Pith. sign in

REVIEW 4 major objections 5 minor 44 references

The Unified Evaluation App for DNA Data Storage Codecs

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read An open-source benchmark of eight DNA storage codecs shows that no single codec is optimal across density, error recovery, runtime, and cost.

desk verdict Genuinely useful open-source benchmarking infrastructure with honest reporting, but the codec rankings are provisional until the reimplemented codecs are validated against their originals. read the letter →

arxiv 2608.09673 v1 pith:V4P75K4B submitted 2026-08-10 cs.ET cs.SE

Aleksandar Anžel , Chisom Anyabolu , Leon Wimbes , Luca Staus , Ihsan Tri Heldian , David Sonnabend , Khawla Elhadri , Samuel Becker
show 7 more authors
Felix Klein Raffael Schoen Michael Schwarz Marius Welzel Bernd Freisleben Dominik Heider Georges Hattab
This is my paper · ORCID
classification cs.ETcs.SE
keywords DNAdatastoragecodecbenchmarkingunifiedevaluationframeworkerrorcorrectioninformationdensityreproducibilitymulti-criteriawebapplication
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to end the fragmented evaluation of DNA data storage codecs, where each new coding scheme is tested on its own files, metrics, and parameter choices. It proposes an open-source web platform that integrates eight published codecs behind uniform encode/decode wrappers and scores them on the same nine-file baseline and the same five criteria: information density, recovery success, runtime, cost, and biochemical constraint adherence. Benchmarking with default parameters and simulated degradation shows that no single codec wins across all dimensions: DNA-Aeon has the strongest error recovery, the NOREC4DNA rateless codecs the highest density, and simpler codecs the best speed when they complete. The point is not to rank codecs but to make the trade-offs visible and reproducible, so codec choice can be matched to the constraints of a real archival system.

What carries the argument

The machinery that carries the comparison is a pair of standardized wrapper functions — encode(file path, bool array) and decode(file path, list of DNA strings) — implemented for every codec, so each method receives and returns the same Python/NumPy representation of the data. Around these wrappers the platform builds a nine-file benchmark set spanning text, image, audio, video, and PDF data, a five-dimensional evaluation aligned with the DNA Data Storage Alliance's codec criteria, and two degradation strategies: error rates from the cited HEDGES study (substitution 0.0238, deletion 0.0082, insertion 0.0039) scaled by multipliers from 0 to 2.5, and the MESA simulator for user-uploaded files. The wrappers isolate each codec in its own process, and the information-density metric deliberately includes headers and all redundancy rather than payload alone, which makes the reported density a fair apples-to-apples quantity.

What would settle it

Take any one of the reimplemented codecs, run it on the same input files through both the platform's wrapper and the original authors' released code or test vectors, and compare the decoded bit arrays; any divergence in decoded output or error-corrected sequences would show that the benchmark scores describe the reimplementations rather than the codecs as published, so the comparative trade-off conclusions would not transfer to the original methods.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that when eight DNA storage codecs — Repetition, Simple, 2D, XOR-based, DNA-Aeon, and three NOREC4DNA rateless erasure codecs — are forced through identical input conversion, identical error simulation, and identical success checks, their performance separates along recognizable axes. DNA-Aeon achieves the highest overall success rate (55.97%), followed by the Raptor-based (27.94%) and LT-based (19.50%) NOREC4DNA codecs, while the Simple codec never recovered a tested file; the NOREC4DNA LT and Raptor codecs reach around 0.9 bits per nucleotide in information density, and the simpler codecs finish encoding and decoding fastest and cheapest when they finish at all. The paper treats this separation as evidence that information density alone is a misleading selection criterion, since high density does not imply recoverability under degradation, and that codec choice is inherently application-dependent.

Load-bearing premise

The benchmark's conclusions hold only if each reimplemented or wrapped codec behaves exactly like the original published method including its error-correction logic, and the paper gives no equivalence tests against the original implementations or published test vectors to confirm this.

