公开(公告)号：US20240287505A1

公开(公告)日：2024-08-29

申请号：US18567697

申请日：2022-06-23

申请人： Illumina, Inc.

发明人： Andrea Manzo ,  Colin Brown ,  Steven Norberg ,  Timothy Harrington

IPC分类号： C12N15/10 ,  C12Q1/6834

CPC分类号： C12N15/1065 ,  C12Q1/6834

摘要： Some embodiments relate to methods and compositions for preparing combinatorially indexed beads. Some embodiments include sequential addition of different indexes to polynucleotides attached to beads. In some embodiments, indexes are added by chemical ligation, polymerase extension, ligation of partially double-stranded adaptors, or short splint ligation.

公开(公告)号：US20240287583A1

公开(公告)日：2024-08-29

申请号：US18571069

申请日：2023-04-06

申请人： ILLUMINA, INC.

发明人： Andrew Slatter ,  Carlo Randise-Hinchliff ,  Andrew Price ,  Niall Anthony Gormley ,  Andrea Manzo ,  Nithya Subramanian ,  Fiona Kaper ,  David Jones ,  Steven Norberg

IPC分类号： C12Q1/682 ,  C12Q1/6839

CPC分类号： C12Q1/682 ,  C12Q1/6839

摘要： Aptamer detection techniques with dynamic range compression are described that permit removal of a portion of more abundant aptamers in an aptamer-based assay. In an embodiment, a mixture of tagged probes and dummy probes can be used such that the dummy probes bind abundant aptamers and in turn are not captured or amplified for detection in downstream steps. Other techniques are also contemplated, including targeted removal of or cleavage of probes that bind to excess aptamers.

公开(公告)号：US20230313271A1

公开(公告)日：2023-10-05

申请号：US18172821

申请日：2023-02-22

申请人： Illumina, Inc. ,  Illumina Cambridge Limited

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

IPC分类号： C12Q1/6809 ,  G16C20/70

CPC分类号： C12Q1/6809 ,  G16C20/70

摘要： 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.