{"id":730,"date":"2021-07-20T13:34:43","date_gmt":"2021-07-20T16:34:43","guid":{"rendered":"https:\/\/pages.cnpem.br\/proteomicsworkshop\/?p=730"},"modified":"2021-07-20T13:42:41","modified_gmt":"2021-07-20T16:42:41","slug":"ix-proteomics-workshop","status":"publish","type":"post","link":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/2021\/07\/20\/ix-proteomics-workshop\/","title":{"rendered":"IX Proteomics Workshop"},"content":{"rendered":"\n

\"\"<\/p>\n

IX Proteomics Workshop<\/p>\n

\"\"Click here to access the Abstracts List<\/a><\/h4>\n

Click here to access the list of selected applicants for the IX Proteomics Workshop.<\/a><\/h4>\n

The IX Proteomics Workshop is promoted by the Brazilian Biosciences National Laboratory (LNBio). The event will be hosted by the Brazilian Center for Research in Energy and Materials (CNPEM) in Campinas-SP, Brazil, on November 12<\/strong>th<\/sup><\/strong>\u00a0and 13<\/strong>th<\/sup><\/strong>, 2019. The meeting program comprises classes ministered by experts and poster sessions.<\/strong><\/p>\n

IX Proteomics Workshop will offer 2-day courses in advanced themes in Omics Data Analysis. The lecturers will be ministered by 3 Brazilians and 4 foreign invited scientists. This edition will cover Data Integration, Computational and Statistical methods applied for Omics. The topics will be addressed in one-hour theoretical lecture and one-hour practical activity for each speaker.<\/p>\n

Why participate?<\/strong><\/p>\n

This meeting represents a training opportunity for current and future users of proteomics and mass spectrometry. Following the example of the previous editions, held in the last eight years, the workshop also promotes the interaction between Brazilian and international researchers and dissemination of knowledge in the scientific community.<\/p>\n

How much will I invest?<\/strong><\/p>\n

The IX Proteomics Workshop registration fee is mandatory for all participants. The registration fee includes all lectures, conference\u00a0material,\u00a0coffee breaks, cocktails and lunches at the event on November 12th<\/sup>\u00a0and 13th\u00a0\u00a0<\/sup>2019.<\/p>\n

Details about the registration and the payment procedures are available in \u201cApplications\u201c.<\/p>\n\n\n\n\n\n\n\n\n
\n

IX Proteomics Workshop Deadlines<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Applications<\/p>\n<\/td>\n

\n

From September 26th to October 6th<\/p>\n<\/td>\n<\/tr>\n

\n

Releasing of shortlisted applicants<\/p>\n<\/td>\n

\n

October 23th<\/p>\n<\/td>\n<\/tr>\n

\n

Waiting list call<\/p>\n<\/td>\n

\n

October 30th<\/p>\n<\/td>\n<\/tr>\n

\n

Workshop<\/p>\n<\/td>\n

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From 12th to 13th of November<\/p>\n<\/td>\n<\/tr>\n

\u00a0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n

Speakers<\/h1>\n

Daniel Clarke, Icahn School of Medicine at Mount Sinai, New York<\/strong>
<\/strong>http:\/\/labs.icahn.mssm.edu\/maayanlab\/team\/<\/strong><\/a>

<\/strong><\/p>\n

Title: Integrating Bioinformatics Tools for Knowledge Exploration Workflows<\/p>\n

To form novel hypotheses biomedical researchers rely more and more on information stored in public databases and bioinformatics tools that can query these databases. We have integrated several widely used bioinformatics tools and databases developed by the Ma\u2019ayan Laboratory into a directed multi-graph with nodes representing fundamental data objects, for example, gene sets, signatures, disease or drug terms, and edges representing the transformations performed by various tools (for example, enrichment analysis, principal component analysis, or a PubMed search). We then use this graph to direct and facilitate user-driven exploration of the landscape of available knowledge stemming from an initial query or from a given dataset. As a case study, we use this system to investigate the role of under-studied protein kinases in diabetic nephropathy.<\/p>\n

Dexter Pratt, UC San Diego School of Medicine, San Diego<\/strong>
<\/strong>https:\/\/home.ndexbio.org\/team-members\/<\/strong><\/a>

<\/strong><\/p>\n

Title:\u00a0Investigation of Proteomic Datasets using Biological Network Analysis Tools in the Cytoscape Ecosystem<\/p>\n

