Microbiome Data Analysis

Microbiome Data Analysis

The microbiome, a complex community of microorganisms, plays a crucial role in the life of the environment it inhabits, including the human body. For example, patients with Alzheimer's disease have substantially different gut microbiomes compared to healthy individuals. The recent biotechnology advances enabled the generation of large amounts of microbiome-related data across different omics levels, such as genomics, transcriptomics, proteomics, and metabolomics. However, effectively analyzing and interpreting this data to fully realize its promise and shed light on the role of the microbiome requires the development and application of specialized computational methods.

This course covers cutting-edge bioinformatics tools and techniques for analyzing microbiome data (all omics levels). Students get hands-on experience by applying these tools to sample datasets. They also explore the computational principles that underlie these methods and implement some basic algorithms on their own.

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Registration

The course is intended for Bioinformatics students (MSc and BSc) who are competent (== not beginners) in command line (Linux/macOS) and programming (preferably Python, but any language should work).

If you are eligible and want to enroll, please send an email to gurevich@cs.uni-saarland.de entitled "Enrollment MDA (WiSe 25/26)" and containing (i) a brief motivation to join the course (not more than 250 words), (ii) written confirmation that the two key requirements are met: competent command-line (Linux/macOS) and programming skills. You will be added to the course MS Teams group after that. Note: there is NO additional registration via CMS; the main teaching platforms will be MS Teams and Cogniterra (see below).

The deadline for registration (sending the abovementioned email) is 19.10.2024 (Sunday) at 23:59.

For the final exam in February, you will need to additionally register in LSF (the registration will open close to the exam date).

For the hands-on component of the course (practical and coding assignments), register on the Cogniterra platform and join the course.

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When / Where / Who

Lectures: Wed, 10:15 – 11:45, E2.1 (CBI) R0.07 -- small seminar room (weekly, from 15.10.2025) by JProf. Dr. Alexey Gurevich

Tutorials: Fri, 12:00 – 13:30, E2.1 (CBI) R0.03 -- CIP pool (bi-weekly, from 24.10.2025) by JProf. Dr. Alexey Gurevich, with assistance from Dr. Azat Tagirdzhanov and MSc. Aleksandra Kushnareva from the HMSB group.

Both events will be held in person. However, if/when we have more active students than available seats (ca. 25), a hybrid connect option will be available (via MS Teams; max until the midterm exam in early December).

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Content and Exam 

The course comprises 12 lectures on (meta)genomics, transcriptomics, proteomics, and metabolomics data analysis and 7 assignments on practical (running state-of-the-art data analysis tools, 4) and algorithmic (implementing the underlying algorithms, 3) aspects. There will be a written midterm exam covering the first half of the course and (supposedly) an oral final exam covering the entire course theory.

Exam admission criteria

• Practical and Coding assignments (each ≥ 50%)
• Midterm exam (≥ 50%)

Grading

• You should score at least 50% in the final exam to pass it, but this grade constitutes only 70% of the final course grade (e.g., 50% in the final exam transforms into 35% of the final course grade).
• The remaining 30% could be obtained via completing practical and coding assignments and midterm exams above 90% (10% for each of the three modules; for results between 50% and 90%, pro rata value is added)
• The final percentage is converted into the German grade scale using this online calculator, with thresholds set to 100 and 50, and rounded to the closest valid grade (1.50 --> 1.3, 1.51 --> 1.7, etc.).

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Course Syllabus (TBA; subject to change; refer to last year's syllabus as an example)

Date	Type	Major Topic	Title	Slides
15.10.2025	lecture	other	Microbiome Data Analysis: Introduction (general info about the course content and grading)	MDA_L01
22.10.2025	lecture	amplicon seq.	Amplicon Sequencing: Part 1 (from raw reads to ASVs/OTUs)	MDA_L02
24.10.2025	tutorial (run)	amplicon seq.	Basic 16S Data Analysis (see L02)	assignments
29.10.2025	lecture	amplicon seq.	Amplicon Sequencing: Part 2 (comparing samples' taxonomical content)	MDA_L03
05.11.2025	lecture	metagenomics	Metagenomics: Introduction and Read-Based Analysis	MDA_L04
07.11.2025	tutorial (code)	metagenomics	Burrows-Wheeler transform (BWT) and Sequence Alignment (see L04)	assignments
12.11.2025	lecture	metagenomics	Metagenomics: Assembly-Based Analysis Part I (from reads to contigs)	MDA_L05
19.11.2025	lecture	metagenomics	Metagenomics: Assembly-Based Analysis Part II (from contigs to MAGs)	MDA_L06
21.11.2025	tutorial (run)	metagenomics	Metagenomics: from Reads to MAGs (see L05-L06)	assingments
26.11.2025	lecture	functional ann.	Taxonomical and Functional Annotation	MDA_L07
03.12.2025	lecture	functional ann.	Annotation of Secondary Metabolite Clusters (functional annotation part II)	MDA_L08
05.12.2025	tutorial (code)	functional ann.	Hidden Markov Models (HMM) and Genome Annotation (see L08)	assignments
10.12.2025	midterm exam	other	Intermediate Exam (during the lecture slot 10:15 -- 11:45; in person; written)	questions  answers
17.12.2025	lecture	transcriptomics	(Meta)transcriptomics: Introduction and Common Tools	MDA_L09
17.12.2025	tutorial (run)	transcriptomics	(Meta)transcriptomics Data Analysis (see L09)	assignments
07.01.2026	lecture	proteomics	(Meta)proteomics and Mass Spectrometry Basics	MDA_L10
14.01.2026	lecture	proteomics	Computational Mass Spectrometry	TBA
16.01.2026	tutorial (code)	proteomics	Basic Computational Mass Spectrometry Problems (see L11)	assignments

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Contact

Feel free to send your questions, suggestions, and concerns directly to JProf. Dr. Alexey Gurevich (gurevich@cs.uni-saarland.de).