IMMUNE RECEPTOR-BARCODE ERROR CORRECTION

• US 20190095578A1
• Filed: 09/24/2018
• Published: 03/28/2019
• Est. Priority Date: 09/25/2017
• Status: Active Application

• Current Assignee: Becton, Dickinson & Co
• Original Assignee: Cellular Research, Inc. (Becton, Dickinson & Co)
• Inventors: Shum, Eleen, Fan, Jue, Tsai, Jennifer
• Application Number:
  US16/139,699
• Publication Number:
  US 20190095578A1
• Time in Patent Office: Days
• CPC Class Codes: C12Q 1/6848 characterised by the means for preventing contamination or increasing the specificity or sensitivity of an amplification reaction
• CPC Class Codes: C12Q 1/6869 Methods for sequencing
• CPC Class Codes: C12Q 1/6876 Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
• CPC Class Codes: C12Q 2537/165 Mathematical modelling, e.g. logarithm, ratio
• CPC Class Codes: C12Q 2549/10 the purpose being that of reducing false positive or false negative signals
• CPC Class Codes: C12Q 2563/179 the label being a nucleic acid
• CPC Class Codes: C12Q 2563/185 Nucleic acid dedicated to use as a hidden marker/bar code, e.g. inclusion of nucleic acids to mark art objects or animals
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 30/10 Sequence alignment; Homology search
• 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 50/00 ICT programming tools or database systems specially adapted for bioinformatics

Abstract

Disclosed herein are methods and systems for determining occurrences of targets. In some embodiments, the method comprises: collapsing putative sequences of the target; collapsing molecular label sequences associated with the putative sequences of the target; and estimating the occurrence of the target, wherein the occurrence of the target estimated correlates with the occurrence of molecular label sequences associated with the putative sequences of the target in the sequencing data after collapsing the occurrence of the putative sequences of the target and the occurrence of noise molecular label sequences.

Claims

1. A method for determining occurrences of targets, comprising:

(a) stochastically barcoding a plurality of targets using a plurality of stochastic barcodes to create a plurality of stochastically barcoded targets, wherein each of the plurality of stochastic barcodes comprises a cell label and a molecular label, wherein molecular labels of at least two stochastic barcodes of the plurality of stochastic barcodes comprise different molecular label sequences, and wherein at least two stochastic barcodes of the plurality of stochastic barcodes comprise cell labels with an identical cell label sequence;

(b) obtaining sequencing data of the stochastically barcoded targets; and

(c) for at least one target of the plurality of targets;

(i) identifying putative sequences of the target in the sequencing data;

(ii) counting occurrences of molecular label sequences associated with the putative sequences of the target in the sequencing data identified in (i);

(iii) identifying clusters of the putative sequences of the target;

(iv) collapsing the sequencing data obtained using the clusters of putative sequences of the target identified in (iii);

(v) identifying clusters of the molecular label sequences associated with the putative sequences of the target;

(vi) collapsing the sequencing data using the clusters of molecular label sequences identified in (v);

(vii) identifying clusters of combination sequences, wherein each combination sequence comprises a sequence of the sequences of the target and an associated molecular label sequence of the molecular label sequences;

(viii) collapsing the sequencing data using the clusters of combination sequences identified in (vii);

(ix) identifying one or more putative sequences of the target that correspond to one or more chimeric sequences of the target, wherein occurrences of the one or more putative sequences of the target that correspond to the one or more chimeric sequences of the target are smaller than occurrences of remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target;

(x) removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target identified in (ix) from the sequencing data; and

(xi) estimating the occurrence of the target, wherein the occurrence of the target estimated correlates with the number of molecular label sequences counted in (ii) after collapsing the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target that correspond to the one or more chimeric sequences of the target in (x).

2. The method of claim 1, wherein the plurality of targets comprises targets of the whole transcriptome of a cell.

3. The method of claim 2, wherein the plurality of targets comprises a gene.

4. The method of claim 3, wherein the gene encodes a T-cell Receptor.

5. The method of claim 1, wherein the putative sequences of the target differ from one another by at least one nucleotide.

6. The method of claim 1, wherein identifying the clusters of the putative sequences of the target comprises identifying the clusters of the putative sequences of the target using directional adjacency.

7. The method of claim 6, wherein putative sequences of the target within a cluster are within a first predetermined directional adjacency threshold of one another.

8. The method of claim 7, wherein the first directional adjacency threshold is a Hamming distance of one.

9. The method of claim 7, wherein the putative sequences of the target within the cluster comprise one or more parent sequences and one or more children sequences of the one or more parent sequences, and wherein an occurrence of the parent sequence is greater than or equal to a first predetermined directional adjacency occurrence threshold.

