Science Score: 23.0%
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: biorxiv.org -
○Committers with academic emails
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○Scientific vocabulary similarity
Low similarity (19.9%) to scientific vocabulary
Last synced: 11 months ago
·
JSON representation
Repository
predict effector protein
Basic Info
- Host: GitHub
- Owner: ruthkr
- License: other
- Language: R
- Default Branch: master
- Homepage: https://ruthkr.github.io/deepredeff
- Size: 1.57 MB
Statistics
- Stars: 5
- Watchers: 1
- Forks: 4
- Open Issues: 1
- Releases: 2
Created over 6 years ago
· Last pushed almost 3 years ago
Metadata Files
Readme
License
README.Rmd
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
ggplot2::theme_set(ggplot2::theme_bw())
```
# deepredeff 
[](https://cran.r-project.org/package=deepredeff)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/ruthkr/deepredeff/actions/workflows/R-CMD-check.yaml)
[](https://codecov.io/gh/ruthkr/deepredeff?branch=master)
[](https://ruthkr.github.io/deepredeff/)
[](https://www.tensorflow.org/)
[](https://www.python.org/)
[](https://www.biorxiv.org/content/10.1101/2020.07.08.193250v1)
**deepredeff** is a package to predict effector protein given amino acid sequences. This tool can be used to predict effectors from three different taxa, which are oomycete, fungi, and bacteria.
## Installation
You can install the released version of `deepredeff` from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("deepredeff")
```
And the development version from [GitHub](https://github.com/) with:
```r
# install.packages("devtools")
devtools::install_github("ruthkr/deepredeff")
```
The `deepredeff` package uses TensorFlow. If you already have TensorFlow 2.0.0 or later in your system, then you can specify the environment where TensorFlow is installed using `reticulate::use_condaenv()`. Otherwise, you can install TensorFlow, by using the `install_tensorflow()` function as follows:
```r
library(deepredeff)
install_tensorflow()
```
**Note that this only needs to be run once**, the first time you use `deepredeff`.
## Documentation
To use `deepredeff`, you can read the documentation on the following topics:
1. [Getting started](https://ruthkr.github.io/deepredeff/articles/overview.html)
2. [Effector prediction with various different input formats and models](https://ruthkr.github.io/deepredeff/articles/predict.html)
## Quick start
This is a basic example which shows you how to predict effector sequences if you have a FASTA file:
```{r example}
# Load the package
library(deepredeff)
# Define the fasta path from the sample data
bacteria_fasta_path <- system.file(
"extdata/example", "bacteria_sample.fasta",
package = "deepredeff"
)
# Predict the effector candidate using bacteria model
pred_result <- predict_effector(
input = bacteria_fasta_path,
taxon = "bacteria"
)
```
```r
# View results
pred_result
```
```{r pred-result, echo=FALSE}
pred_result %>%
dplyr::mutate(
name = stringr::str_replace_all(name, "\\|", "⎮"),
sequence = stringr::str_sub(sequence, 1, 25)
) %>%
knitr::kable()
```
After getting the prediction results, you can plot the probability distribution of the results as follows:
```{r pred_result_plot, fig.width=7, fig.height=3.5, out.width='670px', out.height='335px', fig.align='center'}
plot(pred_result)
```
More examples with different input formats are available on functions documentations and vignettes, please refer to the [documentation](https://ruthkr.github.io/deepredeff/).
Owner
- Name: Ruth Kristianingsih
- Login: ruthkr
- Kind: user
- Location: Norwich, UK
- Website: ruthkr.com
- Repositories: 4
- Profile: https://github.com/ruthkr
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Committers
Last synced: almost 3 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Ruth Kristianingsih | r****0@g****m | 368 |
| clarajegousse | c****e@g****m | 1 |
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 4
- Total pull requests: 1
- Average time to close issues: 11 days
- Average time to close pull requests: 12 months
- Total issue authors: 3
- Total pull request authors: 1
- Average comments per issue: 2.5
- Average comments per pull request: 1.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- bsalehe (2)
- metalichen (1)
- emilecg (1)
Pull Request Authors
- clarajegousse (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 301 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
- Total maintainers: 1
cran.r-project.org: deepredeff
Deep Learning Prediction of Effectors
- Homepage: https://github.com/ruthkr/deepredeff/
- Documentation: http://cran.r-project.org/web/packages/deepredeff/deepredeff.pdf
- License: MIT + file LICENSE
-
Latest release: 0.1.1
published about 5 years ago
Rankings
Forks count: 14.9%
Stargazers count: 28.5%
Dependent packages count: 29.8%
Average: 34.5%
Dependent repos count: 35.5%
Downloads: 64.0%
Maintainers (1)
Last synced:
11 months ago
Dependencies
DESCRIPTION
cran
- R >= 2.10 depends
- Biostrings * imports
- dplyr * imports
- ggplot2 * imports
- ggthemes * imports
- keras * imports
- magrittr * imports
- purrr * imports
- reticulate * imports
- rlang * imports
- seqinr * imports
- tensorflow * imports
- covr * suggests
- kableExtra * suggests
- knitr * suggests
- rmarkdown * suggests
- stringr * suggests
- testthat * suggests