VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Abstract
Der Bereich Praktika, Projekte, Seminare umfasst Lehrveranstaltungen in unterschiedlichen Formaten zum Erwerb von praktischen Kenntnissen und Fertigkeiten. Ausserdem soll selbstständiges Experimentieren und Gestalten gefördert, exploratives Lernen ermöglicht und die Methodik von Projektarbeiten vermittelt werden.
Objective
Genome analysis is a cornerstone for groundbreaking scientific and medical advancements, including personalized healthcare. However, the field faces significant computational challenges, such as algorithmic bottlenecks and the handling of large datasets. This course aims to provide a comprehensive understanding of these computational facets, spanning across the computing stack from algorithms, software & tools, to microarchitecture & hardware accelerators. The course will cover how advanced hardware solutions like FPGAs and GPUs can expedite genome analysis by reducing computational time and energy consumption. In parallel, it will delve into the use and development of heuristic algorithms & tools for accelerating genome analysis across various computational platforms. These algorithms, for example, can offer tradeoffs between computational intensity and accuracy. Students will engage in hands-on projects focused on optimizing existing methods or innovating new solutions for genome analysis. The curriculum’s dual emphasis on hardware solutions and versatile algorithmic strategies offers students a holistic view of the current challenges and potential resolutions within the realm of genome analysis. Prerequisites of the course: + No prior knowledge in bioinformatics or genome analysis is needed. + An interest in optimizing efficiency and solving complex problems is essential. + Basic to good knowledge in C or C++ programming language is required. + Familiarity with accessing servers using SSH and bash scripting. + Previous coursework in Digital Design and Computer Architecture, or an equivalent course, is desirable. + Experience in either FPGA implementation, GPU programming, or algorithm design is beneficial but not mandatory.
Content
The students carry out a hands-on project under the supervision of their mentors. We also offer the following lectures that the students are encouraged to follow to make impactful progress on their projects. Lecture 1a: P&S Course Introduction & Scope Lecture 1b: Project Overview and Q&A Lecture 2: Introduction to Genome Analysis Lecture 3: From Molecules to Data: An Overview of DNA Sequencing Technologies Lecture 4a: Fundamentals of Sequence Alignment: Algorithms and Applications Lecture 4b: Optimizing Sequence Search: Hashing, Indexing, and Filtering Techniques Lecture 5a: Building the Blueprint of Life: Genome Assembly Lecture 5b: Generating Insights from Genome Analysis: Variant Calling and Functional Genomics Lecture 6a: GateKeeper Lecture 6b: SneakySnake Lecture 6c: GRIM-Filter Lecture 7a: GenASM Lecture 7b: Scrooge Lecture 8: SeGraM Lecture 9: GenStore Lecture 10a: GenPIP Lecture 10b: TargetCall Lecture 11a: BLEND Lecture 11b: AirLift Lecture 12a: Raw Nanopore Signal Analysis & RawHash
Resources
Lecture Notes
See:https://safari.ethz.ch/projects_and_seminars/doku.php?id=bioinformatics
Literature
Learning Materials =============== 1. Overview paper on co-designing hardware and software for genome analysis: https://people.inf.ethz.ch/omutlu/pub/AcceleratingGenomeAnalysis_dac23.pdf 2. Survey on the main steps in the genome analysis pipeline and their bottlenecks: https://people.inf.ethz.ch/omutlu/pub/IntelligentGenomeAnalysis_csbj22.pdf 3. Survey on accelerating genome analysis: https://people.inf.ethz.ch/omutlu/pub/AcceleratingGenomeAnalysis_ieeemicro20.pdf 4. Detailed survey on state-of-the-art algorithms for sequencing data: Link 5. Example of accelerating genomic sequence matching with FPGAs or GPUs: Link 6. Example using different computing paradigms for read mapping and alignment, improving energy consumption: - https://arxiv.org/pdf/1711.01177.pdf - https://arxiv.org/pdf/2208.01243.pdf 7. Examples of software/hardware co-design for genomic sequence matching: - Link - Link - https://people.inf.ethz.ch/omutlu/pub/GenStore_asplos22-arxiv.pdf - https://arxiv.org/pdf/2209.08600.pdf 8. Example on analyzing raw nanopore signals: https://arxiv.org/pdf/2301.09200.pdf