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1 Concursos de Projectos de I&D Calls for R&D Projects Voltar à descrição do projecto Back to project description Imprimir esta página Print this page Visão global da candidatura Application overview Ocultar todos as secções desta candidatura Hide all sections for this application Referência do projeto Project reference PTDC/EEI-ESS/3191/2012 (Lacrado a às 18:45) 1. Identificação do projeto 1. Project description Domínio Científico Scientific Domain Ciências Exatas e da Engenharia Área científica principal Main Area Engenharia Eletrotécnica e Engenharia Informática - Engenharia de Software e Sistemas de Informação Área científica Secundária Secondary area Biologia Experimental - Biologia Computacional e Bioinformática Acrónimo Acronym NextGenPop Título do projeto (em português) Project title (in portuguese) Ferramentas para manipulação de dados em larga escala em análise filogenética: inferência de novo conhecimento a partir de genómica de populações de agentes patogénicos Título do projeto (em inglês) Project title (in english) Tools for large scale data handling in phylogenetic analysis: deriving new knowledge from pathogen population genomics Financiamento solicitado Requested funding ,00 Palavra-chave 1 Keyword 1 Análise Filogenética Phylogenetic analysis Palavra-chave 2 Keyword 2 Next Generation Sequencing Palavra-chave 3 Keyword 3 Analise de dados em larga escala Next Generation Sequencing Large data analysis Palavra-chave 4 Keyword 4 Plataforma Web based. Data de início do projeto Starting date Web based framework Duração do projeto em meses Duration in months 2. Instituições envolvidas 2. Institutions and their roles Instituição Proponente Principal Contractor Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC ID/INESC/IST/UTL) Rua Alves Redol, Lisboa da Instituição INESC-ID, Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa, is dedicated to R&D in the fields of information technology, electronics and telecommunications. INESC-ID is a non for profit institution, privately owned institution, and declared officially of public interest. It is owned by IST and INESC. INESC-ID was legally operating in 1999 as a result of a restructuring of INESC. In 2002 it was created the Taguspark campus. In December 2004 the institution was awarded the status of Laboratório Associado. The main objectives of INESC-ID are to integrate competences from researchers in electrical engineering and computer science to advance the state of the 1 of 13 03/29/ :47 PM

2 art in computers, telecommunications, and information systems and to perform technology transfer, to support the creation of technology based startups, and to provide technical support, also through basic research, applied research and advanced education. INESC-ID activities are divided in four scientific areas: -Interactive Intelligent Systems; -Information and Decision Support Systems; -Computer Systems and Communication Networks; -Embedded Electronic Systems; that are structured in several research groups, in both campus Alameda and Taguspark Instituição Participante Participating Institution Fundação Calouste Gulbenkian (FCG) Av. de Berna, Lisboa da Instituição The Instituto Gulbenkian de Ciência (IGC, was founded & is supported by Fundacaõ Calouste Gulbenkian (private, non-profit organization) to carry out modern biomedical research and promote post-graduated education. The IGC operates as a "host institution" offering excellent facilities & services to 40 research groups. The Institute focus on the genetic basis of development and evolution of complex systems, privileging organismcentred approaches in experimental models. A strong theoretical sector is one of its specificities, others being the quality of the services, and a large investment in international exchange in the form of graduate courses, workshops and symposia, presently conducting 3 PhD programmes. Since 1998, IGC research lead to 1237 peer-reviewed publications (many on top journals) that have been cited times (average citation 20.92) resulting in an H factor of 73. In 2011 IGC scientists fund raised through 33 new external national & international competitive grants and prizes. The IGC integrates wider research structures, nationally (ITQB/IGC/IBET/CEDOC Laboratório Associado do Ministério da Ciência e da Tecnologia) & internationally (EC funded infrastructure European Mouse Mutant Archive) & has established many bilateral agreements with national & foreign Universities, Research Institutes & private organizations (e.g. Instituto Nacional de Saúde Ricardo Jorge, Fundação Champalimaud, MIT, Government of S. Tomé & Princ Instituto de Medicina Molecular (IMM/FM/UL) Avenida Professor Egas Moniz Lisboa da Instituição The Instituto de Medicina Molecular (IMM) is a non-profit private research institute affiliated with the University of Lisbon Medical School and located in the campus of the Santa Maria Hospital ( IMM is mainly supported by national public funds, European Union funds, and private foundations. The mission of IMM is to foster basic, clinical and translational biomedical research with the aim of contributing to a better understanding of disease mechanisms, developing novel predictive tests, diagnostics and therapeutic approaches. IMM hosts 31 independent research groups (circa 400 researchers) that are organized in three research Programs: Cell & Developmental Biology, Immunology & Infection, and Neurosciences. The Institute provides state-of-the-art Bioimaging, Flow Cytometry, Animal (fish and rodents), and bio-banking facilities, offers regular scientific seminar series by invited external speakers, and runs an international PhD and MD/PhD program with 110 students. IMM also hosts 3 start-up companies in areas of biomedical technologies. In 2011, IMM researchers have published 287 papers, of which 23 in journals with impact higher than 10. Presently, the Institute hosts 3 European Research Council Starting Grants, 2 Howard Hughes Medical Institute Scholars, 2 EMBO Young Investigators and several grants from EU FP7, the Bill and Melinda Gates Foundation and the Harvard-Medical School-Portugal Program (total of 40 international research grants). Unidade de Investigação Research Unit Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC ID/INESC/IST/UTL) Rua Alves Redol, Lisboa Unidade de Investigação Adicional Additional Research Unit (Vazio) (Void) Instituição de Acolhimento Host Institution Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC ID/INESC/IST/UTL) Rua Alves Redol, Lisboa 3. Componente Científica 3. Scientific Component 3.1. Sumário 3.1 Abstract 3.1.a Em português 3.1.a In Portuguese As tecnologias de Next