A bioinformatics approach for determining sample identity from different lanes of high-throughput sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Quality Control

Background:

• Whole genome sequencing data generation is becoming more accessible.
• Combining data from multiple sequencing lanes requires quality control to ensure sample identity.
• Current methods often rely on upstream sample modifications like barcoding.

Purpose of the Study:

• To develop a post-hoc bioinformatic method for verifying sample identity across sequencing lanes.
• To assess the utility of genotype concordance rates for quality control in whole genome sequencing.
• To provide a simple method for gender determination from sequencing data.

Main Methods:

• Utilized genotype concordance rates between sequencing lanes.
• Analyzed data from three human samples generated on the Illumina HiSeq 2000 platform.
• Compared concordance rates for lanes from the same sample versus different samples.

Main Results:

• Distributions of genotype concordance rates were non-overlapping when comparing same-sample lanes versus different-sample lanes.
• The method demonstrated robustness regardless of the number of reads analyzed.
• A straightforward gender determination method was also presented.

Conclusions:

• Genotype concordance analysis is an effective and simple post-hoc quality control method for whole genome sequencing data.
• This approach confirms the identity and quality of combined sequencing lanes.
• The method enhances the reliability of large-scale genomic datasets.