The Perkin-elmer Corporation

Pacific Biosciences of California, Inc.

James N Labrenz

Scott Edward Helgesen

Mark T Lakata

Marco Milletti

Scott E Helgesen

Exemplary embodiments for a multiprocessor pipeline architecture that converts signals from sequencing sample acquisition into sequence data, comprising: a custom coprocessor card configured to directly receive a stream of serialized sensor data generated by an image sensor, wherein the sensor data represents frame-by-frame intensity values for pixels comprising the image sensor, wherein the image sensor captures images of light emitted from a plurality of reaction cells of a removable integrated sequencing chip; a first coprocessor that continually receives the stream of serialized sensor data and transposes the frame-by-frame intensity values into reaction cell chunks, each of the reaction cell chunks representing movie data of the pixel intensity values of a corresponding reaction cell across the frames over a predetermined time window; a buffer that repeatedly receives the reaction cell chunks and stores in contiguous memory locations the reaction cell chunks for each respective reaction cell over a larger predetermined time window to create larger reaction cell chunks; and a plurality of second coprocessors that retrieve the larger reaction cell chunks from the buffer and convert, in parallel, the pixel intensity values into base-by-base sequence data even as additional reaction cell chunks are received by the buffer, such that the second coprocessors begin raw base calling before all the sensor data for the sequencing sample acquisition is obtained. Aspects of the invention include methods for base calling using single instruction multiple data vector processing units.

Multiprocessor pipeline architecture

A system performs two types of spectral calibration. For the first type, the system collects data from monitoring an analytical separation of several spectrally-distinguishable molecular species and creates a data matrix from the collected data. Each element in the data matrix represents a signal intensity at a particular time and over a particular range of light wavelengths. The system then identifies regions in the data matrix that have spectral response characteristics of at least one of the molecular species, determines a set of pure component spectral responses from the identified regions, groups similar pure component spectral responses into clusters, determines a representative spectral response for each cluster, and correlates the representative spectral response for each cluster with one of the molecular species. For the second type of calibration, the system collects data from monitoring an analytical separation of multiple spectrally-distinguishable molecular species and creates a data matrix from the collected data. Each element in the data matrix represents a signal intensity at a particular time and over a particular range of light wavelengths. The system then identifies binary mixture regions within the data matrix, determines pairs of the binary mixture regions that contain one common spectral response, identifies a component that represents the one common spectral response using data in the determined pairs, groups the one common spectral response from each of the determined pairs into clusters, determines a representative spectral response for each cluster, and correlates the representative spectral response for each cluster with one of the molecular species.

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San Francisco, CA, United States of America

James N Labrenz