Integrated epigenomic exposure signature discovery.

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

This article introduces ESDA, a new analytic tool for integrating multiple data types to identify the most distinguishing features following an exposure. Using the ESDA, we were able to identify signatures of infectious diseases. The results of the study indicate that integration of multiple types of large datasets can be used to identify distinguishing features for infectious diseases. Understanding the changes from different exposures will enable development of diagnostic tests for infectious diseases that target responses from the patient. Using the ESDA, we will be able to build a database of human response signatures to different infections and simplify diagnostic testing in the future.

Sequence-based autosomal STR characterization in four US populations using PowerSeq™ Auto/Y system.

The forensic science community is poised to utilize modern advances in massively parallel sequencing (MPS) technologies to better characterize biological samples with higher resolution. A critical component towards the advancement of forensic DNA analysis with these technologies is a comprehensive understanding of the diversity and population distribution of sequence-based short tandem repeat (STR) alleles. Here we analyzed 786 samples of individuals from different population groups, including four of the mostly commonly encountered in forensic casework in the USA. DNA samples were amplified with the PowerSeq™ Auto/Y System Prototype Kit (Promega Corp.), and sequencing was performed on an Illumina® MiSeq instrument. Sequence data were analyzed using a bioinformatics processing tool, Altius. For additional data analysis and profile comparison, capillary electrophoresis (CE) size-based STR genotypes were generated for a subset of individuals, and where possible, also with a second commercially available MPS STR assay. Autosomal STR loci were analyzed and frequencies were calculated based on sequence composition. Also, population genetics studies were performed, with Hardy-Weinberg equilibrium, polymorphic information content (PIC), and observed and expected heterozygosity all assessed. Overall, sequence-based allelic variants of the repeat region were observed in 20 out of 22 different STR loci commonly used in forensic DNA genotyping, with the highest number of sequence variation observed at locus D12S391. The highest increase in allelic diversity and in PIC through sequence-based genotyping was observed at loci D3S1358 and D8S1179. Such detailed sequence analysis, as the one performed in the present study, is important to help understand the diversity of sequence-based STR alleles across different populations and to demonstrate how such allelic variation can improve statistics used for forensic casework.

Short-read, high-throughput sequencing technology for STR genotyping.

DNA-based methods for human identification principally rely upon genotyping of short tandem repeat (STR) loci. Electrophoretic-based techniques for variable-length classification of STRs are universally utilized, but are limited in that they have relatively low throughput and do not yield nucleotide sequence information. High-throughput sequencing technology may provide a more powerful instrument for human identification, but is not currently validated for forensic casework. Here, we present a systematic method to perform high-throughput genotyping analysis of the Combined DNA Index System (CODIS) STR loci using short-read (150 bp) massively parallel sequencing technology. Open source reference alignment tools were optimized to evaluate PCR-amplified STR loci using a custom designed STR genome reference. Evaluation of this approach demonstrated that the 13 CODIS STR loci and amelogenin (AMEL) locus could be accurately called from individual and mixture samples. Sensitivity analysis showed that as few as 18,500 reads, aligned to an in silico referenced genome, were required to genotype an individual (>99% confidence) for the CODIS loci. The power of this technology was further demonstrated by identification of variant alleles containing single nucleotide polymorphisms (SNPs) and the development of quantitative measurements (reads) for resolving mixed samples.