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===Data integrity===
===Data integrity===
An important goal for [[bioinformatics]] is the continuous improvement of biological databases. Given the rapid nature of this improvement and the rate of data production though, the content of these repositories is not without error. For example, the problem of contaminated sequences has been recognized for nearly two decades, with evidence stating that bacteria and human error are the two most common sources of contamination.<ref name="MerchantUnexp14">{{cite journal |title=Unexpected cross-species contamination in genome sequencing projects |journal=PeerJ |author=Merchant, S.; Wood, D.E.; Salzberg, S.L. |volume=2 |pages=e675 |year=2014 |doi=10.7717/peerj.675 |pmid=25426337 |pmc=PMC4243333}}</ref><ref name="StrongMicro14">{{cite journal |title=Microbial contamination in next generation sequencing: implications for sequence-based analysis of clinical samples |journal=PLoS Pathogens |author=Strong, M.J.; Xu, G.; Morici, L. et al. |volume=10 |issue=11 |pages=e1004437 |year=2014 |doi=10.1371/journal.ppat.1004437 |pmid=25412476 |pmc=PMC4239086}}</ref> Ancient DNA is also particularly affected by human contamination.<ref name="PilliMonit13">{{cite journal |title=Monitoring DNA contamination in handled vs. directly excavated ancient human skeletal remains |journal=PLoS One |author=Pilli, E.; Modi, A.; Serpico, C. et al. |volume=8 |issue=1 |pages=e52524 |year=2013 |doi=10.1371/journal.pone.0052524 |pmid=23372650 |pmc=PMC3556025}}</ref> These contaminants are frequently introduced during experiments<ref name="MerchantUnexp14" /><ref name="BallenghienPatterns17">{{cite journal |title=Patterns of cross-contamination in a multispecies population genomic project: Detection, quantification, impact, and solutions |journal=BMC Biology |author=Ballenghien, M.; Faivre, N;. Galtier, N. |volume=15 |issue=1 |pages=25 |year=2017 |doi=10.1186/s12915-017-0366-6 |pmid=28356154 |pmc=PMC5370491}}</ref> from natural associations and insufficient purification.<ref name="SimionALarge17">{{cite journal |title=A Large and Consistent Phylogenomic Dataset Supports Sponges as the Sister Group to All Other Animals |journal=Current Biology |author=Simion, P.; Philippe, H.; Baurain, D. et al. |volume=27 |issue=7 |pages=958-967 |year=2017 |doi=10.1016/j.cub.2017.02.031 |pmid=28318975}}</ref> In the past few years, additional reports have highlighted cases of DNA contamination in published genome data<ref name="WittAnAss09">{{cite journal |title=An assessment of air as a source of DNA contamination encountered when performing PCR |journal=Journal of Biomolecular Techniques |author=Witt, N.; Rodger, G.; Vandesompele, J. et al. |volume=20 |issue=5 |pages=236–40 |year=2009 |pmid=19949694 |pmc=PMC2777341}}</ref><ref name="LongoAbund11">{{cite journal |title=Abundant human DNA contamination identified in non-primate genome databases |journal=PLoS One |author=Longo, M.S.; O'Neill, M.J.; O'Neill, R.J. |volume=6 |issue=2 |pages=e16410 |year=2011 |doi=10.1371/journal.pone.0016410 |pmid=21358816 |pmc=PMC3040168}}</ref>, suggesting that DNA contamination may be more widespread than previously thought. We recognize that errors and omissions can occur in open databases both at the sequence and at the [[metadata]] levels, but for this article we mainly focus on sequence and taxonomic data concerns for the purposes of illustrating some of the many [[data integrity]] challenges possible.
An important goal for [[bioinformatics]] is the continuous improvement of biological databases. Given the rapid nature of this improvement and the rate of data production though, the content of these repositories is not without error. For example, the problem of contaminated sequences has been recognized for nearly two decades, with evidence stating that bacteria and human error are the two most common sources of contamination.<ref name="MerchantUnexp14">{{cite journal |title=Unexpected cross-species contamination in genome sequencing projects |journal=PeerJ |author=Merchant, S.; Wood, D.E.; Salzberg, S.L. |volume=2 |pages=e675 |year=2014 |doi=10.7717/peerj.675 |pmid=25426337 |pmc=PMC4243333}}</ref><ref name="StrongMicro14">{{cite journal |title=Microbial contamination in next generation sequencing: implications for sequence-based analysis of clinical samples |journal=PLoS Pathogens |author=Strong, M.J.; Xu, G.; Morici, L. et al. |volume=10 |issue=11 |pages=e1004437 |year=2014 |doi=10.1371/journal.ppat.1004437 |pmid=25412476 |pmc=PMC4239086}}</ref> Ancient DNA is also particularly affected by human contamination.<ref name="PilliMonit13">{{cite journal |title=Monitoring DNA contamination in handled vs. directly excavated ancient human skeletal remains |journal=PLoS One |author=Pilli, E.; Modi, A.; Serpico, C. et al. |volume=8 |issue=1 |pages=e52524 |year=2013 |doi=10.1371/journal.pone.0052524 |pmid=23372650 |pmc=PMC3556025}}</ref> These contaminants are frequently introduced during experiments<ref name="MerchantUnexp14" /><ref name="BallenghienPatterns17">{{cite journal |title=Patterns of cross-contamination in a multispecies population genomic project: Detection, quantification, impact, and solutions |journal=BMC Biology |author=Ballenghien, M.; Faivre, N;. Galtier, N. |volume=15 |issue=1 |pages=25 |year=2017 |doi=10.1186/s12915-017-0366-6 |pmid=28356154 |pmc=PMC5370491}}</ref> from natural associations and insufficient purification.