Bioconductor for Sequence Analysis – Introduction

Epigenomics 2014
Author: Martin Morgan (mtmorgan@fhcrc.org)
Date: 24 August, 2014

Overall work flow

1. Experimental design
   • Keep it simple, e.g., 'control' and 'treatment' groups
   • Replicate within treatments!
2. Wet-lab sequence preparation
   • Record covariates, including processing day – likely 'batch effects'

3. (Illumina) Sequencing (Bentley et al., 2008, doi:10.1038/nature07517)

   

4. Alignment

   • Choose to match task, e.g., [Rsubread][], Bowtie2 good for ChIPseq, some forms of RNAseq; BWA, GMAP better for variant calling

5. Analysis a. Reduction, e.g., to 'count table' b. Differential expression, peak identification, …

6. Comprehension

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

• 824 packages
• Discover and navigate via biocViews
• Package 'landing page'
  • 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

• Represent complicated data types
• Foster interoperability
• S4 object system
  • Introspection: getClass(), showMethods(..., where=search()), selectMethod()
  • 'accessors' and other documented functions / methods for manipulation, rather than direct access to the object structure
• Interactive help
  • method?"substr,<tab>" to select help on methods, class?D<tab> for help on classes

Example

require(Biostrings)                     # Biological sequences
data(phiX174Phage)                      # sample data, see ?phiX174Phage
phiX174Phage

##   A DNAStringSet instance of length 6
##     width seq                                          names               
## [1]  5386 GAGTTTTATCGCTTCCATGAC...ATTGGCGTATCCAACCTGCA Genbank
## [2]  5386 GAGTTTTATCGCTTCCATGAC...ATTGGCGTATCCAACCTGCA RF70s
## [3]  5386 GAGTTTTATCGCTTCCATGAC...ATTGGCGTATCCAACCTGCA SS78
## [4]  5386 GAGTTTTATCGCTTCCATGAC...ATTGGCGTATCCAACCTGCA Bull
## [5]  5386 GAGTTTTATCGCTTCCATGAC...ATTGGCGTATCCAACCTGCA G97
## [6]  5386 GAGTTTTATCGCTTCCATGAC...ATTGGCGTATCCAACCTGCA NEB03

m <- consensusMatrix(phiX174Phage)[1:4,] # nucl. x position counts
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

showMethods(class=class(phiX174Phage), where=search())

Case study: Working with DNA sequence data

1. 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.
2. Discover vignettes in the Biostrings package with vignette(package="Biostrings"). Add another argument to the vignette function to view the 'BiostringsQuickOverview' vignette.
3. 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?
4. 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"

'S4' Classes, methods, and packages

This section focuses on classes, methods, and packages, with the goal being to learn to navigate the help system and interactive discovery facilities.

Motivation

Sequence analysis is specialized

• Large data needs to be processed in a memory- and time-efficient manner
• Specific algorithms have been developed for the unique characteristics of sequence data

Additional considerations

• Re-use of existing, tested code is easier to do and less error-prone than re-inventing the wheel.
• Interoperability between packages is easier when the packages share similar data structures.

Solution: use well-defined classes to represent complex data; methods operate on the classes to perform useful functions. Classes and methods are placed together and distributed as packages so that we can all benefit from the hard work and tested code of others.

Case study: IRanges and GRanges

The IRanges package defines an important class for specifying integer ranges, e.g.,

library(IRanges)
ir <- IRanges(start=c(10, 20, 30), width=5)
ir

## IRanges of length 3
##     start end width
## [1]    10  14     5
## [2]    20  24     5
## [3]    30  34     5

There are many interesting operations to be performed on ranges, e.g, flank() identifies adjacent ranges

flank(ir, 3)

## IRanges of length 3
##     start end width
## [1]     7   9     3
## [2]    17  19     3
## [3]    27  29     3

Consult the help page for flank, ?flank, and explore other range-based operations.

