A signal model for forensic DNA mixtures
For forensic purposes, short tandem repeat allele signals are used as DNA fingerprints. The interpretation of signals measured from samples has traditionally been conducted by applying thresholding. More quantitative approaches have recently been developed, but not for the purposes of identifying an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en_US |
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2016
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| Online Access: | http://hdl.handle.net/1721.1/100950 https://orcid.org/0000-0003-4059-407X https://orcid.org/0000-0001-8982-6615 |
| _version_ | 1811080551000440832 |
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| author | Monich, Ullrich Grgicak, Catherine Cadambe, Viveck Wellner, Genevieve Duffy, Ken Medard, Muriel Wu, Yonglin |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Monich, Ullrich Grgicak, Catherine Cadambe, Viveck Wellner, Genevieve Duffy, Ken Medard, Muriel Wu, Yonglin |
| author_sort | Monich, Ullrich |
| collection | MIT |
| description | For forensic purposes, short tandem repeat allele signals are used as DNA fingerprints. The interpretation of signals measured from samples has traditionally been conducted by applying thresholding. More quantitative approaches have recently been developed, but not for the purposes of identifying an appropriate signal model. By analyzing data from 643 single person samples, we develop such a signal model. Three standard classes of two-parameter distributions, one symmetric (normal) and two right-skewed (gamma and log-normal), were investigated for their ability to adequately describe the data. Our analysis suggests that additive noise is well modeled via the log-normal distribution class and that variability in peak heights is well described by the gamma distribution class. This is a crucial step towards the development of principled techniques for mixed sample signal deconvolution. |
| first_indexed | 2024-09-23T11:33:22Z |
| format | Article |
| id | mit-1721.1/100950 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:33:22Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | mit-1721.1/1009502022-09-27T20:20:44Z A signal model for forensic DNA mixtures Monich, Ullrich Grgicak, Catherine Cadambe, Viveck Wellner, Genevieve Duffy, Ken Medard, Muriel Wu, Yonglin Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Research Laboratory of Electronics Monich, Ullrich Wu, Yonglin Medard, Muriel For forensic purposes, short tandem repeat allele signals are used as DNA fingerprints. The interpretation of signals measured from samples has traditionally been conducted by applying thresholding. More quantitative approaches have recently been developed, but not for the purposes of identifying an appropriate signal model. By analyzing data from 643 single person samples, we develop such a signal model. Three standard classes of two-parameter distributions, one symmetric (normal) and two right-skewed (gamma and log-normal), were investigated for their ability to adequately describe the data. Our analysis suggests that additive noise is well modeled via the log-normal distribution class and that variability in peak heights is well described by the gamma distribution class. This is a crucial step towards the development of principled techniques for mixed sample signal deconvolution. United States. Dept. of Justice. National Institute of Justice (2012-DN-BX-K050) 2016-01-20T17:01:29Z 2016-01-20T17:01:29Z 2014-11 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-8297-4 978-1-4799-8295-0 http://hdl.handle.net/1721.1/100950 Monich, Ullrich J., Catherine Grgicak, Viveck Cadambe, Jason Yonglin Wu, Genevieve Wellner, Ken Duffy, and Muriel Medard. “A Signal Model for Forensic DNA Mixtures.” 2014 48th Asilomar Conference on Signals, Systems and Computers (November 2014). https://orcid.org/0000-0003-4059-407X https://orcid.org/0000-0001-8982-6615 en_US http://dx.doi.org/10.1109/ACSSC.2014.7094478 Proceedings of the 2014 48th Asilomar Conference on Signals, Systems and Computers Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf MIT web domain |
| spellingShingle | Monich, Ullrich Grgicak, Catherine Cadambe, Viveck Wellner, Genevieve Duffy, Ken Medard, Muriel Wu, Yonglin A signal model for forensic DNA mixtures |
| title | A signal model for forensic DNA mixtures |
| title_full | A signal model for forensic DNA mixtures |
| title_fullStr | A signal model for forensic DNA mixtures |
| title_full_unstemmed | A signal model for forensic DNA mixtures |
| title_short | A signal model for forensic DNA mixtures |
| title_sort | signal model for forensic dna mixtures |
| url | http://hdl.handle.net/1721.1/100950 https://orcid.org/0000-0003-4059-407X https://orcid.org/0000-0001-8982-6615 |
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