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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# cogeqc <img src='man/figures/logo.png' align="right" height="139" />
<!-- badges: start -->
[](https://github.com/almeidasilvaf/cogeqc/issues)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/almeidasilvaf/cogeqc/actions)
[](https://codecov.io/gh/almeidasilvaf/cogeqc?branch=devel)
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The goal of `cogeqc` is to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. Currently, cogeqc can be used to assess:
1. **Genome assembly and annotation quality:** using two approaches:
- *Statistics in a context:* users can
extract summary assembly and annotation statistics for genomes on NCBI
(via the [NCBI Datasets API](https://www.ncbi.nlm.nih.gov/datasets/)) and
compare their observed values (e.g., genome size, number of genes,
contiguity measures) with previously reported values on NCBI genomes.
- *Gene space completeness with BUSCOs:* users can assess gene space
completeness using Best Universal Single-Copy Orthologs (BUSCOs) through
wrapper functions that run [BUSCO](https://doi.org/10.1093/bioinformatics/btv351)
from the comfort of an R session and create publication-ready plots
with summary statistics.
2. **Orthogroup inference:** orthogroups are assessed based on the percentage of shared protein domains in all ortogroups. The rationale for this approach is that genes in the same orthogroup evolved from a common ancestor, so the percentage of conserved protein domains in an orthogroup should be as high as possible.
3. **Synteny detection:** synteny detection is assessed using network-based approaches, namely the clustering coefficient and degree of a synteny network.
## Installation instructions
Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `cogeqc` using from [Bioconductor](http://bioconductor.org/) the following code:
```{r 'install', eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("cogeqc")
```
And the development version from [GitHub](https://github.com/almeidasilvaf/cogeqc) with:
```{r 'install_dev', eval = FALSE}
BiocManager::install("almeidasilvaf/cogeqc")
```
## Citation
Below is the citation output from using `citation('cogeqc')` in R. Please
run this yourself to check for any updates on how to cite __cogeqc__.
```{r 'citation', eval = requireNamespace('cogeqc')}
print(citation('cogeqc'), bibtex = TRUE)
```
Please note that the `cogeqc` was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.
## Code of Conduct
Please note that the `cogeqc` project is released with a [Contributor Code of Conduct](http://bioconductor.org/about/code-of-conduct/). By contributing to this project, you agree to abide by its terms.
## Development tools
* Continuous code testing is possible thanks to [GitHub actions](https://www.tidyverse.org/blog/2020/04/usethis-1-6-0/) through `r BiocStyle::CRANpkg('usethis')`, `r BiocStyle::CRANpkg('remotes')`, and `r BiocStyle::CRANpkg('rcmdcheck')` customized to use [Bioconductor's docker containers](https://www.bioconductor.org/help/docker/) and `r BiocStyle::Biocpkg('BiocCheck')`.
* Code coverage assessment is possible thanks to [codecov](https://codecov.io/gh) and `r BiocStyle::CRANpkg('covr')`.
* The [documentation website](http://almeidasilvaf.github.io/cogeqc) is automatically updated thanks to `r BiocStyle::CRANpkg('pkgdown')`.
* The documentation is formatted thanks to `r BiocStyle::CRANpkg('devtools')` and `r BiocStyle::CRANpkg('roxygen2')`.
For more details, check the `dev` directory.
This package was developed using `r BiocStyle::Biocpkg('biocthis')`.