R6 Class for loading and analysing sequence sets

R6 Class for loading and analysing sequence sets

R6 Class for loading and analysing sequence sets

Super class

floundeR::FloundeR -> SequencingSet

Active bindings

enumerate

prepares a simple 1D Angenieux enumeration of the provided dataset for quick visualisation of the dataset.

N50

Calculate and return the N50 value for passed quality sequence reads in the current SequencingSet object

mean

Calculate and return the mean sequence length for passed quality reads in the SequencingSet object

Methods

Public methods

• SequencingSet$new()

• SequencingSet$as_tibble()

• SequencingSet$read_length_bins()

• SequencingSet$quality_bins()

• SequencingSet$clone()

Inherited methods

• floundeR::FloundeR$bin_data()

• floundeR::FloundeR$num_scale()

• floundeR::FloundeR$nums_scale()

• floundeR::FloundeR$print()

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Method new()

Initialise a new instance of the R6 Class SequencingSet

Usage

SequencingSet$new(keycol, seqsum = NA)

Arguments

keycol

a pointer to the column of interest in the seqsum to direct parsing and exploration of the file.

seqsum

a tibble of sequencing summary information

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Method as_tibble()

Export the imported dataset(s) as a tibble

This object consumes a sequencing summary file (and optionally the corresponding barcoding_summary file) and creates an object in memory that can be explored, sliced and filtered. This method dumps out the in-memory object for further exploration and development.

Usage

SequencingSet$as_tibble()

Returns

A tibble representation of the starting dataset

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Method read_length_bins()

bin the sequences in seqsum content into bins of sequence length

The nanopore sequencing run is expected to return a collection of sequences that vary in their length distributions; this variance is a function of the sequencing library prepared, the starting DNA etc. This method is used to bin reads into uniform bins to assess the distribution of sequence lengths.

Usage

SequencingSet$read_length_bins(
  normalised = TRUE,
  cumulative = FALSE,
  bins = 20,
  outliers = 0.025
)

Arguments

normalised

• should the sequence collection be reported to normalise for the number of sequence bases sequenced or the number of sequence reads - TRUE by default to normalise for sequenced bases.

cumulative

defines whether cumulative sequence bases (reads) are reported per bin (FALSE by default).

bins

the number of sequence bins that should be prepared (20 by default)

outliers

defines the number of outliers (0.025 = 2.5%) that are excluded from the longest reads to prepare a richer distribution visulation - the plots can be bothered by the long tail of mini-whales.

Returns

Angenieux 2D graph object

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Method quality_bins()

bin the sequences in seqsum content into bins of quality

The nanopore sequencing run is expected to return a collection of sequences that vary in their quality distributions; this variance is a function of the sequencing library prepared, the starting DNA etc. This method is used to bin reads into uniform quality bins to assess the overall quality of the run and to identify potential issues

Usage

SequencingSet$quality_bins(bins = 20, outliers = 0)

Arguments

bins

the number of sequence bins that should be prepared (20 by default)

outliers

defines the number of outliers (0 = 0%) that are excluded from the reads - should probably be deprecated for simplicity??

Returns

Angenieux 2D graph object

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Method clone()

The objects of this class are cloneable with this method.

Usage

SequencingSet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.