Correcting nucleotide-specific biases in high-throughput sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• High-throughput sequencing (HTS) data often contain position-specific nucleotide biases.
• These biases obscure biological signals and hinder downstream analyses, especially in epigenetic assays like ChIP-seq, DNase-seq, FAIRE-seq, and ATAC-seq.
• Factors contributing to these biases include DNA fragmentation, enzyme selectivity, and sequencing/amplification methods.

Purpose of the Study:

• To develop and present a novel method for detecting and correcting position-specific nucleotide biases in HTS short read data.
• To improve the accuracy and utility of HTS data for identifying regulatory DNA regions and transcription factor binding sites.

Main Methods:

• A novel method was developed to calculate read-specific weights based on aligned reads.
• These weights correct over- or underrepresentation of position-specific nucleotide subsequences within and adjacent to aligned reads.
• A baseline is calculated using assay-specific enriched regions.

Main Results:

• The developed method effectively reduces position-specific nucleotide imbalance in HTS reads.
• Weight-adjusted reads demonstrate improved utility for downstream analyses.
• The method enhances the identification and characterization of open chromatin peaks and transcription-factor binding sites in ChIP-seq, DNase-seq, FAIRE-seq, and ATAC-seq data.

Conclusions:

• A general method to correct position-specific nucleotide biases is crucial for HTS-based epigenetic assays.
• This approach systematically addresses binding-site preferences.
• A standardized toolkit for bias correction will be important as these biases are better understood.