Equalizer-based intensity correction for base calling

Patent metadata

Field	Value
Publication number	US-11694309-B2
Application number	US-202117522864-A
Country	US
Kind code	B2
Filing date	Nov 9, 2021
Priority date	May 5, 2020
Publication date	Jul 4, 2023
Grant date	Jul 4, 2023

Abstract

The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

First claim

What is claimed is:

1. A computer-implemented method of base calling, the computer-implemented method including:
accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters;
selecting, from a bank of lookup tables, a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio;
applying the pixel coefficients to intensity values of the pixels in the image to produce an output; and
base calling the target cluster based on the output.

2. The computer-implemented method of claim 1 , wherein the pixel coefficients are applied to the intensity values using a convolution operation.

3. The computer-implemented method of claim 1 , wherein the pixel coefficients are applied to the intensity values using an interpolation operation.

4. The computer-implemented method of claim 1 , wherein a signal increased in the signal-to-noise ratio is the intensity emissions from the target cluster, and a noise decreased in the signal-to-noise ratio is the intensity emissions from the additional adjacent clusters, plus additional noise sources.

5. The computer-implemented method of claim 1 , wherein the pixels include a center pixel that contains a center of the target cluster, and each pixel in the pixels is divisible into a plurality of subpixels.

6. The computer-implemented method of claim 1 , wherein the selected lookup table is a subpixel lookup table and the bank of lookup tables are subpixel lookup tables.

7. The computer-implemented method of claim 6 , further including:
depending upon a particular subpixel, in a plurality of subpixels of a center pixel, which contains a center of the target cluster, selecting, from the bank of subpixel lookup tables, the subpixel lookup table that corresponds to the particular subpixel, the selected subpixel lookup table containing the pixel coefficients;
element-wise multiplying the pixel coefficients to the intensity values of the pixels in the image and summing products of the element-wise multiplications to produce the output, the pixel coefficients serving as weights and the output being a weighted sum of the intensity values; and
using the output to base call the target cluster, including generating the output for each imaging channel in a plurality of imaging channels and base calling the target cluster using the output for each imaging channel.

8. The computer-implemented method of claim 7 , wherein the element-wise multiplication adds a bias for given set of equalizer coefficients, wherein the bias is a DC offset that averages background noise intensity.

9. The computer-implemented method of claim 7 , further including:
selecting additional subpixel lookup tables, from the bank of subpixel look tables, which correspond to subpixels that are contiguously adjacent to the particular subpixel;
generating, based on pixel coefficients of the selected subpixel lookup table and the selected additional subpixel lookup tables, interpolated pixel coefficients that are configured to increase the signal-to-noise ratio;
convolving the interpolated pixel coefficients with the intensity values of the pixels in the image to produce an output; and
base calling the target cluster based on the output.

10. The computer-implemented method of claim 7 , further including:
element-wise multiplying the interpolated pixel coefficients to the intensity values of the pixels in the image and summing products of the multiplications to produce the output, the interpolated pixel coefficients serving as weights and the output being a weighted sum of the intensity values.

11. The computer-implemented method of claim 1 , further including training an equalizer using at least one of least squares estimation, ordinary least squares, least-mean squares, and recursive least-squares to generate the pixel coefficients.

12. The computer-implemented method of claim 11 , further including training the equalizer in an offline mode in which the pixel coefficients of subpixel lookup tables are fixed after being trained on batches of training data from a previously executed sequencing run.

13. The computer-implemented method of claim 12 , further including training the equalizer in an online mode in which the pixel coefficients of subpixel lookup tables are iteratively updated during an ongoing sequencing run.

14. The computer-implemented method of claim 13 , further including accessing base-wise intensity distributions of each of four bases A, C, G, and T generated during prior base calling of images in the training data, selecting respective centers of the base-wise intensity distributions as base-wise ground truth target intensities for corresponding color channels, and using the base-wise ground truth target intensities to train the equalizer.

15. The computer-implemented method of claim 14 , further including pre-training the equalizer in the offline mode and retraining the equalizer in the online mode.

16. The computer-implemented method of claim 11 , further including generating the lookup tables in the bank of subpixel lookup tables by together applying a single set of equalizer coefficients and a precalculated set of interpolation filters, including interpolating pixel intensities to generate inputs for the equalizer.

17. The computer-implemented method of claim 1 , further including making a center of the target cluster concentric with a center of a center pixel by:
registering the image against a template image and determining affine transformation and nonlinear transformation parameters;
using the affine transformation and nonlinear transformation parameters to transform location coordinates of the target cluster and the additional adjacent clusters to image coordinates of the image and generating a transformed image with transformed pixels; and
applying interpolation using the transformed location coordinates of the target cluster and the additional adjacent clusters to make their respective cluster centers concentric with centers of respective transformed pixels that contain cluster centers.

18. A non-transitory computer readable storage medium impressed with computer program instructions to perform base calling, the computer program instructions, when executed on a processor, implement a method comprising:
accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters;
selecting, from a bank of lookup tables, a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio;
applying the pixel coefficients to intensity values of the pixels in the image to produce an output; and
base calling the target cluster based on the output.

19. The non-transitory computer readable storage medium of claim 18 , wherein the pixel coefficients are applied to the intensity values using a convolution operation.

20. The non-transitory computer readable storage medium of claim 18 , wherein the pixel coefficients are applied to the intensity values using an interpolation operation.

21. A system including one or more processors coupled to memory, the memory loaded with computer instructions to perform base calling, the computer instructions, when executed on the one or more processors, implement actions comprising:
accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters;
selecting from a bank of

Assignees

• Illumina Inc

Inventors

• Ojard Eric Jon
• Mehio Rami
• Parnaby Gavin Derek
• Udpa Nitin
• Vieceli John S

Classifications

• G06T2207/30004

  Biomedical image processing · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T7/0012

  Biomedical image inspection · CPC title

• G06T5/20

  using local operators · CPC title