The IRanges class is part of a class hierarchy. To see this, ask R for the class of ir, and for the class definition of the IRanges class

class(ir)

## [1] "IRanges"
## attr(,"package")
## [1] "IRanges"

getClassDef(class(ir))

## Class "IRanges" [package "IRanges"]
## 
## Slots:
##                                                                       
## Name:            start           width           NAMES     elementType
## Class:         integer         integer characterORNULL       character
##                                       
## Name:  elementMetadata        metadata
## Class: DataTableORNULL            list
## 
## Extends: 
## Class "Ranges", directly
## Class "IntegerList", by class "Ranges", distance 2
## Class "RangesORmissing", by class "Ranges", distance 2
## Class "AtomicList", by class "Ranges", distance 3
## Class "List", by class "Ranges", distance 4
## Class "Vector", by class "Ranges", distance 5
## Class "Annotated", by class "Ranges", distance 6
## 
## Known Subclasses: "NormalIRanges"

Notice that IRanges extends the Ranges class. Now try entering ?"flank,<tab>, where <tab> means to press the tab key to ask for tab completion (may not be necessary in Rstudio). You can see that there are help pages for several different classes. Tab-complete to

?"flank,Ranges-method" 

and verify that you're at the page that describes the method relevant to an IRanges instance.

The GenomicRanges package extends the notion of ranges to include features relevant to application of ranges in sequence analysis, particularly the ability to associate a range with a sequence name (e.g., chromosome) and a strand. Create a GRanges instance based on our IRanges instance, as follows

library(GenomicRanges)

## Loading required package: GenomeInfoDb

gr <- GRanges(c("chr1", "chr1", "chr2"), ir, strand=c("+", "-", "+"))
gr

## GRanges with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     chr1  [10, 14]      +
##   [2]     chr1  [20, 24]      -
##   [3]     chr2  [30, 34]      +
##   ---
##   seqlengths:
##    chr1 chr2
##      NA   NA

The notion of flanking sequence has a more nuanced meaning in biology. In particular we might expect that flanking sequence on the + strand would precede the range, but on the minus strand would follow it. Verify that flank applied to a GRanges object has this behavior.

flank(gr, 3)

## GRanges with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     chr1  [ 7,  9]      +
##   [2]     chr1  [25, 27]      -
##   [3]     chr2  [27, 29]      +
##   ---
##   seqlengths:
##    chr1 chr2
##      NA   NA

Discover what classes GRanges extends, find the help page documenting the behavior of flank when applied to a GRanges object, and verify that the help page documents the behavior we just observed.

class(gr)

## [1] "GRanges"
## attr(,"package")
## [1] "GenomicRanges"

getClassDef(class(gr))

## Class "GRanges" [package "GenomicRanges"]
## 
## Slots:
##                                                                       
## Name:         seqnames          ranges          strand elementMetadata
## Class:             Rle         IRanges             Rle       DataFrame
##                                       
## Name:          seqinfo        metadata
## Class:         Seqinfo            list
## 
## Extends: 
## Class "GenomicRanges", directly
## Class "Vector", by class "GenomicRanges", distance 2
## Class "GenomicRangesORmissing", by class "GenomicRanges", distance 2
## Class "GenomicRangesORGRangesList", by class "GenomicRanges", distance 2
## Class "Annotated", by class "GenomicRanges", distance 3

?"flank,GenomicRanges-method"

Notice that the available flank() methods have been augmented by the methods defined in the GenomicRanges package.

It seems like there might be a number of helpful methods available for working with genomic ranges; we can discover some of these from the command line, indicating that the methods should be on the current search() path

showMethods(class="GRanges", where=search())

Use help() to list the help pages in the GenomicRanges package, and vignettes() to view and access available vignettes; these are also available in the Rstudio 'Help' tab.

help(package="GenomicRanges")
vignette(package="GenomicRanges")
vignette(package="GenomicRanges", "GenomicRangesHOWTOs")

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

  library(GenomicRanges)

and the help system queried interactively, as outlined above:

  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 for comparison of peaks across samples, ChIPQC for quality assessment, and ChIPpeakAnno 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.
• 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")
• rtracklayer's 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. Explore the ShortRead vignette and Scalable Genomics labs to see approaches to effectively processing the large files.

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 (we'll do more of this in Thursday's lab) at the annotation work flow on the Bioconductor web site.

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.