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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics.

Richard M Leggett¹, Ricardo H Ramirez-Gonzalez¹, Bernardo J Clavijo¹

¹The Genome Analysis Centre Norwich, UK.

Frontiers in Genetics

|January 2, 2014

Summary

The Genome Analysis Centre developed bioinformatics tools for cross-platform quality control (QC) in next-generation sequencing. These data-driven informatics solutions improve downstream analysis and experimental design.

Keywords:

NGS data analysis QC bioinformatics tools contamination screening quality assessment and improvement quality control run statistics sequence analysis

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Published on: October 5, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Quality assessment and control are critical in genomics research.
The Genome Analysis Centre (TGAC) specializes in diverse sequencing projects and technologies.
TGAC investigates library quality and sequence data assessment across multiple platforms.

Purpose of the Study:

To review the bioinformatics tools developed at TGAC for quality assessment and control.
To highlight the application of data-driven informatics for next-generation sequencing (NGS).
To demonstrate how these tools enhance downstream analysis and experimental design.

Main Methods:

Development and implementation of algorithms, tools, pipelines, and packages for quality metrics.
Creation of systems to ascertain, store, and expose quality metrics.
Facilitation of rapid and in-depth cross-platform Quality Control (QC) bioinformatics.

Main Results:

TGAC offers a suite of tools for comprehensive NGS data quality assessment.
These tools enable efficient cross-platform QC bioinformatics.
The developed informatics solutions support data-driven decision-making.

Conclusions:

The TGAC tools provide a robust framework for data-driven informatics in genomics.
These bioinformatics solutions enhance the reliability and interpretability of sequencing data.
The tools have the potential to significantly inform experimental design and improve research outcomes.