Disciplina Discipline IBI5087
Análise de dados multiômicos para ecologia microbiana e biotecnologia

Multi-OmicAnalysis for MicrobialEcologyandBiotechnology

Área de Concentração: 95131

Concentration area: 95131

Criação: 18/09/2019

Creation: 18/09/2019

Ativação: 25/09/2019

Activation: 25/09/2019

Nr. de Créditos: 4

Credits: 4

Carga Horária:

Workload:

Teórica

(por semana)

Theory

(weekly)

Prática

(por semana)

Practice

(weekly)

Estudos

(por semana)

Study

(weekly)

Duração Duration Total Total
15 15 0 2 semanas 2 weeks 60 horas 60 hours

Docentes Responsáveis:

Professors:

João Carlos Setubal

Ulisses Nunes da Rocha

Objetivos:

Apresentar fundamentos de tecnologias e de análises multi-ômicas para aplicações em ecologia microbiana e biotecnologia

Objectives:

In this course students will be able to understand the fundamentals of multi-omics technologies as well as multi-omics analyses in microbial ecology and biotechnology applications. They should be able to design multi-omics experiments and analyses, then use them in real data sets and present the results in oral form.

Justificativa:

Sabe-se de longa data que comunidades microbianas desempenham papel fundamental em diversos processos da biosfera. O manejo do meio ambiente em termos de riscos causados por substâncias químicas entre outros requer a capacidade de predizer os efeitos de tais substâncias no meio ambiente por meio de dados de larga escala de comunidades microbianas. Neste cursos iremos capacitar os alunos a projetar experimentos abordando essa questão usando tecnologias ômicas, e a analisar os resultados

Rationale:

In the dawn of the Anthropocene, Earth appears to be undergoing rapid changes accelerated by anthropogenic activity. For example, the increasing release of chemicals is endangering the foundations of life and biodiversity. Microbial diversity profoundly impacts terrestrial ecosystems as it provides the key catalysts for the biogeochemical reactions needed to sustain the ecosystem services that control the fate of industrially produced chemicals in terrestrial environments. In nature, microbes do not function in isolation but rather act as members of complex communities. A better understanding of the response of microbial communities to introduced chemicals will help to design scientifically based policies aimed at preventing and halting the loss of ecosystem services. This course aims to educate graduate students at USP about advance risk assessments of chemicals in terrestrial environments based on a predictive understanding of what happens to these chemicals using microbial community ‘Big Data’. Here, we propose an innovative problem based approach that scales metabolic interaction models to the high diversity found in complex microbial communities. The proposed visit aims to capacitate the students to: (a) understand how state of the art omics techniques are linked to concepts and theories in ecology and biotechnology; (b) design experiments using omics with focus on ecology and biotechnology, (c) analyze multi-omics data.

Conteúdo:

Conceitos e teorias de ecologia microbiana com foco em ecologia e biotecnologia. Metagenômica, metatranscritômica, metaproteômica e metabolômica. Planejamento de experimentos simulados, lidando com variáveis tais como número de replicatas, metadados, resolução temporal e espacial. Técnicas de análise usando a metodologia SMART. Introdução a comandos básicos em python e R para realizar tarefas relevantes para a disciplina. Uso prático desses comandos em conjuntos de dados reais

Content:

