Forensic STR allele extraction using a machine learning paradigm.

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dc.contributor.author Liu, Yao-Yuan en
dc.contributor.author Welch, John en
dc.contributor.author England, Ryan en
dc.contributor.author Stacey, Janet en
dc.contributor.author Harbison, SallyAnn en
dc.date.accessioned 2020-02-17T01:26:15Z en
dc.date.issued 2020-01 en
dc.identifier.issn 1872-4973 en
dc.identifier.uri http://hdl.handle.net/2292/50073 en
dc.description.abstract We present a machine learning approach to short tandem repeat (STR) sequence detection and extraction from massively parallel sequencing data called Fragsifier. Using this approach, STRs are detected on each read by first locating the longest repeat stretches followed by locus prediction using k-mers in a machine learning sequence model. This is followed by reference flanking sequence alignment to determine precise STR boundaries. We show that Fragsifier produces genotypes that are concordant with profiles obtained using capillary electrophoresis (CE), and also compared the results with that of STRait Razor and the ForenSeq UAS. The data pre-processing and training of the sequence classifier is readily scripted, allowing the analyst to experiment with different thresholds, datasets and loci of interest, and different machine learning models. en
dc.format.medium Print-Electronic en
dc.language eng en
dc.relation.ispartofseries Forensic science international. Genetics en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Forensic STR allele extraction using a machine learning paradigm. en
dc.type Journal Article en
dc.identifier.doi 10.1016/j.fsigen.2019.102194 en
pubs.begin-page 102194 en
pubs.volume 44 en
dc.rights.holder Copyright: The author en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Journal Article en
pubs.elements-id 786381 en
pubs.org-id Science en
pubs.org-id School of Computer Science en
dc.identifier.eissn 1878-0326 en
pubs.record-created-at-source-date 2019-11-08 en
pubs.dimensions-id 31698330 en


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