SINGLE CELL TRANSCRIPTOME COMPUTATION AND ANALYSIS METHOD AND SYSTEM INCORPORATING DEEP LEARNING MODEL

WIPO Patent Application WO/2022/188785

A1

Provided are a single cell transcriptome computation and analysis method and system incorporating a deep learning model. Specifically, provided is an polyadenylation site (PAS) detection method and system based on an incorporated deep learning model, as well as a polyadenylation site detection and cell typing analysis method and system (the SCAPTURE method and system) used on single cell sequencing data. In the SCAPTURE system of the present invention, a deep learning model is constructed and incorporated, implementing highly accurate and non-position dependent polyadenylation site prediction, and used for filtering whole genome level high confidence polyadenylation sites; single cell transcriptome sequencing data can also be used to identify, from the beginning, whole genome level polyadenylation sites by means of detecting sequencing read distribution peaks of a whole genome region; also, different transcripts and differential expression thereof of a same gene can be identified on the basis of polyadenylation sites, and thus same is applied to single cell typing and analysis.

YANG LI (CN)
LI GUOWEI (CN)
NAN FANG (CN)
YUAN GUOHUA (CN)

PCT/CN2022/079788

September 15, 2022

March 08, 2022

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SHANGHAI INST OF NUTRITION AND HEALTH CHINESE ACADEMY OF SCIENCES (CN)

G16B5/00

CN111192631A	2020-05-22
CN110322925A	2019-10-11
CN111081311A	2020-04-28
CN110010201A	2019-07-12
CN110910950A	2020-03-24
CN111755071A	2020-10-09
US20200098448A1	2020-03-26

XU&PARTNERS, LLC. (CN)

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