Forensic Science International-Genetics最新文献

{"title":"DNA identification of monozygotic twins","authors":"Hsiao-Lin Hwa ,&nbsp;Chun-Yen Lin ,&nbsp;Yu-Jen Yu ,&nbsp;Adrian Linacre ,&nbsp;James Chun-I. Lee","doi":"10.1016/j.fsigen.2023.102998","DOIUrl":"https://doi.org/10.1016/j.fsigen.2023.102998","url":null,"abstract":"<div><p>This study details the differentiation of identical twins<span><span> based on single mutational base differences. There were three pairs of male monozygotic (MZ) twins in this study. DNA samples from blood, a </span>buccal swab<span> or saliva from each individual were all initially genotyped using 22 autosomal STR<span> and 27 Y-STR loci. Preliminary screening confirmed there were no differences in the STR data between each pair of MZ twins. Whole Genome Sequence (WGS) data were generated from DNA extracted from the three body fluids from each individual. Kinship coefficients with 0.4254, 0.4557 and 0.4543 from 3 twins were generated based on WGS data to further confirm that their relationship was that of MZ twins. The fastq data generated by the Illumina Hiseq 2000 between MZ twins were then treated as “normal” as opposed to “tumor” using commercially available software tools to identify mutational single base changes. Sanger DNA sequencing confirmed there were 1, 5 and 9 single base changes found in WGS data from each of the three MZ twin sets. There was individual variation in the mutational base changes when comparing data from the three body fluids. The methods used in this study to differentiate MZ twins based on WGS data can readily be performed in many operational forensic DNA laboratories using user friendly software.</span></span></span></p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138656797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a more supportive and inclusive forensic science community","authors":"Vanessa Lynch","doi":"10.1016/j.fsigen.2023.102997","DOIUrl":"10.1016/j.fsigen.2023.102997","url":null,"abstract":"","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138566750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Title page","authors":"","doi":"10.1016/S1872-4973(23)00158-8","DOIUrl":"https://doi.org/10.1016/S1872-4973(23)00158-8","url":null,"abstract":"","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1872497323001588/pdfft?md5=faa293d8ed2e803b8de4e1ab558e95a1&pid=1-s2.0-S1872497323001588-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138549548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salmon sperm DNA increases sample recovery from cotton swabs","authors":"Crystal M. Oechsle ,&nbsp;Thomas A. Paul ,&nbsp;Joseph D. Seichko ,&nbsp;Travis J. Worst","doi":"10.1016/j.fsigen.2023.102996","DOIUrl":"10.1016/j.fsigen.2023.102996","url":null,"abstract":"<div><p><span>Forensic samples with low DNA template amounts are difficult to analyze and interpret. There is a large body of research demonstrating that adding carrier </span>nucleic acid<span><span> to storage tubes, solid phase extractions<span><span><span>, or filtering devices can improve yields of target DNA. However, the addition of carrier nucleic acid to sampling substrates, like cotton swabs, has not yet been attempted. In this proof-of-concept study, carrier nucleic acids in the form of either Poly (A) </span>RNA or salmon sperm DNA were spotted onto cotton swabs, followed by human genomic DNA, to determine if introducing the carrier prior to sample collection would increase recovery from the swabs post-extraction. Extracts were also evaluated to determine whether adding the carrier nucleic acids to human DNA would interfere with downstream </span>forensic DNA analysis processes such as real-time PCR quantitation, PCR amplification of </span></span>STR<span><span> loci, or capillary electrophoresis. The RNA carrier did not improve human sample recovery from cotton swabs. The extraction efficiency of human DNA from cotton swabs was increased when the DNA carrier was applied to the swabs prior to sample deposition, and the scale of the increase depended on the amount of carrier DNA used. When applying the salmon sperm DNA carrier to cotton swabs, with each increase from no carrier to 0.001–1–10 µg, human DNA recovery went from ∼29 % to ∼50 % to ∼75 % to ∼100 %. Additionally, no inhibitory effects from the carrier DNA were observed post-extraction with quantitation or in the DNA profile after amplification. Therefore, salmon sperm DNA carrier will increase human DNA yield from cotton swabs without negative effects on downstream forensic </span>DNA profiling methods, with the optimal carrier amount being 10 µg.</span></span></p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138528810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic evaluation of the Precision ID GlobalFiler™ NGS STR panel v2 using single-source samples of various quantity and quality and mixed DNA samples","authors":"Vishakha Sharma,&nbsp;Elisa Wurmbach","doi":"10.1016/j.fsigen.2023.102995","DOIUrl":"10.1016/j.fsigen.2023.102995","url":null,"abstract":"<div><p>Massively parallel sequencing (<strong>MPS</strong><span><span>) techniques were developed approximately 15 years ago. Meanwhile, several MPS kits for forensic identification, phenotypic information, ancestry<span>, and mitochondrial DNA analysis have been developed and their use has been established. Sequencing </span></span>short tandem repeats (</span><strong>STR</strong><span>s) has certain