Since 1995, national forensic DNA databases have used a maximum number of contributors, and a minimum number of loci to reduce the risk of providing false leads. DNA profiles of biological traces that do not meet these criteria cannot be loaded into these databases. In 2023, about 10 % of more than 15,000 trace DNA profiles analyzed in western Switzerland were not compared at the national level, even though they were considered to be interpretable, mainly because they contained the DNA from more than two persons. In this situation, police services can request local comparisons with DNA profiles of known persons and/or with other traces, but this occurs in only a small proportion of cases, so that DNA mixtures are rarely used to help detect potential series. The development of probabilistic genotyping software and its associated tools have made possible the efficient performance of this type of comparison, which is based on likelihood ratios (LR) rather than on the number of shared alleles. To highlight potential common contributors for investigation and intelligence purposes, the present study used the mixture-to-mixture tool of the software STRmix v2.7 to compare 235 DNA profiles that cannot be searched the Swiss DNA database. These DNA profiles originated from traces collected by six different police services in 2021 and 2022. Traces were selected by the police based on information that indicated that they were from potential series. Associations between profiles were compared with expected investigative associations to define the value of this approach. Among the 27,495 pairwise comparisons of DNA profiles, 88 pairs (0.3 %) showed at least one potential common contributor when using a LR threshold of 1000. Of these 88 pairs, 60 (68.2 %) were qualified by the police services as \"expected\" (60/88), 22 (25.0 %) as \"possible\", and six (6.8 %) as \"unexpected\". Although it is important to consider the limits of this approach (e.g., adventitious or missed associations, cost/benefit evaluation, integration of DNA mixture comparison in the process), these findings indicate that non CODIS loadable DNA mixtures could provide police agencies with information concerning potential series at both the local and national level.

DNA technology is the gold standard with respect to the identification of individuals from biological evidence. The technology offers the convenience of a universally similar approach and methodology for analysis across the globe. However, the technology has not realised its full potential in India due to the lack of a DNA database and lacunae in sample collection and preservation from the scene of crime and victims (especially those of sexual assault). Further, statistical interpretation of DNA results is non-existent in the majority of cases. Though the latest technologies and developments in the field of DNA analysis are being adopted and implemented,very little has been enacted practically to improve optimise sample collection and preservation. This article discusses current casework scenarios that highlight the pitfalls and ambiguous areas in the field of DNA analysis, especially with respect DNA databases, sampling, andstatistical approaches to genetic data analysis. Possible solutions and mitigation measures are suggested.

Ongoing terrorist attacks in the Sahel region call for strengthening the security system by using human DNA identification technology. In this context, public opinion must be considered when establishing solid standards and universal safeguards for one of the most invasive forms of surveillance and profiling. For this purpose, we gathered internet users\' opinions in Burkina Faso (a country located in the Sahel region) on the use of DNA technology to support criminal investigations. The results revealed that 91.7% (431) of the 470 participants believed that this technology is currently necessary for the Burkina Faso\'s criminal justice system. However, the respondents expressed concerns about the custody and management of a national forensic DNA database. In this particular security setting, the public opinion of this study may provide leaders and political policymakers with clues for considering genetic fingerprints and implementing an national forensic DNA database to support criminal investigations in Burkina Faso whilst also considering the ethical implications.

DNA damage is a critical factor contributing to genetic alterations, directly affecting human health, including developing diseases such as cancer and age-related disorders. DNA repair mechanisms play a pivotal role in safeguarding genetic integrity and preventing the onset of these ailments. Over the past decade, substantial progress and pivotal discoveries have been achieved in DNA damage and repair. This comprehensive review paper consolidates research efforts, focusing on DNA repair mechanisms, computational research methods, and associated databases. Our work is a valuable resource for scientists and researchers engaged in computational DNA research, offering the latest insights into DNA-related proteins, diseases, and cutting-edge methodologies. The review addresses key questions, including the major types of DNA damage, common DNA repair mechanisms, the availability of reliable databases for DNA damage and associated diseases, and the predominant computational research methods for enzymes involved in DNA damage and repair.

An automated system of DNA profile processing (termed a \'lights-out\' workflow) was trialled for no-suspect cases over a three-month period at Forensic Science SA (FSSA). The lights-out workflow utilised automated DNA profile reading using the neural network reading feature in FaSTR™ DNA with no analytical threshold. The profile information from FaSTR™ DNA was then processed in STRmix™ using a top-down analysis and automatically compared to a de-identified South Australian searchable DNA database. Computer scripts were used to generate link reports and upload reports and these were compared to the links and uploads that were obtained for the cases during their standard processing within the laboratory. The results of the lights-out workflow was an increase in both uploads and links compared to the standard workflow, with minimal adventitious links or erroneous uploads. Overall, the proof-of-concept study shows the potential for using automated DNA profile reading and top-down analysis to improve workflow efficiency in a no-suspect workflow.

DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2-10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors\' relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.

