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RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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Updated: Jun 21, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Local read haplotagging enables accurate long-read small variant calling.

Alexey Kolesnikov¹, Daniel Cook¹, Maria Nattestad¹

¹Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA, USA.

Nature Communications

|July 13, 2024

Summary

This summary is machine-generated.

We developed a new method to simplify long-read sequencing variant calling. This approach improves accuracy across various platforms like PacBio and Oxford Nanopore Technologies, aiding genetic diagnosis.

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Last Updated: Jun 21, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

Genomics and Bioinformatics
Computational Biology
Next-Generation Sequencing

Background:

Long-read sequencing technologies (e.g., Pacific Biosciences, Oxford Nanopore Technologies) are crucial for variant detection in complex genomic regions and clinical genetic diagnosis.
Deep neural network-based variant callers leverage local haplotype information to enhance genotyping accuracy with long reads.
Current methods using local haplotypes introduce computational overhead, hindering scalability to new sequencing platforms and data types.

Purpose of the Study:

To develop an efficient local haplotype approximation method for long-read variant calling.
To improve the adaptability and performance of variant calling across diverse sequencing platforms.
To simplify the integration of local haplotype information into existing variant calling frameworks like DeepVariant.

Main Methods:

Development of a novel local haplotype approximation algorithm.
Integration of the approximation method into the DeepVariant variant calling pipeline.
Benchmarking performance on data from Pacific Biosciences (PacBio Revio) and Oxford Nanopore Technologies (ONT R10.4 simplex and duplex).

Main Results:

The local haplotype approximation method achieves state-of-the-art variant calling performance.
The approach demonstrates high accuracy across multiple long-read sequencing platforms, including PacBio Revio and ONT R10.4 data.
Significant simplification of the variant calling workflow, reducing computational overhead compared to traditional local haplotyping.

Conclusions:

The developed local haplotype approximation method effectively enhances variant calling accuracy and efficiency for long-read sequencing data.
This simplified approach facilitates the application of advanced variant calling to emerging sequencing technologies and data types.
The method offers a robust solution for improving genetic diagnosis and genomic research using long-read sequencing.