A Survey of Methods and Tools for Large-Scale DNA Mixture Profiling
Type
Book ChapterKAUST Department
Biological and Environmental Science and Engineering (BESE) DivisionBioscience Program
Computational Bioscience Research Center (CBRC)
Date
2019-06-21Permanent link to this record
http://hdl.handle.net/10754/668875
Metadata
Show full item recordAbstract
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.Citation
Alamoudi, E., Mehmood, R., Albeshri, A., & Gojobori, T. (2019). A Survey of Methods and Tools for Large-Scale DNA Mixture Profiling. EAI/Springer Innovations in Communication and Computing, 217–248. doi:10.1007/978-3-030-13705-2_9Sponsors
The work carried out in this chapter is supported by the HPC Center at the King Abdulaziz University.Publisher
Springer International PublishingISBN
97830301370459783030137052
Additional Links
http://link.springer.com/10.1007/978-3-030-13705-2_9ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-13705-2_9