Editorial extensions

If this is right

  • Archival system designers get a defensible basis for codec choice: density-critical workloads would lean toward the NOREC4DNA rateless codecs, while recovery-critical workloads would lean toward DNA-Aeon, with the platform quantifying the cost of each choice.
  • A newly published codec can be dropped into the platform through two wrapper functions and immediately scored on the same nine files and five metrics, making future codec papers directly comparable with this benchmark.
  • The observed density-versus-recovery split implies that future format designs that optimize density alone risk producing DNA that cannot be read back under realistic degradation, so joint reporting of these metrics becomes a practical necessity.
  • Re-running the benchmark automatically when a codec is added keeps the comparison set alive, so the conclusion that no single codec is optimal can be retested rather than assumed as new methods appear.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the paper fixes each codec to one 'sane' default configuration, its conclusion that no codec dominates may be partly an artifact of parameter choice; sweeping parameters would likely reveal zones where a low-ranked codec wins, turning the headline into a map of codec-specific operating regions.
  • The absence of equivalence tests suggests a cheap, useful extension: a regression suite that replays each original codec's published test vectors through the wrappers, converting the platform from a convenient harness into a certified comparison standard.
  • Simulated degradation with the chosen error rates and simulator defaults may not reproduce real synthesis and sequencing error profiles; cross-checking the same codecs against published wet-lab error datasets would test whether the reported trade-offs survive in the physical channel.
  • The framework's open-source, wrapper-based design is a natural testbed for a community norm where every new DNA codec submission reports its score on this baseline, an editorial and policy consequence the paper only gestures toward.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents UNACORM, an open-source, modular web application for benchmarking DNA data storage codecs. It integrates eight codecs (reimplementing some Pascal/Perl originals in Python and wrapping C++ code), defines four quantitative metrics (information density, success rate, runtime, cost) aligned with a five-dimensional framework from the DNA Data Storage Alliance, and evaluates all codecs on a nine-file baseline data set under a HEDGES-based error model with varying error multipliers. The main empirical claim is that no single codec is optimal across all evaluated dimensions, with DNA-Aeon showing the highest success rate while NOREC4DNA LT/Raptor variants achieve higher information density.

Significance. If the reimplementations and wrappers faithfully reproduce the published codecs, the paper provides a genuinely useful resource: a standardized, openly available evaluation harness with clearly defined metrics, a diverse baseline corpus, and honest reporting of incomplete runs and zero success rates (e.g., Simple codec). The modular wrapper design, Docker packaging, and automated benchmark recalculation scripts are concrete strengths that lower the barrier for community adoption and reproducibility. The comparison framework addresses a real gap in the field, where prior benchmarks (e.g., Ping et al., Gimpel et al.) are narrower in metrics or extensibility. However, the significance of the headline comparative results depends critically on the fidelity of the reimplementations and on the representativeness of the chosen default parameters; without equivalence tests or parameter-sensitivity evidence, the specific success-rate and trade-off numbers remain unverified as statements about the original published codecs.

