In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen.
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課程信息
您將獲得的技能
- Data Structure
- Algorithms
- Algorithm Design
- String (Computer Science)
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加州大学圣地亚哥分校
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
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The 2011 European E. coli Outbreak
In April 2011, hundreds of people in Germany were hospitalized with a deadly disease that often started as food poisoning with bloody diarrhea. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Within a few months, the outbreak had infected thousands and killed 53 people. To prevent the further spread of the outbreak, computational biologists all over the world had to answer the question “What is the genome sequence of E. coli X?” in order to figure out what new genes it acquired to become pathogenic.
Assembling Genomes Using de Bruijn Graphs
DNA sequencing approach that led to assembly of a small virus in 1977 went through a series of transformations that contributed to the emergence of personalized medicine a few years ago. By the late 1980s, biologists were routinely sequencing viral genomes containing hundreds of thousands of nucleotides, but the idea of sequencing a bacterial (let alone the human) genome containing millions (or even billions) of nucleotides remained preposterous and would cost billions of dollars.
Genome Assembly Faces Real Sequencing Data
Our discussion of genome assembly has thus far relied upon various assumptions. In this module, we will face practical challenges introduced by quirks in modern sequencing technologies and discuss some algorithmic techniques that have been devised to address these challenges. Afterwards, you will assemble the smallest bacterial genome that lives symbiotically inside leafhoppers. Its sheltered life has allowed it to reduce its genome to only about 112,091 nucleotides and 137 genes. And afterwards, you will be ready to assemble the E. coli X genome!
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- 5 stars68.85%
- 4 stars17.37%
- 3 stars9.18%
- 2 stars2.95%
- 1 star1.63%
來自GENOME ASSEMBLY PROGRAMMING CHALLENGE的熱門評論
I would like to say thank you to all who have created this course and specialization! Good material, excellent lecturers!
hi, this is a hard course and the videos are not sufficient. but finally i finished. thanks a lot
Really tough and enjoyable Project. Learnt something very special.
very challenging course, but still very good and you learn a lot
關於 数据结构与算法 專項課程
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.

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