scholarly journals Immunohistochemistry and Mutation Analysis of SDHx Genes in Carotid Paragangliomas

2020 ◽  
Vol 21 (18) ◽  
pp. 6950
Author(s):  
Anastasiya V. Snezhkina ◽  
Dmitry V. Kalinin ◽  
Vladislav S. Pavlov ◽  
Elena N. Lukyanova ◽  
Alexander L. Golovyuk ◽  
...  
Keyword(s):  
Data Analysis ◽  
Genetic Testing ◽  
High Sensitivity ◽  
Sequencing Data ◽  
Mutation Status ◽  
Exome Sequencing Data ◽  
Pathogenic Variants ◽  
Positive Immunoreactivity ◽  
Carotid Paragangliomas ◽  
Sequencing Data Analysis

Carotid paragangliomas (CPGLs) are rare neuroendocrine tumors often associated with mutations in SDHx genes. The immunohistochemistry of succinate dehydrogenase (SDH) subunits has been considered a useful instrument for the prediction of SDHx mutations in paragangliomas/pheochromocytomas. We compared the mutation status of SDHx genes with the immunohistochemical (IHC) staining of SDH subunits in CPGLs. To identify pathogenic/likely pathogenic variants in SDHx genes, exome sequencing data analysis among 42 CPGL patients was performed. IHC staining of SDH subunits was carried out for all CPGLs studied. We encountered SDHx variants in 38% (16/42) of the cases in SDHx genes. IHC showed negative (5/15) or weak diffuse (10/15) SDHB staining in most tumors with variants in any of SDHx (94%, 15/16). In SDHA-mutated CPGL, SDHA expression was completely absent and weak diffuse SDHB staining was detected. Positive immunoreactivity for all SDH subunits was found in one case with a variant in SDHD. Notably, CPGL samples without variants in SDHx also demonstrated negative (2/11) or weak diffuse (9/11) SDHB staining (42%, 11/26). Obtained results indicate that SDH immunohistochemistry does not fully reflect the presence of mutations in the genes; diagnostic effectiveness of this method was 71%. However, given the high sensitivity of SDHB immunohistochemistry, it could be used for initial identifications of patients potentially carrying SDHx mutations for recommendation of genetic testing.

Development of pipeline for exome sequencing data analysis

2012 ◽  
Vol 18 (A) ◽  
pp. 98 ◽  
Author(s):  
MR De Filippo ◽  
G Giurato ◽  
C Cantarella ◽  
F Rizzo ◽  
F Cirillo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Exome Sequencing ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Sequencing Data Analysis

Exome Sequencing Data Analysis

2019 ◽  
pp. 164-175
Author(s):  
Anita Sathyanarayanan ◽  
Srikanth Manda ◽  
Mukta Poojary ◽  
Shivashankar H Nagaraj
Keyword(s):  
Data Analysis ◽  
Exome Sequencing ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Sequencing Data Analysis

scholarly journals KidneyNetwork: Using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

2021 ◽  
Author(s):  
Floranne Boulogne ◽  
Laura R. Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Specific Gene ◽  
Sequencing Data ◽  
Specific Expression ◽  
Disease Phenotypes ◽  
Exome Sequencing Data ◽  
Pathogenic Variants ◽  
Gene Functions

AbstractBackgroundGenetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. To help identify these genes, we have developed KidneyNetwork, that utilizes tissue-specific expression to predict kidney-specific gene functions.MethodsKidneyNetwork is a co-expression network built upon a combination of 878 kidney RNA-sequencing samples and a multi-tissue dataset of 31,499 samples. It uses expression patterns to predict which genes have a kidney-related function and which (disease) phenotypes might result from variants in these genes. We applied KidneyNetwork to prioritize rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis.ResultsKidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. Applying it to exome sequencing data of kidney disease patients allowed us to identify a promising candidate gene for kidney and liver cysts: ALG6.ConclusionWe present KidneyNetwork, a kidney-specific co-expression network that accurately predicts which genes have kidney-specific functions and can result in kidney disease. We show the added value of KidneyNetwork by applying it to kidney disease patients without a molecular diagnosis and consequently, we propose ALG6 as candidate gene in one of these patients. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes in order to better understand kidney physiology and pathophysiology.Significance statementGenetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient’s disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.

scholarly journals Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

Genomics ◽  
2017 ◽  
Vol 109 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Yan Guo ◽  
Yulin Dai ◽  
Hui Yu ◽  
Shilin Zhao ◽  
David C. Samuels ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Sequencing Data Analysis
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