major comments (4)
  1. [Section 2.2] The paper states that the authors 'systematically restructured or adapted all original codecs' and reimplemented Pascal and Perl codecs in Python, but it reports no equivalence tests against the original implementations or published test vectors. The central results of Section 3.1 (e.g., DNA-Aeon 55.97% success rate, NOREC4DNA Raptor 27.94%, Simple 0.00%) and the abstract's 'no single algorithm is optimal' claim attribute behavior to the published codecs. If a reimplementation deviates, the benchmark describes the reimplementations rather than the codecs as published. Please add differential tests (e.g., identical encoded outputs on a set of public inputs, and identical round-trip behavior under the same error model) or explicitly restrict all comparative claims to the implementations as integrated in UNACORM.
  2. [Section 2.3 and Section 5] The methods section announces a 'benchmarking suite around five orthogonal dimensions' mapped one-to-one to DNA Data Storage Alliance criteria, including 'biochemical constraint adherence' (GC/homopolymer constraints). However, Section 2.3.2 defines only four quantitative metrics (information density, success rate, runtime, cost), and the Results section reports no measurements of GC content, homopolymer lengths, or any other biochemical constraint metric. The Conclusion's claim that 'Our platform implements the five-dimensional evaluation framework' is therefore not supported by the presented evaluation. Please either operationalize the fifth dimension with concrete metrics or revise the framework description and conclusion to state that four of the five dimensions are currently evaluated and the fifth is planned.
  3. [Abstract and Section 3.1 vs. Section 4] The abstract claims that benchmarking 'demonstrates that no single algorithm is optimal across all evaluated dimensions,' and Section 3.1 draws comparative conclusions such as 'DNA-Aeon achieved the strongest overall success-rate profile.' The Discussion, however, acknowledges that only one default configuration per codec was used, that 'some failed or missing runs may have succeeded under alternative settings,' and that results 'should not be interpreted as definitive indicators of the absolute quality or performance of the individual codecs.' Because the paper reports no parameter-sweep or sensitivity analysis, the unqualified comparative claim in the abstract overreaches the evidence. Please add a stability analysis for at least the headline codecs or rephrase the central claim to 'under the default configurations evaluated in this study' in the abstract and results.
  4. [Section 3.1 and Section 4] The paper frequently reports 'missing entries' for codec–file–error combinations that 'did not complete successfully under the default configuration,' and the Discussion states that 'several implementations returned limited diagnostic information when failures occurred' and calls for 'more explicit failure classification, distinguishing between decoding failure, timeout, unsupported error types, file-size limitations, and implementation-level errors.' Since the overall success rates in Figure 9 are computed over these incomplete runs, a run that fails due to, e.g., a timeout or a wrapper crash is currently counted in the same way as a genuine decoding failure. Please provide a failure classification (or at least per-codec counts of each failure mode) for all missing entries before the success-rate comparisons can be interpreted as measures of codec error-correction performance.
minor comments (5)
  1. [Section 2.1.1] The text cites 'Schneiderman' for the visualization mantra; the correct spelling is 'Shneiderman' (as in reference [20]).
  2. [Figure 3] The screenshot footer reads 'The Unified Evaluation Tool for DNA Data Storage Codecs,' whereas the paper title and text consistently use 'App'; please unify the naming to avoid confusion.
  3. [Section 2.2 and Section 4] The description of parameter selection shifts from 'most representative configuration' in the Methods to 'optimal or default configurations' in the Discussion; since no optimality evidence is presented, please align the wording with what was actually done.
  4. [Section 3.2 and Code availability] Runtime results would be more reproducible if the paper specified the hardware and software environment (e.g., CPU model, memory, OS version, Python/Docker versions) used for the benchmark runs, rather than only stating that Docker is used.
  5. [Figures 10–12] The x-axis label 'Base_error_ratio' in the figure panels contains an underscore; this should be rendered as 'Base error ratio' in the figure files for readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the benchmark's trade-off findings are empirical outputs measured from codec executions, not derivations that reduce to their inputs.

full rationale

This paper is a benchmarking study rather than a derivation chain, and no step reduces by construction to its inputs. The central claim that 'no single algorithm is optimal across all evaluated dimensions' is supported by directly measured results—information density, success rate, runtime, and cost—computed by running the integrated codecs on a fixed baseline dataset, with no fitted parameter renamed as a prediction and no equation in which an output is defined as an input. The self-citations to DNA-Aeon, NOREC4DNA, and MESA identify codecs and a simulator authored by co-authors, but those citations are not used to justify the benchmark's conclusions by themselves; the conclusions are empirical outputs of the executions. The paper's own caveat in Section 4 that the results 'should not be interpreted as definitive indicators of the absolute quality or performance of the individual codecs' and the absence of equivalence tests in Section 2.2 for reimplemented Pascal and Perl codecs are validity and verification limitations, not circularity. The reimplementation-fidelity concern would affect whether the measured behavior is attributed correctly to the published methods, but it does not make the benchmark's reasoning circular. No load-bearing step relies on a self-citation chain or on a definition that presupposes the target result.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The benchmark rests on five unproven operational assumptions: a single error channel, faithful reimplementations, author-chosen defaults, codec-internal read preprocessing, and a NumPy-based binary representation. The error channel and default parameters are stated explicitly; reimplementation fidelity and parameter fairness are not validated. The only hidden numeric inputs are the cost-model prices.

