Crime investigation through DNA methylation analysis: methods and applications in forensics

Solving the twin paradox-forensic strategies to identify the identical twins

Identical twins are also called monozygotic twins which originate from the same zygote that possesses the same genetic make-up. To discriminate between identical monozygotic twins, short tandem repeats has not been found effective, therefore, various techniques, including next-generation sequencing (NGS), are applied. Monozygotic twins can be identified through germ line genomes, through speech using deep learning networks, and through epigenetic analysis. Fingerprint analysis has also been used to distinguish between identical twins, as human beings have unique fingerprints. Two distinct levels of fingerprint are used to distinguish between monozygotic twins based upon the differences in the minutiae points. Examination of the methylation pattern of the genome has an enormous potential to differentiate between identical twins, as the methylation of DNA occurs uniquely to each individual. This article offers an insight into the latest methods and techniques used for the differentiation between the identical twins.

Deep learning based method for predicting DNA N6-methyladenosine sites

DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.

Analysis of Y-STR diversity and DNA methylation variation among Black and Indian males from KwaZulu-Natal, South Africa

Y-chromosome short tandem repeats (Y-STRs) are essential in understanding genetic structure and diversity of human populations and, most importantly, in identification of male perpetrators in criminal investigations. DNA methylation differences have been reported in human populations and methylation pattern at the CpG sites found within or flanking the Y-STR sites could also aid in human identification. Studies based on DNA methylation (DNAm) at Y-STRs are currently limited. The current study aimed to analyze the Y-STR diversity in South African Black and Indian individuals living in KwaZulu-Natal, Durban, South Africa, with the Yfiler™ Plus Kit and to analyze DNAm patterns in Y-STR markers CpG sites. DNA from 247 stored saliva samples were isolated and quantified. Across the 27 Y-STR loci in the Yfiler™ Plus Kit, 253 alleles were observed in 113 South African Black and Indian males, 112 unique haplotypes were observed, and one haplotype appeared twice (two Black individuals). No statistically significant differences were observed in the genetic diversity between the two population groups (Fst = 0.028, p-value ≥ 0.05). The kit showed a high discrimination capacity (DC) of 0.9912 and an overall haplotype diversity (HD) = 0.9995 among the sampled population groups. DYS438 and DYS448 markers displayed 2 and 3 CpG sites, respectively. Based on the two-tailed Fisher's Exact test, there were no statistically significant differences in the DNAm levels at DYS438 CpGs of Black and Indian males (p > 0.05). The Yfiler™ Plus Kit can be considered highly discriminatory among South African Black and Indian males. Studies on the South African population using Yfiler™ Plus Kit are scarce. Hence, accumulating Y-STR data on the diverse South African population will enhance the representation of South Africa in STR databases. Knowing which Y-STR markers are significantly informative for South Africa is essential for developing Y-STR kits better suited for the different ethnic groups. And to the best of our knowledge, DNA methylation analysis in Y-STR for different ethnic groups has never been done before. Complementing Y-STR data with methylation knowledge could provide population-specific information for forensic identification.

A review of methods for predicting DNA N6-methyladenine sites

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species

DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and with growth as well as development of plants. Precisely detecting DNA 6mA sites is of great importance to exploration of 6mA functions. Although many computational methods have been presented for DNA 6mA prediction, there is still a wide gap in the practical application. We presented a convolution neural network (CNN) and bi-directional long-short term memory (Bi-LSTM)-based deep learning method (Deep6mAPred) for predicting DNA 6mA sites across plant species. The Deep6mAPred stacked the CNNs and the Bi-LSTMs in a paralleling manner instead of a series-connection manner. The Deep6mAPred also employed the attention mechanism for improving the representations of sequences. The Deep6mAPred reached an accuracy of 0.9556 over the independent rice dataset, far outperforming the state-of-the-art methods. The tests across plant species showed that the Deep6mAPred is of a remarkable advantage over the state of the art methods. We developed a user-friendly web application for DNA 6mA prediction, which is freely available at http://106.13.196.152:7001/ for all the scientific researchers. The Deep6mAPred would enrich tools to predict DNA 6mA sites and speed up the exploration of DNA modification.

