create_high_nucleotide_content

Creates a data frame of the start and end positions of the
regions_a that are considered high in A% and GC%.

A novel machine learning method for plant viruses diagnostic using
genome sequencing data. This package includes three different machine
learning models, random forest, XGBoost, and elastic net, to train and
predict mapped genome samples. Mappability profile and unreliable regions
are introduced to the algorithm, and users can build a mappability profile
from scratch with functions included in the package. Plotting mapped sample
coverage information is provided.

Xuekui Zhang

iimi

Identifying Infection with Machine Intelligence

Haochen Ning

Ian Boyes

Ibrahim Numanagić

Michael Rott

Li Xing

create_high_nucleotide_content function

<dl><dt>gc</dt>
<dd>The threshold for GC content. It is the proportion of G and C
nucleotides in a sliding window. Default is 0.6.</dd>
<dt>a</dt>
<dd>The threshold for A nucleotide. It is the proportion of A nucleotide
in a sliding window. Default is 0.45.</dd>
<dt>window</dt>
<dd>The sliding window size of your choice. Default is 75.</dd>
<dt>virus_info</dt>
<dd>A DNAStringSet of virus segments. The format should be similar to <code>virus_segments</code>.</dd></dl>

Arguments

create_high_nucleotide_content — create_high_nucleotide_content

<dl>

<dt>gc</dt>
<dd>The threshold for GC content. It is the proportion of G and C
nucleotides in a sliding window. Default is 0.6.</dd>


<dt>a</dt>
<dd>The threshold for A nucleotide. It is the proportion of A nucleotide
in a sliding window. Default is 0.45.</dd>


<dt>window</dt>
<dd>The sliding window size of your choice. Default is 75.</dd>


<dt>virus_info</dt>
<dd>A DNAStringSet of virus segments. The format should be similar to <code>virus_segments</code>.</dd>

</dl>

create_high_nucleotide_content: create_high_nucleotide_content

Description

Usage

Value

Arguments

Examples