free parameters (1)
  • Synthesis and sequencing cost rates = not stated
    Cost metric in Section 2.3.2 is computed from oligo length and 'average costs' of synthesis and sequencing, but the per-base price constants are not reported; Figures 12 cannot be reproduced without them.
assumptions (5)
  • domain assumption HEDGES high-mutagenesis error rates (substitution 0.0238, deletion 0.0082, insertion 0.0039), scaled by multipliers 0 to 2.5, represent realistic DNA storage degradation for benchmarking.
    Section 2.1.2 defines the baseline error model; all success-rate and robustness results use this channel, and the paper does not validate it against other degradation models except for optional MESA use.
  • ad hoc to paper Python reimplementations and wrapper scripts faithfully reproduce the original codecs' encoding and decoding behavior.
    Section 2.2 says codecs were restructured, reimplemented from Pascal or Perl, or wrapped from C++; no equivalence tests against original implementations or published test vectors are provided.
  • ad hoc to paper The selected default parameters are representative and fair across codecs.
    Section 2.2 and the Discussion select 'sane' defaults; because the authors also authored DNA-Aeon and NOREC4DNA, this choice cannot be assumed neutral and is not externally audited.
  • domain assumption Clustering and multistrand alignment should remain internal to each codec rather than standardized as a shared preprocessing stage.
    Section 2.1.2 (Clustering and Multistrand Alignment Considerations) makes this choice to preserve authenticity, but it means codecs are evaluated under their own read-processing assumptions.
  • ad hoc to paper NumPy boolean arrays are an acceptable universal binary representation and do not distort codec comparisons.
    Section 2.1.2 and the Discussion acknowledge this representation is slower and memory-inefficient; the benchmark runtime results therefore include a uniform but unquantified overhead.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Unified Evaluation App for DNA Data Storage Codecs." pith.science (2026). https://pith.science/paper/V4P75K4B

@misc{pith2026260809673,
  author       = {Pith},
  title        = {Pith review of: The Unified Evaluation App for DNA Data Storage Codecs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V4P75K4B}},
  note         = {Machine review of arXiv:2608.09673}
}
read the original abstract

Background: Deoxyribonucleic acid (DNA) data storage is a paradigm with great potential for ultra-dense and durable information preservation. However, the rapid proliferation of coding schemes, or codecs, each with their own design constraints and reporting practices, has led to a fragmented landscape that lacks a standardized comparative assessment. Methods: We developed an open-source, modular benchmarking platform that systematically integrates and evaluates state-of-the-art DNA storage encoding and decoding methods (codecs). Our approach uses a curated, diverse set of baseline data and applies multidimensional assessment criteria that are aligned with the consensus standard of the DNA Data Storage Alliance. These criteria include encoding/decoding throughput, computational efficiency, error correction performance across substitutions, insertions, and deletions, and cost efficiency. Results: The developed platform integrates standardized wrapper functions for encoding and decoding, allows for the integration of new methods, and automates reproducible evaluations with comprehensive visual and tabular reporting. Benchmarking both contemporary and classical codecs using their default parameters and multiple metrics demonstrates that no single algorithm is optimal across all evaluated dimensions. The trade-offs between information density, success rate, runtime, and cost are quantified and shown to be critical factors in the design of future-proof formats. Conclusions: Our work establishes a rigorously standardized, open-source evaluation framework that enables reproducible benchmarking, supports evidence-based codec selection, and provides the necessary foundation for translating DNA data storage from experimental research into deployable archival systems.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 32 canonical work pages