Analyses based on molecular interaction networks and pathway mechanism models have a long history of use in the investigation of high throughput gene expression and proteomic data. The widely used Cytoscape desktop application (cytoscape.org) is one of preeminent tools in this field and is evolving into an ecosystem of both desktop, cloud services and web applications. NDEx, the Network Data Exchange (ndexbio.org), is a central element of the Cytoscape cloud that is a resource for network content and a framework for storing, sharing, publishing and computing with networks. This presentation will review Cytoscape tools relevant to the analysis of proteomic datasets including NDEx, Cytoscape desktop apps, and new web applications. In the following workshop, participants will apply tools described in the presentation to the investigation of an example data set.<\/p>\n

Igor Jurisica, University Health Network, Toronto, Ontario<\/strong>
<\/strong>https:\/\/www.uhnresearch.ca\/researcher\/igor-jurisica<\/strong><\/a>

<\/strong><\/p>\n

Title: Data-driven (precision) medicine: from data to models to insights and treatments<\/p>\n

To fathom complex disease development processes, we need to systematically
integrate diverse types of information and link them using relevant
annotations and relationships, leading to meaningful modeling. This ranges
from multiple high-throughput datasets, functional annotations and
orthology data to expert knowledge about biochemical reactions and
biological pathways. Such integrative systems are used to develop new
hypotheses and answer complex questions such as what type of system
perturbation may result in a desired change in cellular function; what
factors cause disease; will patients respond to a given treatment, etc.<\/p>\n

Precision medicine needs to be data-driven and corresponding analyses
comprehensive and systematic. We will not find new treatments if only
testing known targets and studying characterized pathways. Thousands of
potentially important proteins remain pathway or interactome \u201corphans\u201d.
Computational biology methods can help fill this gap with accurate
predictions, but the biological validation and further experiments are
essential. Intertwining computational prediction and modeling with
biological experiments will lead to more useful findings faster and more
economically.<\/p>\n

These computational predictions improved human interactome coverage
relevant to both basic and translational research, and importantly, helped
us to identify, validate and characterize prognostic signatures. Combined,
these results may lead to unraveling mechanism of action for therapeutics,
re-positioning existing drugs for novel use and prioritizing multiple
candidates based on predicted toxicity, identifying groups of patients
that may benefit from treatment and those where a given drug would be
ineffective.<\/p>\n

Application of graph theory, data mining, machine learning and advanced
visualization enables data-driven, precision medicine. Intertwining
computational prediction and modeling with biological experiments will
lead to more useful findings faster and more economically.<\/p>\n

Jo\u00e3o Carlos Setubal, Chemistry<\/strong><\/p>\n

Institute, University of S\u00e3o Paulo, SP<\/strong>
<\/strong>http:\/\/www.iq.usp.br\/setubal\/<\/strong><\/a>

<\/strong><\/p>\n

Title: A transcriptome-based signature of pathological angiogenesis predicts breast cancer patient survival<\/p>\n

Compostagem termof\u00edlica \u00e9 uma rica fonte de enzimas relacionadas com degrada\u00e7\u00e3o de biomassa. No projeto metazoo estudamos a compostagem realizada no Parque Zool\u00f3gico de S\u00e3o Paulo utilizando sequenciamento de nova gera\u00e7\u00e3o. Com base no sequenciamento do DNA total (shotgun) de dezenas de amostras, montamos um cat\u00e1logo com mais de 10 milh\u00f5es de sequ\u00eancias codificadoras de prote\u00ednas.\u00a0 Esse cat\u00e1logo foi mineirado em busca de enzimas termoest\u00e1veis com bom potencial tecnol\u00f3gico. Atrav\u00e9s de uma metodologia de aprendizado de m\u00e1quina chegamos a um subconjunto de 231 enzimas candidatas promissoras. Para quatro dessas candidatas realizamos ensaios experimentais confirmando termoestabilidade e atividade enzim\u00e1tica.<\/p>\n

Lydia Y. Liu, University of Toronto<\/strong>
<\/strong>http:\/\/kislingerlab.uhnres.utoronto.ca\/people.html<\/strong><\/a>

<\/strong><\/p>\n

Title:\u00a0Proteomics Data Integration in Cancer: The Value of Multimodality<\/p>\n

\u201cIncreasingly translational\u00a0cancer studies will quantify many types of molecular information in specific model systems or patient samples. These most frequently include\u00a0the germline and somatic\u00a0mutation profiles (including point mutations, copy number aberrations and genomic rearrangements), the transcriptome, immune infiltrates,\u00a0the epigenome and the evolutionary timing of variants at each of these levels. Proteomic data analyses\u00a0thus need to leverage that data to better understand information flow in cancer cells, develop robust biomarkers and understand the molecular origins of complex phenotypes. We will discuss the broad data-analytic strategies for these large datasets, and the challenges with integrating proteomic data with other datatypes. This will include practical examples of recent work performing such integrative analyses in primary cancer cohorts, and the value of statistical, machine-learning, information-theoretic and network strategies. Overall, we show that data integration across multiple levels of the central dogma improves our understanding of cancer phenotypes. Indeed biomarkers comprising multiple classes of biomolecules systematically outperform those that include only one: despite being an analytic challenge, multi-modality is a key opportunity for the future development of oncoproteomics.\u201d<\/p>\n

