Example multiple sequences data

A collection of made-up sequences in the style of long reads over a repeat region (e.g. NOTCH2NLC), with meta-data describing the participant each read is from and the family each participant is from. Can be used in visualise_many_sequences(), visualise_methylation(), and helper functions to visualise these sequences.

Generation code is available at data-raw/example_many_sequences.R

Usage

example_many_sequences

Format

example_many_sequences

A dataframe with 23 rows and 10 columns:

family

Participant family

individual

Participant ID

read

Unique read ID

sequence

DNA sequence of the read

sequence_length

Length (nucleotides) of the read

quality

FASTQ quality scores for the read. Each character represents a score from 0 to 40 - see fastq_quality_scores.

These values are made up via pmin(pmax(round(rnorm(n, mean = 20, sd = 10)), 0), 40) i.e. sampled from a normal distribution with mean 20 and standard deviation 10, then rounded to integers between 0 and 40 (inclusive) - see example_many_sequences.R

methylation_locations

Indices along the read (starting at 1) at which methylation probability was assessed i.e. CpG sites. Stored as a single character value per read, condensed from a numeric vector via vector_to_string().

methylation_probabilities

Probability of methylation (8-bit integer i.e. 0-255) for each assessed base. Stored as a single character value per read, condensed from a numeric vector via vector_to_string().

These values are made up via round(runif(n, min = 0, max = 255)) - see example_many_sequences.R

hydroxymethylation_locations

Indices along the read (starting at 1) at which hydroxymethylation probability was assessed i.e. CpG sites. Stored as a single character value per read, condensed from a numeric vector via vector_to_string().

hydroxymethylation_probabilities

Probability of hydroxymethylation (8-bit integer i.e. 0-255) for each assessed base. Stored as a single character value per read, condensed from a numeric vector via vector_to_string().

These values are made up via round(runif(n, min = 0, max = 255 - this_base_methylation_probability)) such that the summed methylation and hydroxymethylation probability never exceeds 255 (100%) - see example_many_sequences.R