Generation Sequencing (NGS) estão a criar uma mudança radical nas ciências da vida, tais como a Microbiologia Clínica e a Epidemiologia Molecular. A capacidade actual para a rápida sequenciação de genomas microbianos promete revolucionar estas áreas, permitindo a identificação de milhares de alvos no genoma clinicamente relevantes. Estes, podem ir desde genes com resistência microbiana, à presença e ausência de factores de virulência com aplicação clínica directa, ou análises filogenéticas complexas considerando todo o genoma. Este último, sem impacto clínico imediato, oferece novas pistas sobre a evolução de agentes patogénicos com implicações futuras críticas para a saúde humana. Estas análises podem ser usadas para a detecção de epidemias em ambientes hospitalares ou na indústria, guiar o desenvolvimento de vacinas verificando se os seus alvos são conservados em toda a população bacteriana, ou monitorizando a resistência anti-microbiana, que é cada vez mais preocupante. No entanto, hoje em dia, a dificuldade deixou de ser a produção de dados de sequenciação para ser a sua análise. Com a capacidade crescente para a análise diária de dados NGS e vigilância epidemiológica, têm também surgido bases de dados para o armazenamento desta informação, com a pressão crescente para tornar todos estes dados públicos de forma a potenciar uma visão global sobre o impacto da evolução microbiana na saúde humana. Estes recursos permitem criar as linhas directrizes da vigilância necessária para uma rápida detecção de epidemias, e para a monitorização da resistência anti-microbiana e novos factores de virulência. Do ponto de vista da Engenharia Informática, a questão coloca-se em relação à escalabilidade dos algoritmos, ferramentas e sistemas de informação usados para a análise e inferência de filogenias, em particular quando temos milhares de loci em milhares de isolados. Os métodos actuais requerem várias horas para criar árvores filogenéticas a partir de dados de sequência, com dezenas ou poucas centenas de isolados, considerando apenas alguns genes. Os métodos Bayesianos demoram ainda mais, vários dias, para conjuntos de dados considerados pequenos. Desta forma, com o aumento do volume de dados, verificamos que estamos apenas a encontrar os primeiros problemas computacionais. O objectivo principal desde projecto é providenciar soluções para estas limitações, fornecendo uma plataforma para a análise de dados filogenéticos com algoritmos e ferramentas optimizadas. Esta plataforma será utilizável em ambientes de computação distribuída, como por exemplo serviços de computação na nuvem. Esta plataforma deverá ainda permitir carregar dados remotos sempre que necessário. Outro objectivo importante tem a haver com interfaces adequadas aos utilizadores finais de forma a que possam correr os algoritmos, visualizar os resultados e proceder com a análise de dados subsequentes baseados na informação epidemiológica disponível. 2 of 13 03/29/ :47 PM

3 Para atingir estes objectivos, precisamos de ultrapassar alguns desafios: (1) os métodos existentes precisam de ser melhorados ou substituídos, através do uso de estruturas de dados e algoritmos eficientes e escaláveis; (2) dado o volume de dados crescente, os métodos desenvolvidos têm de ser passíveis de ser executados em ambientes de computação distribuídos e de consumir informação remota; (3) as ferramentas assim como os recursos necessitam de ser acessíveis através de serviços web, permitindo assim a construção de fluxos de trabalho que possam ser reutilizados dentro do ambiente de computação na nuvem; (4) Como nem todos os utilizadores finais vão ser programadores, será necessário disponibilizar interfaces intuitivas que utilizem técnicas de visualização da informação do estado da arte. A equipa de investigação reflecte o carácter multi-disciplinar do projecto. A equipa é composta por três instituições nacionais, INESC-ID, IMM e IGC. Os investigadores do INESC-ID desenvolveram o seu trabalho em tópicos de algoritmia, análise de grandes quantidades de dados, gestão da informação, mineração de dados e descoberta de conhecimento, com aplicações na web, análise de dados sociais e análise de redes. Investigadores do IMM desenvolveram o seu trabalho na genética evolutiva de populações, epidemiologia molecular e métodos de tipagem microbiana. Investigadores do IGC trabalharam ainda em modelação formal de sistemas biológicos. Colaborações anteriores entre as três instituições em bioinformática e biologia computacional focaram-se na análise de dados de tipagem, inferência de filogenias e no desenvolvimento de ferramentas para estas tarefas. Adicionalmente, este projecto tem como consultores três peritos de renome nos campos da genética evolutiva de populações e em métodos de tipagem microbiana, com uma forte experiência no desenvolvimento de ferramentas e na modelação matemática. Juntos formamos uma equipa única de peritos para a resolução dos desafios mencionados acima. 3.1.b Em inglês 3.1.b In English The recent and continuous development of Next Generation Sequencing (NGS) technologies created a radical shift in many fields of life sciences such as Clinical Microbiology and Molecular Epidemiology. The current ability to rapidly sequence whole microbial genomes, has the promise to revolutionize these fields by allowing the identification of thousands of potentially clinical relevant targets in the genome. These can range from antimicrobial resistance genes to the presence and absence of virulence factors which can have direct clinical application in the treatment of patients, to more complex phylogenetic analysis based on the whole genome data. The latter have a less immediate impact in the everyday treatment of the patient, but offer new insights into pathogen evolution with critical future implications for human health. Those analysis on NGS data can be used to detect outbreaks in hospital settings or in food industry, guide the development of vaccines by determining if vaccine targets conserved in the entire bacterial population or by monitoring the spread of antimicrobial resistance, an ever growing concern. However, it is becoming clear that the bottleneck shifted from the production of sequence data to its analysis. Worldwide there is an increasing ability to use NGS for everyday analysis and epidemiological surveillance, and public databases are emerging to store microbial whole genome data. There is also a growing pressure to make this data publicly available to provide a global picture of the impact of microbial evolution in human health and to create the needed surveillance baselines to rapidly detect outbreaks and to monitor emergence of antimicrobial resistance and