Mariana Boroni, National Cancer Institute, RJ<\/strong>
<\/strong>https:\/\/www.inca.gov.br\/en\/node\/2375<\/strong><\/a>

<\/strong><\/p>\n

Title: Identifying new therapeutic strategies for Colorectal cancer in the Big Data Era.<\/p>\n

O c\u00e2ncer colorretal (CCR) \u00e9 um dos carcinomas de maior incid\u00eancia e mortalidade no mundo e tem como fatores de risco o baixo consumo de vegetais e alto consumo de carne vermelha e\/ou processada, sobrepeso e sedentarismo. O CCR \u00e9 uma doen\u00e7a altamente heterog\u00eanea, apresentando quatro subtipos moleculares, com diferen\u00e7as na localiza\u00e7\u00e3o anat\u00f4mica, no microambiente tumoral e nas vias moleculares alteradas. Essa grande heterogeneidade afeta significativamente a resposta a diferentes tratamentos e o progn\u00f3stico dos pacientes. Neste sentido, novas estrat\u00e9gias terap\u00eauticas devem ser desenvolvidas considerando-se as altera\u00e7\u00f5es moleculares mais relevantes nos subtipos moleculares tumorais. As an\u00e1lises para identifica\u00e7\u00e3o de alvos terap\u00eauticos se baseiam no conceito de \u201cdruggable genome\u201d, isto \u00e9, identifica\u00e7\u00e3o de genes que codificam fam\u00edlias prot\u00e9icas espec\u00edficas que interagem com f\u00e1rmacos, e que est\u00e3o diretamente relacionados com o estabelecimento da doen\u00e7a. Com base nestas informa\u00e7\u00f5es, o objetivo do nosso estudo \u00e9 sugerir o reposicionamento de drogas atualmente utilizadas para o tratamento de outros tipos tumorais, assim como a identifica\u00e7\u00e3o de novos alvos terap\u00eauticos a partir da an\u00e1lise dos padr\u00f5es de express\u00e3o g\u00eanica e do perfil de intera\u00e7\u00e3o prote\u00edna-prote\u00edna nos subtipos moleculares de CCR.<\/p>\n

Nina Hirata, Institute of Mathematics and Statistics, University of S\u00e3o Paulo, SP<\/strong>
<\/strong>https:\/\/www.ime.usp.br\/~nina\/<\/strong><\/a>

<\/strong><\/p>\n

Title: Machine Learning and Computational Thinking<\/p>\n

Machine learning techniques are tools often used to automate certain
types of data processing needed for data analysis. They are
particularly useful to analyze multidimensional data of complex nature
or large amounts of data. In this talk we will start relating
computational algorithms to machine learning and discussing how
computational thinking is essential for the effective use of
computational tools, including machine learning techniques. We will
then introduce basic concepts and methods of machine learning.
At the end, the discussed concepts and methods will be explored
through practical hands-on application examples.<\/p>\n

\u00a0<\/h1>\n

Program<\/h1>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

November 12<\/strong>th<\/sup><\/strong>, 2019<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

8:00-9:00<\/p>\n<\/td>\n

\n

Reception and Registration<\/p>\n<\/td>\n<\/tr>\n

\n

9:00-9:30<\/p>\n<\/td>\n

\n

\u00a0Welcome \u2013 Kleber Franchini and Adriana Paes Leme,<\/p>\n<\/td>\n<\/tr>\n

\n

9:30-10:30<\/p>\n<\/td>\n

\n

Lydia Y. Liu, University of Toronto<\/p>\n

Proteomics Data Integration in Cancer: The Value of Multimodality<\/p>\n<\/td>\n<\/tr>\n

\n

10:30-10:40<\/p>\n<\/td>\n

\n

Viviane Nascimento, Waters<\/p>\n<\/td>\n<\/tr>\n

\n

10:40-11:40<\/p>\n<\/td>\n

\n

Lydia Y. Liu, Hands-on<\/p>\n<\/td>\n<\/tr>\n

\n

11:40-12:10<\/p>\n<\/td>\n

\n

Coffee break and Poster session<\/p>\n<\/td>\n<\/tr>\n

\n

12:10-1:10<\/p>\n<\/td>\n

\n

Dexter Pratt, UC San Diego School of Medicine, San Diego<\/p>\n

Investigation of Proteomic Datasets using Biological Network Analysis Tools in the Cytoscape Ecosystem<\/p>\n<\/td>\n<\/tr>\n

