Datasets:

stefanches
/

genomic-bioimaging

@@ -22,17 +22,18 @@ size_categories:
 Bioimages collection of a *high-content RNAi knockdown screening*.
 <img src="https://cdn-uploads.huggingface.co/production/uploads/6808daf29d3434b451624026/D1qgtyD5wVP_WoebTNM1m.png" alt="cell image example" width="1000"/>
 ## Dataset Details
 ### Dataset Description
 <!-- Provide a longer summary of what this dataset is. -->
-To cluster genes and predict function on a genome-wide scale, we measured the effects of 22 839 siRNA-mediated knockdowns on HeLa cells. Each siRNA effect was summarized by a phenotypic profile.
 Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.

 Bioimages collection of a *high-content RNAi knockdown screening*.
+Author one-liner: "To cluster genes and predict function on a genome-wide scale, we measured the effects of 22 839 siRNA-mediated knockdowns on HeLa cells. Each siRNA effect was summarized by a phenotypic profile."
 <img src="https://cdn-uploads.huggingface.co/production/uploads/6808daf29d3434b451624026/D1qgtyD5wVP_WoebTNM1m.png" alt="cell image example" width="1000"/>
 ## Dataset Details
+**NB! Upload still running. The Croissant files and parquet Dataset Card will be decorated once it's finished.**
 ### Dataset Description
 <!-- Provide a longer summary of what this dataset is. -->
 Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.