novel virulence factors. From the Computer Science and Engineering point of view, a growing concern is how algorithms, software tools and information systems used to analyze and infer phylogenies, can be scaled up to analyze thousands of genetic loci in thousands of isolates. Traditional methods already take several hours to create phylogenetic trees from sequence data, for tens or a few hundreds of isolates with just a few selected genes and Bayesian methods can even take much longer, with running times reaching several days for considerably small data sets. Given the continuous increase in the size of the data sets, we are just hitting the first problems related to computational resources requirements. The main goal of this project is to offer solutions to overcome this caveats by providing a framework for phylogenetic data analysis with optimized algorithms and tools. This framework will also be usable on distributed computing facilities whenever the data volume demands it, using for instance cloud computing services. Such framework must also consume data transparently from remote and publicly available data resources on demand. Another important goal is to provide user friendly interfaces for final users so that they can run the algorithms, visualize the results and proceed with further data analysis based on the available epidemiological relevant data. To achieve the proposed goals, we must overcome a number of challenges: (1) existing phylogenetic methods must be improved, or replaced, by using efficient and scalable data structures and algorithms; (2) given the amount of data being generated, developed tools must be suitable for deployment in distributed computing environments and must be able to consume remote data resources; (3) software tools as well as data resources must be made available as Web services, enabling the construction of more complex pipeline workflows that can be easily reused within a cloud computing environment; (4) since not all end-users will be programmers, intuitive and user-friendly interfaces must also be made available, employing state of the art information visualization techniques. The research team reflects the multidisciplinary character of the project. The team has three national research institutes, INESC-ID, IMM and IGC. Researchers from INESC-ID have developed their work on topics of algorithmics, large scale data analysis, data management, data mining and knowledge discovery, with applications to Web and social data analysis and network mining. Researchers from IMM have developed their work on the topics of population evolutionary genetics, molecular epidemiology, microbial typing methods. Researchers from IGC have worked also on formal modeling of biological systems. Prior collaborations of the three institutions in bioinformatics and computational biology focused on typing data analysis, phylogenetic inference and the development of software tools for these tasks. Moreover, this project has as consultants three renowned experts in the field of population evolutionary genetics and microbial typing methods, with strong background on both analysis tools and mathematical modeling. Together we form a team with the unique expertise to successfully address above mentioned challenges 3.2. Técnica 3.2 Technical Revisão da Literatura Literature Review Nowadays, with the decrease of DNA sequencing costs, we observed a paradigm shift in biology and biomedicine in general, and in microbiology and epidemiological surveillance in particular. The use of the DNA sequence made possible to get comparable and reproducible data on microbial at global scale and, thus, perform a global scale microbial population analysis, effectively replacing the molecular and phenotypic techniques. This trend was initiated by sequence based typing methods, such as MultiLocus Sequence Typing (MLST) (Spratt1999,Maiden2006). MLST was the first method to produce reproducible and comparable results between laboratories all over the world, becoming the gold standard for epidemiological surveillance of bacterial population. When traditional sequence based typing methods rely on few genes for the discrimination of bacterial isolates, the advent of NGS created the capacity to analyze the whole genome and it is being presented as the ultimate tool for defining bacterial population and to be used in clinical settings (Voelkerding2008). Now it is recognized that the bottleneck shifted from the sequencing technologies to the data analysis of their results. This issue has been clearly identified by a recent report of the UK Biotechnology and Biological Sciences Research Council (BBSRC2011). The report concludes that for the analysis of NGS data, one of the main challenges is the major gap in data analysis and data interpretation, which is additionally accelerated by a lack of capacity in computational genomics and bioinformatics software for large datasets. This is especially relevant in phylogenetic data analysis, where the traditional algorithms for sequence comparison and the inference of phylogenetic relationships, originally designed for handling a handful of target DNA sequences or markers and now have to handle thousands of targets for an ever increasing large number of isolates, as more and more data is deposited in public databases. Two analysis strategies have been developed for the analysis of NGS data. One is the determination of of Single Nucleotide Polymorphisms (SNP) between a set of strains and a predetermined reference strain (Harris2010, Croucher2011). The other involves the annotation of entire genomes and expands the MLST strategy to any number of loci in the sequenced strains and comparing the generated allelic profiles (Jolley2010). However, the inference of population structure from these two methodologies for large datasets requires access to high performance computer. In particular, the use of more computationally intensive of Bayesian approaches such as BAPS (Corander2006,Corander2008) and ClonalFrame (Didelot2006), that can provide much more descriptive hypotheses of gene flow on bacterial populations, do not scale up for the number of possible analysis targets that NGS analysis can provide. Only very recently, a library named BEAGLE (Ayres2012) was released to accelerate statistical phylogenetic software using Bayesian method. Also recently, distributed computing platforms such as Hadoop have started being used for NGS data analysis, namely for sequence alignment and SNP analysis (Langmead2009). Nevertheless, the ultimate limiting factor for the use of these libraries, is their integration with user-friendly software to be provided to the end-users in the clinical microbiology labs or research groups. Also, when the results are obtained in a timely manner, their integration with epidemiological data and simultaneous analysis is still limited to visualization techniques. Moreover, not only are the datasets growing in size and number, but they are only partly coordinated and often incompatible, which means that integration tasks are significant challenges. Workflows provide a systematic and automated means of conducting analyses across diverse data sets and applications. 3 of 13 03/29/ :47 PM

