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Microbiome analysis using 2nd and 3rd generation DNA sequencing
Provider: Faculty of Science

Activity no.: 5325-26-00-00	There are 57 available seats
	Enrollment deadline: 27/07/2026

Date and time	10.08.2026, at: 00:00 - 21.08.2026, at: 16:00
Regular seats	60
Lecturers	Lukasz Krych
ECTS credits	5.00

Contact person	Lukasz Krych E-mail address: krych@food.ku.dk

Enrolment Handling/Course Organiser	PhD Administration SCIENCE E-mail address: phdcourses@science.ku.dk
Semester/Block	Summer

Enrolment guidelines This is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come, first-served basis and according to the applicable rules. Anyone can apply for the course, but if you are not a PhD student, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to applicants on the waiting list. Aim and Content Adequate generation and analysis of DNA sequencing data are essential skills for scientists across medicine, research, and industry. This course provides theoretical and practical training in microbiome analysis using 2nd (Illumina) and 3rd (Oxford Nanopore Technologies, ONT) generation sequencing technologies, with particular emphasis on nanopore technology. Participants will engage in hands-on sample preparation, DNA sequencing, and analysis of raw data (e.g., pod5/fastq files). They will learn to map amplicons (e.g., 16S rRNA gene), reconstruct genomes, and estimate abundance tables (e.g., OTU/ASVtables) using state-of-the-art, open-access bioinformatics tools. Exploratory analysis (e.g., alpha- and beta-diversity) and advanced statistical approaches for microbiome data will also be covered, including tools for linking microbiome profiles to other omics and environmental data. The course is designed for PhD students and industry representatives and spans 10 days (2 weeks, Monday to Friday). The first week emphasizes wet lab techniques, including the generation and initial processing of raw sequencing data. The second week focuses on the computational analysis of microbiome data, including the datasets generated during the course. Learning outcomes Intended learning outcome for the students who complete the course: • Assess DNA quality and concentration using spectrophotometric and colorimetric methods. • Prepare barcoded 16S rRNA gene amplicon libraries for Illumina and ONT sequencing. • Generate sequencing libraries for near full-length 16S rRNA genes and full genomes using ONT. • Load and operate sequencing platforms (e.g., MinION, PromethION, flongle, Illumina iSeq). • Analyze raw sequencing data (Illumina fastq and ONT pod5) to generate OTU tables using R based tools. • Perform alpha- and beta-diversity analyses and exploratory statistics for microbiome profiling. • Conduct discriminant analysis (DA) and differential abundance testing using advanced statistical tools. Knowledge: By the end of the course, participants will have knowledge about: • Principles of 2nd and 3rd generation DNA sequencing technologies (Illumina, Oxford Nanopore). • Strengths, limitations, and applications of sequencing platforms for microbiome analysis. • Theoretical background of microbial community profiling, genome reconstruction, and amplicon-based approaches. • Statistical approaches for microbiome data, including alpha-/beta-diversity, discriminant analysis, and differential abundance testing. • Integration of microbiome data with other omics and environmental datasets. Skills: Participants will be able to: • Assess DNA quality and concentration using spectrophotometric and colorimetric methods. • Prepare barcoded 16S rRNA amplicon libraries for Illumina and ONT sequencing. • Generate sequencing libraries for near full-length 16S rRNA genes and full genomes using ONT. • Operate sequencing instruments (MinION, PromethION, flongle, Illumina iSeq). • Analyze raw sequencing data (Illumina fastq, ONT pod5) to produce OTU/ASV tables using R-based tools. • Perform exploratory and advanced statistical analyses on microbiome data. Competences: Upon completion, participants will be competent to: • Design and execute microbiome sequencing experiments from sample preparation to data analysis. • Critically evaluate sequencing data quality and analytical pipelines. • Apply appropriate bioinformatics and statistical methods to their own PhD or professional projects. • Integrate microbiome data with other biological and environmental datasets to draw meaningful scientific conclusions. • Communicate methods, results, and interpretations in a written scientific report. Target Group The course is primarily aimed at PhD students working with microbiome research, DNA sequencing, and related omics technologies. It is also highly relevant for laboratory technicians and specialists from private companies and industry who are engaged in microbiome studies, sequencing-based