Invention Grant
- Patent Title: Systems and methods for de novo peptide sequencing from data-independent acquisition using deep learning
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Application No.: US16226575Application Date: 2018-12-19
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Publication No.: US11694769B2Publication Date: 2023-07-04
- Inventor: Baozhen Shan , Ngoc Hieu Tran , Ming Li , Lei Xin , Rui Qiao , Xin Chen , Chuyi Liu
- Applicant: BIOINFORMATICS SOLUTIONS INC.
- Applicant Address: CA Waterloo
- Assignee: BIOINFORMATICS SOLUTIONS INC.
- Current Assignee: BIOINFORMATICS SOLUTIONS INC.
- Current Assignee Address: CA Waterloo
- Agency: Norton Rose Fulbright Canada LLP
- Main IPC: G01N33/48
- IPC: G01N33/48 ; G01N33/50 ; G16B40/10 ; H01J49/00 ; G01N33/68 ; G16B30/20 ; G16B40/20 ; G06N3/02

Abstract:
The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data, and in particular mass spectrometry data obtained by data-independent acquisition. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. To sequence peptides from mass spectrometry data obtained by data-independent acquisition, precursor profiles representing intensities of one or more precursor ion signals associated with a precursor retention time and fragment ion spectra representing signals from fragment ions and fragment retention times are fed into a neural network.
Public/Granted literature
- US20190147983A1 SYSTEMS AND METHODS FOR DE NOVO PEPTIDE SEQUENCING FROM DATA-INDEPENDENT ACQUISITION USING DEEP LEARNING Public/Granted day:2019-05-16
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