README.Rmd

---
output: github_document
---


```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "inst/extdata/README",
  out.width = "100%"
)
```

# snpCYP

## Description

The purpose of snpCYP is to streamline and automate the process of
reading and analyzing single nucleotide polymorphisms (SNP) on CYP genes
It is supposed to allow for easier exploratory analysis by graphically 
outputting the nsSNP distribution and their potential disruptions on drug 
metabolism by correlating the SNPs to known catalytic activity disruption
from DIDB databases. The package uniquely improves current 
ability of researchers to automate their work flow by adding non existing 
R language support for correlating sample’s SNP’s/haplotypes to potential 
disruption on drug metabolism. This project was developed using R 4.0.2 on 
MacOs 11.6.


## Installation

To download the package (user should also load basic data regarding wild-type
CYP isoforms in order to run examples or shiny, unless if custom wild-type data 
is to be provided):

```{r eval=FALSE}
require("devtools")
devtools::install_github("a-albuquerque/snpCYP", build_vignettes = TRUE)
library("snpCYP")
load(file = "./data/baseSeqs.rda")
load(file = "./data/drugs.rda")
library("snpCYP") # second time
```

To run the shinyApp:
```{r eval=FALSE}
snpCYP::runsnpCYP()
```

## Overview

```{r eval=FALSE}
ls("package:snpCYP")
data(package="snpCYP")
```

Data sets available to the users are:
baseSeqs.rda
drugs.rda


Functions available to the user are: 
snpDist()
detectSNP()
snpToDrug()
runsnpCYP()

Please check this package's vignette for detailed information on the functions
provided: 

```{r eval=FALSE}
browseVignettes("snpCYP")
```

This package is structured according to the following tree:

```{r eval=FALSE}
- snpCYP
  |- snpCYP.Rproj
  |- DESCRIPTION
  |- NAMESPACE
  |- NEWS.md
  |- LICENSE
  |- LICENSE.md
  |- README
  |- data
    |- baseSeqs.rda
    |- drugs.rda
  |- inst
    |- extdata
      |- README
      |- shiny-scripts
        |- app.R
  |- man
    |- figures
    |- baseSeqs.Rd
    |- detectSNP.Rd
    |- drugs.Rd
    |- runsnpCYP.Rd
    |- snpDist.Rd
    |- snpToDrug.Rd
    |- snpToPred.Rd
  |- R
    |- data.R
    |- detectSNP.R
    |- snpDist.R
    |- snpToDrug.R
    |- runsnpCYP.R
    |- snpToPred.R
  |- vignettes
    |- nsSNP.png
    |- snpCYPVignette.Rmd
    |- snpDistPlot.png
  |- tests
    |- testthat.R
    |- testthat
      |- test-test-detectSNP.R
      |- test-test-snpDist.R
      |- test-test-snpToDrug.R
      |- test-test-snpToPred.R
```
A possible use of the package is exploration of SNP on CYP sequences:
![](./inst/extdata/README/Overview.png)

## Contributions
The author of this package is Alcides Albuquerque.


## References
Adzhubei, I., Jordan, D. M., & Sunyaev, S. R. (2013). Predicting functional effect of human missense mutations using PolyPhen-2. *Current Protocols in Human Genetics*, 76 (1), 7–20.

Banerjee, P., Dunkel, M., Kemmler, E., & Preissner, R. (2020). SuperCYPsPred—a web server for the prediction of cytochrome activity. *Nucleic Acids Research*, 48 (W1), W580–W585. 

DIDB - the drug interaction database. (2021). *UW Drug Interaction Solutions* Washington University. https://www.druginteractionsolutions.org/solutions/drug-interaction-database/

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Rostkowski, M., Spjuth, O., & Rydberg, P. (2013). WhichCyp: Prediction of cytochromes P450 inhibition. *Bioinformatics*, 29 (16), 2051–2052.

Wang, L.-L., Li, Y., & Zhou, S.-F. (2009). A bioinformatics approach for the phenotype prediction of nonsynonymous single nucleotide polymorphisms in human cytochromes P450. *Drug Metabolism and Disposition*, 37 (5), 977–991.

Zhang, T., Zhou, Q., Pang, Y., Wang, Y., Jin, C., Huo, J., Liu, L. A., & Wei, D. (2012). CYP-nsSNP: A specialized database focused on effect of non-synonymous SNPs on function of CYPs. *Interdisciplinary Sciences: Computational Life Sciences*, 4 (2), 83–89.


## Acknowledgements
This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA