Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Enterprise Systems for Networked Smart Cities Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

9. A Survey of Methods and Tools for Large-Scale DNA Mixture Profiling

verfasst von : Emad Alamoudi, Rashid Mehmood, Aiiad Albeshri, Takashi Gojobori

Erschienen in: Smart Infrastructure and Applications

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

DNA typing or profiling is being widely used for criminal identification, paternity tests, and diagnosis of genetic diseases. DNA typing is considered one of the hardest problems in the forensic science domain, and it is an active area of research. The computational complexity of DNA typing increases significantly with the number of unknowns in the mixture and has been the major deterring factor holding its advancements and applications. In this chapter, we provide an extended review of DNA profiling methods and tools with a particular focus on their computational performance and accuracy. The process of DNA profiling within the broader context of forensic science and genetics is explained. The various classes of DNA profiling methods including general methods, and those based on maximum likelihood estimators, are reviewed. The reviewed DNA profiling tools include LRmix Studio, TrueAllele, DNAMIX V.3, Euroformix, CeesIt, NOCIt, DNAMixture, Kongoh, LikeLTD, LabRetriever, and STRmix. A review of high-performance computing literature in bioinformatics and HPC frameworks is also given. Faster interpretations of DNA mixtures with a large number of unknowns and higher accuracies are expected to open up new frontiers for this area.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Parallel Shortest Path Big Data Graph Computations of US Road Network Using Apache Spark: Survey, Architecture, and Evaluation
Nächstes Kapitel An Architecture to Improve the Security of Cloud Computing in the Healthcare Sector
Literatur
1.
The American Heritage medical dictionary. Houghton Mifflin Co., Boston (2007)
2.
Butler, J.M.: Fundamentals of Forensic DNA Typing. Academic Press/Elsevier (2010)
3.
Swaminathan, H., Grgicak, C.M., Medard, M., Lun, D.S.: NOCIt: a computational method to infer the number of contributors to DNA samples analyzed by STR genotyping. Forensic Sci. Int. Genet. 16, 172–180 (2015)CrossRef
4.
Alamoudi, E., Mehmood, R., Albeshri, A., Gojobori, T.: DNA profiling methods and tools: a review. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. pp. 216–231. Springer, Cham (2018)
5.
Arfat, Y., Aqib, M., Mehmood, R., Albeshri, A., Katib, I., Albogami, N., Alzahrani, A.: Enabling smarter societies through Mobile big data fogs and clouds. Procedia Comput. Sci. 109, 1128–1133 (2017)CrossRef
6.
Alam, F., Mehmood, R., Katib, I., Albogami, N.N., Albeshri, A.: Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access. 5, 9533–9554 (2017)CrossRef
7.
Mehmood, R., Alam, F., Albogami, N.N., Katib, I., Albeshri, A., Altowaijri, S.M.: UTiLearn: a personalised ubiquitous teaching and learning system for smart societies. IEEE Access. 5, 2615–2635 (2017)CrossRef
8.
Butler, J.M.: The future of forensic DNA analysis. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 370, 577–579 (2015)CrossRef
9.
Paoletti, D.R., Krane, D.E., Raymer, M.L., Doom, T.E.: Inferring the number of contributors to mixed DNA profiles. IEEE/ACM Trans. Comput. Biol. Bioinforma. 9, 113–122 (2012)CrossRef
10.
Perez, J., Mitchell, A.A., Ducasse, N., Tamariz, J., Caragine, T.: Estimating the number of contributors to two-, three-, and four-person mixtures containing DNA in high template and low template amounts. Croat. Med. J. 52, 314–326 (2011)CrossRef
11.
Gill, P., Haned, H.: A new methodological framework to interpret complex DNA profiles using likelihood ratios. Forensic Sci. Int. Genet. 7, 251–263 (2013)CrossRef
12.
Weedn, V.W., Foran, D.R.: Forensic DNA typing. In: Molecular pathology in clinical practice. pp. 793–810. Springer International Publishing, Champions (2016)CrossRef
13.
Monich, U.J., Grgicak, C., Cadambe, V., Wu, J.Y., Wellner, G., Duffy, K., Medard, M.: A signal model for forensic DNA mixtures. In: 2014 48th Asilomar Conference on Signals, Systems and Computers. pp. 429–433. IEEE (2014)
14.
Tao, R., Wang, S., Zhang, J., Zhang, J., Yang, Z., Sheng, X., Hou, Y., Zhang, S., Li, C.: Separation/extraction, detection, and interpretation of DNA mixtures in forensic science (review)
15.
Inman, K., Rudin, N., Cheng, K., Robinson, C., Kirschner, A., Inman-Semerau, L., Lohmueller, K.E.: Lab retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles. BMC Bioinformatics. 16, 298 (2015)CrossRef
16.
Schmidt, B., Hildebrandt, A.: Next-generation sequencing: big data meets high performance computing. Drug Discov. Today. 22, 712–717 (2017)CrossRef
17.
Chang, Y.-J., Chen, C.-C., Chen, C.-L., Ho, J.-M.: A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework. BMC Genomics. 13 Suppl 7, S28 (2012)
18.
Li, D., Liu, C.-M., Luo, R., Sadakane, K., Lam, T.-W.: MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 31, 1674–1676 (2015)CrossRef
19.
Liu, Y., Schmidt, B., Maskell, D.L.: DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI. BMC Bioinformatics. 12, 85 (2011)CrossRef
20.
Erbert, M., Rechner, S., Müller-Hannemann, M.: Gerbil: a fast and memory-efficient k-mer counter with GPU-support. Algorithms Mol. Biol. 12, 9 (2017)CrossRef
21.
Varma, B.S.C., Paul, K., Balakrishnan, M., Lavenier, D.: FAssem: FPGA Based Acceleration of De Novo Genome Assembly. In: 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines. pp. 173–176. IEEE (2013)
22.
23.
Kang, S.J., Lee, S.Y., Lee, K.M.: Performance comparison of OpenMP, MPI, and MapReduce in practical problems. Adv. Multimed. 2015, 1–9 (2015)CrossRef
24.
Hamidi, B., Hamidi, L.: Synchronization Possibilities and Features in Java, vol. 1, p. 75 (2015)
25.
Carpenter, B., Getov, V., Judd, G., Skjellum, A., Fox, G.: MPJ: MPI-like message passing for Java. Concurr. Pract. Exp. 12, 1019–1038 (2000)CrossRef
26.
Memeti, S., Pllana, S.: A machine learning approach for accelerating DNA sequence analysis. Int. J. High Perform. Comput. Appl. 1–17
27.
Bell, G., Gray, J.: What’ S Next in Computing ? 45, 91–95 (2002)
28.
Diegoli, T.M., Rohde, H., Borowski, S., Krawczak, M., Coble, M.D., Nothnagel, M.: Genetic mapping of 15 human X chromosomal forensic short tandem repeat (STR) loci by means of multi-core parallelization. Forensic Sci. Int. Genet. 25, 39 (2016)CrossRef
29.
Laguna, I., Ahn, D.H., De Supinski, B.R., Gamblin, T., Lee, G.L., Schulz, M., Bagchi, S., Kulkarni, M., Zhou, B., Chen, Z., Qin, F.: Debugging high-performance computing applications at massive scales. Commun. ACM. 58, 72–81 (2015)CrossRef
30.
Butler, J.M.: Advanced topics in forensic DNA typing: interpretation
31.
Bille, T., Bright, J.-A., Buckleton, J.: Application of random match probability calculations to mixed STR profiles. J. Forensic Sci. 58, 474–485 (2013)CrossRef
32.
Garofano, P., Caneparo, D., D’Amico, G., Vincenti, M., Alladio, E.: An alternative application of the consensus method to DNA typing interpretation for low template-DNA mixtures. Forensic Sci. Int. Genet. Suppl. Ser. 5, e422–e424 (2015)CrossRef
33.
Kelly, H., Bright, J.-A., Buckleton, J.S., Curran, J.M.: A comparison of statistical models for the analysis of complex forensic DNA profiles. Sci. Justice. 54, 66–70 (2014)CrossRef
34.
Bleka, Ø., Storvik, G., Gill, P.: EuroForMix: an open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Sci. Int. Genet. 21, 35 (2016)CrossRef
35.
Perlin, M.W., Dormer, K., Hornyak, J., Schiermeier-Wood, L., Greenspoon, S.: TrueAllele casework on Virginia DNA mixture evidence: computer and manual interpretation in 72 reported criminal cases. PLoS One. 9, e92837 (2014)CrossRef
36.
Gill, P., Haned, H., Eduardoff, M., Santos, C., Phillips, C., Parson, W.: The Open-source software LRmix can be used to analyse SNP mixtures. Forensic Sci. Int. Genet. Suppl. Ser. 5, e50 (2015)CrossRef
37.
Swaminathan, H., Garg, A., Grgicak, C.M., Medard, M., Lun, D.S.: CEESIt: a computational tool for the interpretation of STR mixtures. Forensic Sci. Int. Genet. 22, 149–160 (2016)CrossRef
38.
Balding, D.J., Steele, C.: The likeLTD software: an illustrative analysis, explanation of the model, results of performance tests and version history. UCL Genet. Inst. 1, 1–49 (2014)
39.
Moretti, T.R., Just, R.S., Kehl, S.C., Willis, L.E., Buckleton, J.S., Bright, J.-A., Taylor, D.A., Onorato, A.J.: Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles. Forensic Sci. Int. Genet. 29, 126–144 (2017)CrossRef
40.
Taylor, D., Bright, J.-A., Buckleton, J.: Interpreting forensic DNA profiling evidence without specifying the number of contributors. Forensic Sci. Int. Genet. 13, 269–280 (2014)CrossRef
41.
Russell, D., Christensen, W., Lindsey, T.: A simple unconstrained semi-continuous model for calculating likelihood ratios for complex DNA mixtures. Forensic Sci. Int. Genet. Suppl. Ser. 5, e37–e38 (2015)CrossRef
42.
Paoletti, D.R., Doom, T.E., Krane, C.M., Raymer, M.L., Krane, D.E.: Empirical analysis of the STR profiles resulting from conceptual mixtures. J. Forensic Sci. 50, JFS2004475–JFS2004476 (2005)CrossRef
43.
Biedermann, A., Bozza, S., Konis, K., Taroni, F.: Inference about the number of contributors to a DNA mixture: comparative analyses of a Bayesian network approach and the maximum allele count method. Forensic Sci. Int. Genet. 6, 689–696 (2012)CrossRef
44.
Haned, H., Pène, L., Sauvage, F., Pontier, D.: The predictive value of the maximum likelihood estimator of the number of contributors to a DNA mixture. Forensic Sci. Int. Genet. 5, 281–284 (2011)CrossRef
45.
Haned, H., Pène, L., Lobry, J.R., Dufour, A.B., Pontier, D.: Estimating the number of contributors to forensic DNA mixtures: does maximum likelihood perform better than maximum allele count? J. Forensic Sci. 56, 23–28 (2011)CrossRef
46.
Haned, H., Benschop, C.C.G., Gill, P.D., Sijen, T.: Complex DNA mixture analysis in a forensic context: evaluating the probative value using a likelihood ratio model. Forensic Sci. Int. Genet. 16, 17–25 (2015)CrossRef
47.
Egeland, T., Dalen, I., Mostad, P.F.: Estimating the number of contributors to a DNA profile. Int. J. Legal Med. 117, 271–275 (2003)CrossRef
48.
Marciano, M.A., Adelman, J.D.: PACE: probabilistic assessment for contributor estimation— a machine learning-based assessment of the number of contributors in DNA mixtures. Forensic Sci. Int. Genet. 27, 82–91 (2017)CrossRef
49.
Curran, J.M., Triggs, C.M., Buckleton, J., Weir, B.S.: Interpreting DNA mixtures in structured populations. J. Forensic Sci. 44, 987–995 (1999)
50.
Haned, H., De Jong, J.: LRmix Studio 2.1 user manual. (2016)
51.
52.
Forensim: An open-source initiative for the evaluation of statistical methods in forensic genetics. Forensic Sci. Int. Genet. 5, 265–268 (2011)
53.
Gill, P., Sparkes, R., Pinchin, R., Clayton, T., Whitaker, J., Buckleton, J.: Interpreting simple STR mixtures using allele peak areas. Forensic Sci. Int. 91, 41–53 (1998)CrossRef
54.
Kling, D., Egeland, T., Tillmar, A.O.: FamLink – a user friendly software for linkage calculations in family genetics. Forensic Sci. Int. Genet. 6, 616–620 (2012)CrossRef
55.
Tvedebrink, T., Eriksen, P.S., Mogensen, H.S., Morling, N.: Evaluating the weight of evidence by using quantitative short tandem repeat data in DNA mixtures. J. R. Stat. Soc. Ser. C Applied Stat. 59, 855–874 (2010)MathSciNetCrossRef
56.
Developmental validation of STRmix™, expert software for the interpretation of forensic DNA profiles. Forensic Sci. Int. Genet. 23, 226–239 (2016)
57.
Perlin, M.W., Hornyak, J.M., Sugimoto, G., Miller, K.W.: TrueAllele genotype identification on DNA mixtures containing up to five unknown contributors*, vol. 60, p. 857 (2015)
58.
Cowell, R.G., Graversen, T., Lauritzen, S.L., Mortera, J.: Analysis of forensic DNA mixtures with artefacts. J. R. Stat. Soc. Ser. C Applied Stat., 64. 1–48 (2015)MathSciNet
59.
Manabe, S., Morimoto, C., Hamano, Y., Fujimoto, S., Tamaki, K.: Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model. PLoS One. 12, e0188183 (2017)CrossRef
60.
Bleka, Ø.: An introduction to EuroForMix (v1.8). 2016, 1–59 (2016)
61.
Manabe, S.: Kongoh version 1.0.1 User Manual. 1–12 (2017)
62.
Mehmood, R., Crowcroft, J.: Parallel iterative solution method for large sparse linear equation systems. Comput. Lab. Univ. 22 (2005)
63.
Mehmood, R.: Serial disk-based analysis of large stochastic models. In: Validation of Stochastic Systems. pp. 230–255. Springer, Berlin, (2004)
64.
Altowaijri, S., Mehmood, R., Williams, J.: A quantitative model of grid systems performance in healthcare organisations. In: 2010 International Conference on Intelligent Systems, Modelling and Simulation. pp. 431–436. IEEE (2010)
65.
Mehmood, R., Crowcroft, J., Hand, S., Smith, S.: Grid-level computing needs pervasive debugging. In: The 6th IEEE/ACM International Workshop on Grid Computing, 2005. p. 8 pp. IEEE (2005)
66.
Tawalbeh, L.A., Mehmood, R., Benkhlifa, E., Song, H.: Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access. 4, 6171–6180 (2016)CrossRef
67.
Tawalbeh, L.A., Bakhader, W., Mehmood, R., Song, H.: Cloudlet-Based Mobile Cloud Computing for Healthcare Applications. In: 2016 IEEE Global Communications Conference (GLOBECOM). pp. 1–6. IEEE (2016)
68.
Metadaten
Titel
A Survey of Methods and Tools for Large-Scale DNA Mixture Profiling
verfasst von
Emad Alamoudi
Rashid Mehmood
Aiiad Albeshri
Takashi Gojobori
Copyright-Jahr
2020
Buch
Smart Infrastructure and Applications

Print ISBN: 978-3-030-13704-5

Electronic ISBN: 978-3-030-13705-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-13705-2_9

Neuer Inhalt

    Bildnachweise