Level: Beginner

 

Pat Lynch, Director, Systems Engineering, Bionano Genomics

 

The Bionano Genomics Saphyr® System combines NanoChannel arrays with optical mapping to image extremely long, high molecular weight DNA that has been linearized. This allows for the detection of structural variants in the genome that are from 500 base pairs up to megabases in length, exceeding the detection range of any other technology. As part of the complete workflow, molecules imaged by the Saphyr instrument are computationally assembled into genome maps by the Bionano Solve pipeline. This process is computationally intensive. In order to better predict the time needed to complete the genome assembly, we used JMP to come up with a model that would predict how long it would take. Input parameters initially included all data quality metrics available, then were reduced to a smaller number of variables that were shown to have a statistical impact. An initial data set was used to establish the model. The model was then used on subsequent data sets to predict the resources needed for the genome assembly. This poster presents the methods used to determine the prediction model, then assesses the efficacy of the model.

Presented At Discovery Summit 2019

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Published on ‎03-24-2025 08:36 AM by Community Manager Community Manager | Updated on ‎03-26-2025 04:43 PM

Level: Beginner

 

Pat Lynch, Director, Systems Engineering, Bionano Genomics

 

The Bionano Genomics Saphyr® System combines NanoChannel arrays with optical mapping to image extremely long, high molecular weight DNA that has been linearized. This allows for the detection of structural variants in the genome that are from 500 base pairs up to megabases in length, exceeding the detection range of any other technology. As part of the complete workflow, molecules imaged by the Saphyr instrument are computationally assembled into genome maps by the Bionano Solve pipeline. This process is computationally intensive. In order to better predict the time needed to complete the genome assembly, we used JMP to come up with a model that would predict how long it would take. Input parameters initially included all data quality metrics available, then were reduced to a smaller number of variables that were shown to have a statistical impact. An initial data set was used to establish the model. The model was then used on subsequent data sets to predict the resources needed for the genome assembly. This poster presents the methods used to determine the prediction model, then assesses the efficacy of the model.



Start:
Mon, Oct 14, 2019 09:00 AM EDT
End:
Fri, Oct 18, 2019 05:00 PM EDT
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