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Detection and Removal of Biases in the Analysis of Next-Generation Sequencing Reads

Summary: Detection and Removal of Biases in the Analysis of Next-
Generation Sequencing Reads
Schraga Schwartz.Īa
*, Ram Oren.ĪbĪcĪd
, Gil Ast
Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv, Israel
Since the emergence of next-generation sequencing (NGS) technologies, great effort has been put into the development of
tools for analysis of the short reads. In parallel, knowledge is increasing regarding biases inherent in these technologies.
Here we discuss four different biases we encountered while analyzing various Illumina datasets. These biases are due to
both biological and statistical effects that in particular affect comparisons between different genomic regions. Specifically,
we encountered biases pertaining to the distributions of nucleotides across sequencing cycles, to mappability, to
contamination of pre-mRNA with mRNA, and to non-uniform hydrolysis of RNA. Most of these biases are not specific to one
analyzed dataset, but are present across a variety of datasets and within a variety of genomic contexts. Importantly, some of
these biases correlated in a highly significant manner with biological features, including transcript length, gene expression
levels, conservation levels, and exon-intron architecture, misleadingly increasing the credibility of results due to them. We
also demonstrate the relevance of these biases in the context of analyzing an NGS dataset mapping transcriptionally
engaged RNA polymerase II (RNAPII) in the context of exon-intron architecture, and show that elimination of these biases is
crucial for avoiding erroneous interpretation of the data. Collectively, our results highlight several important pitfalls,
challenges and approaches in the analysis of NGS reads.


Source: Ast, Gil - Department of Molecular Genetics and Biochemistry, Tel Aviv University


Collections: Biology and Medicine