  1. [1]

    Data storage 2030: Building a fully connected, intelligent world

    HUAWEI TECHNOLOGIES CO., LTD. Data storage 2030: Building a fully connected, intelligent world. Tech. Rep., HUAWEI TECHNOLO- GIES CO., LTD., Huawei Industrial Base, Bantian Longgang, Shenzhen 518129, P. R. China (2024). URL https://www-file.huawei.com/-/media/ corp2020/pdf/giv/2024/data storage whitepaper 2030 en.pdf. Accessed August 2025

    work page 2024

  2. [2]

    & Hattab, G

    Anˇ zel, A., Heider, D. & Hattab, G. The visual story of data stor- age: From storage properties to user interfaces.Computational and Structural Biotechnology Journal19, 4904–4918 (2021). URL https: //www.sciencedirect.com/science/article/pii/S2001037021003627. https: //doi.org/https://doi.org/10.1016/j.csbj.2021.08.031

    work page doi:10.1016/j.csbj.2021.08.031 2021

  3. [3]

    & Yaakobi, E

    Bar-Lev, D., Sabary, O. & Yaakobi, E. The zettabyte era is in our dna.Nature Computational Science4(11), 813–817 (2024). URL https://doi.org/10.1038/s43588-024-00717-1. https://doi.org/10.1038/ s43588-024-00717-1

    work page doi:10.1038/s43588-024-00717-1 2024

  4. [4]

    Hoose, A., Vellacott, R., Storch, M., Freemont, P. S. & Ryadnov, M. G. Dna synthesis technologies to close the gene writing gap.Nature Reviews Chemistry7(3), 144–161 (2023). URL https://doi.org/10.1038/ s41570-022-00456-9. https://doi.org/10.1038/s41570-022-00456-9

    work page doi:10.1038/s41570-022-00456-9 2023

  5. [5]

    M., Gao, Y

    Church, G. M., Gao, Y. & Kosuri, S. Next-generation digital information storage in dna.Science337(6102), 1628–1628 (2012). URL https://www.science.org/doi/abs/10. 1126/science.1226355. https://doi.org/10.1126/science.1226355, https://www.science.org/doi/pdf/10.1126/science.1226355

    doi:10.1126/science.1226355 2012

  6. [6]

    URL https://doi.org/10.1038/nature11875

    Goldman, N.et al.Towards practical, high-capacity, low-maintenance information storage in synthesized dna.Nature494(7435), 77–80 (2013). URL https://doi.org/10.1038/nature11875. https://doi.org/10. 1038/nature11875

    doi:10.1038/nature11875 2013

  7. [7]

    N., Nguyen, B

    Takahashi, C. N., Nguyen, B. H., Strauss, K. & Ceze, L. Demonstration of end-to-end automation of dna data storage.Scientific Reports9(1), Springer Nature 2021 LATEX template 30The Unified Evaluation App for DNA Data Storage Codecs 4998 (2019). URL https://doi.org/10.1038/s41598-019-41228-8. https: //doi.org/10.1038/s41598-019-41228-8

    work page doi:10.1038/s41598-019-41228-8 2019

  8. [8]

    F., Welzel, M., Hattab, G., Hauschild, A.-C

    L¨ ochel, H. F., Welzel, M., Hattab, G., Hauschild, A.-C. & Hei- der, D. Fractal construction of constrained code words for dna storage systems.Nucleic Acids Research50(5), e30–e30 (2021). URL https://doi.org/10.1093/nar/gkab1209. https://doi. org/10.1093/nar/gkab1209, https://academic.oup.com/nar/article- pdf/50/5/e30/42917805/gkab1209.pdf

    work page doi:10.1093/nar/gkab1209 2021

Show all 44 references
  1. [9]