<ref name="SimionALarge17">{{cite journal |title=A Large and Consistent Phylogenomic Dataset Supports Sponges as the Sister Group to All Other Animals |journal=Current Biology |author=Simion, P.; Philippe, H.; Baurain, D. et al. |volume=27 |issue=7 |pages=958-967 |year=2017 |doi=10.1016/j.cub.2017.02.031 |pmid=28318975}}</ref> In the past few years, additional reports have highlighted cases of DNA contamination in published genome data<ref name="WittAnAss09">{{cite journal |title=An assessment of air as a source of DNA contamination encountered when performing PCR |journal=Journal of Biomolecular Techniques |author=Witt, N.; Rodger, G.; Vandesompele, J. et al. |volume=20 |issue=5 |pages=236–40 |year=2009 |pmid=19949694 |pmc=PMC2777341}}</ref><ref name="LongoAbund11">{{cite journal |title=Abundant human DNA contamination identified in non-primate genome databases |journal=PLoS One |author=Longo, M.S.; O'Neill, M.J.; O'Neill, R.J. |volume=6 |issue=2 |pages=e16410 |year=2011 |doi=10.1371/journal.pone.0016410 |pmid=21358816 |pmc=PMC3040168}}</ref>, suggesting that DNA contamination may be more widespread than previously thought. We recognize that errors and omissions can occur in open databases both at the sequence and at the [[metadata]] levels, but for this article we mainly focus on sequence and taxonomic data concerns for the purposes of illustrating some of the many [[data integrity]] challenges possible.
In addition to contamination, two high-profile examples of sequence errors include the reassembly of a misassembled ''Francisella tularensis'' genome<ref name="PuiuReass08">{{cite journal |title=Re-assembly of the genome of ''Francisella tularensis'' subsp. holarctica OSU18 |journal=PLoS One |author=Puiu, D.; Salzberg, S.L. |volume=3 |issue=10 |pages=e3427 |year=2008 |doi=10.1371/journal.pone.0003427 |pmid=18927608 |pmc=PMC2561293}}</ref> and the identification of single nucleotide errors in a reference ''Tobacco mosaic virus'' (TMV) genome.<ref name="CooperProof14">{{cite journal |title=Proof by synthesis of ''Tobacco mosaic virus'' |journal=Genome Biology |author=Cooper, P. |volume=15 |issue=5 |pages=R67 |year=2014 |doi=10.1186/gb-2014-15-5-r67 |pmid=24887356 |pmc=PMC4072989}}</ref> Without a way to flag or remove the erroneous entries, future researchers are left to continually rediscover them. The errors in the reference TMV sequence are particularly disturbing. The taxonomic assignment corresponds to a pathogenic strain, but due to two erroneous single nucleotide polymorphisms (SNPs), virions synthesized from the published reference sequence are atypically not infectious. Overlooked contamination in reference genomes can thereby lead to wrong or confusing results and may have major detrimental effects on biological conclusions.<ref name="PhilippeResolv11">{{cite journal |title=Resolving difficult phylogenetic questions: Why more sequences are not enough |journal=PLoS Biology |author=Philippe, H;. Brinkmann, H.; Lavrov, D.V. et al. |volume=9 |issue=3 |pages=e1000602 |year=2011 |doi=10.1371/journal.pbio.1000602 |pmid=21423652 |pmc=PMC3057953}}</ref><ref name="Laurin-LemayOrigin12">{{cite journal |title=Resolving difficult phylogenetic questions: Why more sequences are not enough |journal=PLoS Biology |author=Laurin-Lemay, S.; Brinkmann, H.; Philippe, H. |volume=22 |issue=15 |pages=R593–4 |year=2012 |doi=10.1016/j.cub.2012.06.013 |pmid=22877776}}</ref> While resequencing could be used to identify and correct sequence errors, it is only possible when the original source material is available. For the given example of single nucleotide errors in the TMV genome, the biological sample (sequenced in 1982) no longer exists. In addition to missing samples, samples of high consequence human and agricultural pathogens may not be available for resequencing.
Database integrity considerations for proteomics are generally similar to those for genomics because databases of protein sequences are derived from genome sequencing, via genome annotation and ''in silico'' translation. A sequence database error is unlikely to result in spurious detection of a protein that is present in the [[Sample (material)|sample]] (false positive), but it could easily lead to a failure to detect a protein that is present (false negative). This is particularly concerning for discovery of accurate peptide signatures for use in targeted assays, a rapidly growing area of research.
In this section we discussed the issue of errors in genomic and proteomic databases and their impacts for research and application. Sources of these errors may include, among others, entry errors derived from data transfer, original errors derived from source data, and metadata errors (typically provenance-related) derived from the analysis pipeline. Original errors can arise from sequencing and sample preparation instrumentation chemistry, hardware, and software. Metadata errors can arise from bioinformatics software and faulty human interpretation. Each of these errors may be considered noise or the result of some other unintentional cause, but the key problem to note is that each element of the analytical process introduces some level of artifact when creating the analytical product, i.e., what is defined as a peak or a spot, what is the gene scaffold, what is the closed genome, etc. Any difference in process would therefore by its nature have some impact on the final genome. Our goal here is to start drawing connections between these process elements and genome anomalies.