advantages over the currently used length-based genotyping methods, which are based on PCR amplification followed by capillary electrophoresis (</span><strong>CE</strong><span>). MPS is more discriminative and includes the possibility of testing high numbers of targets (&gt; 100), different types of markers [STRs and single nucleotide polymorphisms (</span><strong>SNP</strong><span><span>s)], as well as the use of smaller amplicons (&lt; 300 bp). This study evaluated in 24 experimental runs the Precision ID GlobalFiler™ NGS STR panel v2 from ThermoFisher, which targets 31 autosomal STRs, </span>amelogenin<span>, and three Y-markers (one STR, SRY, and Yindel). Single-source samples were used in 18 experimental runs, for systematic evaluation. These included assessing library preparation benchmark conditions, limited DNA input, as well as testing repeatability, number of samples per run, and degraded DNA samples. Full profiles were consistently obtained from as little as 50 pg DNA input. Using the optional recovery PCR method improved outcomes for samples with low DNA input. Full profiles were also obtained from severely degraded DNA samples with degradation indices (</span></span><strong>DI</strong>) of &gt; 60. In addition, six experimental runs were performed testing various two-person mixtures with mixture ratios ranging from 1:20 to 20:1. Major and minor contributors were distinguishable by their read counts (coverage), because less DNA input yielded lower read counts, analogous to the traditional CE technology, where less DNA produces lower peak heights. Mixture ratios of approximately 1:1 were indistinguishable, while a greater imbalance, i.e., higher mixture ratios, made the mixture more distinguishable between major and minor contributors. Based on this information, the highest success rate of correctly deconvoluted four-allelic loci was from mixtures with 1:3 ratios. At higher mixture ratios, the drop-out rate of the minor contributor increased, reducing the number of four-allelic loci.</p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138528801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning applications in forensic DNA profiling: A critical review","authors":"Mark Barash ,&nbsp;Dennis McNevin ,&nbsp;Vladimir Fedorenko ,&nbsp;Pavel Giverts","doi":"10.1016/j.fsigen.2023.102994","DOIUrl":"10.1016/j.fsigen.2023.102994","url":null,"abstract":"<div><p>Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.</p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1872497323001692/pdfft?md5=2fe1beb79f1af33b24269b194601dd7f&pid=1-s2.0-S1872497323001692-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138528814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An mRNA profiling assay incorporating coding region InDels for body fluid identification and the inference of the donor in mixed samples","authors":"Shouyu Wang ,&nbsp;Tingting Jiang ,&nbsp;Chunyan Yuan ,&nbsp;Liming Wu ,&nbsp;Xiaoyuan Zhen ,&nbsp;Yinlei Lei ,&nbsp;Baoyan Xie ,&nbsp;Ruiyang Tao ,&nbsp;Chengtao Li","doi":"10.1016/j.fsigen.2023.102979","DOIUrl":"https://doi.org/10.1016/j.fsigen.2023.102979","url":null,"abstract":"<div><p><span><span>Biological traces discovered at crime scenes hold significant significance in forensic investigations. In cases involving mixed body fluid stains, the evidentiary value of DNA profiles depends on the type of body fluid from which the DNA was obtained. Recently, coding region polymorphism analysis has proved to be a promising method for directly linking specific body fluids to their respective DNA contributors in mixtures, which may help to avoid \"association fallacy\" between separate DNA and </span>RNA<span> evidence. In this study, we present an update on previously reported coding region Single Nucleotide Polymorphisms (cSNPs) by exploring the potential application of coding region Insertion/Deletion polymorphisms (cInDels). Nine promising cInDels, selected from 70 mRNA markers based on stringent screening criteria, were integrated into an existing mRNA profiling assay. Subsequently, the body fluid specificity of our cInDel assay and the genotyping consistency between complementary DNA (cDNA) and genomic DNA (gDNA) were examined. Our study demonstrates that cInDels can function as important multifunctional </span></span>genetic markers, as they provide not only the ability to confirm the presence of forensically relevant body fluids, but also the ability to associate/dissociate specific body fluids with particular donors.</p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138475234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixture detection with Demixtify","authors":"August E. Woerner ,&nbsp;Benjamin Crysup ,&nbsp;Jonathan L. King ,&nbsp;Nicole M. Novroski ,&nbsp;Michael D. Coble","doi":"10.1016/j.fsigen.2023.102980","DOIUrl":"https://doi.org/10.1016/j.fsigen.2023.102980","url":null,"abstract":"<div><p>The <em>de facto</em><span><span><span><span> genetic markers of forensics are </span>short tandem repeats (STRs). There are many analytical tools designed to work with STRs, including techniques for analyzing and assessing </span>DNA mixtures. In contrast, the nascent field of forensic </span>genetic genealogy<span> often relies on biallelic single nucleotide polymorphisms (SNPs). Tools designed for the forensic assessment of SNPs are somewhat lacking, especially for DNA mixtures. In this paper we introduce Demixtify, a program that detects DNA mixtures using biallelic SNPs. Demixtify is quite powerful; highly imbalanced mixtures can be detected (≤1:99, considering </span></span><span><em>in silico</em></span> and <em>in vitro</em> mixtures) when coverage is ample. Demixtify can also detect mixtures in low coverage (∼1×) samples (when the mixture is relatively balanced). Demixtify includes an empirical estimator of sequence error that is specific to the markers assayed, making it especially relevant to the forensic community. Orthogonal techniques are also developed to characterize <em>in vitro</em> mixtures, as well as samples thought to be single source, and the results of these approaches serve to validate the techniques presented.