Context: Over the last few decades, advances in sequencing have improved greatly. One of the most important achievements of Next Generation Sequencing (NGS) is to produce millions of sequence reads in a short period of time, and to produce large sequences of DNA in fragments of any size. Libraries can be generated from whole genomes or any DNA or RNA region of interest without the need to know its sequence beforehand. This allows for looking for variations and facilitating genetic identification. Objectives: A deep analysis of current NGS technologies and their application, especially in forensics, including a discussion about the pros and cons of these technologies in genetic identification. Methods: A systematic literature search in PubMed, Science Direct and Scopus electronic databases was performed for the period of December 2012 to June 2015. Results: In the forensic field, one of the main problems is the limited amount of sample available, as well as its degraded state. If the amount of DNA input required for preparing NGS libraries continues to decrease, nearly any sample could be sequenced; therefore, the maximum information from any biological remains could be obtained. Additionally, microbiome typification could be an interesting application to study for crime scene characterisation. Conclusions: NGS technologies are going to be crucial for DNA human typing in cases like mass disasters or other events where forensic specimens and samples are compromised and degraded. With the use of NGS it will be possible to achieve the simultaneous analysis of the standard autosomal DNA (STRs and SNPs), mitochondrial DNA, and X and Y chromosomal markers.

Human identification has made great strides over the past 2 decades due to the advent of DNA typing. Forensic DNA typing provides genetic data from a variety of materials and individuals, and is applied to many important issues that confront society. Part of the success of DNA typing is the generation of DNA databases to help identify missing persons and to develop investigative leads to assist law enforcement. DNA databases house DNA profiles from convicted felons (and in some jurisdictions arrestees), forensic evidence, human remains, and direct and family reference samples of missing persons. These databases are essential tools, which are becoming quite large (for example the US Database contains 10 million profiles). The scientific, governmental and private communities continue to work together to standardize genetic markers for more effective worldwide data sharing, to develop and validate robust DNA typing kits that contain the reagents necessary to type core identity genetic markers, to develop technologies that facilitate a number of analytical processes and to develop policies to make human identity testing more effective. Indeed, DNA typing is integral to resolving a number of serious criminal and civil concerns, such as solving missing person cases and identifying victims of mass disasters and children who may have been victims of human trafficking, and provides information for historical studies. As more refined capabilities are still required, novel approaches are being sought, such as genetic testing by next-generation sequencing, mass spectrometry, chip arrays and pyrosequencing. Single nucleotide polymorphisms offer the potential to analyze severely compromised biological samples, to determine the facial phenotype of decomposed human remains and to predict the bioancestry of individuals, a new focus in analyzing this type of markers.

Prostate cancer is one of the most prevalent forms of cancer in men worldwide. Traditional screening strategies such as serum PSA levels, which are not necessarily cancer-specific, or digital rectal exams, which are often inconclusive, are still the screening methods used for the disease. Some studies have focused on identifying biomarkers of the disease but none have been reported for diagnosis in routine clinical practice and few studies have provided tools to assist the pathologist in the decision-making process when analyzing prostate tissue. Therefore, a classifier is proposed to predict the occurrence of PCa that provides physicians with accurate predictions and understandable explanations. A selection of 47 genes was made based on differential expression between PCa and normal tissue, GO gene ontology as well as the literature to be used as input predictors for different machine learning methods based on eXplainable Artificial Intelligence. These methods were trained using different class-balancing strategies to build accurate classifiers using gene expression data from 550 samples from 'The Cancer Genome Atlas'. Our model was validated in four external cohorts with different ancestries, totaling 463 samples. In addition, a set of SHapley Additive exPlanations was provided to help clinicians understand the underlying reasons for each decision. An in-depth analysis showed that the Random Forest algorithm combined with majority class downsampling was the best performing approach with robust statistical significance. Our method achieved an average sensitivity and specificity of 0.90 and 0.8 with an AUC of 0.84 across all databases. The relevance of DLX1, MYL9 and FGFR genes for PCa screening was demonstrated in addition to the important role of novel genes such as CAV2 and MYLK. This model has shown good performance in 4 independent external cohorts of different ancestries and the explanations provided are consistent with each other and with the literature, opening a horizon for its application in clinical practice. In the near future, these genes, in combination with our model, could be applied to liquid biopsy to improve PCa screening.