Company: BLLN
Filing Date: 2025-08-11
Form Type: DRS/A
Source: 0000950123-25-007483
Chunk: 179

Company: BillionToOne, Inc.
Filing Date: 2025-08-11
Form: DRS/A
Chunk 179
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 rate * Quantitative Counting Templates ** multiple issued patents QCTs are used in our testing workflow with the following steps:

| (1) |     | Add an aliquot of traceable and specific QCTs into the patient’s blood sample, which contains an unknown number of DNA fragments of interest (m1) among a vast background of the patient’s genome. The 
 precise number of QCT molecules (n1) is also unknown at this stage but is determined in a subsequent step.                                                                                             |

| (2) |     | Amplify the cfDNA fragments of interest using PCR at the same, unknown amplification rate as QCTs. |

| (3) |     | Count the number of clusters of sequences with identifier tags (i.e., the number of distinct QCTs with different diversity regions), which is equal to the number of QCTs that were added to the sample (n1). |

| (4) |     | Determine the amplification multiplier (x) by dividing the number of total sequencing reads that map to QCTs by the number of QCTs (n1). |

| (5) |     | Remove bias by dividing the total number of sequencing reads that map to cfDNA fragments of interest by x to find the absolute number of molecules in the cfDNA sample (m1). |

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We believe that QCTs provide unprecedented insight into disease biology across the human genome. We have developed custom machine learning models to analyze the tiny, individualized variations across hundreds of thousands of patients, uncovering novel patterns of disease biology. These insights are incorporated into our assays to significantly improve performance. We continuously refine our understanding of disease biology to support existing and future diagnostic products. Custom bioinformatics with machine learning Due to the vast amounts of data generated by genetic sequencing, robust bioinformatics are required to analyze the data and identify mutations of interest. Our smNGS platform requires the use of proprietary bioinformatics to further analyze the data generated by the genetic sample and QCTs to accurately quantify the presence of disease. A selection of proprietary bioinformatic tools enabling the single-molecule sensitivity of smNGS includes the following:

| • |     | Our bioinformatic tools implement numerous quality control metrics that enable single molecule sensitivity, for instance, 
 the QCTs’ ability to identify cross-contamination between samples down to a 0.001% level.                                 |

| • |     | Our bioinformatic models enable us to effectively combine signals across thousands of loci,