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Algorithms for sequence determination

a sequence determination and algorithm technology, applied in the field can solve the problems that the technology and method of biomolecule sequence determination do not always produce perfect sequence data, and achieve the effect of improving the accuracy of consensus biomolecule sequence determination and improving the validation of sequence reads

Inactive Publication Date: 2013-08-22
PACIFIC BIOSCIENCES
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present invention provides powerful and flexible methods and systems for accurately determining the sequence of biomolecules from replicate sequence data. This helps improve validity of sequence reads and reduce errors, allowing for a higher accuracy of consensus biomolecule sequence determination. The invention uses algorithms to integrate data from multiple sequencing reads to create a final consensus sequence, outperforming any one-pass analysis system.

Problems solved by technology

Technologies and methods for biomolecule sequence determination do not always produce sequence data that is perfect.

Method used

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Examples

Experimental program
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example i

VII. Example I

Modeling Errors in a Sequencing-by-Synthesis Experiment with Hidden Markov Models

[0103]Assessing the performance of a single-molecule, real-time sequencing system relies on comparisons between the sequence of a known template and the sequence emitted by an experiment. A hidden Markov model (HMM) was constructed to analyze the accuracy of sequencing-by-synthesis experiments and provide accurate estimates of system parameters, and this model is described in detail below.

Mapping Between Sequencing Procedures and Hidden Markov Models

[0104]The hidden Markov model used employs a very simple underlying state structure that models the progression of the polymerase along its template. At each time step the molecule can move forward to the next template location or stay at the current template location (FIG. 7). The states of the HMM (except the final state) are in one to one correspondence with template locations.

[0105]At each transition the model either emitted a symbol that r...

example ii

VIII. Example II

[0153]A consensus basecall was determined in an MSA using a pattern classification model that accounted for the local context of the sequence alignment and comprised two steps: (1) estimation of the context-dependent alignment model parameter using whole genome multiple sequence alignment; and (2) use of the estimated model parameters to both determine consensus basecalls and provide a value indicating the discrimination power for the position so basecalled.

[0154]For a position in the E. coli K12 genome (position 1904826 of the K12 NCBI reference sequence), it was observed that (nj,A, nj,c,nj,G, nj,T, nj,-,)=(2, 5, 10, 8, 7) for the “T” base in the “CGTGG”5-mer. Using the following context-dependent base frequencies from the whole genome multiple sequence alignment of a 47-fold coverage of sequencing reads:

fCGAGG=(A:0.688953,C:0.0921108,G:0.103457,T:0.0351911,-:0.0802874)

fCGCGG=(A:0.0331184,C:0.785788,G:0.0947676,T:0.0317243,-: 0.0546016)

fCGGGG=(A:0.0384701,C:0.08739...

example iii

IX. Example III

[0156]Consensus basecalls were determined in an MSA using a pattern classification model that applied an all path probabilistic alignment to assign confidence values to basecalls. For each of the four possible bases (“A”, “C”,“G”, and “T”) at each template position, the confidence values from each read at a given location were summed. The summed confidence values were defined as the consensus confidence values, and were correlated with empirical Phred-style empirical quality values (EQV) as shown in FIG. 12. To demonstrate the usefulness of such consensus confidence values, the number of bases in the test data set that were still retained after application of the quality filter was determined. This number was compared with the empirical quality value derived using fold-coverage as the binning method for deriving the EQV. FIG. 13 shows how many basecalls remained when the consensus bases below a given QV threshold were filtered out. When the QV derived by the confidenc...

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Abstract

The present invention is generally directed to powerful and flexible methods and systems for consensus sequence determination from replicate biomolecule sequence data. It is an object of the present invention to improve the accuracy of consensus biomolecule sequence determination from replicate sequence data by providing methods for assimilating replicate sequence into a final consensus sequence more accurately than any one-pass sequence analysis system.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application No. 61 / 116,439, filed Nov. 20, 2008, the disclosure of which is incorporated herein by reference in its entirety for all purposes.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH[0002]Not Applicable.BACKGROUND OF THE INVENTION[0003]Advances in biomolecule sequence determination, in particular with respect to nucleic acid and protein samples, has revolutionized the fields of cellular and molecular biology. Facilitated by the development of automated sequencing systems, it is now possible to sequence whole genomes. However, the quality of the sequence information must be carefully monitored, and may be compromised by many factors related to the biomolecule itself or the sequencing system used, including the composition of the biomolecule (e.g., base composition of a nucleic acid molecule), experimental and systematic noise, variations in observed signal strength, and differ...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06F19/22G16B30/10G16B30/20
CPCG06F19/22G16B30/00G16B30/10G16B30/20
Inventor SORENSON, JONTANG, SUSANMARKS, PATRICKCHIN, CHEN-SHAN
Owner PACIFIC BIOSCIENCES
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