[BBC] Summer School Trends in multi-omics data analysis for Microbial Ecology and Biotechnology

[BeNeLux bioinformaticians forum]

Summer School Trends in multi-omics data analysis for Microbial Ecology and Biotechnology

In 2023, the summer school will take place from July 6 to July 27 at the Helmholtz Centre for Environmental Research (Permoserstraße 15, 04318 Leipzig).

If you are interested to enroll in this course please email
Dr. Ulisses Nunes da Rocha<https://www.ufz.de/index.php?de=43142> - ulisses.rocha at ufz.de


The Microbial Data Science group at the UFZ designed this course focusing on students at different levels of their academic careers (B.Sc., M.Sc., and Ph.D.) and postdoctoral researchers. When we created this course, we had two groups in mind. On the one hand, experienced bioinformaticians would understand how their skills fit microbial ecology and biotechnology research; on the other, those working in microbial ecology and biotechnology would develop their multi-omics analysis skills.


This course is taught in collaboration with the University of Leipzig under the supervision of Prof. Dr. Peter Stadler (Webpage<http://www.bioinf.uni-leipzig.de/~studla/>). Further, it participates in the efforts of the Helmholtz Interdisciplinary GRADuate School for Environmental Research (HIGRADE<https://www.ufz.de/higrade/>) and the German National research Data Infrastructure for Microbiota (NFDI4Microbiota<https://nfdi4microbiota.de/>).
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Teaching Phylosophy

Our teaching philosophy is centered on the conviction that educators should empower students to take control of their learning, nurturing the qualities that we expect our students to exhibit: dedication, professionalism, and self-motivation. To foster an environment where students are stimulated to learn, it is crucial to create a classroom where independent thought is highly valued, and all students are encouraged to achieve their full potential. To this end, we nurture intellectual skills (e.g., thinking, reasoning, interpreting, analyzing, reflecting, and questioning. I also believe in continually relating lecture and laboratory subjects to real-life situations. In this context, our team tries to foster problem-oriented learning. We believe this approach will help to make classes more interesting for students.

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Teaching format

  *   Lectures on 'Trends in multi-omics analysis' (3 ECTs) = 30 h in presence and 70 h self-study.
  *   Lectures on 'Multi-omics analysis in microbial ecology and biotechnology' (3 ECTs) = 30 h in presence and 35 h self-study.
  *   Practical course on 'From data to answers: making sense of multi-omics data sets' (4 ECTs) = 30 h in presence and 105 h of self-study.

Workload

10 ECTs = 300 working hours

Competence Goals
After active participation in the module 'Trends in multi-omics data analysis for Microbial Ecology and Biotechnology', students will be able to:

  *   Define basic concepts and differents between genomics; transcriptomics and proteomics and metabolomics in single species and complex communities
  *   Determine how wet-lab and experimental design may influence data mining and analysis of multi-omics data sets
  *   Define basic concepts of multi-omics analysis in ecological and biotechnological applications
  *   Design multi-omics data analyis related to ecology and biotechnology and to apply them in real multi-omics data sets
  *   Give a scientific presentation
  *   Translate multi-Omics data analyses into scientific texts and presentations

Summary of Module's content

The contents that make up this module are: Functional potential analysis; Description of ecosystem processes; Omics history; Omics technologies; Biodiversity analysis of high-throughput sequencing data; Research data management in the omics era; Phylogenomic analysis; Genome reconstruction from metagenomes; Amplicon sequencing analysis; Virus ecology and biotechnology; Reconstruction of metabolism at the genome level; Transcriptomics; Use of machine learning for omics analysis. The computational laboratory project will include: Genome reconstruction from the metagenome, machine learning, analysis of omics data, or metabolic reconstruction at the genome level (students can decide which of the projects they would like to participate in).



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