Introduction to (R and) R/Bioconductor and Regular Expressions

Introduction to (R and) R/Bioconductor

Task 1

• Load the DNA sequence fishes.fna.gz using functions from the seqinr package and the Biostrings package. Note the differences between the created variables.

Task 2

• Next, focus on the Biostrings package. Practice working with loaded data:
  • Check the number of loaded sequences:

    length(seq)

  • Determine the lengths of each sequence:

    width(seq[1])

  • View the sequence names (FASTA headers):

    names(seq)

  • Assign the first sequence including the name to the variable seq1:

    seq1 <- seq[1]

  • Assign the first sequence without the name to the variable seq1_sequence:

    seq1_sequence <- seq[[1]]

  • Assign the first sequence as a vector of characters to the variable seq1_string:

    seq1_string <- toString(seq[1])

  • Learn more about the XStringSet class and the Biostrings package:

    help(XStringSet)

Task 3

• Globally align the two selected sequences using the BLOSUM62 matrix, a gap opening cost of -1 and a gap extension cost of 1.

Regular Expressions

Task 4

• Practice working with regular expressions:
  • Create a list of names, e.g.:

    names_list <- c("anna", "jana", "kamil", "norbert", "pavel", "petr", "stanislav", "zuzana")

  • Search for name jana:

    grep("jana", names_list, perl = TRUE)

  • Search for all names containing letter n at least once:

    grep("n+", names_list, perl = TRUE)

  • Search for all names containing letters nn:

    grep("n{2}", names_list, perl = TRUE)

  • Search for all names starting with n:

    grep("^n", names_list, perl = TRUE)

  • Search for names Anna or Jana:

    grep("Anna|Jana", names_list, perl = TRUE)

  • Search for names starting with z and ending with a:

    grep("^z.*a$", names_list, perl = TRUE)

Task 5

• Load an amplicon sequencing run from 454 Junior machine fishes.fna.gz.
• Get a sequence of a sample (avoid conditional statements), that is tagged by forward and reverse MID ACGAGTGCGT.
• How many sequences are there in the sample?

Task 6

• Create a function Demultiplexer() for demultiplexing of sequencing data.

• Input:

  • a string with path to fasta file
  • a list of forward MIDs
  • a list of reverse MIDs
  • a list of samples labels

• Output:

  • fasta files that are named after the samples and contain sequences of the sample without MIDs (perform MID trimming)
  • table named report.txt containing samples‘ names and the number of sequences each sample has

• Check the functionality again on the fishes.fna.gz file, the list of samples and MIDs can be found in the corresponding table fishes_MIDs.csv.

Download files from GitHub

Basic Git settings

• Configure the Git editor

  git config --global core.editor notepad

• Configure your name and email address

  git config --global user.name "Zuzana Nova"
  git config --global user.email z.nova@vut.cz

• Check current settings

  git config --global --list

• Create a fork on your GitHub account. On the GitHub page of this repository find a Fork button in the upper right corner.

• Clone forked repository from your GitHub page to your computer:

git clone <fork repository address>

• In a local repository, set new remote for a project repository:

git remote add upstream https://github.com/mpa-prg/exercise_02.git

Send files to GitHub

Create a new commit and send new changes to your remote repository.

• Add file to a new commit.

git add <file_name>

• Create a new commit, enter commit message, save the file and close it.

git commit

• Send a new commit to your GitHub repository.

git push origin main