Other Data Structures

Remind students to install Bioconductor.

Remember to download Archaea genome data.

Set up R console:

source("https://bioconductor.org/biocLite.R")
biocLite("ShortRead")
library(ShortRead)

Lists

• Lists are generic vectors that can hold other things.

sites <- c("a", "b", "c")
notes <- "It was a good day in the field today. Warm, sunny, lots of gators."
helpers <- 4
field_notes <- list(sites, notes, helpers)
field_notes[1]
field_notes[[1]]

• We can also give the values names.

field_notes <- list(sites=sites, notes=notes, helpers=helpers)
field_notes$sites
field_notes[["sites"]]

Objects

• Data structures are defined in R as class() and stored in objects().
  • vector()
    • "character"
    • "integer"
    • "numeric"
  • data.frame()
    • "data.frame"
  • list()
    • "list"
• We can also make arbitrary objects that store whatever kinds of data we need.
  • Genome sequences
  • Geographical information
  • Evolutionary trees

Bioconductor

• Bioconductor is software for bioinformatics, that includes ShortRead for working with genomic data in R.

• We’re using genomic data from Genbank.

  • Archaea genome
  • Coding regions
  • .FASTA
    • Format stores nucleotide sequences

reads <- readFasta("data/archaea_dna/t-pendens.fasta")

• reads is a special kind of object class, ShortRead.

reads

• The str() of ShortRead includes other kinds of objects.
  • Object access:
    • using the @ operator
    • ShortRead functions
  • "DNAStringSet" holds groups of sequences
    • @sread
    • sread()

str(reads)

reads@sread
reads@sread[[1]]

reads@sread@ranges
reads@sread@ranges@start

sread(reads)

• Managing and manipulating complex data structures
  • Hard to get right in all cases
  • Best to rely on existing tools
  • Someone has probably already developed a tool for your data structure.
• Other useful ShortRead functions

reverse(reads@sread)
complement(reads@sread)
reverseComplement(reads@sread)
alphabetFrequency(reads@sread)
translate(reads@sread)

Assign Exercise 6 - Multiple Files.