TY - JOUR
T1 - An algorithm for random match probability calculation from peptide sequences
AU - Woerner, August E.
AU - Hewitt, F. Curtis
AU - Gardner, Myles W.
AU - Freitas, Michael A.
AU - Schulte, Kathleen Q.
AU - LeSassier, Danielle S.
AU - Baniasad, Maryam
AU - Reed, Andrew J.
AU - Powals, Megan E.
AU - Smith, Alan R.
AU - Albright, Nicolette C.
AU - Ludolph, Benjamin C.
AU - Zhang, Liwen
AU - Allen, Leah W.
AU - Weber, Katharina
AU - Budowle, Bruce
N1 - Funding Information:
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) (https://www.iarpa.gov), via contract number 2018-18041000003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. The funders reviewed and approved the manuscript for publication but had no role in study design, data collection and analysis, or preparation of the manuscript. Award received by Signature Science, LLC (co-PIs FCH and MWG). Sub-award received by University of North Texas (AEW).
Funding Information:
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) ( https://www.iarpa.gov ), via contract number 2018-18041000003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. The funders reviewed and approved the manuscript for publication but had no role in study design, data collection and analysis, or preparation of the manuscript. Award received by Signature Science, LLC (co-PIs FCH and MWG). Sub-award received by University of North Texas (AEW).
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7
Y1 - 2020/7
N2 - For the past three decades, forensic genetic investigations have focused on elucidating DNA signatures. While DNA has a number of desirable properties (e.g., presence in most biological materials, an amenable chemistry for analysis and well-developed statistics), DNA also has limitations. DNA may be in low quantity in some tissues, such as hair, and in some tissues it may degrade more readily than its protein counterparts. Recent research efforts have shown the feasibility of performing protein-based human identification in cases in which recovery of DNA is challenged; however, the methods involved in assessing the rarity of a given protein profile have not been addressed adequately. In this paper an algorithm is proposed that describes the computation of a random match probability (RMP) resulting from a genetically variable peptide signature. The approach described herein explicitly models proteomic error and genetic linkage, makes no assumptions as to allelic drop-out, and maps the observed proteomic alleles to their expected protein products from DNA which, in turn, permits standard corrections for population structure and finite database sizes. To assess the feasibility of this approach, RMPs were estimated from peptide profiles of skin samples from 25 individuals of European ancestry. 126 common peptide alleles were used in this approach, yielding a mean RMP of approximately 10−2.
AB - For the past three decades, forensic genetic investigations have focused on elucidating DNA signatures. While DNA has a number of desirable properties (e.g., presence in most biological materials, an amenable chemistry for analysis and well-developed statistics), DNA also has limitations. DNA may be in low quantity in some tissues, such as hair, and in some tissues it may degrade more readily than its protein counterparts. Recent research efforts have shown the feasibility of performing protein-based human identification in cases in which recovery of DNA is challenged; however, the methods involved in assessing the rarity of a given protein profile have not been addressed adequately. In this paper an algorithm is proposed that describes the computation of a random match probability (RMP) resulting from a genetically variable peptide signature. The approach described herein explicitly models proteomic error and genetic linkage, makes no assumptions as to allelic drop-out, and maps the observed proteomic alleles to their expected protein products from DNA which, in turn, permits standard corrections for population structure and finite database sizes. To assess the feasibility of this approach, RMPs were estimated from peptide profiles of skin samples from 25 individuals of European ancestry. 126 common peptide alleles were used in this approach, yielding a mean RMP of approximately 10−2.
KW - Exome sequencing
KW - Genetically variable peptides
KW - Liquid chromatography–tandem mass spectrometry
KW - Proteomics
KW - Random match probability
UR - http://www.scopus.com/inward/record.url?scp=85083018630&partnerID=8YFLogxK
U2 - 10.1016/j.fsigen.2020.102295
DO - 10.1016/j.fsigen.2020.102295
M3 - Article
C2 - 32289731
AN - SCOPUS:85083018630
SN - 1872-4973
VL - 47
JO - Forensic Science International: Genetics
JF - Forensic Science International: Genetics
M1 - 102295
ER -