Bioconductor
Analysis and comprehension of high-throughput genomic data
- Statistical analysis: large data, technological artifacts, designed experiments; rigorous
- Comprehension: biological context, visualization, reproducibility
- High-throughput
- Sequencing: RNASeq, ChIPSeq, variants, copy number, …
- Microarrays: expression, SNP, …
- Flow cytometry, proteomics, images, …
Packages, vignettes, work flows
- 1211 packages
- Discover and navigate via biocViews
- Package ‘landing page’, e.g., Gviz
- Title, author / maintainer, short description, citation, installation instructions, …, download statistics
- All user-visible functions have help pages, most with runnable examples
- ‘Vignettes’ an important feature in Bioconductor – narrative documents illustrating how to use the package, with integrated code
- ‘Release’ (every six months) and ‘devel’ branches
Objects
Load the Biostrings and GenomicRanges package
library(Biostrings)
library(GenomicRanges)
- Bioconductor makes extensive use of classes to represent complicated data types
- Classes foster interoperability – many different packages can work on the same data – but can be a bit intimidating for the user.
- Formal ‘S4’ object system
- Often a class is described on a particular home page, e.g.,
?GRanges, and in vignettes, e.g., vignette(package="GenomicRanges"), vignette("GenomicRangesIntroduction")
- Many methods and classes can be discovered interactively , e.g.,
methods(class="GRanges") to find out what one can do with a GRanges instance, and methods(findOverlaps) for classes that the findOverlaps() function operates on.
- In more advanced cases, one can look at the actual definition of a class or method using
getClass(), getMethod()
- Interactive help
?findOverlaps,<tab> to select help on a specific method, ?GRanges-class for help on a class.
Example: Biostrings for DNA sequences
library(Biostrings)
data(phiX174Phage)
phiX174Phage
## A DNAStringSet instance of length 6
## width seq names
## [1] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA Genbank
## [2] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA RF70s
## [3] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA SS78
## [4] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA Bull
## [5] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA G97
## [6] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA NEB03
m <- consensusMatrix(phiX174Phage)[1:4,]
polymorphic <- which(colSums(m != 0) > 1)
m[, polymorphic]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## A 4 5 4 3 0 0 5 2 0
## C 0 0 0 0 5 1 0 0 5
## G 2 1 2 3 0 0 1 4 0
## T 0 0 0 0 1 5 0 0 1
methods(class=class(phiX174Phage))
Exercises
- Load the Biostrings package and phiX174Phage data set. What class is phiX174Phage? Find the help page for the class, and identify interesting functions that apply to it.
- Discover vignettes in the Biostrings package with
vignette(package="Biostrings"). Add another argument to the vignette function to view the ‘BiostringsQuickOverview’ vignette.
- If the internet is available, navigate to the Biostrings landing page on http://bioconductor.org. Do this by visiting the biocViews page. Can you find the BiostringsQuickOverview vignette on the web site?
The following code loads some sample data, 6 versions of the phiX174Phage genome as a DNAStringSet object.
library(Biostrings)
data(phiX174Phage)
Explain what the following code does, and how it works
m <- consensusMatrix(phiX174Phage)[1:4,]
polymorphic <- which(colSums(m != 0) > 1)
mapply(substr, polymorphic, polymorphic, MoreArgs=list(x=phiX174Phage))
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## Genbank "G" "G" "A" "A" "C" "C" "A" "G" "C"
## RF70s "A" "A" "A" "G" "C" "T" "A" "G" "C"
## SS78 "A" "A" "A" "G" "C" "T" "A" "G" "C"
## Bull "G" "A" "G" "A" "C" "T" "A" "A" "T"
## G97 "A" "A" "G" "A" "C" "T" "G" "A" "C"
## NEB03 "A" "A" "A" "G" "T" "T" "A" "G" "C"
A sequence analysis package tour
This very open-ended topic points to some of the most prominent Bioconductor packages for sequence analysis. Use the opportunity in this lab to explore the package vignettes and help pages highlighted below; many of the material will be covered in greater detail in subsequent labs and lectures.
Basics
- Bioconductor packages are listed on the biocViews page. Each package has ‘biocViews’ (tags from a controlled vocabulary) associated with it; these can be searched to identify appropriately tagged packages, as can the package title and author.
- Each package has a ‘landing page’, e.g., for GenomicRanges. Visit this landing page, and note the description, authors, and installation instructions. Packages are often written up in the scientific literature, and if available the corresponding citation is present on the landing page. Also on the landing page are links to the vignettes and reference manual and, at the bottom, an indication of cross-platform availability and download statistics.