\n

1:10-2:30<\/p>\n<\/td>\n

\n

Lunch<\/p>\n<\/td>\n<\/tr>\n

\n

2:30-3:30<\/p>\n<\/td>\n

\n

Dexter Pratt, Hands-on<\/p>\n<\/td>\n<\/tr>\n

\n

3:30-3:40<\/p>\n<\/td>\n

\n

Felipe Lug\u00e3o, NovaAnal\u00edtica\/Thermo Scientific<\/p>\n<\/td>\n<\/tr>\n

\n

3:40-4:40<\/p>\n<\/td>\n

\n

Nina Hirata, Institute of Mathematics and Statistics, University of S\u00e3o Paulo, SP<\/p>\n

Machine Learning and Computational Thinking<\/p>\n<\/td>\n<\/tr>\n

\n

4:40-5:10<\/p>\n<\/td>\n

\n

Coffee break and Poster session<\/p>\n<\/td>\n<\/tr>\n

\n

5:10-6:10<\/p>\n<\/td>\n

\n

Nina Hirata, Hands-on<\/p>\n<\/td>\n<\/tr>\n

\n

6:10-6:30<\/p>\n<\/td>\n

\n

Discussion<\/p>\n<\/td>\n<\/tr>\n

\n

6:30-8:30<\/p>\n<\/td>\n

\n

Welcome Reception<\/p>\n<\/td>\n<\/tr>\n

\n

November 13<\/strong>th<\/sup><\/strong>, 2019<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

8:00-9:00<\/p>\n<\/td>\n

\n

Jo\u00e3o Carlos Setubal, Chemistry Institute, University of S\u00e3o Paulo, SP<\/p>\n

Mining of thermostable enzymes from compost metagenomic data<\/p>\n<\/td>\n<\/tr>\n

\n

9:00-9:10<\/p>\n<\/td>\n

\n

Diego Assis, Bruker<\/p>\n

timsTOF fleX with ESI and MALDI for Proteomics and SpatialOMx<\/p>\n<\/td>\n<\/tr>\n

\n

9:10-10:10<\/p>\n<\/td>\n

\n

Jo\u00e3o Carlos Setubal, Hands-on<\/p>\n<\/td>\n<\/tr>\n

\n

10:10-10:40<\/p>\n<\/td>\n

\n

Coffee break and Poster session<\/p>\n<\/td>\n<\/tr>\n

\n

10:40-11:40<\/p>\n<\/td>\n

\n

Mariana Boroni, National Cancer Institute, RJ<\/p>\n

Identifying new therapeutic strategies for Colorectal cancer in the Big Data Era<\/p>\n<\/td>\n<\/tr>\n

\n

11:40-12:40<\/p>\n<\/td>\n

\n

Mariana Boroni, Hands-on<\/p>\n<\/td>\n<\/tr>\n

\n

1:00-2:00<\/p>\n<\/td>\n

\n

Lunch<\/p>\n<\/td>\n<\/tr>\n

\n

2:00-3:00<\/p>\n<\/td>\n

\n

Daniel Clarke, Icahn School of Medicine at Mount Sinai, New York<\/p>\n

Integrating Bioinformatics Tools for Knowledge Exploration Workflows<\/p>\n<\/td>\n<\/tr>\n

\n

3:10-3:20<\/p>\n<\/td>\n

\n

Maur\u00edcio Marques, Agilent<\/p>\n<\/td>\n<\/tr>\n

\n

3:20-4:20<\/p>\n<\/td>\n

\n

Daniel Clarke, Hands-on<\/p>\n<\/td>\n<\/tr>\n

\n

4:20-4:50<\/p>\n<\/td>\n

\n

Coffee break and Poster session<\/p>\n<\/td>\n<\/tr>\n

\n

4:50-5:50<\/p>\n<\/td>\n

\n

Igor Jurisica, University Health Network, Toronto, Ontario<\/p>\n

Data-driven (precision) medicine: from data to models to insights and treatments<\/p>\n<\/td>\n<\/tr>\n

\n

5:50-6:50<\/p>\n<\/td>\n

\n

Igor Jurisica, Hands-on<\/p>\n<\/td>\n<\/tr>\n

\n

6:50-7:00<\/p>\n<\/td>\n

\n

Final Remarks<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/strong><\/p>\n

List of selected applicants<\/h1>\n

Click here to access the Abstracts List<\/a><\/h4>\n

The application form is available until October 6th.<\/strong><\/p>\n

Please, read all the information below carefully before filling the form.<\/p>\n

How to apply<\/strong><\/p>\n