MODELS FOR TARGETED SEQUENCING OF RNA
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.
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.
33 . The method of claim 27 , wherein the model includes a multinomial distribution grouping lengths of insertions or deletions at anchor positions of sequence reads into latent classes.
34 . The method of claim 27 , wherein an expected mean total count of insertions or deletions at a given anchor position is modeled by a distribution based on covariates and anchor positions of a genome.
35 . The method of claim 1 , wherein the plurality of sequence reads are obtained from sequencing RNA molecules from a sample of blood, whole blood, plasma, serum, urine, cerebrospinal fluid, fecal, saliva, tears, a tissue biopsy, pleural fluid, pericardial fluid, or peritoneal fluid of an individual.
36 . The method of claim 1 , wherein the plurality of sequence reads are obtained from sequencing RNA molecules from a tumor biopsy.
37 . The method of claim 1 , wherein the plurality of sequence reads are obtained from sequencing a RNA molecules from an isolate of cells from blood, the isolate of cells including at least buffy coat white blood cells or CD4+ cells.
38 . A system comprising a computer processor and a memory, the memory storing computer program instructions that when executed by the computer processor cause the processor to:
obtain a plurality of sequence reads each derived from a RNA molecule obtained from the test sample;
filter the plurality of sequence reads;
identify one or more candidate variants from the filtered plurality of sequence reads;
determine 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
output the one or more candidate variants having a quality score greater than a threshold quality score.
39 . A non-transitory computer-readable storage medium storing instructions that when executed by a processor cause the processor to:
obtain a plurality of sequence reads each derived from a RNA molecule obtained from the test sample;
filter the plurality of sequence reads;
identify one or more candidate variants from the filtered plurality of sequence reads;
determine 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
output the one or more candidate variants having a quality score greater than a threshold quality score.