Scarborough Biotechnology LTD

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Predicting diseases from patient data using snapshot

Posted by mcoggins on July 24, 2018 at 10:10 AM

Introduction


According to Genetic Disorders UK.org there are between 4,000 to 6,000 diagnosed genetic disorders ranging from common disorders such as sickle cell disease (1 in 500 africans) to more rare disorders such as lactate dehydrogenase A deficiency which has a prevelance of only 1 in 1 milion. Given the amount of potential disorders there is a push for more rapid yet accurate diagnosis for patients. 


With Snapshot we have recently released a function called realconv  that can take gene expression data from patients and highlight potential diseases based upon that data. 


Lactate Dehydrogenase Example


Imagine we have a patient whose symptoms include brown urine and rhabdomyolysis (breakdown of skeletal muscle). We obtain a gene expression profile from the patient from which we will see if there are any genetic conditions which maybe associated with the patients symptoms. Then we can put the gene expression data in to snapshot. 


To do this we need to use the realconv function. This function takes gene expression values and will knockout any genes whose expression falls below a certain threashold value determined by the user. This value must be low such that it will turn off any genes whose expressions would be  associated with an enzyme deficiency caused by that gene. In the code below we have set this value to 0.3 where 0 would be a complete knockdown of that gene and 1 would be normal expressions and anything higher would be determined to be overexpression. After we ahve determined the threshold we can start to add in the expressions of each gene from the expression profile. To add these expressions we must first declare the HGNC ID of that gene followed by it's expression on the next line below. 


For example:


6541

0.8 


Here is the code:


knockin 

start:realconv

threshold 

0.3 

21481 

19708

1

30866

0.3

6541

0.1

6535

0.1

9040

1

61

1

66

1

2625

0.4

1122

0.8

1516

0.8

end:realconv

cal 




From the disease table we can see that it has highlighted Exerctional Myglobinuria due to deficiency of HGNC 6535.  





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