1.1 Main table: Merged analyses sources
This table shows the merged results of all analyses files as a wide table with summarized information.
- - + +We used ten common diagnostic panels that can be ordered for kidney disease analysis and extracted the screened genes from them. Here we show the overlap of the genes in the different panels.
- - + + diff --git a/docs/analyses-plots.html b/docs/analyses-plots.html index 31070b6..18028d5 100644 --- a/docs/analyses-plots.html +++ b/docs/analyses-plots.html @@ -23,7 +23,7 @@ - + @@ -205,8 +205,8 @@This table shows the merged results of all analyses files as a wide table with summarized information.
- - + +This table shows results of the first analysis searching kidney disease associated genes from the PanelApp project in the UK and Australia.
- - + +This table shows results of the second analysis searching kidney disease associated genes from various publications.
- - + +This table shows results of the third analysis searching kidney disease associated genes from clinical diagnostic panels for kidney disease.
- - + +This table shows results of the fourth analysis searching kidney disease associated genes from a Human Phenotype Ontology (HPO)-based search in rare disease databases (OMIM, Orphanet).
- - + +This table shows results of the fifth analysis searching kidney disease associated genes from a PubTator API-based automated literature extraction from PubMed.
- - + +2023-10-18
+2023-11-22
The scientific literature highlights the need for such a database and emphasizes the importance of genetic research in kidney disease (e.g. (Boulogne et al., 2023)).
In summary, our research question and its approach have the potential to provide a deeper scientific understanding of KD genetics, improve diagnostic accuracy, guide treatment selection, advance precision medicine, and facilitate research collaboration. The establishment of the “Kidney-Genetics” database addresses an important gap in the field and provides a valuable resource for researchers, clinicians, and patients involved in the discovery and treatment of KD.
@@ -223,14 +223,14 @@Methods -
Utilized data from Genomics England and Australia PanelApp (Martin et al., 2019) +Utilized data from Genomics England and Australia PanelApp (Martin et al., 2019) Conducted a comprehensive literature review of published gene lists
Collected information from clinical diagnostic panels for kidney disease -
Performed a Human Phenotype Ontology (HPO)-based (Köhler et al., 2021)) search in rare disease databases (OMIM)
+Performed a Human Phenotype Ontology (HPO)-based (Köhler et al., 2021)) search in rare disease databases (OMIM) -
Employed a PubTator (Wei et al., 2013) API-based automated literature extraction from PubMed +Employed a PubTator (Wei et al., 2013) API-based automated literature extraction from PubMed We also developed an evidence-scoring system to differentiate highly confirmed disease genes from candidate genes. We defined the presence of a certain gene in 3 or more of the 5 resources as highly evident genes. These genes were then manually curated according to predetermined criteria or, in the case of existing ClinGen curation, their data and scores were used. Genes with a score of 2 or less were accordingly more likely to be classified as candidate genes.
@@ -245,22 +245,22 @@Methods(Bleyer et al., 2022) -
(Knoers et al., 2022) -(Alaamery et al., 2022) -(KDIGO Conference Participants, 2022) -(Tanudisastro et al., 2021) -(Devarajan et al., 2022) -(Rasouly et al., 2019) -(Elhassan et al., 2022) -(Cormican et al., 2019) -(Murray et al., 2020) -(Claus et al., 2022) -(Bullich et al., 2018) -(Ottlewski et al., 2019) -(Al-Hamed et al., 2016) -(Domingo-Gallego et al., 2022) -(Jordan et al., 2022) +(Bleyer et al., 2022) +(Knoers et al., 2022) +(Alaamery et al., 2022) +(KDIGO Conference Participants, 2022) +(Tanudisastro et al., 2021) +(Devarajan et al., 2022) +(Rasouly et al., 2019) +(Elhassan et al., 2022) +(Cormican et al., 2019) +(Murray et al., 2020) +(Claus et al., 2022) +(Bullich et al., 2018) +(Ottlewski et al., 2019) +(Al-Hamed et al., 2016) +(Domingo-Gallego et al., 2022) +(Jordan et al., 2022) We used ten common diagnostic panels that can be purchased for genome analysis and extracted the screened genes from them. Those included following panels:
@@ -294,13 +294,13 @@
Methods
Results
--The “Kidney-Genetics” database currently contains detailed information on 3001 kidney-associated genes with detailed annotations on gene function, kidney phenotype, incidence, possible syndromic disease expression and genetic variation. +
The “Kidney-Genetics” database currently contains detailed information on 3025 kidney-associated genes with detailed annotations on gene function, kidney phenotype, incidence, possible syndromic disease expression and genetic variation. To automatically group the genes, we will present the results of phenotypic and functional clustering.
The number of genes extracted from the five analyzed sources of information is as follows: (1) 550, (2) 822, (3) 936, (4) 791, and (5) 2133
+
-Notably, 437 genes (14.6%) of the total 3001 genes are present in three or more of the analyzed information sources, thus meeting our evidence criteria, indicating high confidence and their potential for diagnostic use. -Of these high evidence genes, 423 (96.8%) are present in at least one, and 56 (12.8%) are present in all 10 comprehensive diagnostic laboratory panels.The number of genes extracted from the five analyzed sources of information is as follows: (1) 534, (2) 822, (3) 956, (4) 789, and (5) 2158
+Notably, 598 genes (19.8%) of the total 3025 genes are present in three or more of the analyzed information sources, thus meeting our evidence criteria, indicating high confidence and their potential for diagnostic use. +Of these high evidence genes, 526 (88.0%) are present in at least one, and 56 (9.4%) are present in all 10 comprehensive diagnostic laboratory panels.To ensure currency, Kidney-Genetics will be updated regularly and automatically at XXX week intervals. We will also provide phenotypic and functional clustering results to facilitate gene grouping.