Forensic STR allele extraction using a machine learning paradigm.
Identifieur interne : 000197 ( Main/Exploration ); précédent : 000196; suivant : 000198Forensic STR allele extraction using a machine learning paradigm.
Auteurs : Yao-Yuan Liu [Nouvelle-Zélande] ; David Welch [Nouvelle-Zélande] ; Ryan England [Nouvelle-Zélande] ; Janet Stacey [Nouvelle-Zélande] ; Sallyann Harbison [Nouvelle-Zélande]Source :
- Forensic science international. Genetics [ 1878-0326 ] ; 2020.
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.
DOI: 10.1016/j.fsigen.2019.102194
PubMed: 31698330
Affiliations:
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Electronic address: sallyann.harbison@esr.cri.nz.</nlm:affiliation><country xml:lang="fr">Nouvelle-Zélande</country><wicri:regionArea>Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142</wicri:regionArea><wicri:noRegion>Auckland 1142</wicri:noRegion></affiliation></author></analytic><series><title level="j">Forensic science international. Genetics</title><idno type="eISSN">1878-0326</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>Nouvelle-Zélande</li></country></list><tree><country name="Nouvelle-Zélande"><noRegion><name sortKey="Liu, Yao Yuan" sort="Liu, Yao Yuan" uniqKey="Liu Y" first="Yao-Yuan" last="Liu">Yao-Yuan Liu</name></noRegion><name sortKey="England, Ryan" sort="England, Ryan" uniqKey="England R" first="Ryan" last="England">Ryan England</name><name sortKey="Harbison, Sallyann" sort="Harbison, Sallyann" uniqKey="Harbison S" first="Sallyann" last="Harbison">Sallyann Harbison</name><name sortKey="Stacey, Janet" sort="Stacey, Janet" uniqKey="Stacey J" first="Janet" last="Stacey">Janet Stacey</name><name sortKey="Welch, David" sort="Welch, David" uniqKey="Welch D" first="David" last="Welch">David Welch</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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