Update README.md

README.md

@@ -6,7 +6,7 @@ Given one or several [FASTQ](https://en.wikipedia.org/wiki/FASTQ_format) file(s)
 _ROCK_ can therefore be used to reduce and/or homogenize the overall coverage depth within large sets of HTS reads, which is often required to quickly infer accurate _de novo_ genome assemblies (e.g. Desai et al. 2013, Chen et al. 2015).
 _ROCK_ can also be used to discard low-covering HTS reads, as those ones are often artefactual, highly erroneous or contaminating sequences. 
 
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+For more details, see the associated publication: Legrand et al. (2022) [![DOI](https://joss.theoj.org/papers/10.21105/joss.03790/status.svg)](https://doi.org/10.21105/joss.03790)
 
 
 ## Compilation and installation
@@ -305,6 +305,8 @@ Durai DA, Schulz MH (2019) _Improving in-silico normalization using read weights
 
 Kokot M, Długosz M, Deorowicz S (2017) _KMC 3: counting and manipulating k-mer statistics_. **Bioinformatics**, 33(17):2759-2761. [doi:10.1093/bioinformatics/btx304](https://doi.org/10.1093/bioinformatics/btx304).
 
+Legrand V, Kergrohen T, Joly N, Criscuolo A (2022) _ROCK: digital normalization of whole genome sequencing data_. **Journal of Open Source Software**, 7(73):3790. [doi:10.21105/joss.03790](https://doi.org/10.21105/joss.03790).
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 Liu B, Shi Y, Yuan J, Hu X, Zhang H, Li N, Li Z, Chen Y, Mu D, Fan W (2013) _Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects_. **arXiv**:[1308.2012v2](https://arxiv.org/abs/1308.2012v2).
 
 Melsted P, Halldórsson BV (2014) _KmerStream: streaming algorithms for k-mer abundance estimation_. **Bioinformatics**, 30(24):3541-3547. [doi:10.1093/bioinformatics/btu713](https://doi.org/10.1093/bioinformatics/btu713).