chunk_rows

The number of rows in the dataset to be chunked

nrows

The number of cores that will be used for parallel processing

n_cores

Determine row breakpoints for dividing a dataset into chunks for parallel processing

A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale.
It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated
physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>.
In order to support genome-wide analyses, these models are designed to accept any type of protein as input
and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately
represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited
for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) <doi:10.1093/bioinformatics/btaa653>.
The 'ampir' package is also available via a Shiny based GUI at <https://ampir.marine-omics.net/>.

Legana Fingerhut

chunk_rows: Determine row breakpoints for dividing a dataset into chunks for parallel processing

Description

Usage

Arguments

Value