    & Zhang, Q

    Yin, Q., Zheng, Y., Wang, B. & Zhang, Q. Design of constraint coding sets for archive dna storage.IEEE/ACM Transactions on Computational Biology and Bioinformatics19(6), 3384–3394 (2022). https://doi.org/ 10.1109/TCBB.2021.3127271

    2022

  2. [10]

    Limbachiya, D., Gupta, M. K. & Aggarwal, V. Family of constrained codes for archival dna data storage.IEEE Communications Let- ters22(10), 1972–1975 (2018). https://doi.org/10.1109/LCOMM.2018. 2861867

    2018 doi

  3. [11]

    Tabatabaei Yazdi, S. M. H., Yuan, Y., Ma, J., Zhao, H. & Milenkovic, O. A rewritable, random-access dna-based storage system.Scientific Reports5(1), 14138 (2015). URL https://doi.org/10.1038/srep14138. https://doi.org/10.1038/srep14138

    2015 doi

  4. [12]

    URL https://doi.org/10.1038/nbt

    Organick, L.et al.Random access in large-scale dna data storage.Nature Biotechnology36(3), 242–248 (2018). URL https://doi.org/10.1038/nbt

    2018 doi

  5. [13]

    URL https://doi.org/10.1016/j.patter.2025.101288

    Cao, B.et al.Achieving handle-level random access in an encrypted dna archival storage system via frequency dictionary mapping coding.Pat- terns6(9) (2025). URL https://doi.org/10.1016/j.patter.2025.101288. https://doi.org/10.1016/j.patter.2025.101288

    2025

  6. [14]

    URL https://doi.org/10.1038/s43588-022-00231-2

    Ping, Z.et al.Towards practical and robust dna-based data archiv- ing using the yin–yang codec system.Nature Computational Science 2(4), 234–242 (2022). URL https://doi.org/10.1038/s43588-022-00231-2. https://doi.org/10.1038/s43588-022-00231-2

    2022 doi

  7. [15]

    bioRxiv(2020)

    Ping, Z.et al.Chamaeleo: a robust library for dna storage coding schemes. bioRxiv(2020). URL https://www.biorxiv.org/content/early/2020/ 03/19/2020.01.02.892588. https://doi.org/10.1101/2020.01.02.892588, https://www.biorxiv.org/content/early/2020/03/19/2020.01.02.892588.full.pdf

    2020 doi

  8. [16]

    L., Remschak, A., Stark, W

    Gimpel, A. L., Remschak, A., Stark, W. J., Heckel, R. & Grass, R. N. Comparison of state-of-the-art error-correction Springer Nature 2021 LATEX template The Unified Evaluation App for DNA Data Storage Codecs31 coding for sequence-based dna data storage.bioRxiv(2025). URL https...

    2025 doi

  9. [17]

    URL https://doi.org/10.1038/ s41467-023-36297-3

    Welzel, M.et al.Dna-aeon provides flexible arithmetic coding for constraint adherence and error correction in dna storage.Nature Communications14(1), 628 (2023). URL https://doi.org/10.1038/ s41467-023-36297-3. https://doi.org/10.1038/s41467-023-36297-3

    2023 doi

  10. [18]

    & Yaakobi, E

    Bar-Lev, D., Orr, I., Sabary, O., Etzion, T. & Yaakobi, E. Scalable and robust dna-based storage via coding theory and deep learning.Nature Machine Intelligence7(4), 639–649 (2025). URL https://doi.org/10. 1038/s42256-025-01003-z. https://doi.org/10.1038/s42256-025-01003-z

    2025 doi

  11. [19]

    Dna data storage codecs — examples, requirements, and metrics

    DNA Data Storage Alliance. Dna data storage codecs — examples, requirements, and metrics. Tech. Rep., The Storage Networking Industry Association (SNIA) (2025). URL https://www.snia.org/educational-library/ dna-data-storage-codecs-examples-requirements-and-metrics-2025