4 Each data processing can be viewed as a workflow task and, thus, each workflow represents a process pipeline or a more complex model, with, for instance, a graph topology, that orchestrates the flow of data, towards data analysis. General-purpose open source workflow systems include Taverna (Hull2006) and Kepler (Altintas2004). More domain specifics are also available, such as Galaxy (Goecks2010). These existing workflow engines, such as Taverna, provides platforms for running the workflows locally or in a specific server. These workflow engines have been used in various domains and deployed using various platforms and technologies. In this proposal we aim to advance the state of the art, not only by providing novel techniques for the optimization of phylogenetic analysis methods, but also to provide software and corresponding interfaces to be used by researchers to obtain and interpret results, typically phylogenetic trees or graphs for epidemiological surveillance and inference of evolutionary parameters for the bacterial population under study. The assembled team has a large multidisciplinary experience in several fields needed for the success of this proposal. Concerning typing methods we developed goeburst, an optimized algorithm for the analysis of MLST data (Francisco2009). The algorithm, is now provided in PHYLOViZ (Francisco2012), which extended the algorithm for any sequence based typing method and includes visualization capabilities for epidemiological data. Team members were also involved in the introduction of quantitative comparison methods for typing methodologies (Carrico2006,Pinto2007) and the implementation of their statistical support (Severiano2011,Severiano2011b). Also, we have large experience in Web services specification and formalization (Vaz2007,Vaz2009,Lanese2010), namely on the context of the FP6 Project SENSORIA (Vaz2008). The research team has also worked on biological information systems and Web services, through the use of ontologies and RESTful interfaces (Almeida2012, Dario2011), and on large data analysis (Francisco2011) Plano e Métodos Plan and Methods The analyses of phylogenetic data present several challenges for epidemiologists and microbiologists. One of the major challenges is taking into account data from several sources and generated by different sequence based typing methods. The appearance of NGS technologies further increased this challenge by the substantial growth on the amount of genomic data that can be used to characterize a population. Traditional algorithms and software are proving inadequate to handle the increase in the number of genes available to be included in the phylogenetic analysis, and the constant decrease in costs per isolate, allowed the increase of the number of strains under studies from dozens to thousands. Moreover, the integration of the results from the typing methods with epidemiological data and simultaneous analysis is still limited to visualization techniques. For visualizing and analysing the outputs, there are some free software tools. For instance, for data analysis and visualization of microbial typing data focused on MLST analysis, we tools such as START (Jolley2001), eburst (Feil2004) and goeburst (Francisco2009). However, these tools lack the capacity to integrate epidemiological data. Other well known software tools, provide the ability for network visualization and data integration, usually of generic use, such as Gephi (Bastian2009) or more focused on biological networks such as regulatory, protein interaction or metabolic networks, as is the case of Cytoscape (Suderman2007). Neither these nor any other current tools for network visualization are specifically directed towards population or evolutionary analysis and all depend on already defined trees or networks, lacking the ability to infer them directly from data originating from sequence-based typing methods. Moreover, given their focus on general use, their configuration and customization for epidemiological data analysis requires expertise on graph theory and network mining and frequently calls for programming skills, so that their use for this purpose is restricted to only a few advanced users. A recent work by our team, PHYLOViZ (Francisco2012), which extended the algorithm goeburst(francisco2009) for any sequence based typing method and includes also some visualization capabilities for epidemiological data. Nevertheless, as more data becomes available, the better our understanding of the microbial population evolution and its impact in Human health issues, such as the evolution of antibiotic resistance in some species and the knowledge for the development of vaccine targets that can cover the entire bacterial population of a given species. Other important field is the creation of the needed surveillance databases to rapidly detect microbial outbreaks and for the analysis of the geographic dissemination of clinically relevant strains. However, most of the existing phylogenetic methods tend to consider only the DNA sequence for inferring the phylogenetic relationships and ignore available ancillary data, such as demographics, geographic information and the temporal dimension. This project will investigate and propose algorithms to assist in the analysis of large scale epidemiological and microbial whole genome sequence data, by combining optimized traditional phylogenetic methodologies with data mining techniques capable, and making this new advances available by the use of user-friendly interfaces by utilizing advanced visualization techniques. This will allow the end users, epidemiologists and microbiologists, to access algorithms running either locally or, when needed due to the data set sizes, in high performance computing facilities or in cloud computing providers, without having to have the technical skills usually needed to set up, configure and run the available pipelines. In Task 1, we propose to develop suitable data structures and algorithms for storing and manipulate such volume of data, taking into account parallel processing techniques. Since most of the data to be considered may appear in an unstructured way and may required