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公开(公告)号:US20170336419A1
公开(公告)日:2017-11-23
申请号:US15599431
申请日:2017-05-18
Applicant: BIOINFORMATICS SOLUTIONS INC.
Inventor: Ngoc Hieu TRAN , Mohammad Ziaur RAHMAN , Lin HE , Lei XIN , Baozhen SHAN , Ming LI
CPC classification number: G01N33/6818 , G01N33/6848 , G01N33/6854 , G01N2560/00 , G06F19/22 , G06F19/24 , G06F19/26
Abstract: Methods and systems for determining amino acid sequence of a polypeptide or protein from mass spectrometry data is provided, using a weighted de Bruijn graph. Extracted and purified protein is cleaved into a mixture of peptide and then analyzed using mass spectrometry. A list of peptide sequences is derived from mass spectrometry fragment data by de novo sequencing, and amino acid confidence scores are determined from peak fragment ion intensity. A weighted de Bruijn graph is constructed for the list of peptide sequences having node weights defined by k−1 mer confidence scores. At least one contig is assembled from the de Bruijn graph by identifying node weights having the highest k-1 mer confidence scores.
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2.
公开(公告)号:US20200326348A1
公开(公告)日:2020-10-15
申请号:US16846817
申请日:2020-04-13
Applicant: BIOINFORMATICS SOLUTIONS INC.
Inventor: Rui QIAO , Ngoc Hieu Tran , Lei XIN , Xin CHEN , Baozhen Shan , Ali GHODSI , Ming LI
Abstract: The present systems and methods are directed to de novo identification of peptide sequences from tandem mass spectrometry data. The systems and methods uses unconverted mass spectrometry data from which features are extracted. Using unconverted mass spectrometry data reduces the loss of information and provides more accurate sequencing of peptides. The systems and methods combine deep learning and neural networks to sequencing of peptides.
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