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Curs

Introduction to Bioconductor in R

IntermediarNivel de competențe

Actualizat 12.2022

Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

Începe cursul gratuit

RProbability & Statistics

4 h

14 videoclipuri

54 Exerciții

4,050 XP

18,466

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Descrierea cursului

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!

Cerințe prealabile

Introduction to R Introduction 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.

Introduction to the Bioconductor Project

Bioconductor version

BiocManager to install packages

The role of S4 in Bioconductor

S4 class definition

Interaction with classes

Introducing biology of genomic datasets

Discovering the yeast genome

Partitioning the yeast genome

Available genomes

Începe capitolul

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.

Introduction to Biostrings

Exploring the Zika virus sequence

Biostrings containers

Manipulating Biostrings

Sequence handling

From a set to a single sequence

Subsetting a set

Common sequence manipulation functions

Why are we interested in patterns?

Searching for a pattern

Finding Palindromes

Finding a conserved region within six frames

Looking for a match

Începe capitolul

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.

IRanges and Genomic Structures

Constructing IRanges

Interacting with IRanges

Gene of interest

From tabular data to Genomic Ranges

GenomicRanges accessors

ABCD1 mutation

Human genome chromosome X

Manipulating collections of GRanges

A sequence window

Is it there?

More about ABCD1

How many transcripts?

From GRangesList object into a GRanges object

Începe capitolul

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!

Sequence files

Reading in files

Exploring a fastq file

Extract a sample from a fastq file

Sequence quality

Exploring sequence quality

Base quality plot

Try your own nucleotide frequency plot

Match and filter

Filtering reads on the go!

Removing duplicates

More filtering!

Multiple assessment

Plotting cycle average quality

Introduction to Bioconductor

Începe capitolul

Introduction to Bioconductor in R

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