Contents

Original Authors: Martin Morgan, Sonali Arora
Presenting Authors: Martin Morgan, Lori Shepherd
Date: 12 June, 2017
Back: Monday labs

Objective: An overview of software available in Bioconductor.

Lessons learned:

1 Bioconductor

Analysis and comprehension of high-throughput genomic data

1.1 Packages, vignettes, work flows

Alt Sequencing Ecosystem

1.2 Objects

Load the Biostrings and GenomicRanges package

library(Biostrings)
library(GenomicRanges)

1.3 Example: Biostrings for DNA sequences

library(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
methods(class=class(phiX174Phage))      # 'DNAStringSet' methods

1.4 Exercises

  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. 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?
  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"

2 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

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.

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.

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:

A number of Bioconductor packages help with visualization and reporting, in addition to functions provided by indiidual packages.

3 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.