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Introduction to Bioconductor in R

IntermediateSkill Level
4.8+
99 reviews
Updated 12/2022
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
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RProbability & Statistics4 hr14 videos54 Exercises4,050 XP18,126Statement of Accomplishment

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Course Description

Much of the biological research, from medicine to biotech, is moving toward sequence analysis. We are now generating targeted and whole genome big data, which needs to be analyzed to answer biological questions. To help you get started, you will be introduced to The Bioconductor project. Bioconductor is and builds the infrastructure to share software tools (packages), workflows and datasets for the analysis and comprehension of genomic data. Bioconductor is a great platform accessible to you, and it is a community developed open software resource. By the end of this course, you will be able to use essential Bioconductor packages and get a grasp of its infrastructure and some built-in datasets. Using BSgenome, Biostrings, IRanges, GenomicRanges, TxDB, ShortRead and Rqc with real datasets from different species is going to be an exceptional experience!

Prerequisites

Introduction to RIntroduction to the Tidyverse
1

What is Bioconductor?

In this chapter, you will get hands-on with Bioconductor. Bioconductor is the specialized repository for bioinformatics software, developed and maintained by the R community. You will learn how to install and use bioconductor packages. You'll be introduced to S4 objects and functions, because most packages within Bioconductor inherit from S4. Additionally, you will use a real genomic dataset of a fungus to explore the BSgenome package.
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2

Biostrings and When to Use Them?

Biostrings are memory efficient string containers. Biostring has matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. How efficient you can become by using the right containers for your sequences? You will learn about alphabets, and sequence manipulation by using the tiny genome of a virus.
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3

IRanges and GenomicRanges

The IRanges and GenomicRanges packages are also containers for storing and manipulating genomic intervals and variables defined along a genome. These packages provide infrastructure and support to many other Bioconductor packages because of their enriching features. You will learn how to use these containers and their associated metadata, for manipulation of your sequences. The dataset you will be looking at is a special gene of interest in the human genome.
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4

Introducing ShortRead

ShortRead is the package for input, manipulation and assessment of fasta and fastq files. You can subset, trim and filter the sequences of interest, and even do a report of quality. An extra bonus towards the last exercises will give you the tools for parallel quality assessment, wink, wink Rqc. Exciting enough, for this you will use plant genome sequences!
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Introduction to Bioconductor in R
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*4.8
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  • Maren
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  • Sasha
    2 months ago

    It has been very helpful for my training as a researcher. I would also like to participate in projects related to this topic so that I can put what I’ve learned into practice.

Maren

Hang

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FAQs

What is Bioconductor and who is this course designed for?

Bioconductor is an open-source R platform for analyzing genomic data. This beginner course is for anyone starting in bioinformatics who knows basic R and tidyverse.

Which Bioconductor packages are covered in this course?

You work with BSgenome, Biostrings, IRanges, GenomicRanges, ShortRead, and Rqc. Each package is introduced with real genomic datasets from species including fungi, viruses, humans, and plants.

What types of biological datasets will I analyze?

You explore a fungus genome with BSgenome, a virus genome with Biostrings, a human gene of interest with GenomicRanges, and plant genome sequences with ShortRead and Rqc.

What are S4 objects and why are they relevant?

S4 objects are a formal class system in R used by most Bioconductor packages. Chapter 1 introduces how they work so you can interact with Bioconductor's infrastructure effectively.

Is this course heavy on coding or mostly conceptual?

It is hands-on with 67 exercises across 4 chapters. You write R code to manipulate genomic sequences, work with specialized containers, and assess sequence quality throughout.

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