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Pan-cancer analysis of systematic batch effects on somatic sequence variations
Ji-Hye Choi1,2, Seong-Eui Hong1, Hyun Goo Woo3,4
1Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, South Korea.
BMC Bioinformatics
|April 13, 2017
Summary
Batch effects in The Cancer Genome Atlas (TCGA) data significantly impact somatic sequence variations, introducing 999 biased variants. Understanding these batch-biased mutations is crucial for accurate cancer genome interpretation.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- The Cancer Genome Atlas (TCGA) is a vital resource for multi-layered cancer genome profiles.
- Large-scale data generation introduces batch effects due to processing variations.
- Batch effects on sequence variation characteristics remain understudied.
Purpose of the Study:
- To systematically evaluate batch effects on somatic sequence variations within TCGA pan-cancer data.
- To characterize the nature and patterns of batch-biased sequence variants.
Main Methods:
- Statistical analysis of somatic sequence variations in TCGA data.
- Identification of batch-biased variants using Fisher's exact test and false discovery rate.
- Characterization of variant locations, mutation types, and sequence motifs.
Main Results:
- Identified 999 statistically significant batch-biased somatic variants (P < 0.00001, FDR ≤ 0.0027).
- Most batch-biased variants were linked to specific sample plates and exhibited a unique mutational spectrum (frequent indels, homopolymer runs).
- Non-indel batch-biased variants occurred at splicing sites with a specific motif ('TTDTTTAGTT'); some affected known cancer genes.
Conclusions:
- Developed a strategy to identify and characterize batch-biased variants in cancer sequencing data.
- This approach can help eliminate false variants and improve the interpretation of mutation profiles.
- Findings highlight the importance of accounting for batch effects in large-scale genomic studies.