==References==
==References==

Revision as of 21:01, 18 June 2019

Full article title Defending out public biological databases as a global critical infrastructure
Journal Frontiers in Bioengineering and Biotechnology
Author(s) Caswell, Jacob; Gans, Jason D.; Generous, Nicolas; Hudson, Corey M.; Merkley, Eric; Johnson, Curtis; Oehmen, Christopher;
Omberg, Kristin; Purvine, Emilie; Taylor, Karen; Ting, Christina L.; Wolinsky, Murray; Xie, Gary
Author affiliation(s) Sandia National Laboratories, Los Alamos National Laboratory, Pacific Northwest National Laboratory
Primary contact Email: karen at pnnl dot gov
Editors Murch, Randall S.
Year published 2019
Volume and issue 7
Page(s) 58
DOI 10.3389/fbioe.2019.00058
ISSN 2296-4185
Distribution license Creative Commons Attribution 4.0 International
Website https://www.frontiersin.org/articles/10.3389/fbioe.2019.00058/full
Download https://www.frontiersin.org/articles/10.3389/fbioe.2019.00058/pdf (PDF)
This article should not be considered complete until this message box has been removed. This is a work in progress.

Abstract

Progress in modern biology is being driven, in part, by the large amounts of freely available data in public resources such as the International Nucleotide Sequence Database Collaboration (INSDC), the world's primary database of biological sequence (and related) information. INSDC and similar databases have dramatically increased the pace of fundamental biological discovery and enabled a host of innovative therapeutic, diagnostic, and forensic applications. However, as high-value, openly shared resources with a high degree of assumed trust, these repositories share compelling similarities to the early days of the internet. Consequently, as public biological databases continue to increase in size and importance, we expect that they will face the same threats as undefended cyberspace. There is a unique opportunity, before a significant breach and loss of trust occurs, to ensure they evolve with quality and security as a design philosophy rather than costly “retrofitted” mitigations. This perspective article surveys some potential quality assurance and security weaknesses in existing open genomic and proteomic repositories, describes methods to mitigate the likelihood of both intentional and unintentional errors, and offers recommendations for risk mitigation based on lessons learned from cybersecurity.

Keywords: cyberbiosecurity, biosecurity, cybersecurity, biological databases, machine learning, bioeconomy

Introduction

Although an openly shared interaction platform confers great value to the biological research community, it may also introduce quality and security risks. Without a system for trusted correction and revision, these shared resources may facilitate widespread dissemination and use of low-quality content, for instance, taxonomically misclassified or erroneous sequences. Furthermore, as these public databases increase in size and importance, they may fall victim to the same security issues and abuses that plague cyberspace to this day. If we act now by developing the databases with quality and security as a design philosophy, we can protect these databases at a much lower cost and with fewer challenges than we currently face with the internet.

