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.

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.

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.

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.

Abstract: A method for labeling sequence reads includes retrieving a sequence read having an associated flow measurement and an associated flow order; matching a sequence selected from a plurality of sequences with the sequence read, the sequence having a position within the sequence that has more than one acceptable variants; determining which variant of the more than one acceptable variants matches the sequence; generating a predicted flow measurement based on the matched sequence, the variant, and a flow order; and labeling the sequence read and associated flow measurement with the predicted flow measurement.

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.

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.

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.

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.

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.

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.

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.

Abstract: The invention relates to a rake receiver and a method for de-spreading thereof. A plurality of noise branches is adopted for producing a plurality of noise components in the rake receiver. Next, a noise combining unit adjusts each noise component according to a plurality of noise weights, so as to combine the noise components to obtain an interference-plus-noise estimation value. The rake receiver eliminates the noises in the main signal generated by the signal branches through using the interference-plus-noise estimation value. Therefore, the performance of a receiving terminal can be enhanced.