Evaluation of Quality Assessment Protocols for High Throughput Genome Resequencing Data
Matteo Chiara1, Giulio Pavesi1
1Dipartimento di Bioscienze, Università di MilanoMilan, Italy.
Frontiers in Genetics
|July 25, 2017
Summary
This study explores Next-Generation Sequencing (NGS) data analysis for human genomics. We propose best practices for data pre-processing to improve genotyping accuracy in clinical diagnostics and genetic variation discovery.
Area of Science:
- Genomics
- Bioinformatics
- Medical Diagnostics
Background:
- Large-scale human genome sequencing initiatives aim to catalog genetic variation.
- Next-Generation Sequencing (NGS) is revolutionizing medical science but faces bioinformatic challenges.
- Current bioinformatic pipelines for NGS data are complex and data-type dependent.
Purpose of the Study:
- To discuss the strengths and limitations of genome resequencing protocols in molecular diagnostics.
- To identify best practices for pre-processing NGS data to enhance genotyping outcomes.
- To address the impact of quality assessment and read pre-processing on NGS data analysis.
Main Methods:
- Analysis of publicly available human resequencing data.
- Evaluation of various genome resequencing protocols.
- Devising and suggesting pre-processing strategies for NGS data.
Main Results:
- Identified limitations in current bioinformatic pipelines for NGS data.
- Proposed best practices for data pre-processing that improve genotyping.
- Demonstrated minimal impact of suggested strategies on computational costs.
Conclusions:
- Optimized pre-processing of NGS data is crucial for effective human genomics and diagnostics.
- Standardized bioinformatic strategies are needed to overcome data variability.
- Implementing best practices can enhance the reliability of genetic variation discovery.
