(in Polish) Metagenomics 2600-MK-MTBIOL-2-S2

Class 1
Introduction to metagenomics. Metagenomics as a useful tool in the ecology of microorganisms. The importance and perspectives of metagenomics.

Class 2
Isolation of biological material for metagenomic. Methods and tools for DNA / RNA isolation. Requirements and quality of genetic material.

Class 3
Bioinformatics analysis used in NGS data processing.

Class 4.
Taxonomy analysis of microbial communities. Abundant and rare OTUs, Venn diagrams. Taxonomy visualization.

Class 5
Alpha diversity. Analysis of biodiversity of microbial communities. Rarefraction curves, indicators of species richness (Sobs, Chao1, ACE).

Class 6
Alpha diversity. Biodiversity indicators of microbial communities (Shannon-Wiener index, Simpson index). Eveness indicators.

Class 7
Beta diversity. Distance, similarity/dissimilarity of microbial communities. Cluster analysis.

Class 8
Beta diversity. Consulting methods (PCA, PCoA).

Class 9
Beta diversity. Hypothesis testing (ANOSIM, SIMPER, PERMANOVA).

Class 10
Other methods for visualizing data from NGS sequencing. Correlations and heatmaps.

Class 11
Genome sequencing.

Class 12
Microbial identification based on the whole genome sequence (ANI, in silico DDH).

Class 13
Genome annotation in Rast Server. The role of microorganisms in the environment.

Class 14, 15
Students' own work. Compilation of the results of the analysis, preparing a report and presenting its conclusions.

Total student workload

60 h including: • work with teacher: 30 h • individual student consultation: 1 h • individual work: 25 hours • preparations for assessment: 4 hours

Learning outcomes - knowledge

W1: Describes and explains complicated phenomena occurring in microbial communities - K_W04 W2: Explains the interactions of the environment and organisms living in it - K_W07. W3: Provides in-depth knowledge of statistics and knowledge of specialized IT tools allowing to describe the course of natural phenomena - K_W08. W4: Has knowledge in the field of molecular biology that allows the assessment of biological material - K_W10. W5: Has current knowledge in the field of detailed biological sciences (biochemistry, genetics, microbiology, and physiology) used in research - K_W11.

Learning outcomes - skills

U1: Applies advanced knowledge in statistics in the description of biological phenomena - K_U01 U2: Uses the computer to search for information, for communication, organizing and analyzing data, preparing reports and presenting results - K_U04. U3: Uses source information in English, performs analysis, synthesis, summarizes and makes a critical assessment, which allows correct inference - K_U09.

Learning outcomes - social competencies

K1: Understands the need to continually improve knowledge using scientific and popular scientific journals - K_K01. K2: Is aware of the responsibility for the reliability of conducted analyzes and expert opinions - K_K04. K3: Shows criticism in relation to the results of his work - K_K07. K4: Shows the ability to use mathematical-statistical and IT methods to develop and present results and analyzes - K_K08. K5. Is aware of the importance of foreign language skills in communication and assimilation of information - K_K13.

Teaching methods

Theoretical and practical classes using the Moodle platform.

Expository teaching methods

- description
- discussion
- informative (conventional) lecture

Exploratory teaching methods

- practical
- seminar

Online teaching methods

- content-presentation-oriented methods
- cooperation-based methods

Type of course

compulsory course

Prerequisites

Basic knowledge in molecular biology and statistics. Ability to use the R and Past. English language proficiency both written and spoken.

Course coordinators

Agnieszka Kalwasińska

Assessment criteria

Class attendance, preparation for classes, active participation, preparation of reports on conducted analyzes.

Practical placement

Not applicable

Bibliography

1. Benedetti C (Ed) Metagenomics, Methods, Application and Perspectives (2014) Nova Publishers, New York
2. Izard J, Rivers M.C (Eds) Metagenomics for Microbiology, Academic Press, Elsevier
3. Li R.W (Ed) Metagenomics and its applications in agriculture, biomedicine, and environmental studies (2011) Nova Science Publishers.
4. Magurran AE, McGill BJ (Eds) Biological diversity: frontiers in measurement and assessment (2011) Oxford University Press

Notes

Term 2022/23Z:

All the materials and tasks designed for the classes are available from moodle.umk.pl

Term 2024/25Z:

All the materials and tasks designed for the classes are available from moodle.umk.pl

Schedule;
11.10. Installing R and its packages
18.10. Matagenomics as a tool in microbial ecology. Significance and perspectives.
25.10. Methods and tools for DNA/RNA isolation. Quality and quantity of genetic material
8.11. Bioinformatics at early stages of NGS data processing
15.11. Taxonomy visualization
22.11. Rarefraction curves. Alpha diversity metrics
29.11. Distance, similarity/dissimilarity, cluster analysis
6.12. Beta diversity. Ordination methods (PCA, PCoA, NMDS).
13.12. Beta diversity. Hypothesis testing (ANOSIM, SIMPER, PERMANOVA).
20.12. Correlations, heatmaps, networks.
10.01. Genome sequencing (WGS)
17.01. Identification of bacteria based on WGS
24.01. Genome mining. KEGG database
31.01. Students' projects
7.02. Students' projects

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2600-MK-MTBIOL-2-S2 in USOSweb