In this perspective article, the authors aim to outline some potential quality assurance and security weaknesses in existing public biological repositories. In the background section we provide a discussion of errors present in public biological databases and discuss possible security vulnerabilities inherent in their access, publication, and distribution models and systems. Both unintentional and intentional errors are discussed, the latter of which has not been given significant consideration in literature.[1] Afterwards, we attempt to introduce greater trust in the data and analyses by providing recommendations to mitigate or account for these errors and vulnerabilities and point to approaches used by other internet databases. Finally, we summarize our recommendations in the conclusions section.

This article focuses on databases which contain public and freely available data. We recognize that other biological databases exist which contain private, sensitive, or otherwise valuable data (e.g., human genomes). While unauthorized disclosure is not a formal concern in public, non-human databases, safeguarding against intentional or unintentional erroneous content is. Some approaches have been proposed to protect unauthorized disclosure[2][3][4] and, while we don't survey these approaches in this perspective, we note that the public database community may benefit from these ideas as well.

Background: Problems with public biological databases

Data integrity

An important goal for bioinformatics is the continuous improvement of biological databases. Given the rapid nature of this improvement and the rate of data production though, the content of these repositories is not without error. For example, the problem of contaminated sequences has been recognized for nearly two decades, with evidence stating that bacteria and human error are the two most common sources of contamination.[5][6] Ancient DNA is also particularly affected by human contamination.[7] These contaminants are frequently introduced during experiments[5][8] from natural associations and insufficient purification.[9] In the past few years, additional reports have highlighted cases of DNA contamination in published genome data[10][11], suggesting that DNA contamination may be more widespread than previously thought. We recognize that errors and omissions can occur in open databases both at the sequence and at the metadata levels, but for this article we mainly focus on sequence and taxonomic data concerns for the purposes of illustrating some of the many data integrity challenges possible.

In addition to contamination, two high-profile examples of sequence errors include the reassembly of a misassembled Francisella tularensis genome[12] and the identification of single nucleotide errors in a reference Tobacco mosaic virus (TMV) genome.[13] Without a way to flag or remove the erroneous entries, future researchers are left to continually rediscover them. The errors in the reference TMV sequence are particularly disturbing. The taxonomic assignment corresponds to a pathogenic strain, but due to two erroneous single nucleotide polymorphisms (SNPs), virions synthesized from the published reference sequence are atypically not infectious. Overlooked contamination in reference genomes can thereby lead to wrong or confusing results and may have major detrimental effects on biological conclusions.[14][15] While resequencing could be used to identify and correct sequence errors, it is only possible when the original source material is available. For the given example of single nucleotide errors in the TMV genome, the biological sample (sequenced in 1982) no longer exists. In addition to missing samples, samples of high consequence human and agricultural pathogens may not be available for resequencing.

Database integrity considerations for proteomics are generally similar to those for genomics because databases of protein sequences are derived from genome sequencing, via genome annotation and in silico translation. A sequence database error is unlikely to result in spurious detection of a protein that is present in the sample (false positive), but it could easily lead to a failure to detect a protein that is present (false negative). This is particularly concerning for discovery of accurate peptide signatures for use in targeted assays, a rapidly growing area of research.

In this section we discussed the issue of errors in genomic and proteomic databases and their impacts for research and application. Sources of these errors may include, among others, entry errors derived from data transfer, original errors derived from source data, and metadata errors (typically provenance-related) derived from the analysis pipeline. Original errors can arise from sequencing and sample preparation instrumentation chemistry, hardware, and software. Metadata errors can arise from bioinformatics software and faulty human interpretation. Each of these errors may be considered noise or the result of some other unintentional cause, but the key problem to note is that each element of the analytical process introduces some level of artifact when creating the analytical product, i.e., what is defined as a peak or a spot, what is the gene scaffold, what is the closed genome, etc. Any difference in process would therefore by its nature have some impact on the final genome. Our goal here is to start drawing connections between these process elements and genome anomalies.