heavy processing, we plan to implement these algorithms on a distributed setting, namely on top of Apache Hadoop ( We will work together with the project consultors, Jukka Corander and Xavier Didelot, to extend available Bayesian analysis softwares BAPS and Clonalframe on top such distributed computing environments. By analysing and reimplementing two diferent softwares, we aim to analyze and understand the guiding principles behind the general implementation of these Bayesian approaches in a distributed computing setting, while the development of optimized and compressed data structures will be aimed to speed up their manipulation and minimize data transfers. The optimized algorithms will be tested using large public NGS data sets from Short Read Archive ( from NCBI (National Center for Biotechnology Information), and data sets contributed by the IMM team and the consultants of the project for clinically relevant bacteria, such as Staphylococcus aureus and Streptococcus pneumoniae. The Short Read Data stores raw sequencing data from a variety of the NGS platforms, such as Roche 454 GS System, Illumina Genome Analyzer and Life Technologies Ion Torrent. The main outcomes of Task 1 will be used in Tasks 2 and 3. Given the quantity of phylogenetic data available and the increasing, we also expect challenges at knowledge discovery level. In Task 2 we will address the problem of extracting information from data, providing algorithms and employing machine learning techniques for mining genome typing data and related ancillary data, such as demographic, geographic, temporal and spatial information. The contribute of the consultor David Aanensen will be fundamental for this task given his previous experience in such analysis (Grundmann2010) and as the main developer of the website. These algorithms will include clustering algorithms for mining temporal and spatial patterns, which will contribute to better understand genomic population evolution. Moreover, we will also plan to contribute with techniques that allow evolutionary parameter estimation, such as mutation and recombination population rates. We propose to develop a Web based framework in Task 3, for making available the algorithms that we have developed in the previous tasks. We will make the algorithms available as software tools and the framework will be suitable for composing these tools, creating more complex workflow pipelines. Thus, the framework will support an online repository of the developed tools, where users can download and or construct workflows by composing available software tools. The main inovation in this taks will be the development of an orchestrator that can run on the user machine, and after the user providing it with a given a pipeline workflow description, it will use a code mobility approach (Meldvidovic2010) to download required tools and execute it using local computing resources or, depending on the computing needs of the data under analysis, move it to a Hadoop cluster or a cloud computing environment. The workflow construction will rely well known approaches, such as OWL-S and agent-based systems. Part of these results will be also implemented in the PHYLOViZ software developed by the project team, that it will serve as the basis for the integration of multiple methodologies. The web based framework will also include data integration mechanisms from several public or private sources as well as mechanisms for data transformation in a user friendly way. Based on team expertise, INESC-ID team will work together with IMM team on Task 1 and Task 2. IGC team will join on Task 2 and Task 3. Given the required effort on development to achieve an usable prototype framework, four BIs are needed to work on this project, two will collaborate on Task 1 and Task 2, and the other two will collaborate with on Task1, Task 2 and Task 3. Their work will consist on developing methods, perform validation experiments and framework integration Tarefas Tasks Lista de tarefas (3) Task list (3) 4 of 13 03/29/ :47 PM

5 Ordem Order Designação da tarefa Task denomination Data de início Start date Data de fim End date Duração Duration Pessoas * mês Person * months 1 NGS data analysis and processing tools ,7 da tarefa e Resultados Esperados Task description and Expected results This task aims to develop new methods, and improve existing ones, to process and analyze NGS data efficiently and effectively. Although typing methods, in particular sequence-based ones, have been successfully used to characterize bacterial and viral isolates, the amount of data generated by NGS technologies demands new tools capable of processing the sequence typing data infered from the whole genome sequence. Moreover, since we are dealing with large amounts of data, new tools must rely on succinct and efficient data structures, possibly including compression techniques, for text, graphs and structured data representation, and also on efficient and scalable algorithm implementations. Thus, we plan to: (1) identify and improve state of the art methods for the analysis whole genome typing data; (2) design and develop underlying data structures and algorithms to achieve desired scalability; and (3) deploy such methods on a distributed computing environment to allow big data processing and analysis. The analysis of NGS data includes several phases: genome assembly and resequencing, genome annotation, genome characterization and extraction of typing data, and finally typing data analysis and processing, where microbial strains are compared using phylogenetic analysis. In this project we will focus on this latter phase. Hence, given a collection of isolate typing data, which can include both sequence data, such as gene sequences, and typing profiles, such as MLST or SNP profiles, we want to improve existing methods, or develop better alternatives, for epidemiological analysis and phylogenetic reconstruction. Most of such methods rely on tree or graph reconstruction such as Minnimum Spanning Trees identification, graph clustering or Bayesian analysis (such as BAPS and ClonalFrame) to infer the relationships between the microbial strains under study. For this reason, we propose to identify, improve and reimplement these techniques as scalable as possible, following state of art algorithmics. In particular, several available tools have implemented the traditional techniques in a naive way, that although it worked with small datasets,but that can no longer handle larger data sets derived from NGS data. In these cases we plan to use the Hadoop framework ( and related technologies, such as the Mahout machine learning library ( Research from this task will result in a collection of