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Updated: Apr 19, 2026

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Semiconductor sequencing: how many flows do you need?

Jan Budczies¹, Michael Bockmayr², Denise Treue²

¹Institute of Pathology, Charité University Hospital, Charitéplatz 1, 10117 Berlin, Germany, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK), Berlin partner site, Charitéplatz 1, 10117 Berlin, Germany Institute of Pathology, Charité University Hospital, Charitéplatz 1, 10117 Berlin, Germany, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK), Berlin partner site, Charitéplatz 1, 10117 Berlin, Germany Institute of Pathology, Charité University Hospital, Charitéplatz 1, 10117 Berlin, Germany, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK), Berlin partner site, Charitéplatz 1, 10117 Berlin, Germany.

Bioinformatics (Oxford, England)

|December 7, 2014

Summary

This summary is machine-generated.

Optimizing semiconductor sequencing requires knowing the number of nucleotide flows for complete template coverage. This study provides calculations and an algorithm to determine required flows, enhancing instrument throughput for targeted cancer research panels.

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Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

Area of Science:

Genomics and Bioinformatics
Semiconductor Technology

Background:

Semiconductor sequencing converts genetic information (A, C, G, T) into electrical signals.
This method relies on detecting pH changes during nucleotide incorporation.
Minimizing sequencing time and cost necessitates accurate prediction of required nucleotide flows.

Purpose of the Study:

To develop a method for calculating the number of nucleotide flows needed for complete template coverage in semiconductor sequencing.
To provide tools for optimizing sequencing experiments and enhancing instrument throughput.

Main Methods:

Developed a random sequence model to calculate the number of required flows.
Derived exact expressions for cumulative distribution function, expected value, and variance.
Created an algorithm to determine flows for specific amplicon panels using BED files.

Main Results:

The algorithm was applied to six amplicon panels for cancer research.
Calculated upper bounds for the number of flows required for template coverage.
Demonstrated potential to increase instrument throughput from two to three chips per day for certain panels.

Conclusions:

The developed algorithm and calculations provide essential parameters for optimizing semiconductor sequencing.
This optimization can lead to significant improvements in sequencing efficiency and cost-effectiveness.
The findings are directly applicable to targeted sequencing in cancer research and beyond.