10. The method of claim 9, wherein the first predetermined directional adjacency occurrence threshold is twice an occurrence of a child sequence less one.

11. The method of claim 1, wherein collapsing the sequencing data obtained in (b) using the clusters of putative sequences of the target identified in (iii) comprises:

attributing an occurrence of a child sequence of the one or more children sequences to the parent sequence of the child sequence.

12. The method of claim 1, wherein identifying the clusters of the molecular label sequences associated with the putative sequences of the target comprises identifying the clusters of the molecular label sequences associated with the putative sequences of the target using directional adjacency.

13. The method of claim 12, wherein molecular label sequences of the target within a cluster are within a second predetermined directional adjacency threshold of one another.

14. The method of claim 13, wherein the second directional adjacency threshold is a Hamming distance of one.

15. The method of claim 13, wherein the putative molecular label sequences of the target within the cluster comprise one or more parent molecular label sequences and one or more children molecular label sequences of the one or more parent molecular label sequences, and wherein the occurrence of the parent molecular label sequence is greater than or equal to a second predetermined directional adjacency occurrence threshold.

16. The method of claim 15, wherein the second predetermined directional adjacency occurrence threshold is twice an occurrence of a child molecular label sequence less one.

17. The method of claim 1, wherein collapsing the sequencing data using the clusters of molecular label sequences associated with the sequences of the target identified in (v) comprises:

attributing an occurrence of a child molecular label sequence of the one or more children molecular label sequences to the parent molecular label of the child molecular label sequence.

18. The method of claim 1, wherein identifying the clusters of combination sequences comprises identifying clusters of combination sequences using directional adjacency.

19. The method of claim 18, wherein combination sequences within a cluster are within a third predetermined directional adjacency threshold of one another.

20. The method of claim 19, wherein the third directional adjacency threshold is a Hamming distance of one.

21. The method of claim 19, wherein the combination sequences within the cluster comprise one or more parent combination sequences and one or more children combination sequences of the one or more parent combination sequences, and wherein an occurrence of the parent combination sequence is greater than or equal to a third predetermined directional adjacency occurrence threshold.

22. The method of claim 21, wherein the third predetermined directional adjacency occurrence threshold is twice an occurrence of a child combination sequence less one.

23. The method of claim 1, wherein collapsing the sequencing data using the clusters of combination sequences identified in (vii) comprises:

attributing an occurrence of a child combination sequence of the one or more children combination sequences to the parent combination sequence of the child combination sequence.

24. The method of claim 1, wherein identifying the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target:

identifying putative sequences of the target associated with one molecular label sequence of the plurality of molecular sequences;

identifying a putative sequence of the putative sequences of the target associated with the one molecular label sequence with an occurrence smaller than a chimeric occurrence threshold as corresponding to a chimeric sequence of the one or more chimeric sequences of the target.

25. The method of claim 24, wherein a value of the chimeric occurrence threshold is an occurrence of a putative sequence of the putative sequences of the target associated with the one molecular label sequence that is greater than an occurrence of any other sequence of the putative sequences of the target.

26. The method of claim 1, further comprising:

adjusting the sequencing data after collapsing the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target that correspond to the one or more chimeric sequences of the target in (x).

27. The method of claim 26, wherein adjusting the sequencing data after collapsing the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target that correspond to the one or more chimeric sequences of the target in (x) comprises:

thresholding molecular label sequences associated with the putative sequences of the target to determine signal molecular label sequences and noise molecular label sequences associated with the sequences of the target in the sequencing data counted in (b) after collapsing the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target in (x).

28. The method of claim 27, wherein thresholding the molecular label sequences associated with the putative sequences of the target comprises performing a statistical analysis on the molecular label sequences of the target.

29. The method of claim 28, wherein performing the statistical analysis comprises:

fitting the molecular label sequences associated with the putative sequences of the target and their occurrences to two negative binomial distributions;

determining an occurrence of signal molecular label sequences n using the two negative binomial distributions; and

removing the noise molecular label sequences from the sequencing data obtained in (b) after collapsing the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target that correspond to the one or more chimeric sequences of the target in (x), wherein the noise molecular label sequences comprise molecular label sequences with occurrences lower than an occurrence of the nth most abundant molecular label, and wherein the signal molecular label sequences comprise molecular label sequences with occurrences greater than or equal to the occurrence of the nth most abundant molecular label.

30. The method of claim 29, wherein the two negative binomial distributions comprise a first negative binomial distribution corresponding for the signal molecular label sequences and a second negative binomial distribution for the noise molecular label sequences.