The bioinformatician applies a quality filter that removes 18% of sequencing reads. If a sample initially has 2.5 million reads, and each retained read requires 200 bytes of storage, how many megabytes of data remain after filtering?

Sun, 03 May 2026 23:14:07 +0000

How Bioinformaticians Use Quality Filters to Reduce Sequencing Data: A Practical Example In bioinformatics, managing vast volumes of sequencing data is critical for efficient analysis and storage. A common step in the data preprocessing pipeline is applying quality filters to remove low-quality sequencing reads. These filters significantly improve data reliability but also reduce data size—impactful for downstream processing, storage, and cost. This article explores a practical example of how a quality filter operates and its measurable impact on sequencing data. A bioinformatician often applies a quality filter that removes 18% of sequencing reads to enhance accuracy. In one scenario, a sample begins with 2.5 million raw sequencing reads. After filtering, only the high-quality reads are retained. Each retained read requires 200 bytes of storage. We now calculate how much data remains after this quality control step. First, determine how many reads are retained:
18% of 2.5 million reads are removed, so 82% of reads pass the filter.
Retained reads = 2,500,000 × (1 – 0.18) = 2,500,000 × 0.82 = 2,050,000 reads