Bioconductor
Bioconductor
An Introduction to Core Technologies
About the Book
The Bioconductor project is a widely used open source and open development platform for software for computational biology. It is a leading platform for doing data science in Genomics. This book covers the core functionality needed to deploy Bioconductor on modern datasets, and will lay the foundation for you to learn and explore parts of the project devoted to specific application domains.
You will learn about the core data classes in Bioconductor, tools for manipulating genomic intervals and genome sequences, tools for getting your data into Bioconductor and for accessing online annotation data. Finally, the book concludes with a number of short introductions to standard analysis packages.
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This package includes the book as well as high-definition lecture videos (6 hours in total). The videos are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
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Instructional videos
Instructional videos associated with the book, for a total of more than 35 videos with a total length of 6 hours. The file format is mp4 and the total size is almost 8 GB. These videos have no subtitles. The audio is in english; note that the author does not have english as a first language.
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Table of Contents
- Preface
-
1. What is Bioconductor
- 1.1 What is this book
-
2. Installing Bioconductor
- 2.1 Installing Bioconductor
-
3. Online Resources for Bioconductor
- 3.1 Overview
- 3.2 Bioconductor packages and documentation
- 3.3 The Bioconductor site
- 3.4 Other resources
-
4. An overview of base types in R
- 4.1 Dependencies
- 4.2 Overview
- 4.3 Atomic Vectors
- 4.4 Matrices
- 4.5 Lists
- 4.6 Data frames
- 4.7 Conversion
- 4.8 Other Resources
-
5. IRanges - Basic Usage
- 5.1 Dependencies
- 5.2 Overview
- 5.3 Basic IRanges
- 5.4 Normal IRanges
- 5.5 Disjoin
- 5.6 Manipulating IRanges, intra-range
- 5.7 Manipulating IRanges, as sets
- 5.8 Finding Overlaps
- 5.9 Counting Overlaps
- 5.10 Finding nearest IRanges
- 5.11 Other Resources
-
6. GenomicRanges - GRanges
- 6.1 Dependencies
- 6.2 GRanges
- 6.3 GRanges, seqinfo
- 6.4 Other Resources
- 6.5 References
-
7. GenomicRanges - Basic GRanges Usage
- 7.1 Dependencies
- 7.2 DataFrame
- 7.3 GRanges, metadata
- 7.4 findOverlaps
- 7.5 subsetByOverlaps
- 7.6 makeGRangesFromDataFrame
- 7.7 Biology usecase I
- 7.8 Biology usecase II
- 7.9 Other Resources
- 7.10 References
-
8. GenomicRanges - More on seqinfo
- 8.1 Dependencies
- 8.2 Overview
- 8.3 Drop and keep seqlevels
- 8.4 Changing style
- 8.5 Using information from BSgenome packages
- 8.6 Other Resources
-
9. AnnotationHub
- 9.1 Dependencies
- 9.2 Overview
- 9.3 Usage
- 9.4 Other Resources
- 9.5 References
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10. Usecase - Basic GRanges and AnnotationHub
- 10.1 Dependencies
- 10.2 Overview
- 10.3 Accomplishing our goals
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11. Biostrings
- 11.1 Dependencies
- 11.2 Overview
- 11.3 Representing sequences
- 11.4 Basic functionality
- 11.5 Biological functionality
- 11.6 Counting letters
- 11.7 References
-
12. BSgenome
- 12.1 Dependencies
- 12.2 Overview
- 12.3 Genomes
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13. Biostrings - Matching
- 13.1 Dependencies
- 13.2 Overview
- 13.3 Pattern matching
- 13.4 Specialized alignments
- 13.5 References