</p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138448651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metatranscriptomic characterization of six types of forensic samples and its potential application to body fluid/tissue identification: A pilot study","authors":"Zhiyong Liu (刘志勇) ,&nbsp;Jiajun Liu (刘佳俊) ,&nbsp;Jiaojiao Geng ,&nbsp;Enlin Wu ,&nbsp;Jianzhang Zhu ,&nbsp;Bin Cong ,&nbsp;Riga Wu ,&nbsp;Hongyu Sun","doi":"10.1016/j.fsigen.2023.102978","DOIUrl":"10.1016/j.fsigen.2023.102978","url":null,"abstract":"<div><p><span><span>Microorganisms are potential markers for identifying body fluids (venous and menstrual blood, semen, saliva, and vaginal secretion) and skin tissue in forensic genetics<span><span>. Existing published studies have mainly focused on investigating microbial DNA by 16 S </span>rRNA<span> gene sequencing or metagenome<span><span> shotgun sequencing. We rarely find microbial </span>RNA<span> level investigations on common forensic body fluid/tissue. Therefore, the use of metatranscriptomics to characterize common forensic body fluids/tissue has not been explored in detail, and the potential application of metatranscriptomics in forensic science remains unknown. Here, we performed 30 </span></span></span></span></span>metatranscriptome<span> analyses on six types of common forensic sample from healthy volunteers by massively parallel sequencing. After quality control and host RNA filtering, a total of 345,300 unigenes were assembled from clean reads. Four kingdoms, 137 phyla, 267 classes, 488 orders, 985 families, 2052 genera, and 4690 species were annotated across all samples. Alpha- and beta-diversity and differential analysis were also performed. As a result, the saliva and skin groups demonstrated high alpha diversity (Simpson index), while the venous blood group exhibited the lowest diversity despite a high Chao1 index. Specifically, we discussed potential microorganism contamination and the “core </span></span>microbiome<span><span>,” which may be of special interest to forensic researchers. In addition, we implemented and evaluated artificial neural network (ANN), random forest (RF), and </span>support vector machine<span> (SVM) models for forensic body fluid/tissue identification (BFID) using genus- and species-level metatranscriptome profiles. The ANN and RF prediction models discriminated six forensic body fluids/tissue, demonstrating that the microbial RNA-based method could be applied to BFID. Unlike metagenomic research, metatranscriptomic analysis can provide information about active microbial communities; thus, it may have greater potential to become a powerful tool in forensic science for microbial-based individual identification. This study represents the first attempt to explore the application potential of metatranscriptome profiles in forensic science. Our findings help deepen our understanding of the microorganism community structure at the RNA level and are beneficial for other forensic applications of metatranscriptomics.</span></span></p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selecting mRNA markers in blood for age estimation of the donor of a biological stain","authors":"Guro Dørum ,&nbsp;Nadescha Viviane Hänggi ,&nbsp;Dario Burri ,&nbsp;Yael Marti ,&nbsp;Regine Banemann ,&nbsp;Galina Kulstein ,&nbsp;Cornelius Courts ,&nbsp;Annica Gosch ,&nbsp;Thorsten Hadrys ,&nbsp;Cordula Haas ,&nbsp;Jacqueline Neubauer","doi":"10.1016/j.fsigen.2023.102976","DOIUrl":"10.1016/j.fsigen.2023.102976","url":null,"abstract":"<div><p>RNA has gained a substantial amount of attention within the forensic field over the last decade. There is evidence that RNAs are differentially expressed with biological age. Since RNA can be co-extracted with DNA from the same piece of evidence, RNA-based analysis appears as a promising molecular alternative for predicting the biological age and hence inferring the chronological age of a person. Using RNA-Seq data we searched for markers in blood potentially associated with age. We used our own RNA-Seq data from dried blood stains as well as publicly available RNA-Seq data from whole blood, and compared two different approaches to select candidate markers. The first approach focused on individual gene analysis with DESeq2 to select the genes most correlated with age, while the second approach employed lasso regression to select a set of genes for optimal prediction of age. We present two lists with 270 candidate markers, one for each approach.</p></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1872497323001515/pdfft?md5=06b05bb2fc3072b8427d8773a9e07f6c&pid=1-s2.0-S1872497323001515-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138435627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}