Copy number variant caller
Direct targeted sequencing (DTS) methods and a hidden Markov model (HMM) can be used to call the copy number of a segment of interest within a region of interest. Described herein are methods for calling a copy number variant or a copy number variant abnormality using an HMM, and methods for determining a copy number based on a copy number likelihood model, in a test sequencing library that has be sequenced using DTS methods. Also described herein are methods for determining a copy number of a segment, including accounting for spurious capture probes that may arise from the DTS methods.
1. A method for determining a copy number of an interrogated segment of nucleic acids within a region of interest of a genome, comprising:
(a) mapping a plurality of sequencing reads of nucleic acids generated from a test sequencing library to the interrogated segment;
(b) determining a number of sequencing reads mapped to the interrogated segment;
(c) determining a copy number likelihood model using a plurality of likelihood distributions associated with an expected number of sequencing reads mapped to the interrogated segment;
(d) building a hidden Markov model comprising:
(i) one or more hidden states comprising a copy number corresponding to the interrogated segment or a plurality of sub-segments within the interrogated segment,
(ii) an observation state comprising the number of sequencing reads mapped to the interrogated segment; and
(iii) the copy number likelihood model;
(e) parameterizing the hidden Markov model by adjusting the copy number likelihood model to fit the determined number of sequencing reads mapped to the interrogated segment by allowing portions of the likelihood distributions to float; and
(f) determining a most probable copy number of the interrogated segment by optimizing the parameterized hidden Markov model.
2. The method of claim 1 , wherein:
step (a) comprises mapping the plurality of sequencing reads generated from the test sequencing library to a plurality of spatially adjacent segments, wherein the plurality of spatially adjacent segments comprises the interrogated segment;
step (b) comprises determining a number of sequencing reads mapped to each spatially adjacent segment;
step (c) comprises determining a copy number likelihood model for each spatially adjacent segment using a plurality of likelihood distributions associated with an expected number of mapped sequencing reads at the spatially adjacent segment;
step (d) comprises building the hidden Markov model, wherein the hidden Markov model comprises comprising:
(i) a plurality of hidden states comprising a copy number for each of the spatially adjacent segments or a plurality of sub-segments within each of the spatially adjacent segments,
(ii) a plurality of observation states comprising the number of sequencing reads mapped to each spatially adjacent segment, and
(iii) the copy number likelihood model for each spatially adjacent segment; and
step (e) comprises parameterizing the hidden Markov model comprising adjusting each copy number likelihood model to fit the determined number of sequencing reads mapped to each spatially adjacent segment by allowing portions of the likelihood distributions to float.
3. The method of claim 1 , further comprising determining a most probable copy number of a section within the region of interest, wherein the section comprises a plurality of spatially adjacent segments comprising the interrogated segment.
4. The method of claim 1 , wherein the copy number likelihood model comprises a distribution for two or more copy number states.
5. The method of claim 1 , wherein the copy number likelihood model comprises a negative binomial distribution, wherein the negative binomial distribution is not a Poisson distribution.
6. The method of claim 1 , wherein the expected number of sequencing reads is based on an average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries and an average number of mapped sequencing reads across a plurality of segments of interest within the test sequencing library, wherein the average number of mapped sequencing reads at a corresponding segment across a plurality of sequencing libraries or the average number of mapped sequencing reads across a plurality of segments of interest within the test sequencing library is a normalized average.
7. The method of claim 1 , wherein the copy number likelihood model is adjusted to account for the presence of GC content bias.
8. The method of claim 7 , wherein adjustment of the copy number likelihood model depends on the GC content of the capture probe corresponding to the interrogated segment or the GC content of the interrogated segment.
9. The method of claim 1 , wherein the hidden Markov model comprises a transition probability of the copy number of the interrogated segment for a given copy number of a spatially adjacent segment.
10. The method of claim 1 , wherein the hidden Markov model comprises a plurality of transition probabilities of the copy number of a sub-segment in the plurality of sub-segments within the interrogated segment for a given copy number of a spatially adjacent sub-segment.
11. The method of claim 9 , wherein the transition probability accounts for an average length of a copy number variant.
12. The method of claim 9 , wherein the transition probability accounts for a prior probability of a copy number variant at the interrogated segment or a spatially adjacent segment.
13. The method of claim 11 , wherein the average length of a copy number variant or the probability of a copy number variant at the interrogated segment are determined based on observations in a human population.
14. The method of claim 1 , wherein parameterizing the hidden Markov model comprises accounting for one or more spurious capture probes.
15. The method of claim 14 , wherein accounting for one or more spurious capture probes comprises weighting the one or more observation states in the plurality of observation states with a spurious capture probe indicator.
16. The method of claim 15 , wherein the spurious capture probe indicator comprises a Bernoulli distribution of a prior observation state in the hidden Markov model.
17. The method of claim 15 , wherein accounting for one or more of the capture probes being spurious comprises optimizing the parameterized hidden Markov model using an expectation-maximization step.
18. The method of claim 14 , wherein if a capture probe is determined to be spurious, the likelihood information from that capture probe is disregarded in the copy number likelihood model.
19. The method of claim 1 , wherein the parameterizing of the hidden Markov model comprises accounting for noise in the number of mapped sequencing reads.
20. The method of claim 19 , wherein accounting for noise in the number of mapped sequencing reads comprises adjusting the copy number likelihood model by adjusting a dispersion of a copy number likelihood distribution in the copy number likelihood model.
21. The method of claim 20 , wherein adjusting the copy number likelihood model to account for the noise comprises adjusting the dispersion of the copy number likelihood distribution in the copy number likelihood model via an expectation-maximization step.
22. The method of claim 21 wherein the expectation-maximization step comprises weighing a level of noise in the number of mapped sequencing reads from the test sequencing library.
23. The method of claim 22 , wherein the expectation-maximization step comprises using a Quasi-Newtonian solver.
24. The method of claim 19 , wherein the most probable copy number of the interrogated segment is not called if the noise in the number of mapped sequencing reads is above a predetermined threshold.
25. The method of claim 1 , wherein sequencing reads from overlapping capture probes are merged.
26. The method of claim 1 , wherein a Viterbi algorithm is used to determine the most probable copy number of the interrogated segment.
27. The method of claim 1 , further comprising determining a confidence of the most probable copy number of the segment.