Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data

Thomas S Rask; Bent Petersen; Donald S Chen; Karen P Day; Anders Gorm Pedersen

doi:10.1186/s12859-016-1032-7

Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data

Thomas S Rask, Bent Petersen, Donald S Chen, Karen P Day, Anders Gorm Pedersen

9 Citationer (Scopus)

Abstract

BACKGROUND: Amplicon pyrosequencing targets a known genetic region and thus inherently produces reads highly anticipated to have certain features, such as conserved nucleotide sequence, and in the case of protein coding DNA, an open reading frame. Pyrosequencing errors, consisting mainly of nucleotide insertions and deletions, are on the other hand likely to disrupt open reading frames. Such an inverse relationship between errors and expectation based on prior knowledge can be used advantageously to guide the process known as basecalling, i.e. the inference of nucleotide sequence from raw sequencing data.

RESULTS: The new basecalling method described here, named Multipass, implements a probabilistic framework for working with the raw flowgrams obtained by pyrosequencing. For each sequence variant Multipass calculates the likelihood and nucleotide sequence of several most likely sequences given the flowgram data. This probabilistic approach enables integration of basecalling into a larger model where other parameters can be incorporated, such as the likelihood for observing a full-length open reading frame at the targeted region. We apply the method to 454 amplicon pyrosequencing data obtained from a malaria virulence gene family, where Multipass generates 20 % more error-free sequences than current state of the art methods, and provides sequence characteristics that allow generation of a set of high confidence error-free sequences.

CONCLUSIONS: This novel method can be used to increase accuracy of existing and future amplicon sequencing data, particularly where extensive prior knowledge is available about the obtained sequences, for example in analysis of the immunoglobulin VDJ region where Multipass can be combined with a model for the known recombining germline genes. Multipass is available for Roche 454 data at http://www.cbs.dtu.dk/services/MultiPass-1.0 , and the concept can potentially be implemented for other sequencing technologies as well.

Originalsprog	Engelsk
Tidsskrift	BMC Bioinformatics
Vol/bind	17
Sider (fra-til)	176
ISSN	1471-2105
DOI	https://doi.org/10.1186/s12859-016-1032-7
Status	Udgivet - 22 apr. 2016
Udgivet eksternt	Ja

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10.1186/s12859-016-1032-7Licens: CC BY

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title = "Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data",

abstract = "BACKGROUND: Amplicon pyrosequencing targets a known genetic region and thus inherently produces reads highly anticipated to have certain features, such as conserved nucleotide sequence, and in the case of protein coding DNA, an open reading frame. Pyrosequencing errors, consisting mainly of nucleotide insertions and deletions, are on the other hand likely to disrupt open reading frames. Such an inverse relationship between errors and expectation based on prior knowledge can be used advantageously to guide the process known as basecalling, i.e. the inference of nucleotide sequence from raw sequencing data.RESULTS: The new basecalling method described here, named Multipass, implements a probabilistic framework for working with the raw flowgrams obtained by pyrosequencing. For each sequence variant Multipass calculates the likelihood and nucleotide sequence of several most likely sequences given the flowgram data. This probabilistic approach enables integration of basecalling into a larger model where other parameters can be incorporated, such as the likelihood for observing a full-length open reading frame at the targeted region. We apply the method to 454 amplicon pyrosequencing data obtained from a malaria virulence gene family, where Multipass generates 20 % more error-free sequences than current state of the art methods, and provides sequence characteristics that allow generation of a set of high confidence error-free sequences.CONCLUSIONS: This novel method can be used to increase accuracy of existing and future amplicon sequencing data, particularly where extensive prior knowledge is available about the obtained sequences, for example in analysis of the immunoglobulin VDJ region where Multipass can be combined with a model for the known recombining germline genes. Multipass is available for Roche 454 data at http://www.cbs.dtu.dk/services/MultiPass-1.0 , and the concept can potentially be implemented for other sequencing technologies as well.",

keywords = "Algorithms, DNA, Protozoan/genetics, Models, Molecular, Open Reading Frames, Plasmodium falciparum/genetics, Protozoan Proteins/genetics, Sequence Alignment, Sequence Analysis, DNA",

author = "Rask, {Thomas S} and Bent Petersen and Chen, {Donald S} and Day, {Karen P} and Pedersen, {Anders Gorm}",

year = "2016",

month = apr,

day = "22",

doi = "10.1186/s12859-016-1032-7",

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journal = "B M C Bioinformatics",

