Bioinformatic Methods I

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Beschrijving

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About this course: Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and prote…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts. The first part, Bioinformatic Methods I (this one), deals with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics. This, the second part, Bioinformatic Methods II, covers motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions. This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. Both provide an overview of the many different bioinformatic tools that are out there. These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course.

Created by:  University of Toronto
  • Taught by:  Nicholas James Provart, Professor

    Cell & Systems Biology
Commitment 12-18 hours videos and labs Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.6 stars Average User Rating 4.6See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Syllabus


WEEK 1


NCBI/Blast I



In this module we'll be exploring the amazing resources available at NCBI, the National Centre for Biotechnology Information, run by the National Library of Medicine in the USA. We'll also be doing a Blast search to find similar sequences in the enormous NR sequence database. We can use similar sequences to infer homology, which is the primary predictor of gene or protein function.


4 videos, 4 readings expand


  1. Reading: Acknowledgements
  2. Reading: Course Logistics
  3. Video: Introduction
  4. Video: Lecture
  5. Reading: Lecture Materials
  6. Reading: Lab 1 -- Exploring NCBI
  7. Video: Lab Discussion
  8. Video: Summary

Graded: Lab 1 Quiz

WEEK 2


Blast II/Comparative Genomics



In this module we'll continue exploring the incredible resources available at NCBI, the National Centre for Biotechnology Information. We will be performing several different kinds of Blast searches: BlastP, PSI-Blast, and Translated Blast. We can use similar sequences identified by such methods to infer homology, which is the primary predictor of gene or protein function. We'll also be comparing parts of the genomes of a couple of different species, to see how similar they are.


4 videos, 2 readings expand


  1. Video: Introduction
  2. Video: Lecture
  3. Reading: Lecture Materials
  4. Reading: Lab 2 -- Advanced Blast and Comparative Genomics
  5. Video: Lab Discussion
  6. Video: Summary

Graded: Lab 2 Quiz

WEEK 3


Multiple Sequence Alignments



In this module we'll be doing multiple sequence alignments with Clustal (as implemented in MEGA), DiAlign, and MAFFT. Multiple sequences alignments can tell you where in a sequence the conserved and variable regions are, which is important for understanding the biology of the sequences under investigation. It also has practical applications, such as being able to design PCR primers that will amplify sequences from a number of different species, for example.


4 videos, 2 readings expand


  1. Video: Introduction
  2. Video: Lecture
  3. Reading: Lecture Materials
  4. Reading: Lab 3 -- Multiple Sequence Alignment
  5. Video: Lab Discussion
  6. Video: Summary

Graded: Lab 3 Quiz

WEEK 4


Review: NCBI/Blast I, Blast II/Comparative Genetics, and Multiple Sequence Alignments





    Graded: Quiz: Modules 1-3

    WEEK 5


    Phylogenetics



    In this module we'll be using the multiple sequence alignments we generated last lab to do some phylogenetic analyses with both neighbour-joining and maximum likelihood methods. The tree-like structure generated by such analyses tells us how closely sequences are related one to another, and suggests when in evolutionary time a speciation or gene duplication event occurred.


    4 videos, 2 readings expand


    1. Video: Introduction
    2. Video: Lecture
    3. Reading: Lecture Materials
    4. Reading: Lab 4 -- Phylogenetics
    5. Video: Lab Discussion
    6. Video: Summary

    Graded: Lab 4 Quiz

    WEEK 6


    Selection Analysis



    In this module we'll take a set of orthologous sequences from bacteria and use DataMonkey to analyze them for the presence of certain sites under positive, negative or neutral selection. Such an analysis can help understand the biology of a set of protein coding sequences by identifying residues that might be important for biological function (those residues under negative selection) or those that might be involved in response to external influences, such as drugs, pathogens or other factors (residues under positive selection).


    4 videos, 2 readings expand


    1. Video: Introduction
    2. Video: Lecture
    3. Reading: Lecture Materials
    4. Reading: Lab 5 -- Selection Analysis
    5. Video: Lab Discussion
    6. Video: Summary

    Graded: Lab 5 Quiz

    WEEK 7


    'Next Gen' Sequence Analysis (RNA-Seq) / Metagenomics



    In this module we'll explore some of the data that have been generated as a result of the rapid decrease in the cost of sequencing DNA. We'll be exploring a couple of RNA-Seq data sets that can tell us where any given gene is expressed, and also how that gene might be alternatively spliced. We'll also be looking at a couple of metagenome data sets that can tell us about the kinds of species (especially microbial species that might otherwise be hard to culture) that are in a given environmental niche.


    4 videos, 2 readings expand


    1. Video: Introduction
    2. Video: Lecture
    3. Reading: Lecture Materials
    4. Reading: Lab 6 -- Next Generation Sequencing Applications: RNA-Seq and Metagenomics
    5. Video: Lab Discussion
    6. Video: Summary

    Graded: Lab 6 Quiz

    WEEK 8


    Review: Phylogenetics, Selection Analysis, and 'Next Gen' Sequence Analysis (RNA-seq)/Metagenomics + Final Assignment



    1 reading expand


    1. Reading: Final Assignment Instructions

    Graded: Review: Modules 5-7
    Graded: Final Assignment

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