Sources of erroneous sequences and artifact chimeric reads in next generation sequencing of genomic DNA from formalin-fixed paraffin-embedded samples. Issue 2 (12th November 2018)

• Record Type: Journal Article
• Authors: Haile, Simon; Corbett, Richard D; Bilobram, Steve; Bye, Morgan H; Kirk, Heather; Pandoh, Pawan; Trinh, Eva; MacLeod, Tina; McDonald, Helen; Bala, Miruna; Miller, Diane; Novik, Karen; Coope, Robin J; Moore, Richard A; Zhao, Yongjun; Mungall, Andrew J; Ma, Yussanne; Holt, Rob A; Jones, Steven J; Marra, Marco A
• Is Part Of: Nucleic acids research. Volume 47:Issue 2(2019)
• Page Start: e12
• Page End: e12
• Publication Date: 2018-11-12
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky1142
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Tissues used in pathology laboratories are typically stored in the form of formalin-fixed, paraffin-embedded (FFPE) samples. One important consideration in repurposing FFPE material for next generation sequencing (NGS) analysis is the sequencing artifacts that can arise from the significant damage to nucleic acids due to treatment with formalin, storage at room temperature and extraction. One such class of artifacts consists of chimeric reads that appear to be derived from non-contiguous portions of the genome. Here, we show that a major proportion of such chimeric reads align to both the 'Watson' and 'Crick' strands of the reference genome. We refer to these as strand-split artifact reads (SSARs). This study provides a conceptual framework for the mechanistic basis of the genesis of SSARs and other chimeric artifacts along with supporting experimental evidence, which have led to approaches to reduce the levels of such artifacts. We demonstrate that one of these approaches, involving S1 nuclease-mediated removal of single-stranded fragments and overhangs, also reduces sequence bias, base error rates, and false positive detection of copy number and single nucleotide variants. Finally, we describe an analytical approach for quantifying SSARs from NGS data.