(Pro-)Seminar: Comparison and Clustering of Biological Molecules

Description

In bioinformatics, the comparison and clustering of biological molecules serve as essential methodologies for deciphering the intricate complexities of biological systems. These processes play a pivotal role in elucidating evolutionary relationships, functional similarities, and structural motifs across diverse organisms and biomolecules. Through sequence alignment, structural superposition, and clustering techniques, researchers can discern patterns, similarities, and differences within vast datasets of DNA, RNA, proteins, and small molecules. Such analyses not only aid in understanding the fundamental mechanisms governing cellular processes but also pave the way for advancements in drug discovery, protein engineering, and personalized medicine. Moreover, by organizing biological data into meaningful clusters based on shared characteristics, these methodologies streamline data interpretation, enabling researchers to extract valuable insights and make informed decisions in their quest to unravel the mysteries of life at the molecular level.

In this seminar, we will look at two groups of tools. The first group computes clusters directly while the second group computes pairwise inter-sample similarities or distances that can then be used by classical clustering algorithms such as spectral clustering or agglomerative clustering. The goal for this seminar is to equip participants with a comprehensive understanding of the principles, methodologies, and practical applications of comparing and clustering biological molecules using cutting-edge tools such as FoldSeek, MASH, and CD-HIT. By the end of the seminar, participants should be able to proficiently utilize these tools to perform sequence alignment, structural comparison, and clustering analyses on various biological datasets. Moreover, they should gain insights into how these analyses contribute to advancements in genomics, proteomics, and drug discovery.

Requirements

This (pro-)seminar has no formal requirements.

Team

• Prof. Dr. Olga Kalinina
• M.Sc. Roman Joeres
• M.Sc. Guangyi Chen
• M.Sc. Amay Agrawal

What Do You Need To Do?

• Read and present one of the assigned seminar papers (assignment made at the kick-off meeting).
• Bachelor students in their proseminar have to
• Give a 20 minutes presentation with additional 10 minutes discussion and
• Write a 5-pages-long report about their method.
• Students in their seminar have to
• Give a 30 minutes presentation with additional 10 minutes discussion and
• Write a 7-pages-long report about their method.
• You need to attend all talks and participate in discussions.

How Are the Grades Computed?

Presentation (40%)

Assessed on clarity, depth of understanding, and the quality of your answers to audience questions.

Report (40%)

The report should include:

• A short explanation of how the the algorithm in your paper works.
• What are its (dis-)advantages over other tools presented in the seminar?
• Your own thoughts on this method.

Participation (20%)

Active engagement during other students' presentations. Ask questions — it counts toward your grade and improves the seminar for everyone.

Plagiarism

Registration

Please register to this seminar by writing an email to Roman Joeres before 19.04.2024 23:59. Please also attach your transcript of records which can be downloaded from the LSF/HISPOS. We will distribute the topics among students in the mandatory-to-attend kickoff meeting at 23.04.2024 12 PM in E2.1 SR 007.

Organisational Details

Important Dates

Topics

1. DIAMOND - Comparison of amino-acid sequences Assigned to: Anastasia Lesnikov, Supervisor: Roman Joeres
   DOI GitHub
2. MMseqs2 - Comparison & Clustering of amino-acid sequences Assigned to: Johanna Becher, Supervisor: Amay Agrawal
   DOI GitHub
3. TM-align - Comparison of amino-acid sequences Assigned to: Johanna Straub, Supervisor: Guangyi Chen
   DOI GitHub
4. CD-HIT - Clustering of amino-acid sequences & DNA/RNA Assigned to: Maximilian Bähr, Supervisor: Amay Agrawal
   DOI GitHub
5. FoldSeek - Comparison of protein structures Assigned to: Zyad Ahmed, Supervisor: Roman Joeres
   DOI GitHub
6. Weisfeiler-Lehman Graph Kernels - Comparison of protein structures & chemical molecules Assigned to: Varvara Kotelnikova, Supervisor: Roman Joeres
   PDF GitHub
7. MCES (Maximum Common Edge Subgraph) - Comparison of protein structures & chemical molecules Assigned to: Katja Räde, Supervisor: Roman Joeres
   DOI GitHub
8. MashMap - Comparison of long DNA strands Assigned to: Pranjali Jain, Supervisor: Guangyi Chen
   DOI GitHub
9. MCL - Clustering of genomes & amino acid sequences Assigned to: Misbah Sayeeda Musheer, Supervisor: Amay Agrawal
   DOI GitHub
10. MASH - Comparison of genomes Assigned to: Max Asenow, Supervisor: Amay Agrawal
    DOI GitHub
11. GClust - Comparison of genomes Assigned to: Saransh Shiva Nair, Supervisor: Guangyi Chen
    DOI GitHub
12. SANS - Clustering of genomes & amino acid sequences Assigned to: Vishak Kadamalithaya, Supervisor: Guangyi Chen
    DOI (update) DOI (main paper) GitHub

Comparison means a tool computes a matrix of pairwise distances or similarities. Clustering means a tool computes clusters of samples without telling how similar/distance these are.

Schedule