公开(公告)号：US20190087539A1

公开(公告)日：2019-03-21

申请号：US16136463

申请日：2018-09-20

Applicant: LIFE TECHNOLOGIES CORPORATION

Inventor： Rajesh GOTTIMUKKALA ,  Cheng-Zong BAI ,  Dumitru BRINZA ,  Jeoffrey SCHAGEMAN ,  Varun BAGAI

IPC: G06F19/22 ,  C12Q1/6853

Abstract: A method for compressing nucleic acid sequence data wherein each sequence read is associated with a molecular tag sequence, wherein a portion of the sequence reads alignments correspond to sequence reads mapped to a targeted fusion reference sequence includes determining a consensus sequence read for each family of sequence reads based on flow space signal measurements corresponding to the family of sequence reads, determining a consensus sequence alignment for each family of sequence reads, wherein a portion of the consensus sequence alignments correspond to the consensus sequence reads aligned with the targeted fusion reference sequence, generating a compressed data structure comprising consensus compressed data, the consensus compressed data including the consensus sequence read and the consensus sequence alignment for each family, and detecting a fusion using the consensus sequence reads and the consensus sequence alignments from the compressed data structure.

公开(公告)号：US20230360733A1

公开(公告)日：2023-11-09

申请号：US18312663

申请日：2023-05-05

Applicant: Life Technologies Corporation

Inventor： Cheng-Zong BAI ,  Eugene INGERMAN ,  Alison LAI ,  Werner PUSCHITZ ,  Chao WANG

IPC: G16B40/10 ,  G16B30/00

CPC classification number: G16B40/10 ,  G16B30/00

Abstract: A method for correcting signal measurements comprises an artificial neural network (ANN). The ANN receives a plurality of signal measurements in a channel of an input layer. The ANN is applied to the signal measurements and produces a plurality of signal correction values. The signal correction values may be subtracted from the signal measurements to form corrected signal measurements. The corrected signal measurements may be provided to a base caller to produce a sequence of base calls. The ANN may comprise a convolutional neural network (CNN). The CNN may have a U-NET architecture that includes an encoder and a decoder. The U-NET may include a Convolutional Block Attention Module (CBAM). The CBAM may applied to the outputs of a last pooling layer of the encoder and provides refined feature maps to a first layer of the decoder. The input signal measurements may be generated by a nucleic acid sequencing instrument.

公开(公告)号：US20240274241A1

公开(公告)日：2024-08-15

申请号：US18392060

申请日：2023-12-21

Applicant: Life Technologies Corporation

Inventor： Cheng-Zong BAI

IPC: G16B50/50 ,  C12Q1/6869 ,  G16B20/00 ,  G16B20/20 ,  G16B20/40 ,  G16B30/00 ,  G16B30/10 ,  G16B40/00 ,  G16B40/10 ,  G16B50/00 ,  H03M7/30

CPC classification number: G16B50/50 ,  G16B20/00 ,  G16B20/20 ,  G16B20/40 ,  G16B30/00 ,  G16B40/00 ,  G16B40/10 ,  G16B50/00 ,  H03M7/70 ,  C12Q1/6869 ,  G16B30/10

Abstract: A method for compressing molecular tagged sequence data includes: grouping sequence reads associated with a molecular tag sequence to form a family of sequence reads, corresponding vectors of flow space signal measurements and corresponding sequence alignments, calculating an arithmetic mean of the corresponding vectors of flow space signal measurements to form a vector of consensus flow space signal measurements, calculating a standard deviation of the corresponding vectors of flow space signal measurements to form a vector of standard deviations, determining a consensus base sequence based on the vector of consensus flow space signal measurements, determining a consensus sequence alignment and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, the vector of standard deviations and the number of members.