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公开(公告)号：US11694769B2
公开(公告)日：2023-07-04
申请号：US16226575
申请日：2018-12-19
发明人： Baozhen Shan , Ngoc Hieu Tran , Ming Li , Lei Xin , Rui Qiao , Xin Chen , Chuyi Liu
IPC分类号： G01N33/48 , G01N33/50 , G16B40/10 , H01J49/00 , G01N33/68 , G16B30/20 , G16B40/20 , G06N3/02
CPC分类号： G16B40/10 , G01N33/6818 , G01N33/6848 , G06N3/02 , G16B30/20 , G16B40/20 , H01J49/0036
摘要： 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.
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公开(公告)号：US11573239B2
公开(公告)日：2023-02-07
申请号：US16037949
申请日：2018-07-17
发明人： Baozhen Shan , Ngoc Hieu Tran , Ming Li , Lei Xin , Xianglilan Zhang
IPC分类号： G01N33/48 , G01N33/50 , G01N33/68 , G06F17/16 , G16B20/00 , G16B40/00 , G16B50/00 , G16B30/20 , G16B40/10 , G16B40/20 , G16B50/20 , G16B50/10
摘要： The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data. 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. The present systems and methods are re-trainable to adapt to new sources of data and provides a complete end-to-end training and prediction solution, which is advantageous given the growing massive amount of data. The systems and methods combine deep learning and dynamic programming to solve optimization problems.
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3.发明申请公开(公告)号：US20200326348A1
公开(公告)日：2020-10-15
申请号：US16846817
申请日：2020-04-13
发明人： Rui QIAO , Ngoc Hieu Tran , Lei XIN , Xin CHEN , Baozhen Shan , Ali GHODSI , Ming LI
摘要： 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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4.发明授权公开(公告)号：US11644470B2
公开(公告)日：2023-05-09
申请号：US16846817
申请日：2020-04-13
发明人： Rui Qiao , Ngoc Hieu Tran , Lei Xin , Xin Chen , Baozhen Shan , Ali Ghodsi , Ming Li
CPC分类号： G01N33/6848 , G16B20/00 , G16B30/00 , G16B40/00
摘要： 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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公开(公告)号：US20200243164A1
公开(公告)日：2020-07-30
申请号：US16775947
申请日：2020-01-29
发明人： Rui Qiao , Ngoc Hieu Tran , Lei Xin , Xin Chen , Baozhen Shan , Ming Li
摘要： The present systems and workflows identify neoantigens for cancer immunotherapy by introducing deep learning to de novo peptide sequencing from tandem mass spectrometry data. The systems and workflow allows for patient specific identification of neoantigens for personalized immunotherapy.
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公开(公告)号：US10309968B2
公开(公告)日：2019-06-04
申请号：US15599431
申请日：2017-05-18
发明人： Ngoc Hieu Tran , Mohammad Ziaur Rahman , Lin He , Lei Xin , Baozhen Shan , Ming Li
摘要： 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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7.发明申请公开(公告)号：US20190147983A1
公开(公告)日：2019-05-16
申请号：US16226575
申请日：2018-12-19
发明人： Baozhen Shan , Ngoc Hieu Tran , Ming Li , Lei Xin , Rui Qiao , Xin Chen , Chuyi Liu
摘要： 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.
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公开(公告)号：US20190018019A1
公开(公告)日：2019-01-17
申请号：US16037949
申请日：2018-07-17
发明人： Baozhen Shan , Ngoc Hieu Tran , Ming Li , Lei Xin , Xianglilan Zhang
摘要： The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data. 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. The present systems and methods are re-trainable to adapt to new sources of data and provides a complete end-to-end training and prediction solution, which is advantageous given the growing massive amount of data. The systems and methods combine deep learning and dynamic programming to solve optimization problems.
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