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Ultra-long Read Sequencing for Whole Genomic DNA Analysis
Published on: March 15, 2019
24.9K
Large scale comparison of non-human sequences in human sequencing data
Hongseok Tae1, Enusha Karunasena1, Jasmin H Bavarva1
1Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA.
Genomics
|September 1, 2014
Summary
Unmapped reads in next-generation sequencing data can indicate contamination. Analysis of the 1000 Genomes Project revealed sequencing centers have unique contamination signatures, and some non-human genome similarities are due to sequence homology.
Area of Science:
- Genomics
- Bioinformatics
- Next-Generation Sequencing
Background:
- Unmapped reads in next-generation sequencing (NGS) data are known to potentially identify infectious agents or structural variants.
- However, a comprehensive analysis and classification of non-human sequences within large datasets have been lacking.
Purpose of the Study:
- To identify commonalities in non-human sequences related to infectious agents and potential contamination events.
- To analyze non-human sequences in genomic data from the 1000 Genomes Project.
Main Methods:
- Analysis of non-human sequences in 150 genomic sequencing data files from the 1000 Genomes Project.
- Comparison of results across different ethnic groups, sequencing centers, and enrichment methods (whole genome sequencing vs. exome sequencing).
Main Results:
- An average of 0.13% of reads showed similarity to non-human genomes.
- Sequencing centers exhibited distinct "time stamp" signatures of contaminating genomes.
- A significant number of unmapped reads falsely suggested contamination due to high human sequence similarity to non-human genomes like mouse and Nicotiana.
Conclusions:
- Sequencing centers can introduce specific contamination profiles into genomic data.
- Sequence homology between human and non-human genomes can lead to misinterpretation of unmapped reads as contamination.
- Understanding these patterns is crucial for accurate genomic data analysis and interpretation.
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