METHODS AND COMPUTER PROGRAM PRODUCTS FOR COMPRESSION OF SEQUENCING DATA

• US 20130231870A1
• Filed: 01/30/2013
• Published: 09/05/2013
• Est. Priority Date: 01/31/2012
• Status: Active Grant

• Current Assignee: Life Technologies Corporation (Thermo Fisher Scientific Incorporated)
• Original Assignee: Life Technologies Corporation (Thermo Fisher Scientific Incorporated)
• Inventors: CAWLEY, Simon, REARICK, Todd, HUBBELL, Earl, SUGNET, Charles, GUPTA, Mohit, MARJANOVIC, Iztok, BEAUCHEMIN, Mark
• Granted Patent: US 9,515,676 B2
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: H03M 7/30 Compression speech analysis-synthesis for redundancy reduction G10L19/00; for image communication H04N; Expansion; Suppression of unnecessary data, e.g. redundancy reduction

Abstract

A compression method includes measuring a waveform associated with a chemical event occurring on a sensor array, wherein the waveform comprises at least one region associated with expected measured values and at least one region associated with unpredictable measured values; applying a first compression process to the waveform, the first compression process including an averaging of one or more frames in one or more portions of the waveform; and applying a second compression process to the waveform, the second compression process including a truncating of data corresponding to a portion of the waveform that is not related to a nucleotide incorporation component of the waveform.

Claims

1. A compression method, comprising:

measuring a waveform associated with a chemical event occurring on a sensor array, wherein the waveform comprises at least one region associated with expected measured values and at least one region associated with unpredictable measured values;

applying a first compression process to the waveform, the first compression process including an averaging of one or more frames in one or more portions of the waveform; and

applying a second compression process to the waveform, the second compression process including a truncating of data corresponding to a portion of the waveform that is not related to a nucleotide incorporation component of the waveform.

2. The method of claim 1, wherein the truncating of data comprises determining, for each of a plurality of sensors in the sensor array, a cut-off time point for the waveform for that sensor defining a data range to be truncated.

3. The method of claim 2, wherein each cut-off time point is determined by mining a plurality of past analysis runs for a given sensor array geometry.

4. The method of claim 2, wherein each cut-off time point is determined prior to every run or during a calibration procedure.

5. The method of claim 2, wherein each cut-off time point is factory pre-determined for a given sensor array geometry.

6. The method of claim 2, wherein the sensors are arranged in a plurality of regions in the sensor array, and wherein each cut-off time point for sensors in a given region is determined to have a common cut-off time point determined for sensors for that region.

7. The method of claim 6, wherein each common cut-off time point is determined by finding a best fit to a linear hinge model on a median trace for a region.

8. The method of claim 6, wherein each common cut-off time point is determined empirically and depends on a position of that region relative to other regions along a fluidic flow of nucleotides onto the sensor array.

9. The method of claim 8, wherein the cut-off time point of sensors in a region substantially near a fluidic inlet is different from the cut-off time point of sensors in a region substantially near a fluidic outlet.

10. The method of claim 1, wherein the averaging comprises:

applying a first frame averaging to the at least one portion associated with the expected measured values, wherein the first frame averaging comprises averaging a first number of frames during the at least one portion associated with the expected measured values; and

applying a second frame averaging to the at least one portion associated with the unpredictable measured values, wherein the second frame averaging comprises averaging a second number of frames during the at least one portion associated with the unpredictable measured values, the second number of frames being lower than the first number of frames.

11. The method of claim 10, further comprising:

applying a third frame averaging to a second portion of the characteristic waveform, wherein the second portion is associated with another portion of the waveform with expected measured values, and wherein the third frame averaging comprises averaging a third number of frames during the second portion, the third number of frames being higher than the second number of frames; and

applying a keyframe delta compression to results from the first, second, and third frame averaging, wherein the keyframe delta compression comprises comparing a current frame of data from frame-averaged data to a previous frame of frame-averaged data associated with the waveform.

12. The method of claim 1, wherein the measuring the waveform comprises measuring the waveform of a dynamic response of an ion-sensitive field effect transistor (ISFET) array to a change in ionic strength of an analyte solution in fluid contact with the ISFET array.

13. The method of claim 12, wherein the measuring the waveform of the dynamic response of the ISFET array comprises associating the at least one portion with the unpredictable measured values to a stepwise increase in ion concentration in the analyte solution and associating the at least one portion with the expected measured values to at least one portion of the dynamic response outside of the stepwise increase in ion concentration.

14. The method of claim 13, wherein the applying the first frame averaging comprises averaging the first number of frames during the at least one portion of the dynamic response outside of the stepwise increase in ion concentration.

15. The method of claim 13, wherein the applying the second frame averaging comprises averaging the second number of frames during the stepwise increase in ion concentration in the analyte solution.

16. A computer program product comprising a computer-usable medium having computer program logic recorded thereon that, when executed by one or more processors, samples and compresses data from a sensor array, the computer program logic comprising:

first computer readable program code that enables a processor to measure a waveform associated with a chemical event occurring on a sensor array, wherein the waveform comprises at least one portion associated with expected measured values and at least one portion associated with unpredictable measured values;

second computer readable program code that enables a processor to apply a first compression process to the waveform, the first compression process including an averaging of one or more frames in one or more portions of the waveform; and

third computer readable program code that enables a processor to apply a second compression process to the waveform, the second compression process including a truncating of data corresponding to a portion of the waveform that is not related to a nucleotide incorporation component of the waveform.

17. A method for compressing nucleic acid sequencing data, comprising:

obtaining raw data from a semiconductor-based sequencing sensor array comprising a plurality of sensors during a data acquisition time period, the raw data comprising at least a non-informative portion corresponding to a subinterval of the data acquisition time period having a location within the data acquisition time period that varies for different sensors according to a position of the sensor in the sensor array; and

transforming the raw data into compressed data using both a lossless compression process including a keyframe delta compression process and lossy compression processes including a variable frame averaging process and a data truncation process, the data truncation process being related for each sensor to the position of the sensor in the sensor array and configured to discard the non-informative portion of the raw data.

18. The method of claim 17, wherein the data truncation process comprises determining, for each of the plurality of sensors in the sensor array, a cut-off time point for that sensor defining the non-informative portion of the raw data to be truncated.

19. The method of claim 18, wherein each cut-off time point is determined by mining a plurality of past analysis runs for a given sensor array geometry.

20. The method of claim 18, wherein the plurality of sensors in the sensor array are arranged in a plurality of regions in the sensor array, and wherein each cut-off time point for sensors in a given region is determined to have a common cut-off time point determined for sensors for that region.