-
14. BSgenome - Views
- 14.1 Dependencies
- 14.2 Overview
- 14.3 Views
-
15. GenomicRanges - Rle
- 15.1 Dependencies
- 15.2 Overview
- 15.3 Coverage
- 15.4 Rle
- 15.5 Useful functions for Rle
- 15.6 Views and Rles
- 15.7 RleList
- 15.8 Rles and GRanges
- 15.9 Biology Usecase
-
16. GenomicRanges - Lists
- 16.1 Dependencies
- 16.2 Overview
- 16.3 Why
- 16.4 GrangesList
- 16.5 Other Lists
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17. GenomicFeatures
- 17.1 Dependencies
- 17.2 Overview
- 17.3 Examples
- 17.4 Caution: Terminology
- 17.5 Gene, exons and transcripts
- 17.6 Other Resources
-
18. Using the rtracklayer package for data import
- 18.1 Dependencies
- 18.2 Overview
- 18.3 The import function
- 18.4 BED files
- 18.5 BigWig files
- 18.6 Other file formats
- 18.7 Extensive example
- 18.8 LiftOver
- 18.9 Importing directly from UCSC
- 18.10 Tabix indexing
- 18.11 Other Resources
-
19. ExpressionSet
- 19.1 Dependencies
- 19.2 Overview
- 19.3 Data Containers
- 19.4 The structure of an ExpressionSet
- 19.5 Example
- 19.6 Subsetting
- 19.7 featureData and annotation
- 19.8 Note: phenoData and pData
- 19.9 The eSet class
- 19.10 Other Resources
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20. SummarizedExperiment
- 20.1 Dependencies
- 20.2 Overview
- 20.3 Details
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21. GEOquery
- 21.1 Dependencies
- 21.2 Overview
- 21.3 NCBI GEO
- 21.4 GEOquery
- 21.5 Other packages
- 21.6 Other Resources
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22. biomaRt
- 22.1 Dependencies
- 22.2 Overview
- 22.3 Specifiying a mart and a dataset
- 22.4 Building a query
- 22.5 Other Resources
-
23. R - S4 Classes and Methods
- 23.1 Dependencies
- 23.2 Overview
- 23.3 S3 and S4 classes
- 23.4 Constructors and getting help
- 23.5 Slots and accessor functions
- 23.6 Class inheritance
- 23.7 Outdated S4 classes
- 23.8 S4 Methods
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24. Getting Data into Bioconductor
- 24.1 Dependencies
- 24.2 Overview
- 24.3 Application Area
- 24.4 File types
- 24.5 Get data from databases of publicly available data
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25. ShortRead
- 25.1 Dependencies
- 25.2 Overview
- 25.3 ShortRead
- 25.4 Reading FASTQ files
- 25.5 A word on quality scores
- 25.6 Reading alignment files
- 25.7 Other Resources
-
26. Rsamtools
- 26.1 Dependencies
- 26.2 Overview
- 26.3 Rsamtools
- 26.4 The BAM / SAM file format
- 26.5 scanBam
- 26.6 Reading in parts of the file
- 26.7 BAM summary
- 26.8 Other functionality from Rsamtools
- 26.9 Other Resources
-
27. The oligo package
- 27.1 Dependencies
- 27.2 Overview
- 27.3 Getting the data
- 27.4 Normalization
- 27.5 Other Resources
-
28. limma
- 28.1 Dependencies
- 28.2 Overview
- 28.3 Analysis Setup and Design
- 28.4 A two group comparison
- 28.5 More on the design
- 28.6 Background: Data representation in limma
- 28.7 Background: The targets file
- 28.8 Other Resources
-
29. Analysis of 450k DNA methylation data with minfi
- 29.1 Dependencies
- 29.2 Overview
- 29.3 DNA methylation
- 29.4 Array Design
- 29.5 Data
- 29.6 Preprocessing
- 29.7 Differential Methylation
- 29.8 Other Resources
-
30. Count Based RNA-seq analysis
- 30.1 Dependencies
- 30.2 Overview
- 30.3 RNA-seq count data
- 30.4 Statistical issues
- 30.5 The Data
- 30.6 edgeR
- 30.7 DESeq2
- 30.8 Comments
- 30.9 Other Resources
- Details on R and Bioconductor
- About the Author
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