Overview

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. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.

Syllabus

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. The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this Genome Assembly Programming Challenge, you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the deadly E. coli X strain. However, before you embark on building a program for assembling the E. coli X strain, we have to explain some genomic concepts and warm you up by having you solve a simpler problem of assembling a small virus.

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. In 1988, three biologists (independently and simultaneously!) came up with an idea to reduce sequencing cost and proposed the futuristic and at the time completely implausible method of DNA arrays. None of these three biologists could have possibly imagined that the implications of his own experimental research would eventually bring him face-to-face with challenging algorithmic problems. In this module you will learn about the algorithmic challenge of DNA sequencing using information about short k-mers provided by DNA arrays. You will also travel to the 18the century to learn about the Bridges of Konigsberg and solve a related problem of assembling a jigsaw puzzle!

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!