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TY - JOUR

T1 - Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data

AU - Rask, Thomas S

AU - Petersen, Bent

AU - Chen, Donald S

AU - Day, Karen P

AU - Pedersen, Anders Gorm

PY - 2016/4/22

Y1 - 2016/4/22

N2 - BACKGROUND: Amplicon pyrosequencing targets a known genetic region and thus inherently produces reads highly anticipated to have certain features, such as conserved nucleotide sequence, and in the case of protein coding DNA, an open reading frame. Pyrosequencing errors, consisting mainly of nucleotide insertions and deletions, are on the other hand likely to disrupt open reading frames. Such an inverse relationship between errors and expectation based on prior knowledge can be used advantageously to guide the process known as basecalling, i.e. the inference of nucleotide sequence from raw sequencing data.RESULTS: The new basecalling method described here, named Multipass, implements a probabilistic framework for working with the raw flowgrams obtained by pyrosequencing. For each sequence variant Multipass calculates the likelihood and nucleotide sequence of several most likely sequences given the flowgram data. This probabilistic approach enables integration of basecalling into a larger model where other parameters can be incorporated, such as the likelihood for observing a full-length open reading frame at the targeted region. We apply the method to 454 amplicon pyrosequencing data obtained from a malaria virulence gene family, where Multipass generates 20 % more error-free sequences than current state of the art methods, and provides sequence characteristics that allow generation of a set of high confidence error-free sequences.CONCLUSIONS: This novel method can be used to increase accuracy of existing and future amplicon sequencing data, particularly where extensive prior knowledge is available about the obtained sequences, for example in analysis of the immunoglobulin VDJ region where Multipass can be combined with a model for the known recombining germline genes. Multipass is available for Roche 454 data at http://www.cbs.dtu.dk/services/MultiPass-1.0 , and the concept can potentially be implemented for other sequencing technologies as well.

AB - BACKGROUND: Amplicon pyrosequencing targets a known genetic region and thus inherently produces reads highly anticipated to have certain features, such as conserved nucleotide sequence, and in the case of protein coding DNA, an open reading frame. Pyrosequencing errors, consisting mainly of nucleotide insertions and deletions, are on the other hand likely to disrupt open reading frames. Such an inverse relationship between errors and expectation based on prior knowledge can be used advantageously to guide the process known as basecalling, i.e. the inference of nucleotide sequence from raw sequencing data.RESULTS: The new basecalling method described here, named Multipass, implements a probabilistic framework for working with the raw flowgrams obtained by pyrosequencing. For each sequence variant Multipass calculates the likelihood and nucleotide sequence of several most likely sequences given the flowgram data. This probabilistic approach enables integration of basecalling into a larger model where other parameters can be incorporated, such as the likelihood for observing a full-length open reading frame at the targeted region. We apply the method to 454 amplicon pyrosequencing data obtained from a malaria virulence gene family, where Multipass generates 20 % more error-free sequences than current state of the art methods, and provides sequence characteristics that allow generation of a set of high confidence error-free sequences.CONCLUSIONS: This novel method can be used to increase accuracy of existing and future amplicon sequencing data, particularly where extensive prior knowledge is available about the obtained sequences, for example in analysis of the immunoglobulin VDJ region where Multipass can be combined with a model for the known recombining germline genes. Multipass is available for Roche 454 data at http://www.cbs.dtu.dk/services/MultiPass-1.0 , and the concept can potentially be implemented for other sequencing technologies as well.

KW - Algorithms

KW - DNA, Protozoan/genetics

KW - Models, Molecular

KW - Open Reading Frames

KW - Plasmodium falciparum/genetics

KW - Protozoan Proteins/genetics

KW - Sequence Alignment

KW - Sequence Analysis, DNA

U2 - 10.1186/s12859-016-1032-7

DO - 10.1186/s12859-016-1032-7

M3 - Journal article

C2 - 27102804

SN - 1471-2105

VL - 17

SP - 176

JO - B M C Bioinformatics

JF - B M C Bioinformatics

ER -

Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data

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