new and improved algorithms for the analysis of whole genome typing data. This collection will be made available as independent and reusable modules that, in particular, whenever possible, will be compatible with Hadoop framework for big data analysis. New algorithms and innovative implementations will be published in renown venues, namely in key international journals. Public versions of the software packages will be made publicly available to the research community. This task will provide basic building blocks for remaining tasks. Both Task 2 and Task 3 will rely on the algorithms made available in this task for data mining and integration, Web services implementation and visualization data processing. The team from INESC-ID will contribute to this task with experience on algorithmics and large data processing. The team from IMM will contribute with experience with epidemiology, techniques for analyzing typing data, and related mathematical models. Two BI (BI 1 and BI 3) are needed to work in the implementation of algorithms and, latter, on large data analysis which allow us to test our tools. Membros da equipa de investigação nesta tarefa Members of the research team in this task (BI) Bolseiro de Investigação (Lic. ou Bacharel) 1; (BI) Bolseiro de Investigação (Lic. ou Bacharel) 3; (BI) Bolseiro de Investigação (Lic. ou Bacharel) 4; (BI) Bolseiro de Investigação (Lic. ou Bacharel) 6; Alexandre Paulo Lourenço Francisco; Cátia Raquel Jesus Vaz; João André Nogueira Custódio Carriço; Mário Nuno Ramos de Almeida Ramirez; Ordem Order Designação da tarefa Task denomination Data de início Start date Data de fim End date Duração Duration Pessoas * mês Person * months 2 Population surveillance and outbreak ,2 da tarefa e Resultados Esperados Task description and Expected results This task focuses on new techniques for mining whole genome typing data and associated information. Most of existing methods focus on typing data analysis and phylogenetic reconstruction, ignoring ancillary data such as demographic and geographic information. Another issue is related with the fact that the temporal dimensionis not usually taken into consideration, with most methods comparing data collected at rather different dates. A third issue is related with bacterial population sampling. Note that in general we only have access to a tiny fraction of isolate population and, thus, we must be careful when estimating parameters for population evolution models. These models, combined with ancillary data available, are crucial for the understanding of population evolution and for the detection of outbreaks. Hence, this task has the following objectives: (1) research data mining and integration methods for combining whole genome typing data and ancillary data for better understanding host-pathogen relationships and bacterial population evolution; (2) study methods for mining temporal and spatial patterns; and (3) develop parameter estimation techniques for model determination, such as mutation and recombination rates. All these goals will be used to refine the output (descendency tree or graph) from the phylogenetic methods from Task 1. In this task we will rely on algorithms implemented in Task 1, namely in what concerns the analysis of whole genome typing data. In particular, we plan to combine and run together those algorithms, superimposing their results. For data mining, integration and knowledge discovery, we will use already existing data mining and machine learning tools, such as WEKA ( and, for large data processing, Mahout ( For mining temporal and spatial patterns, we will use state of the start clustering techniques such as item set mining and biclustering techniques. With respect to parameter estimation, beyond standard approaches, we plan to integrate features extracted from data through previous methods and to use statistical and numerical parameter estimation. In the context of this task, we plan also to use recent techniques for mining social networks and for understanding diffusion processes and cascades on complex networks, to simulate the usual form of contact and interaction, between a network of pathogen hosts. One of the main challenges in this task will be dealing with missing and noisy data, as it is usually the case with ancillary data, which typically has less quality than typing data. Thus, we plan to rely on standard techniques for noise reduction and deal with missing values. The work done in this task will provide new methods for mining whole genome typing data and related ancillary, including demographic, geographic and temporal information. Such methods will take an integrative approach, relying on different kinds of data. Public versions of implemented methods will be made publicly available to the research community. This task will rely on algorithms implemented in Task 1, namely those related with whole genome typing data characterization. Methods developed within this task will be also made available as Web services and will benefit from intuitive and user-friendly interfaces that will be developed in Task 3. The team from INESC-ID will contribute to this task with experience on data mining and integration techniques, machine learning and knowledge discovery. The team from IMM will contribute with experience on population genetics, evolution and related mathematical models. The team from IGC will add experience on formal modelling and large simulation studies, particularly relevant for model validation. The BIs will process the data and to analyze it by integrating required algorithms from Task 1 with algorithms developed in this task. Membros da equipa de investigação nesta tarefa Members of the research team in this task (BI) Bolseiro de Investigação (Lic. ou Bacharel) 1; (BI) Bolseiro de Investigação (Lic. ou Bacharel) 3; (BI) Bolseiro de Investigação (Lic. ou Bacharel) 4; (BI) Bolseiro de Investigação (Lic. ou Bacharel) 6; Alexandre Paulo Lourenço Francisco; Cátia Raquel Jesus Vaz; João André Nogueira Custódio Carriço; Mário Nuno Ramos de Almeida Ramirez; Pedro Tiago Gonçalves Monteiro; Ordem Order Designação da tarefa Task denomination Data de início Start date Data de fim End date Duração Duration Pessoas * mês Person * months 3 Data visualization and interoperabili ,9 da tarefa e Resultados Esperados Task description and Expected results 5 of 13 03/29/ :47 PM