• Lecture block 1: “Trends in Multi-Omics Analyses – from concepts to data and back” In the first block of lectures, the class will be introduced to concepts and theories in microbial ecology with focus on ecology and biothechnologies. The different omic technologies will be discussed: (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. Later, we will introduce the state of the art examples of such techniques; first individually and late in combination (multi-omics studies). During this lecture block the students will be constant stimulated to think how these concept and theories could be extrapolated to their field of study. • Problem solving block 1: “Drawing experimental design (wet lab) to answer ecologic and biotechnologic questions with multi-omics” Wewill divide the students in small group with similar interest. Based on the Lecture Block 1, the students will discuss what questions in their field can be answered with the different omics technologies. Then we will plan mock experiments to discuss the important of number of replicates, associated metadata, space and temporal resolution. • Lecture block 2: “Multi-Omics-Analyzes in Microbes Ecology and Biotechnology" Most often, bioinformatics and data analysis classes teach the students to think about a specific method to make one set of analysis. In this lecture block, the students will be stimulated to discuss the rationale involved with analysis. We will work on the concept of ‘plan the work’ and work the plan. Also, the students will be introduced to SMART goals. The SMART strategy means: Specific, Measurable, Achievable, Relevant and Time bound plans. In this block, wewant to capacitate the students to think in an output oriented manner. When working on their SMART strategy, they can think about a need to learn basis that can be used to analyze progress and completion of their projects. • Problem solving block 2: “Planning necessary output – what and how much data a project needs” Following up Lecture Block 2, this problem solving session will raise the discussion of what omics or multi-omics data combination is necessary to answer different research questions. The same groups of the first problem solving block will work on the questions they raised and how to answer them. Also, they will prepare a SMART plan to be discussed among all students. • Lecture block 3: “Basic computational skills in python and R to work with bioinformatics” Wewill divide the students in two groups depending on their experience level. With those with no or little experiencewewill introduce basic python and R commands to be used in the computer lab. To the experienced students wewill give the SMART plan for the Computer Lab (see next item). The experienced students will work as monitors for the computer lab. • Computer Lab: "From data to answers: Understanding Multi-Omics Data" In the last problem solving part of the module, the students will be introduced to: (a) amplicon sequencing analysis, and (b) recovery of metagenome assembled genomes. The first part of the computer lab will be on hands on data sets that we have recently published using high-throughput amplicon sequencing data. The students will discuss the experimental design of ideal datasets and those used by recent publications. In such a manner, they will not only learn how to do the analysis, but to think about how decisions they make during their experimental design have implications till the completion of their projects. Wewill introduce the students to a pipeline developed by Dr. Nunes da Rocha that is based in a structure similar to QIIME II. Such structure was planned to make it easier for people that have experience in amplicon sequence analysis to advance to recovery of metagenome assembled genomes. Competency Goals This course will introduce the students for the following competences: • To be able to participate actively in the module Artificial Neural Networks and Machine Learning. • To understand the basic concepts and differences between "omics" (genomics, transcriptomics, proteomics, and metabolomics) for individual species and complex communities • To determine how wet-lab and experiment design affect data yield and multi-omics data set analysis. • To define basic concepts of multi-omics analysis in environmental and biotechnological applications • To design multi-omics microbial data analysis on ecology and biotechnology and apply them to real-world datasets • To give a scientific lecture • To produce a SMART plan for their own work • to learn amplicon sequencing analysis • To learn recovery of metagenome assembled genomes.

Forma de Avaliação:

Apresentação oral dos trabalhos feitos em aula (60%); e participação nas aulas (40%).

Type of Assessment:

After completing the Computer Lab project the different groups will present a summary of what they learned during the class. The presentation will last 20 min.

Observação:

Bibliografia:

A. Konopka. Microbial Ecology. Encyclopaedia of Microbiology, 4th edition, Elsevier, 2019. Roumpeka et al. A review of bioinformatics tools for bio-prospecting from metagenomic sequence data (2017), Frontiers in Genetics, 8:23. Kumar et al. Modelling approaches for studying the microbiome. Nature Microbiology, vol. 4, agosto de 2019, pp. 1253-1267

Bibliography:

A. Konopka. Microbial Ecology. Encyclopaedia of Microbiology, 4th edition, Elsevier, 2019. Roumpeka et al. A review of bioinformatics tools for bio-prospecting from metagenomic sequence data (2017), Frontiers in Genetics, 8:23. Kumar et al. Modelling approaches for studying the microbiome. Nature Microbiology, vol. 4, agosto de 2019, pp. 1253-1267