This supplementary information discusses the estimation and correction of GC-content bias in high-throughput sequencing. GC-content bias can affect the accuracy of sequencing data, particularly in regions with high or low GC content. The paper presents methods to estimate and correct for this bias, using data from various sources, including human cell lines, Arabidopsis thaliana, and cancer patient samples. The paper describes the use of TV scores to estimate GC effects and correct for GC bias in sequencing data. It also discusses the impact of sequencing quality on GC bias and how to distinguish between biases caused by library preparation and sequencing. The paper compares two protocols for reducing GC bias: a PCR-free protocol and an optimized PCR protocol. It also presents results from two technical replicates of ChIP-seq samples, showing the effect of GC on coverage and the importance of correcting for GC bias. The paper also compares the performance of two methods for correcting GC bias: a fragment model and the BEADS method. The fragment model uses TV scores to estimate fragment size and correct for GC bias, while BEADS uses a different approach to correct for GC, mappability, and other biases. The paper shows that both methods produce similar results in high-mappability regions, but that BEADS may have more variability in low-mappability regions. The paper also describes the methods used to estimate and correct for GC bias, including the use of TV scores, fragment models, and the BEADS method. It discusses the importance of separating the prediction and correction steps in the fragment model, and how this can affect the accuracy of predictions in low-coverage regions. The paper concludes that correcting for GC bias is essential for accurate analysis of sequencing data.This supplementary information discusses the estimation and correction of GC-content bias in high-throughput sequencing. GC-content bias can affect the accuracy of sequencing data, particularly in regions with high or low GC content. The paper presents methods to estimate and correct for this bias, using data from various sources, including human cell lines, Arabidopsis thaliana, and cancer patient samples. The paper describes the use of TV scores to estimate GC effects and correct for GC bias in sequencing data. It also discusses the impact of sequencing quality on GC bias and how to distinguish between biases caused by library preparation and sequencing. The paper compares two protocols for reducing GC bias: a PCR-free protocol and an optimized PCR protocol. It also presents results from two technical replicates of ChIP-seq samples, showing the effect of GC on coverage and the importance of correcting for GC bias. The paper also compares the performance of two methods for correcting GC bias: a fragment model and the BEADS method. The fragment model uses TV scores to estimate fragment size and correct for GC bias, while BEADS uses a different approach to correct for GC, mappability, and other biases. The paper shows that both methods produce similar results in high-mappability regions, but that BEADS may have more variability in low-mappability regions. The paper also describes the methods used to estimate and correct for GC bias, including the use of TV scores, fragment models, and the BEADS method. It discusses the importance of separating the prediction and correction steps in the fragment model, and how this can affect the accuracy of predictions in low-coverage regions. The paper concludes that correcting for GC bias is essential for accurate analysis of sequencing data.