial-time solvable, and I will discuss a few examples of such problems.
Friday, November 22, 2024
9:00 - 9:45 AM EST
Evolving RevBayes into an Open-Source Ecosystem
11th Floor Lecture Hall
Speaker
Tracy Heath, Iowa State University
Session Chair
Katharina Huber, University of East Anglia
Abstract
Phylogenetic methods and software have clearly played an essential role in advancing biological and biomedical discovery, yet more can be done to meet researchers’ needs. Significant technical and pedagogical barriers––both for software developers and users––limit the adoption of these powerful modeling tools. Thus, strengthening community-level relationships among users and developers, as well as enhancing access to cutting-edge statistical analysis is a critical direction for research in the life sciences. RevBayes is a highly flexible platform for Bayesian phylogenetic inference that allows users to build complex statistical models using a graphical modeling framework and an interpreted programming language. As part of an open-source project, the RevBayes team develops and maintains a variety of software resources and educational materials that are used by researchers, teachers, and students, including modeling and inference tools, development environments, documentation, visualization packages, and detailed user tutorials. These products and materials are the result of dedicated efforts from over 50 contributors and help to sustain a large international user base. We are investigating and piloting strategies for enhancing the experiences of community members at all levels (user, contributor, developer), with the goal of making the software and opportunities to join the development team more accessible, and to ultimately ensure the long-term stability and continued growth of RevBayes.
10:00 - 10:30 AM EST
Coffee Break
11th Floor Collaborative Space
10:30 - 11:15 AM EST
Optimizing computation for complex tree priors
11th Floor Lecture Hall
Speaker
Yucai Shao, UCLA
Session Chair
Katharina Huber, University of East Anglia
Abstract
Episodic birth-death-sampling (EBDS) models and structured coalescent approximations (SCAs) are essential frameworks for modeling the branching processes that generate phylogenetic trees, yet significant computational challenges have constrained their applicability to large datasets and complex parameter spaces. This talk will focus on the methodological advancements that enhance the scalability and practical utility of these models. EBDS models are powerful tools for capturing shifts in pathogen transmission dynamics through piecewise-constant rates across different time periods. By incorporating Hamiltonian Monte Carlo (HMC) sampling into EBDS models, we enable a more extensive exploration of parameter space and reduce autocorrelation in samples, which accelerates convergence when analyzing real-world sequence data from disease outbreaks. Separately, SCAs model the genealogical process and unobserved migration histories of ancestral lineages, offering less biased inference for migration rates and population sizes. For SCAs, we address computational limitations by reformulating the likelihood using a reverse-level-order tree traversal method that allows for massive parallelization, making the computation of the likelihood feasible for large-scale analyses. These advancements demonstrate how theoretical developments in phylodynamic models can be effectively translated into practice, highlighting the interplay between algorithmic techniques and computational strategies, and providing insights into optimizing scalability in phylogenetic inference.
11:30 AM - 12:15 PM EST
Computational Tools for Analyzing and Visualizing Large-Scale Phylogenetic Relationships
11th Floor Lecture Hall
Speaker
Tiffani Williams, University of Illinois at Urbana-Champaign
Session Chair
Katharina Huber, University of East Anglia
Abstract
In this talk, I will discuss novel information-rich and holistic approaches to analyze and visualize large-scale data from phylogenetic analyses.
12:30 - 1:30 PM EST
Lunch/Free Time
1:30 - 2:15 PM EST
Progress in Large-Scale Phylogenomic Estimation Methods
11th Floor Lecture Hall
Speaker
Tandy Warnow, University of Illinois Urbana-Champaign
Session Chair
Katherine St. John, City University of New York
Abstract
The estimation of phylogenetic trees for individual genes or multi-locus datasets is a basic part of considerable biological research. In order to enable large trees to be computed, Disjoint Tree Mergers (DTMs) have been developed; these methods operate by dividing the input sequence dataset into disjoint sets, constructing trees on each subset, and then combining the subset trees (using auxiliary information) into a tree on the full dataset. In this talk I will survey the techniques used in DTMs, and evaluate the impact of using DTMs for use with maximum likelihood gene tree estimation and species tree estimation using methods, such as ASTRAL, that address incomplete lineage sorting.
2:30 - 3:15 PM EST
Adventures with dating and dealing with fog
11th Floor Lecture Hall
Speaker
Brian O'Meara, Univeristy of Tennessee, Knoxville
Session Chair
Katherine St. John, City University of New York
Abstract
Most modern uses of phylogenetic trees require chronograms: trees with branch lengths proportional to time. There are many challenges to making these, and further challenges to using them. This talk will cover two main avenues for this. The first is developing practical ways to enable reuse of existing knowledge, including ways to aggregate information from shared chronograms and ways to make techniques usable via R packages and web interfaces. The second is the importance of incorporating the biasing effect of uncertainty in estimating chronograms and in using them.
3:30 - 4:00 PM EST
Coffee Break
11th Floor Collaborative Space
