1 / 8

Bioinformatics and Mathematical Genetics

Bioinformatics and Mathematical Genetics. Simon Myers myers@stats.ox.ac.uk. An example of a modern genetic dataset. We are gathering data in Oxford (in collaboration with Chicago colleagues) 10 chimpanzees Having their DNA sequence read across the ~3 billion base chimp genome

rea
Download Presentation

Bioinformatics and Mathematical Genetics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Bioinformatics and Mathematical Genetics Simon Myers myers@stats.ox.ac.uk

  2. An example of a modern genetic dataset • We are gathering data in Oxford (in collaboration with Chicago colleagues) • 10 chimpanzees • Having their DNA sequence read across the ~3 billion base chimp genome • First time this has been done in chimps • Modern technology means this is an affordable project Q: Why are we doing this? A: Because of the insights it can yield into evolutionary processes, not just in chimps, but in humans

  3. Data for a small region ~300,000,000,000 base pairs of sequence in total A project to sequence 1000 human genomes: ~20,000,000,000,000 base pairs

  4. Opportunities (I) • Genome-wide data carries an immense amount of information • We can compare genomes between humans and chimps: • Chimpanzees and humans are 98.6% similar at the DNA level...but • What places do we differ? • Can we explain what makes us human? • Many places that differ are the result of random chance • We can look within the set of chimps • How are chimps evolving? • How do individual chimpanzees differ at the DNA level? • Lots of similar variation data has been or is being gathered in humans • Is the chimpanzee data similar, or different, in terms of overall patterns?

  5. Opportunities (II) • Variation patterns that we see reflect evolutionary forces • Mutation, selection, recombination, migration,... • Do these forces work in similar ways in both species? • Are, e.g., similar types of gene under selection in humans and chimpanzees? • We can learn how dynamic, or conserved, we ought to think of these forces • A particular interest is recombination: • This does differ, strongly, between the species, at least at scales of thousands of bases • Is there any sharing? At what scales? • How does DNA sequence relate to recombination in chimps and humans? • Knowing the answer will lead directly to information about constraints on recombination • In turn, perhaps insights in humans: disease, infertility, and differences among populations Father Mother Child

  6. Challenges ....as night follows day A lot of data • Computationally intensive • Need for careful algorithm construction Go from “raw” sequence to something useful: • Must align to compare species, dealing with errors in the data Understand how the forces we care about influence the data • Evolutionary modelling • Think about relationships among individuals in the sample • Development of inference techniques • Must be applicable to these large datasets The aim of this module is to give an introduction to: • Approaches to address these types of challenges • What we have already learnt using Bioinformatics and mathematical genetics

  7. Brief overview • Mixture of lectures, practicals, exercises and some reading • Week 1:Genomes, genetic variation and evolutionary forces • Today: how and why do genomes evolve? • Later in the week: • Alignment of genomes, to e.g. discover genes • Phylogenetics: to build trees • Modelling evolution, to relate biological parameters and data • Exploring variation patterns in practice • Inference on biological parameters • Week 2:Phenotype and function • 3-day project, led by Jotun Hein, to identify, and analyse, the evolution of a unique functional element in our genome • This is also the basis of the assessment, by presentation • Relating variation among individuals to human phenotypes • What mutations cause disease, differences in metabolite levels....

  8. Introductory practical The first practical explores the role of randomness in evolution Some mutations carry a benefit to those who carry them, or are deleterious • Individuals with beneficial mutations have, on average, more children on average • However, inheritance is highly stochastic • Luck is involved in successfully finding a mate, and raising children • Parents pass on a random subset of the mutations they carry to their children • Randomness and selection act in opposition • How does randomness affect selection? • How often do “neutral” mutations, that are neither beneficial nor deleterious, • succeed? • These questions can be explored with the Wright-Fisher model

More Related