Models for targeted sequencing of rna

Patent metadata

Field	Value
Publication number	US-2020105375-A1
Application number	US-201916584936-A
Country	US
Kind code	A1
Filing date	Sep 26, 2019
Priority date	Sep 28, 2018
Publication date	Apr 2, 2020
Grant date	—

Abstract

Systems and methods for processing sequencing data of ribonucleic acid (RNA) molecules from a test sample include obtaining a plurality of sequence reads each derived from a RNA molecule obtained from the test sample, filtering the plurality of sequence reads, identifying one or more candidate variants from the filtered plurality of sequence reads, determining a quality score for each of the identified one or more candidate variants, the quality score indicating a likelihood that the candidate variant is a false positive detection of a mutation in the RNA molecule, and outputting the one or more candidate variants having a quality score greater than a threshold quality score.

First claim

What is claimed is:

1 . A method for processing sequencing data of ribonucleic acid (RNA) molecules from a test sample, the method comprising:
obtaining a plurality of sequence reads each derived from a RNA molecule obtained from the test sample; filtering the plurality of sequence reads; identifying one or more candidate variants from the filtered plurality of sequence reads; determining a quality score for each of the identified one or more candidate variants, the quality score indicating a likelihood that the candidate variant is a false positive detection of a mutation in the RNA molecule; and outputting the one or more candidate variants having a quality score greater than a threshold quality score.

2 . The method of claim 1 , wherein obtaining the plurality of sequence reads comprises:
obtaining the test sample from an individual, the test sample comprising a plurality of RNA molecules; preparing a RNA sequencing library from the plurality of RNA molecules; and generating the plurality of sequence reads from the RNA sequencing library.

3 . The method of claim 2 , wherein the sequencing library is enriched for one or more targeted RNA molecules prior to obtaining the plurality of sequence reads.

4 . The method of claim 2 , wherein the plurality of sequence reads are obtained using next-generation sequencing of the RNA sequencing library.

5 . The method of claim 2 , wherein the plurality of RNA molecules are RNA transcripts, wherein the RNA transcripts are messenger RNA, transfer RNA, or ribosomal RNA.

6 . The method of claim 1 , wherein filtering the plurality of sequence reads comprises:
filtering at least one sequence read of the plurality of sequence reads having a least a threshold number of continuous nucleotide base mutations; filtering at least one sequence read of the plurality of sequence reads having at least a threshold depth; and/or filtering out a number of leading nucleotide bases of at least one sequence read of the plurality of sequence reads.

7 . The method of claim 6 , wherein the threshold number of continuous nucleotide base mutations is at least three, the threshold depth is at least 50,000, or the number of leading nucleotide bases is six.

8 . The method of claim 1 , wherein the threshold quality score is determined by performing calibration using a plurality of calibration samples, each calibration sample including one or more control RNA molecules and a plurality of RNA molecules from one or more individuals.

9 . The method of claim 8 , wherein the one or more control RNA molecules are associated with External RNA Controls Consortium (ERCC) Spike-In Control Mixes, and wherein the one or more individuals are healthy.

10 . The method of claim 8 , wherein performing the calibration using calibration samples comprises:
for each of the plurality of calibration samples:
determining a depth of the calibration sample; and
determining a sensitivity of the calibration sample, the sensitivity indicating a likelihood of detecting false positive mutations in the calibration sample.

11 . The method of claim 1 , wherein determining the quality score for a candidate variant comprises:
accessing a plurality of parameters including a dispersion parameter r and a mean rate parameter m specific to the candidate variant, the r and m having been derived using a model; inputting read information of the plurality of sequence reads into a function parameterized by the plurality of parameters; and determining the quality score for the candidate variant using an output of the function based on the input read information.

12 . The method of claim 11 , wherein the plurality of parameters represent mean and shape parameters of a gamma distribution, and wherein the function is a negative binomial based on the plurality of sequence reads and the plurality of parameters.

13 . The method of claim 11 , wherein the plurality of parameters represent parameters of a distribution that encodes an uncertainty level of nucleotide mutations with respect to a given position of a sequence read.

14 . The method of claim 13 , wherein a gamma distribution is one component of a mixture of the distribution.

15 . The method of claim 11 , wherein the plurality of parameters are derived from a training sample of sequence reads from a plurality of healthy individuals.

16 . The method of claim 15 , wherein the training sample excludes a subset of the sequence reads from the plurality of healthy individuals based on filtering criteria when the sequence reads that have (i) a depth less than a threshold value or (ii) an allele frequency greater than a threshold frequency.

17 . The method of claim 11 , wherein the plurality of parameters are derived using a Bayesian Hierarchical model.

18 . The method of claim 17 , wherein the Bayesian Hierarchical model includes a multinomial distribution grouping positions of sequence reads into latent classes.

19 . The method of claim 17 , wherein the Bayesian Hierarchical model includes fixed covariates unrelated to training samples from healthy individuals, wherein the covariates are based on a plurality of nucleotides adjacent to a given position of a sequence read, or wherein the covariates are based on a level of uniqueness of a given sequence read relative to a target region of a genome.

20 . The method of claim 17 , wherein the Bayesian Hierarchical model is estimated using a Markov chain Monte Carlo method.

21 . The method of claim 20 , wherein the Markov chain Monte Carlo method uses a Metropolis-Hastings algorithm, a Gibbs sampling algorithm, or Hamiltonian mechanics.

22 . The method of claim 11 , wherein the sequence read information includes a depth d of the plurality of sequence reads, the function parameterized by m·d.

23 . The method of claim 11 , wherein the quality score is a Phred-scaled likelihood.

24 . The method of claim 11 , further comprising:
determining that the candidate variant is a false positive mutation by comparing the quality score to a threshold quality score.

25 . The method of claim 24 , wherein the candidate variant is a single nucleotide variant.

26 . The method of claim 25 , wherein the model encodes noise levels of nucleotide mutations for one base of A, U, C, and G to each of the other three bases.

27 . The method of claim 11 , wherein the candidate variant is an insertion or deletion of at least one nucleotide.

28 . The method of claim 27 , wherein the model includes a distribution of lengths of insertions or deletions.

29 . The method of claim 28 , wherein the model separates an inference for determining a likelihood of an alternate allele from an inference for determining a length of the alternate allele using the distribution of lengths.

30 . The method of claim 28 , wherein the distribution of lengths comprises a multinomial with Dirichlet prior, wherein the Dirichlet prior on the multinomial distribution of lengths is determined by covariates of anchor positions of a genome.

31 . The method of claim 27 , wherein the model includes a distribution ω determined based on covariates.

32 . The method of claim 27 , wherein the model includes a distribution φ determined based on covariates and anchor positions of a genome.

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Assignees

• Grail Inc

Inventors

• Pan Wenying
• Kim Hyunsung John
• Larson Matthew H
• Blocker Alexander W
• Hubbell Earl
• Jamshidi Arash

Classifications

• G16B40/20

  Supervised data analysis · CPC title

• G16B30/10 Primary

  Sequence alignment; Homology search · CPC title

• G16B20/20 Primary

  Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection · CPC title

• G16B40/00

  ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title

• G16B30/20

  Sequence assembly · CPC title