doc: some BiocStyle cosmetics

inst/CITATION

@@ -0,0 +1,19 @@
+citHeader("To cite koinaR in publications, please use:")
+
+citEntry(
+  entry = "article",
+  title = "Koina: Democratizing machine learning for proteomics research",
+  author = c(
+    "Lautenbacher, Ludwig", "Yang, Kevin L.", "Kockmann, Tobias",
+    "Panse, Christian", "Chambers, Matthew", "Kahl, Elias",
+    "Yu, Fengchao", "Gabriel, Wassim", "Bold, Dulguun",
+    "Schmidt, Tobias", "Li, Kai", "MacLean, Brendan",
+    "Nesvizhskii, Alexey I.", "Wilhelm, Mathias"
+  ),
+  Journal = "bioRxiv",
+  year = "2024",
+  month = "jun",
+  url = "http://dx.doi.org/10.1101/2024.06.01.596953",
+  doi = "10.1101/2024.06.01.596953",
+  publisher = "Cold Spring Harbor Laboratory"
+)

vignettes/koina.Rmd

@@ -11,7 +11,7 @@ author:
     - Swiss Institute of Bioinformatics (SIB), Quartier Sorge - Batiment Amphipole, CH-1015 Lausanne, Switzerland
 package: koinar
 abstract: |
-  How to use `r BiocStyle::Biocpkg('KoinaR')` to fetch predictions from [Koina](https://koina.wilhelmlab.org/)
+  How to use `r BiocStyle::Biocpkg('koinar')` to fetch predictions from [Koina](https://koina.wilhelmlab.org/)
 output:
   BiocStyle::html_document:
     toc_float: true
@@ -42,7 +42,7 @@ set_requester(function(request) {
 ```
 
 # Introduction
-Koina is a repository of machine learning models enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic. As such, HTTP/S requests can be easily generated in any programming language without requiring specialized hardware. This design enables users to easily access ML/DL models that would normally required specialized hardware from any device and in any programming language. It also means that the hardware is used more efficiently and it allows for easy horizontal scaling depending on the demand of the user base.
+Koina [@Lautenbacher2024] is a repository of machine learning models enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic. As such, HTTP/S requests can be easily generated in any programming language without requiring specialized hardware. This design enables users to easily access ML/DL models that would normally required specialized hardware from any device and in any programming language. It also means that the hardware is used more efficiently and it allows for easy horizontal scaling depending on the demand of the user base.
 
 To minimize the barrier of entry and “democratize” access to ML models, we provide a public network of Koina instances at koina.wilhelmlab.org. The computational workload is automatically distributed to processing nodes hosted at different research institutions and spin-offs across Europe. Each processing node provides computational resources to the service network, always aiming at just-in-time results delivery.
 
@@ -52,7 +52,7 @@ Koina is a community driven project. It is fuly open-source. We welcome all cont
 
 At the moment Koina mostly focuses on the Proteomics domain but the design can be easily extended to any machine learning model. Active development to expand it into Metabolomics is underway. If you are interested in using Koina to interface with a machine learning model not currently available feel free to [create a request](https://github.com/wilhelm-lab/koina/issues).
 
-Here we take a look at KoinaR the R package to simplify getting predictions from Koina. 
+Here we take a look at `r BiocStyle::Biocpkg('koinar')` the R package to simplify getting predictions from Koina. 
 
 # Install
 ```{r, eval=FALSE}
@@ -65,8 +65,8 @@ BiocManager::install("koinar")
 
 
 # Basic usage
-Here we show the basic usage principles of KoinaR. The first step to interact with Koina is to pick a model and server you wish to use.
-Here we use the model Prosit_2019_intensity published by Gessulat et al [@prosit2019] and the public Koina network available via koina.wilhelmlab.org.
+Here we show the basic usage principles of `r BiocStyle::Biocpkg('koinar')`. The first step to interact with Koina is to pick a model and server you wish to use.
+Here we use the model `Prosit_2019_intensity` published by Gessulat et al [@prosit2019] and the public Koina network available via koina.wilhelmlab.org.
 For a complete overview of models available on Koina have a look at the documentation available at https://koina.wilhelmlab.org/docs. 
 
 ```{r create, eval=TRUE, message=FALSE}
@@ -235,7 +235,7 @@ title(main = paste(peptide_sequence, "| Spectrum similarity", round(sim, 3)))
 # Example 3: Loading rawdata with the Spectra package
 
 The main application of predicted fragment mass spectra is to be compared with experimental spectra.
-Here we use the `Spectra` package to read a rawfile (provided by the `msdata` package).
+Here we use the `r BiocStyle::Biocpkg('Spectra')` package to read a rawfile (provided by the `r BiocStyle::Biocpkg('msdata')` package).
 
 ```{r, message=FALSE}
 library(Spectra)

vignettes/koina.bib

@@ -11,3 +11,13 @@ @article{prosit2019
   title = {Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning},
   journal = {Nature Methods}
 }
+
+@article{Lautenbacher2024,
+  title = {Koina: Democratizing machine learning for proteomics research},
+  url = {http://dx.doi.org/10.1101/2024.06.01.596953},
+  DOI = {10.1101/2024.06.01.596953},
+  publisher = {Cold Spring Harbor Laboratory},
+  author = {Lautenbacher,  Ludwig and Yang,  Kevin L. and Kockmann,  Tobias and Panse,  Christian and Chambers,  Matthew and Kahl,  Elias and Yu,  Fengchao and Gabriel,  Wassim and Bold,  Dulguun and Schmidt,  Tobias and Li,  Kai and MacLean,  Brendan and Nesvizhskii,  Alexey I. and Wilhelm,  Mathias},
+  year = {2024},
+  month = jun
+}