    2025

  12. [20]

    The eyes have it: a task by data type taxonomy for information visualizations.Proceedings 1996 IEEE Symposium on Visual Languages336–343 (1996)

    Shneiderman, B. The eyes have it: a task by data type taxonomy for information visualizations.Proceedings 1996 IEEE Symposium on Visual Languages336–343 (1996). https://doi.org/10.1109/VL.1996.545307

    1996

  13. [21]

    URL https://doi.org/10.1093/ bioinformatics/btaa140

    Schwarz, M.et al.MESA: automated assessment of synthetic DNA fragments and simulation of DNA synthe- sis, storage, sequencing and PCR errors.Bioinformatics 36(11), 3322–3326 (2020). URL https://doi.org/10.1093/ bioinformatics/btaa140. https://doi.org/10.1093/bioinformatics/ bt...

    2020 doi

  14. [22]

    R.et al.Array programming with NumPy.Nature585(7825), 357–362 (2020)

    Harris, C. R.et al.Array programming with NumPy.Nature585(7825), 357–362 (2020). URL https://doi.org/10.1038/s41586-020-2649-2. https: //doi.org/10.1038/s41586-020-2649-2

    2020 doi

  15. [23]

    H., Hawkins, J

    Press, W. H., Hawkins, J. A., Jones, S. K., Schaub, J. M. & Finkel- stein, I. J. Hedges error-correcting code for dna storage corrects indels and allows sequence constraints.Proceedings of the National Academy of Sciences117(31), 18489–18496 (2020). URL https://www.pnas. org/d...

    2020 doi

  16. [24]

    & Heckel, R.Information-Theoretic Foundations of DNA Data Storage(Now Foundations and Trends, 2022)

    Shomorony, I. & Heckel, R.Information-Theoretic Foundations of DNA Data Storage(Now Foundations and Trends, 2022). URL https:// Springer Nature 2021 LATEX template 32The Unified Evaluation App for DNA Data Storage Codecs ieeexplore.ieee.org/document/9721712

    2022

  17. [25]

    N., Heckel, R., Puddu, M., Paunescu, D

    Grass, R. N., Heckel, R., Puddu, M., Paunescu, D. & Stark, W. J. Robust chemical preservation of digital information on dna in silica with error- correcting codes.Angewandte Chemie International Edition54(8), 2552– 2555 (2015). URL https://onlinelibrary.wiley.com/doi/abs/10.10...

    2015 doi

  18. [26]

    51(4), 637–649 (2016)

    Bornholt, J.et al.A dna-based archival storage system.SIGPLAN Not. 51(4), 637–649 (2016). URL https://doi.org/10.1145/2954679.2872397. https://doi.org/10.1145/2954679.2872397

    2016

  19. [27]

    Schwarz, P. M. & Freisleben, B. Norec4dna: using near-optimal rate- less erasure codes for dna storage.BMC Bioinformatics22(1), 406 (2021). URL https://doi.org/10.1186/s12859-021-04318-x. https://doi. org/10.1186/s12859-021-04318-x

    2021 doi

  20. [28]

    Raptor codes.IEEE Transactions on Information Theory 52(6), 2551–2567 (2006)

    Shokrollahi, A. Raptor codes.IEEE Transactions on Information Theory 52(6), 2551–2567 (2006). https://doi.org/10.1109/TIT.2006.874390

    2006

  21. [29]

    Online codes

    Maymounkov, P. Online codes. Tech. Rep., Computer Science Depart- ment at New York University, Warren Weaver Hall, Room 305, 251 Mercer Street, New York, NY 10012 (2002). URL https://pdos.csail.mit.edu/ ∼petar/papers/maymounkov-online.pdf

    2002

  22. [30]

    Lt codes.The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002

    Luby, M. Lt codes.The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings.271–280 (2002). https://doi.org/ 10.1109/SFCS.2002.1181950

