DeSP: a systematic DNA storage error simulation pipeline

Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundan...

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Published inBMC bioinformatics Vol. 23; no. 1; pp. 185 - 14
Main Authors Yuan, Lekang, Xie, Zhen, Wang, Ye, Wang, Xiaowo
Format Journal Article
LanguageEnglish
Published London BioMed Central 17.05.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects
Algorithms
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer Simulation
Data storage
Density
Deoxyribonucleic acid
Design optimization
DeSP
DNA
DNA - genetics
DNA storage
Encoding optimization
Error correction
Error-correcting codes
Experiments
Information storage
Information Storage and Retrieval
Life Sciences
Methods
Microarrays
Noise
Redundancy
Sequence Analysis, DNA - methods
Simulation
Systematic error simulation
Systematic errors
Web application
Web applications
China
DNA storage
Systematic error simulation
Web application
DeSP
Encoding optimization
Online AccessGet full text
ISSN1471-2105
1471-2105
DOI10.1186/s12859-022-04723-w

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Abstract Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Results Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. Conclusions DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.
AbstractList Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Results Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. Conclusions DeSP implemented in Python is freely available on Github ( Keywords: DeSP, DNA storage, Systematic error simulation, Encoding optimization, Web application
Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.
Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure.BACKGROUNDUsing DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure.Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments.RESULTSHere, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments.DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.CONCLUSIONSDeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.
Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Results Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. Conclusions DeSP implemented in Python is freely available on Github (https://github.com/WangLabTHU/DeSP). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.
Abstract Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Results Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. Conclusions DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.
Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Results Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. Conclusions DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.
Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. DeSP implemented in Python is freely available on Github (https://github.com/WangLabTHU/DeSP). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.
ArticleNumber 185
Audience Academic
Author Wang, Ye
Yuan, Lekang
Xie, Zhen
Wang, Xiaowo
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Issue 1
Keywords DNA storage
Systematic error simulation
Web application
DeSP
Encoding optimization
Language English
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Snippet Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A...
Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant...
Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A...
Abstract Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A...
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SubjectTerms Algorithms
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer Simulation
Data storage
Density
Deoxyribonucleic acid
Design optimization
DeSP
DNA
DNA - genetics
DNA storage
Encoding optimization
Error correction
Error-correcting codes
Experiments
Information storage
Information Storage and Retrieval
Life Sciences
Methods
Microarrays
Noise
Redundancy
Sequence Analysis, DNA - methods
Simulation
Systematic error simulation
Systematic errors
Web application
Web applications
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Title DeSP: a systematic DNA storage error simulation pipeline
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