公开(公告)号：US20240182881A1

公开(公告)日：2024-06-06

申请号：US18538262

申请日：2023-12-13

Applicant: ILLUMINA, INC.

Inventor： Dewei Joel Toh ,  Leslie Yee Ming Beh ,  Shu Ting Tan ,  Anna Traczyk ,  Saurabh Nirantar ,  Eric Brustad ,  Hamed Tabatabaei Ghomi ,  Zahra Fahmi ,  Lekha Ravichandraprabhu ,  Colin Brown ,  Kayla Busby ,  Stephen Gross ,  Rebekah Karadeema ,  Huy Lam ,  Pascale Mathonet ,  Sarah E. Shultzaberger ,  Kathleen Tzeng ,  Allison Kathleen Yunghans

IPC: C12N9/78

CPC classification number: C12N9/78 ,  C12Y305/04005

Abstract: The present disclosure is concerned with proteins, methods, compositions, and kits for mapping of methylation status of nucleic acids, including 5-methylcytosine and 5-hydroxymethyl cytosine (5 hmC). In one embodiment, proteins are provided that selectively act on certain modified cytosines of target nucleic acids and converts them to thymidine or modified thymidine analogues. In another embodiment, proteins are provided that selectively act on certain modified cytosines of target nucleic acids and converts them to uracil or thymidine and selectively do not act on other certain modified cytosines of target nucleic acids. Also provided are compositions and kits that include one or more of the proteins and methods for using one or more of the proteins.

公开(公告)号：US20230313271A1

公开(公告)日：2023-10-05

申请号：US18172821

申请日：2023-02-22

Applicant: Illumina, Inc. ,  Illumina Cambridge Limited

Inventor： Steven Norberg ,  Luis Fernando Camarillo Guerrero ,  Colin Brown ,  Andrea Manzo ,  Sarah E. Shultzaberger ,  Michael Eberle ,  Sepideh Almasi ,  Suzanne Rohrback ,  Pascale Mathonet ,  Egor Dolzhenko

IPC: C12Q1/6809 ,  G16C20/70

CPC classification number: C12Q1/6809 ,  G16C20/70

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning to determine factors or scores indicating an error level with which a given methylation assay detects methylation of cytosine bases. For instance, the disclosed systems use a machine-learning model to generate a bias score indicating a degree to which a given methylation assay errs in detecting cytosine methylation when specific sequence contexts surround such cytosines compared to other sequence contexts. The machine-learning model may take various forms of models, including a decision-tree model, a neural network, or a combination of a decision-tree model and a neural network. In some cases, the disclosed system combines or uses bias scores from multiple machine-learning models to generate a consensus bias score.