6 The purpose of this task is to develop a web based framework for on demand analysis of epidemiological data. One of the most common problems with data analysis has to do with how to find the right tools, integrate them and having the needed computing power to run them. In recent years, the majority of proposed solutions try to rely on Web services, and on their composition, to simultaneously make available tools and enable richer (more complex) pipelines. Although these approaches work reasonably well for some cases, problems may arise when dealing with large data. First, data transferring and computing power come with a cost that must be paid by Web services providers. A second problem has to do with data integration from several sources, which implies data transformation operations and transfer problems. A third problem is related with data and information visualization in a user-friendly environment. Our objective in this task is to apply state of the art techniques to address this problems, namely: (1) make our tools available as building blocks, with well documented interfaces, that can be downloaded from an online repository, much like an app store, and that can be used in a distributed computing environment; (2) design a Web user interface for pipeline workflow construction by selecting among available building blocks and by composing them; (3) integrate data transformation and visualization tools also as building blocks on our framework. Our approach will rely on existing technologies as much as possible. First, we will formally document our tools interfaces, using open standards and formats. This will be particularly useful for data integration, for which data transformation mappings have to be also defined. For data and information visualization we will rely on libraries such as d3.js ( and prefuse ( Our idea is that the output of both tools and built pipeline workflows will be available as Web resources, if client-server interaction is required beyond just data retrieval, and that end-users will be able to explore results through Web user interfaces. The most challenging step in this task is related with making tools available within a Web framework, namely in what concerns pipelines and workflows definition. We will base our research on existing proposals for services compositions, such as OWL-S, and on existing workflow engines, such as Taverna. Resulting pipelines should be then stored and be reusable. Most of current approaches rely on making tools available as Web services. Our goal is to move a step forward, and although we will reuse existing engines, we will develop an orchestrator that can be locally run and that, given a pipeline workflow description, it will download required tools and execute it using local computing resources, including for instance an Hadoop cluster or in a cloud computing environment. This task will provide a Web framework that will work as a tool and pipeline workflow repository, including tools developed in the context of this project, data transformation helper tools and data export endpoints, that may be just simple resources or Web services including data and information visualization. The framework will be available to all research community and it constitutes the end product of this project. This task incorporates all developments delivered in previous tasks, making developed tools and methods available in a user-friendly and reusable way through the developed Web framework. The team from INESC-ID and IGC will contribute to this task with experience on web services, information systems and on information visualization techniques. The team from IMM will contribute with experience on how analysis results and data can be better visualized and they will help on workflows definition and testing. Membros da equipa de investigação nesta tarefa Members of the research team in this task (BI) Bolseiro de Investigação (Lic. ou Bacharel) 4; (BI) Bolseiro de Investigação (Lic. ou Bacharel) 6; Cátia Raquel Jesus Vaz; João André Nogueira Custódio Carriço; Mário Nuno Ramos de Almeida Ramirez; Pedro Tiago Gonçalves Monteiro; Calendarização e Gestão do Projeto Project Timeline and Management a da Estrutura de Gestão a of the Management Structure To accomplish the objectives of this project, the expertise, skills and technologies, available within this group of internationally recognized experts, the teams need to operate within a coherent and transparent management framework. An essential requirement will be an efficient and accurate exchange of information and decisions between the teams. This procedure must respect the publication rights and the protection of intellectual property. The regular meetings and the project web site will provide the mechanism for joint decisionmaking that is required to achieve the stated deliverables and milestones. The management structure that will be adopted will be organized in two distinct areas: 1) the administration of the financial plan and 2) the coordination of the scientific and research issues. The financial administration of the project will be conducted by the PI, supported by a specialized department of the host research unit (INESC-ID), devoted to provide all the management support to ongoing scientific projects running in this institution. In what concerns the scientific structure, in order to ensure a perfect coordination of the partners and teams, regular meetings with the PI and researchers that are involved in each task will be promoted. Each task has several teams working within it, and within each team there will be several scientists. Therefore, to ensure proper coordination of activities at this tactical level, each task will have a task leader, who will coordinate the activities of all the other researchers within the task and report on the progress of the task to the PI. To support this coordination, the PI will set up a web site that will include a public and a confidential environment. The public site will have available the project description, the main results achieved and the planned dissemination venues, and the confidential site will support the communication and interaction among all the researchers in the project. The project results will be summarized and delivered in a progress report per year, with a final report at the end of the project. The achievement of the milestones will be the assessment criteria. If milestones are not met, the PI will decide whether to: increase the efforts directed towards the specific task, use alternative strategies, or redirect the resources to other main target. Results will be primarily disclosed to the international scientific community as conference communications and papers in peer-reviewed journals. A final workshop will be organized. Personal contact among the partners is essential to ensure good communication and will be pursued through project meetings. A kick-off meeting will be done in the first month of the project and one meeting in the middle of the second year gathering all the