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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Updated: Feb 23, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Variant genotyping with gap filling.

Riku Walve¹, Leena Salmela¹, Veli Mäkinen¹

¹Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.

|September 9, 2017

Summary

This summary is machine-generated.

This study presents a novel method for genotyping insertion variants by modeling them as gaps in genome assembly. This approach enhances accuracy, particularly for long insertions, improving genetic variation analysis.

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Last Updated: Feb 23, 2026

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Advances in DNA sequencing boost genome assembly quality and quantity.
De novo genome assemblies still require more efficiency and accuracy for individual genetic variation studies.
Genotyping insertion variants is challenging as it requires assembling the insertion sequence from reads.

Purpose of the Study:

To develop efficient and accurate methods for genotyping insertion variants.
To adapt gap-filling techniques from genome assembly for insertion genotyping.
To improve the scalability and performance of insertion genotyping methods.

Main Methods:

Modeling insertion genotyping as a gap-filling problem in the reference genome.
Utilizing existing gap-filling tools and methods for insertion sequence assembly.
Implementing a general read filtering scheme to enhance scalability for large datasets.

Main Results:

Gap-filling methods demonstrate competitive performance against existing insertion genotyping tools.
Read filtering significantly improves insertion genotyping performance, especially for long insertions.
The proposed method achieves the highest accuracy for long insertions and comparable performance for short insertions.

Conclusions:

Adapting gap-filling techniques offers a robust approach to insertion genotyping.
Read filtering is crucial for improving the scalability and accuracy of insertion genotyping.
The developed method provides a valuable tool for accurate genetic variation analysis, particularly for complex insertions.