References

  1. ↑ Moussouni, F.; Berti‐Équille, L. (2014). "Cleaning, Integrating, and Warehousing Genomic Data From Biomedical Resources". In Elloumi, M.; Zomaya, A.Y.. Biological Knowledge Discovery Handbook. John Wiley & Sons. pp. 35–58. doi:10.1002/9781118617151.ch02. ISBN 9781118617151. 
  2. ↑ Kim, M.; Lauter, K. (2015). "Private genome analysis through homomorphic encryption". BMC Medical Informatics and Decision Making 15 (Suppl 5): S3. doi:10.1186/1472-6947-15-S5-S3. PMC PMC4699052. PMID 26733152. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4699052. 
  3. ↑ Mandal, A.; Mitchell, J.C.; Montgomery, H.; Roy, A. (2018). "Data oblivious genome variants search on Intel SGX". In Garcia-Alfaro, J.; Herrera-Joancomartí, J.; Livraga, G.; Rios, R.. Data Privacy Management, Cryptocurrencies and Blockchain Technology. Lecture Notes in Computer Science. Springer International Publishing. pp. 21. doi:10.1007/978-3-030-00305-0_21. ISBN 9783030003050. 
  4. ↑ Ozercan, H.I.; Ileri, A.M.; Ayday, E.; Alkan, C. (2018). "Realizing the potential of blockchain technologies in genomics". Gemone Research 28 (9): 1255–63. doi:10.1101/gr.207464.116. PMC PMC6120626. PMID 30076130. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6120626. 
  5. ↑ 5.0 5.1 Merchant, S.; Wood, D.E.; Salzberg, S.L. (2014). "Unexpected cross-species contamination in genome sequencing projects". PeerJ 2: e675. doi:10.7717/peerj.675. PMC PMC4243333. PMID 25426337. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243333. 
  6. ↑ Strong, M.J.; Xu, G.; Morici, L. et al. (2014). "Microbial contamination in next generation sequencing: implications for sequence-based analysis of clinical samples". PLoS Pathogens 10 (11): e1004437. doi:10.1371/journal.ppat.1004437. PMC PMC4239086. PMID 25412476. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239086. 
  7. ↑ Pilli, E.; Modi, A.; Serpico, C. et al. (2013). "Monitoring DNA contamination in handled vs. directly excavated ancient human skeletal remains". PLoS One 8 (1): e52524. doi:10.1371/journal.pone.0052524. PMC PMC3556025. PMID 23372650. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556025. 
  8. ↑ Ballenghien, M.; Faivre, N;. Galtier, N. (2017). "Patterns of cross-contamination in a multispecies population genomic project: Detection, quantification, impact, and solutions". BMC Biology 15 (1): 25. doi:10.1186/s12915-017-0366-6. PMC PMC5370491. PMID 28356154. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5370491. 
  9. ↑ Simion, P.; Philippe, H.; Baurain, D. et al. (2017). "A Large and Consistent Phylogenomic Dataset Supports Sponges as the Sister Group to All Other Animals". Current Biology 27 (7): 958-967. doi:10.1016/j.cub.2017.02.031. PMID 28318975. 
  10. ↑ Witt, N.; Rodger, G.; Vandesompele, J. et al. (2009). "An assessment of air as a source of DNA contamination encountered when performing PCR". Journal of Biomolecular Techniques 20 (5): 236–40. PMC PMC2777341. PMID 19949694. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2777341. 
  11. ↑ Longo, M.S.; O'Neill, M.J.; O'Neill, R.J. (2011). "Abundant human DNA contamination identified in non-primate genome databases". PLoS One 6 (2): e16410. doi:10.1371/journal.pone.0016410. PMC PMC3040168. PMID 21358816. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3040168. 
  12. ↑ Puiu, D.; Salzberg, S.L. (2008). "Re-assembly of the genome of Francisella tularensis subsp. holarctica OSU18". PLoS One 3 (10): e3427. doi:10.1371/journal.pone.0003427. PMC PMC2561293. PMID 18927608. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2561293. 
  13. ↑ Cooper, P. (2014). "Proof by synthesis of Tobacco mosaic virus". Genome Biology 15 (5): R67. doi:10.1186/gb-2014-15-5-r67. PMC PMC4072989. PMID 24887356. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4072989. 
  14. ↑ Philippe, H;. Brinkmann, H.; Lavrov, D.V. et al. (2011). "Resolving difficult phylogenetic questions: Why more sequences are not enough". PLoS Biology 9 (3): e1000602. doi:10.1371/journal.pbio.1000602. PMC PMC3057953. PMID 21423652. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3057953. 
  15. ↑ Laurin-Lemay, S.; Brinkmann, H.; Philippe, H. (2012). "Resolving difficult phylogenetic questions: Why more sequences are not enough". PLoS Biology 22 (15): R593–4. doi:10.1016/j.cub.2012.06.013. PMID 22877776. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added. The footnote in the original material were turned into an inline references for this version.

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