team will take place in Portugal. The project consultants will travel to Lisbon in these occasions. The final meeting and final workshop will occur in the same week b Lista de Milestones b Milestone List Data Date Designação da milestone Milestone denomination Optimized Data Structures for Phylogenetic Analysis of optimized data structures for the implementation of parallelized phylogenetic analysis algorithms. First prototypes. Data Date Designação da milestone Milestone denomination Parallelized versions of Phylogenetic Analysis Algorithms Finished the implementation of parallelized versions of Phylogenetic analysis algorithms for sequence analysis, namely the Bayesian methods of Clonalframe and BAPS. Data Date Designação da milestone Milestone denomination Knowledge discovery on Phylogenetic Algorithm results Data Mining Algorithms that will explain the impact of the inferred microbial population structure inferred from the phylogenetic methods and additional epidemiological and clinical data made available. These will focus on methods for mining the data for temporal and spatial patterns. Data Designação da milestone 6 of 13 03/29/ :47 PM

7 Date Milestone denomination Web Based Framework A Web based framework that will work as a tool and a pipeline workflow repository, including the developed tools in the previous milestones of the project, as well as provide data and information visualization c Cronograma c Timeline Ficheiro com a designação "timeline.pdf", no 9. Ficheiros Anexos, desta Visão Global (caso exista). File with the name "timeline.pdf" at 9. Attachments (if exists) Referências Bibliográficas 3.3. Bibliographic References Referência Reference Ano Year Spratt Maiden Voelkerding BBSRC Corander Corander Harris Croucher Jolley Didelot Ayres Langmead Hull Altintas Goecks Carrico Pinto Severiano Severiano2011b 2011 Jolley Feil Bastian Suderman Grundmann Medvidovic Grundmann Vaz Lanese Publicação Publication Spratt BG. Multilocus sequence typing: molecular typing of bacterial pathogens in an era of rapid DNA sequencing and the internet. Curr Opin Microbiol Jun;2(3):312-6 Maiden, M. C. J. (2006). Multilocus sequence typing of bacteria. Annual review of microbiology, 60, Voelkerding, K. V., Dames, S. A., & Durtschi, J. D. (2009). Next-generation sequencing: from basic research to diagnostics. Clinical Chemistry, 55(4), Biotechnology and Biological Sciences Research Council. Next generation sequencing review. UK BBSRC, 2011 Corander J, Marttinen P. Bayesian identification of admixture events using multi-locus molecular markers. Molecular Ecology, 2006, 15, Corander J, Marttinen P, Sirén J, Tang J. Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. BMC Bioinformatics, 2008, 9:539. Harris, S. R., Feil, E. J., Holden, M. T. G., Quail, M. A., Nickerson, E. K., Chantratita, N., Gardete, S., et al. (2010). Evolution of MRSA During Hospital Transmission and Intercontinental Spread. Science, 327(5964), Croucher, N., Harris, S., Fraser, C., & Quail, M. (2011). Rapid pneumococcal evolution in response to clinical interventions. Science, 331, Jolley, K. A., & Maiden, M. C. (2010). BIGSdb: Scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics, 11(1), 595. Didelot, X., & Falush, D. (2006). Inference of Bacterial Microevolution Using Multilocus Sequence Data. Genetics, 175(3), BEAGLE: an application programming interface and high-performance computing library for statistical phylogenetics. Ayres DL, Darling A, Zwickl DJ, Beerli P, Holder MT, Lewis PO, Huelsenbeck JP, Ronquist F, Swofford DL, Cummings MP, Rambaut A, Suchard MA. Syst Biol Jan 1;61(1): Epub 2011 Oct 1. Langmead B, Schatz MC, Lin J, Pop M, Salzberg SL. Searching for SNPs with cloud computing. Genome Biol 10:R134 D. Hull, K. Wolstencroft, R. Stevens, C. Goble, M. Pocock, P. Li, and T. Oinn, Taverna: a tool for building and running workflows of services., Nucleic Acids Research, vol. 34, iss. Web Server issue, pp , Kepler: an extensible system for design and execution of scientific workflows. Altintas, I. and Berkley, C. and Jaeger, E. and Jones, M. and Ludascher, B. and Mock, S. (2004) Scientific and Statistical Database Management, Proceedings. 16th International Conference on. Pages: Jeremy Goecks, Anton Nekrutenko, James Taylor, The Galaxy Team.Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biology 2010, 11:R86 Carriço, J.A.; C. Silva-Costa; J. Melo-Cristino; F. R. Pinto; H. de Lencastre; J.S.Almeida; M.Ramirez, Illustration of a Common Framework for Relating Multiple Typing Methods by Application to Macrolide-Resistant Streptococcus pyogenes, J Clin Microbiol Jul;44(7): Francisco R. Pinto, João A. Carriço, Mário Ramirez and Jonas S Almeida,Ranked Adjusted Rand: integrating distance and partition information in a measure of clustering agreement, BMC Bioinformatics 2007, 8:44 (07 Feb 2007) Severiano A, Carriço JA, Robinson DA, Ramirez M, Pinto FR (2011) Evaluation of Jackknife and Bootstrap for Defining Confidence Intervals for Pairwise Agreement Measures. PLoS ONE 6(5): e Severiano A., F. R. Pinto, M. Ramirez, and J. A. Carriço Adjusted Wallace as a measure of congruence between typing methods. J Clin Microbiol Nov;49(11): Epub 2011 Sep 14. Jolley KA, Feil EJ, Chan MS, Maiden MCJ: Sequence type analysis and recombinational tests(start). Bioinformatics 2001, 17(12):1230{1231. Feil EJ, Li BC, Aanensen DM, Hanage WP, Spratt BG: eburst: Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequence Typing Data. Journal of Bacteriology 2004, 186(5):1518{1530. Bastian M, Heymann S, Jacomy M: Gephi: An open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media Suderman M, Hallett M: Tools for visually exploring biological networks. Bioinformatics 2007, 23(20):2651{2659. Grundmann H, Aanensen DM, van den Wijngaard CC, Spratt BG, Harmsen D, Friedrich AW; European Staphylococcal Reference Laboratory Working Group.Geographic distribution of Staphylococcus aureus causing invasive infections in Europe: a molecular-epidemiological analysis. PLoS Med Jan 12;7(1):e Nenad Medvidovic, George Edwards. Software architecture and mobility: A roadmap. The Journal of Systems and Software 83 (2010) Grundmann H, Aanensen DM, van den Wijngaard CC, Spratt BG, Harmsen D, Friedrich AW. Geographic distribution of Staphylococcus aureus causing invasive infections in Europe: a molecular-epidemiological analysis. PLoS Med Jan 12;7(1):e C. Vaz and C. Ferreira. Towards Automated Verification of Web Services, IADIS WWW/Internet 2007, Oct I. Lanese, C. Vaz and C. Ferreira. On the Expressive Power of Primitives for Compensation Handling. In 19th European Symposium on Programming, ESOP 2010, Lecture Notes in Computer Science 6012, pp , Springer. 7 of 13 03/29/ :47 PM