Epigenetic age prediction in semen - marker selection and model development

DNA methylation analysis is becoming increasingly useful in biomedical research and forensic practice. The discovery of differentially methylated sites (DMSs) that continuously change over an individual's lifetime has led to breakthroughs in molecular age estimation. Although semen samples are often used in forensic DNA analysis, previous epigenetic age prediction studies mainly focused on somatic cell types. Here, Infinium MethylationEPIC BeadChip arrays were applied to semen-derived DNA samples, which identified numerous novel DMSs moderately correlated with age. Validation of the ten most age-correlated novel DMSs and three previously known sites in an independent set of semen-derived DNA samples using targeted bisulfite massively parallel sequencing, confirmed age-correlation for nine new and three previously known markers. Prediction modelling revealed the best model for semen, based on 6 CpGs from newly identified genes SH2B2, EXOC3, IFITM2, and GALR2 as well as the previously known FOLH1B gene, which predict age with a mean absolute error of 5.1 years in an independent test set. Further increases in the accuracy of age prediction from semen DNA will require technological progress to allow sensitive, simultaneous analysis of a much larger number of age correlated DMSs from the compromised DNA typical of forensic semen stains.

Progress in forensic bone DNA analysis: Lessons learned from ancient DNA

Research on ancient and forensic DNA is related in many ways, and the two fields must deal with similar obstacles. Therefore, communication between these two communities has the potential to improve results in both research fields. Here, we present the insights gained in the ancient DNA community with regard to analyzing DNA from aged skeletal material and the potential use of the developed protocols in forensic work. We discuss the various steps, from choosing samples for DNA extraction to deciding between classical PCR amplification and massively parallel sequencing approaches. Based on the progress made in ancient DNA analyses combined with the requirements of forensic work, we suggest that there is substantial potential for incorporating ancient DNA approaches into forensic protocols, a process that has already begun to a considerable extent. However, taking full advantage of the experiences gained from ancient DNA work will require comparative studies by the forensic DNA community to tailor the methods developed for ancient samples to the specific needs of forensic studies and case work. If successful, in our view, the benefits for both communities would be considerable.

Age estimation using DNA methylation technique in forensics: a systematic review

Background

In addition to the DNA sequence, epigenetic markers have become substantial forensic tools during the last decade. Estimating the age of an individual from human biological remains may provide information for a forensic investigation. Age estimation in molecular strategies can be obtained by telomere length, mRNa mutation, or by sjTRECs but the accuracy is not sufficient in forensic practice because of high margin error.

Main body

One solution to this problem is to use DNA methylation methods. DNA methylation markers for tissue identification at age-associated CpG sites have been suggested as the most informative biomarkers for estimating the age of an unknown donor. This review aims to give an overview of DNA methylation profiling for estimating the age in cases of forensic relevance and the important aspects in determining the mean absolute deviation (MAD) or mean absolute error (MAE) of the estimated age. Online database searching was performed through PubMed, Scopus, and Google Scholar with keywords selected for forensic age estimation. Thirty-two studies were included in the review, with variable DNA samples but blood commonly as a source. Pyrosequencing and EpiTYPER were methods mostly used in DNA analysis. The MAD in the estimates from DNA methylation was about 3 to 5 years, which was better than other methods such as those based on telomere length or signal-joint T-cell receptor excision circles. The ELOVL2 gene was a commonly used DNA methylation marker in age estimation.

Conclusion

DNA methylation is a favorable candidate for estimating the age at the time of death in forensic profiling, with an uncertainty mean absolute deviation of about 3 to 5 years in the predicted age. The sample type, platform techniques used, and methods to construct age predictive models were important in determining the accuracy in mean absolute deviation or mean absolute error. The DNA methylation outcome suggests good potential to support conventional STR profiling in forensic cases.

Ethnicity, age and disease-associated variation in body fluid-specific CpG sites in a diverse South African cohort

Tissue-specific differential DNA methylation has been an attractive target for the development of markers for discrimination of body fluids found at crime scenes. Though mostly stable, DNA methylation patterns have been shown to vary between different ethnic groups, in different age groups as well as between healthy and diseased individuals. To the best of our knowledge, none of the markers for body fluid identification have been applied to different ethnic groups to ascertain if variability exists. In the present study, saliva and blood were collected to determine the effects of ethnicity (Blacks, Whites, Coloureds and Indians), age (20-30 years, 40-50years and above 60 years) and diabetes on methylation profiles of potential saliva- and blood-specific DMSs. Both DMSs were previously shown to exhibit hypermethylation in their target body fluids at single CpG sites, however in the present study, additional CpG sites flanking the reported sites were also screened. Bisulfite sequencing revealed that Coloureds showed highest methylation levels for both body fluids, and blacks displayed significant differences between other ethnic groups in the blood-specific CpG sites. A decline in methylation for both potential DMRs was observed with increasing age. Heavily methylated CpG sites in different ethnic groups and previously reported DMSs displayed hypomethylation with increasing age and disease status. Diabetic status did not show any significant difference in methylation when compared to healthy counterparts. Thus, the use of methylation markers for forensics needs thorough investigation of influence of external factors and ideally, several CpG sites should be co-analysed instead of a single DMS.