- A package needs to be installed once, using the instructions on the landing page. Once installed, the package can be loaded into an R session and the help system queried interactively, as outlined above:
library(GenomicRanges)
help(package="GenomicRanges")
vignette(package="GenomicRanges")
vignette(package="GenomicRanges", "GenomicRangesHOWTOs")
?GRanges
Domain-specific analysis – explore the landing pages, vignettes, and reference manuals of two or three of the following packages.
- Important packages for analysis of differential expression include edgeR and DESeq2; both have excellent vignettes for exploration. Additional research methods embodied in Bioconductor packages can be discovered by visiting the biocViews web page, searching for the ‘DifferentialExpression’ view term, and narrowing the selection by searching for ‘RNA seq’ and similar.
- Popular ChIP-seq packages include DiffBind and csaw for comparison of peaks across samples, ChIPQC for quality assessment, and ChIPpeakAnno and ChIPseeker for annotating results (e.g., discovering nearby genes). What other ChIP-seq packages are listed on the biocViews page?
- Working with called variants (VCF files) is facilitated by packages such as VariantAnnotation, VariantFiltering, and ensemblVEP; packages for calling variants include, e.g., h5vc and VariantTools.
- Single-cell ‘omics are increasingly important. From the biocViews page, enter ’single cell’ in the ‘search table’ field.
- Several packages identify copy number variants from sequence data, including cn.mops; from the biocViews page, what other copy number packages are available? The CNTools package provides some useful facilities for comparison of segments across samples.
- Microbiome and metagenomic analysis is facilitated by packages such as phyloseq and metagenomeSeq.
- Metabolomics, chemoinformatics, image analysis, and many other high-throughput analysis domains are also represented in Bioconductor; explore these via biocViews and title searches.
Working with sequences, alignments, common web file formats, and raw data; these packages rely very heavily on the IRanges / GenomicRanges infrastructure that we will encounter later in the course.
- The Biostrings package is used to represent DNA and other sequences, with many convenient sequence-related functions. Check out the functions documented on the help page
?consensusMatrix, for instance. Also check out the BSgenome package for working with whole genome sequences, e.g., ?"getSeq,BSgenome-method"
- The GenomicAlignments package is used to input reads aligned to a reference genome. See for instance the
?readGAlignments help page and vigentte(package="GenomicAlignments", "summarizeOverlaps")
- The rtracklayer
import and export functions can read in many common file types, e.g., BED, WIG, GTF, …, in addition to querying and navigating the UCSC genome browser. Check out the ?import page for basic usage.
- The ShortRead and Rsamtools packages can be used for lower-level access to FASTQ and BAM files, respectively.
- Many genomics data files are very large. We’ll explore strategies of restriction (only input some of the data in the file) and iteration (read the file in chunks, rather than its entirety) for processing large data in other labs.
Annotation: Bioconductor provides extensive access to ‘annotation’ resources (see the AnnotationData biocViews hierarchy); these are covered in greater detail in Thursday’s lab, but some interesting examples to explore during this lab include:
- biomaRt, PSICQUIC, KEGGREST and other packages for querying on-line resources; each of these have informative vignettes.
- AnnotationDbi is a cornerstone of the Annotation Data packages provided by Bioconductor.
- org packages (e.g., org.Hs.eg.db) contain maps between different gene identifiers, e.g., ENTREZ and SYMBOL. The basic interface to these packages is described on the help page
?select
- TxDb packages (e.g., TxDb.Hsapiens.UCSC.hg19.knownGene) contain gene models (exon coordinates, exon / transcript relationships, etc) derived from common sources such as the hg19 knownGene track of the UCSC genome browser. These packages can be queried, e.g., as described on the
?exonsBy page to retrieve all exons grouped by gene or transcript.
- BSgenome packages (e.g., BSgenome.Hsapiens.UCSC.hg19) contain whole genomes of model organisms.
- VariantAnnotation and ensemblVEP provide access to sequence annotation facilities, e.g., to identify coding variants; see the Introduction to VariantAnnotation vignette for a brief introduction; we’ll re-visit this during the Thursday lab.
- Take a quick look (there are more activites in other labs) at the annotation work flow on the Bioconductor web site.
A number of Bioconductor packages help with visualization and reporting, in addition to functions provided by indiidual packages.
- Gviz provides a track-like visualization of genomic regions; it’s got an amazing vignette.
- ComplexHeatmap does an amazing job of all sorts of heatmaps, including OncoPrint-style summaries.
- ReportingTools provides a flexible way to generate static and dynamic HTML-based reports.
Summary
Bioconductor is a large collection of R packages for the analysis and comprehension of high-throughput genomic data. Bioconductor relies on formal classes to represent genomic data, so it is important to develop a rudimentary comfort with classes, including seeking help for classes and methods. Bioconductor uses vignettes to augment traditional help pages; these can be very valuable in illustrating overall package use.