    2002 arXiv

  23. [31]

    PASCAL user manual and report(Springer-Verlag, Berlin, Heidelberg, 1974)

    Jensen, K.et al. PASCAL user manual and report(Springer-Verlag, Berlin, Heidelberg, 1974). URL http://pascal.hansotten.com/uploads/ books/Pascal User Manual and Report Fourth Edition.pdf

    1974

  24. [32]

    D., Wall, L

    Christiansen, T., Foy, B. D., Wall, L. & Orwant, J.Programming Perl: Unmatched power for text processing and scripting(O’Reilly Media, Inc., 2012)

    2012

  25. [33]

    & Drake, F

    Van Rossum, G. & Drake, F. L.Python 3 Reference Manual(CreateS- pace, Scotts Valley, CA, 2009)

    2009

  26. [34]

    Stroustrup, B.The C++ Programming Language, Third Edition3rd edn (Addison-Wesley Longman Publishing Co., Inc., USA, 1997)

    1997

  27. [35]

    URL https://www.gutenberg.org/ebooks/103

    Verne, J.Around the World in Eighty Days By Jules Verne(BookRix, 1994). URL https://www.gutenberg.org/ebooks/103. Springer Nature 2021 LATEX template The Unified Evaluation App for DNA Data Storage Codecs33

    1994

  28. [36]

    URL https://www

    Wilde, O.The Picture of Dorian GrayCollection of British Authors: Tauchnitz Edition (Bernhard Tauchnitz, 1908). URL https://www. gutenberg.org/ebooks/174

    1908

  29. [37]

    BBC Sound Effects — sound-effects.bbcrewind.co.uk

    BBC Archive. BBC Sound Effects — sound-effects.bbcrewind.co.uk. https://sound-effects.bbcrewind.co.uk/search?q=07002136. [Accessed 01-11-2024]

    2024

  30. [38]

    BBC Sound Effects — sound-effects.bbcrewind.co.uk

    BBC Archive. BBC Sound Effects — sound-effects.bbcrewind.co.uk. https://sound-effects.bbcrewind.co.uk/search?q=NHU05015024. [Accessed 01-11-2024]

    2024

  31. [39]

    & de Boer, J

    van Rossum, G. & de Boer, J. Interactively testing remote servers using the python programming language.CWI Quarterly4(4), 283–304 (1991). URL https://ir.cwi.nl/pub/18204/18204A.pdf

    1991

  32. [40]

    B., Jette, M

    Yoo, A. B., Jette, M. A. & Grondona, M. Feitelson, D., Rudolph, L. & Schwiegelshohn, U. (eds)Slurm: Simple linux utility for resource management. (eds Feitelson, D., Rudolph, L. & Schwiegelshohn, U.) Job Scheduling Strategies for Parallel Processing, 44–60 (Springer Berlin Hei...

    2003

  33. [41]

    & Strauss, K

    Landsman, D. & Strauss, K. The dna data storage model.Computer 56(7), 78–85 (2023). https://doi.org/10.1109/MC.2023.3272188

    2023

  34. [42]

    Chaykin, G., Sabary, O., Furman, N., Shabat, D. B. & Yaakobi, E. Dna-storalator: a computational simulator for dna data storage.BMC Bioinformatics26(1), 204 (2025). URL https://doi.org/10.1186/ s12859-025-06222-0. https://doi.org/10.1186/s12859-025-06222-0

    2025 doi

  35. [43]

    URL https://doi.org/10.1038/ s41467-026-71485-x

    Zhang, J.et al.Gungnir codec enabling high error-tolerance and low- redundancy dna storage through substantial computing power.Nature Communications17(1), 4828 (2026). URL https://doi.org/10.1038/ s41467-026-71485-x. https://doi.org/10.1038/s41467-026-71485-x . Springer Nature...

    2026 doi

  36. [4079]

    https://doi.org/10.1038/nbt.4079

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.