Biodiversity is proposed as a sustainable alternative for the economic development of high-biodiversity regions. Especially in the field of biodiversity genomics, the development of low-cost DNA sequencing opens an opportunity for new actors beyond academia to engage in genomic sequencing. However, it is challenging to adequately compensate non-academic actors such as local populations for their contribution to the innovation process, preventing better bioeconomy development. Although many repositories register genomic data to support biodiversity research, they do not facilitate the fair sharing of economic benefits. In this work, we propose the creation of the Amazon Biobank, a community-based genetic database. We employed blockchain to build a transparent and verifiable log of transactions involving genomic data, and we used smart contracts to implement an internal monetary system for all participants who collect, insert, process, store, and validate genomic data. We also used peer-to-peer solutions to allow users with commodity computers to collaborate with the storage and distribution of DNA files. By combining emerging technologies, Amazon Biobank provides adequate benefit-sharing among all participants that collaborate with data, knowledge, and computational resources. It also provides traceability and auditability, allowing easy association between biotechnological research and DNA data. In addition, the solution is highly scalable and less dependent on the trust deposited in any system player. Therefore, Amazon Biobank can become an important stepping stone to unlock the potential of bioeconomy in rich ecosystems such as the Amazon Rainforest.

Not all blockchains are created equal, and many cannot accommodate all of the primary characteristics of big data: Variety, Velocity, Volume and Veracity. Currently, public blockchains are slow and clunky, it can be expensive to keep up with the velocity of genomic data production. Further, the transparent and universally accessible nature of public blockchain doesn\'t necessarily accommodate all of the variety of sequence data, including very private information. Bespoke private permissioned blockchains, however, can be created to optimally accommodate all of the big data features of genomic data. Further, private permissioned chains can be implemented to both protect the privacy and security of the genetic information therein, while also providing access to researchers. An NFT marketplace associated with that private chain can provide the discretized sale of anonymous and encrypted data sets while also incentivizing individuals to share their data through payments mediated by smart contracts. Private blockchains can provide a transparent chain of custody for each use of the customers\' data, and validation that this data is not corrupted. However, even with all of these benefits there remain some concerns with the implementation of this new technology including the ethical, legal and social implications typically associated with DNA databases.

Short tandem repeat (STR) markers for autosomal STR are used in forensic deoxyribonucleic acid (DNA) typing to track down the missing, verify family connections, and potentially connect suspects to crime sites. It is well acknowledged that forensically relevant genetic markers cannot predict phenotype. There is no evidence to support the claim that directly using forensic STR variations causes or indicates illness. Such an example would have significant ethical and permissible repercussions. It is essential to check the necessity to alert a blood donor or if a medical problem is identified during routine sample analysis. In this study, we assess the likelihood that forensic STRs might offer details beyond those required for primary identification. However, as the role of non-coding STRs in gene regulation is understood, the probability of discovering meaningful links is rising. For this review, Google Scholar, ScienceDirect, PubMed, and Google Search were all used to conduct a thorough electronic literature search. If they linked to the topic, thoughts, retrospective studies, observational studies, and first publications were considered. The case studies presented here highlight the critical role forensic DNA typing plays in reducing criminal risk and delivering conclusive evidence in cases. The primary method for forensic DNA typing is short tandem repeat (STR) typing. This discussion on the importance of STR markers to the criminal justice system is part of the present study. As unflinching proof of false beliefs and invaluable connections to the genuine culprits, DNA typing offers proof that may be utilised to prosecute and punish criminals. It may even deter certain offenders from committing more terrible offences. Additionally, forensic experts have used DNA typing techniques to re-examine ancient cases previously closed due to a lack of evidence.

The importance of DNA evidence for gaining investigative leads demands a fast workflow for forensic DNA profiling performed in large volumes. Therefore, we developed software solutions for automated DNA profile analysis, contamination check, major donor inference, DNA database (DDB) comparison and reporting of the conclusions. This represents the Fast DNA IDentification Line (FIDL) and this study describes its development, validation and implementation in criminal casework at the authors\' institute. This first implementation regards single donor profiles and major contributors to mixtures. The validation included testing of the software components on their own and examination of the performance of different DDB search strategies. Furthermore, end-to-end testing was performed under three conditions: (1) testing of scenarios that can occur in DNA casework practice, (2) tests using three months of previous casework data, and (3) testing in a casework production environment in parallel to standard casework practices. The same DNA database candidates were retrieved by this automated line as by the manual workflow. The data flow was correct, results were reproducible and robust, results requiring manual analysis were correctly flagged, and reported results were as expected. Overall, we found FIDL valid for use in casework practice in our institute. The results from FIDL are automatically reported within three working days from receiving the trace sample. This includes the time needed for registration of the case, DNA extraction, quantification, polymerase chain reaction and capillary electrophoresis. FIDL itself takes less than two hours from intake of the raw CE data to reporting. Reported conclusions are one of five options: (1) candidate retrieved from DDB, (2) no candidate retrieved from DDB, (3) high evidential value with regards to reference within the case, (4) results require examination of expert, or (5) insufficient amount of DNA obtained to generate a DNA profile. In our current process, the automated report is sent within three working days and a complete report, with confirmation of the FIDL results, and signed by a reporting officer is sent at a later time. The signed report may include additional analyses regarding e.g. minor contributors. The automated report with first case results is quickly available to the police enabling them to act upon the DNA results prior to receiving the full DNA report. This line enables a uniform and efficient manner of handling large numbers of traces and cases and provides high value investigative leads in the early stages of the investigation.