workflows, or bioinformatics data analysis. Recommended Academic Qualifications Participants are expected to have a background in life sciences (e.g., microbiology, molecular biology, biotechnology, bioinformatics, food science, or related disciplines) and an interest in applying state-of-the-art sequencing technologies and computational tools to microbiome research. Research Area The course is relevant to research areas within microbiology, molecular biology, biotechnology, bioinformatics, food science, and environmental science. In particular, it targets research focusing on microbiome analysis, DNA sequencing technologies, microbial ecology, and multi-omics integration across both academic and industrial contexts. Teaching and Learning Methods The course will combine theoretical teaching and practical training to ensure participants gain both conceptual understanding and hands-on experience. Instruction will include: • Lectures (25 hours): Introducing principles of 2nd and 3rd generation sequencing technologies, microbiome analysis, and statistical approaches. • Laboratory Sessions (20 hours): Hands-on training in DNA extraction, library preparation, and operation of Illumina and Oxford Nanopore sequencing platforms. • Theoretical Exercises (30 hours): Guided computational work on microbiome datasets, covering raw data processing, OTU/ASV table generation, and advanced statistical analyses using R-based tools. • Course Preparation (15 hours): Pre-reading and familiarization with course material. • Report Writing (47.5 hours): Independent work where participants analyze data (optionally including their own PhD project datasets) and prepare a written report. This blended format ensures alignment between theory and practice, reflecting the workload distribution specified for the 5.5 ECTS course. Type of Assessment • Write and document a dedicated analysis script (e.g., in R or bash) for a specific dataset type, such as raw sequencing data (Illumina fastq or ONT pod5 files) or processed feature/abundance tables. • Include both the code (lines of script) and comments explaining the analysis steps to ensure reproducibility and clarity. • Perform analyses such as OTU/ASV table generation, alpha-/beta-diversity estimation, or advanced statistical testing. • Interpret and discuss the results, with the option to integrate data from their own PhD project or professional context. • Assessment will be conducted on a pass/fail basis, determined by the quality and completeness of the submitted script and report, as well as active course participation. Course coordinator Associate professor Lukasz Krych Guest Lecturers 1. Pablo Atienza Lopez Bioinformatician, Genomics of Gene Expression Lab, Valencia Topic: Genome assembly, annotations, and raw data treatment 2. Farhad M. Panah, PhD Unseen Bio, Denmark Topic: 16S rRNA gene amplicon sequencing data treatment 3. Witold Kot , PhD Tenure Track Assistant Professor, Department of Plant and Environmental Sciences, University of Copenhagen Topic: Illumina sequencing lectures Dates 10-08-2026 to 21-08-2026 Expected frequency The course is offered annually. The 2026 edition will mark the 8th edition of the course, and it is expected to continue in the coming years with further development and refinement of content and teaching methods. Course location The course will take place at the Department of Food Science, Faculty of Science, University of Copenhagen. Deadline for registration The deadline for registration is 27 July 2026. Applications received after this date will only be considered if seats remain available. Registration is binding, and available seats are limited to 60 participants. Course fee • Participant fee: 1.000 DKK • PhD student enrolled at SCIENCE: 0 DKK • PhD student from Danish PhD school Open market: 0 DKK • PhD student from Danish PhD school not Open market: 6.000 DKK • PhD student from foreign university: 6.000 DKK • Master's student from Danish university: 0 DKK • Master's student from foreign university: 6.000 DKK • Non-PhD student employed at a university (e.g., postdocs): 6.000 DKK • Non-PhD student not employed at a university (e.g., from a private company): 16.800 DKK Cancellation policy • Cancellations made up to two weeks before the course starts are free of charge. • Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000 • Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000 • No-show will result in a fee of DKK 5.000 • Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 Course fee and participant fee PhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types. In addition to the course fee, there might also be a participant fee. If the course has a participant fee, this will apply to all participants regardless of participant type - and in addition to the course fee.

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