Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality.

Identifying distant relatives using benchtop-scale sequencing.

The genetic component of forensic genetic genealogy (FGG) is an estimate of kinship, often conducted at genome scales between a great number of individuals. The promise of FGG is substantial: in concert with genealogical records and other nongenetic information, it can indirectly identify a person of interest. A downside of FGG is cost, as it is currently expensive and requires chemistries uncommon to forensic genetic laboratories (microarrays and high throughput sequencing).

Mixture detection with Demixtify.

The de facto genetic markers of forensics are short tandem repeats (STRs). There are many analytical tools designed to work with STRs, including techniques for analyzing and assessing DNA mixtures. In contrast, the nascent field of forensic genetic genealogy often relies on biallelic single nucleotide polymorphisms (SNPs).

Using unique molecular identifiers to improve allele calling in low-template mixtures.

PCR artifacts are an ever-present challenge in sequencing applications. These artifacts can seriously limit the analysis and interpretation of low-template samples and mixtures, especially with respect to a minor contributor. In medicine, molecular barcoding techniques have been employed to decrease the impact of PCR error and to allow the examination of low-abundance somatic variation.

Optimized variant calling for estimating kinship.

One of the fundamental goals of forensic genetics is sample attribution, i.e., whether an item of evidence can be associated with some person or persons.

A genotype likelihood function for DNA mixtures.

The recent advent of genetic genealogy has brought about a renewed interest in genome-scale forensic analyses, of which kinship estimation is a critical component. Most genomic kinship estimators consider SNPs (single nucleotide polymorphisms), often leveraging the co-inheritance of shared alleles to inform their analyses. While current estimators cannot directly evaluate mixed samples, there exist well-established SNP-based kinship estimators tailored to considering challenged samples, including low-pass whole genome sequencing.

Evaluating the Impact of Dropout and Genotyping Error on SNP-Based Kinship Analysis With Forensic Samples.

Technological advances in sequencing and single nucleotide polymorphism (SNP) genotyping microarray technology have facilitated advances in forensic analysis beyond short tandem repeat (STR) profiling, enabling the identification of unknown DNA samples and distant relationships. Forensic genetic genealogy (FGG) has facilitated the identification of distant relatives of both unidentified remains and unknown donors of crime scene DNA, invigorating the use of biological samples to resolve open cases. Forensic samples are often degraded or contain only trace amounts of DNA.

vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework.

Genotyping error can impact downstream single nucleotide polymorphism (SNP)-based analyses. Simulating various modes and levels of error can help investigators better understand potential biases caused by miscalled genotypes. We have developed and validated vcferr, a tool to probabilistically simulate genotyping error and missingness in variant call format (VCF) files.

Techniques for estimating genetically variable peptides and semi-continuous likelihoods from massively parallel sequencing data.

Forensic genetic investigations typically rely on analysis of DNA for attribution purposes. There are times, however, when the amount and/or the quality of the DNA is limited, and thus little or no information can be obtained regarding the source of the sample. An alternative biochemical target that also contains genetic signatures is protein.

Determining Informative Microbial Single Nucleotide Polymorphisms for Human Identification.

The skin microbiome is a highly abundant and relatively stable source of DNA that may be utilized for human identification (HID). In this study, a set of single nucleotide polymorphisms (SNPs) with a high mean estimated Wright's fixation index (F) (>0.1) and widespread abundance (found in ≥75% of samples compared) were selected from a diverse set of markers in the hidSkinPlex panel.

skater: an R package for SNP-based kinship analysis, testing, and evaluation.

: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream process- ing and manipulation. A dedicated software package that consistently and intuitively imple- ments this analysis functionality is needed. : Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R.

ProSynAR: a reference aware read merger.

Motivation: Read-merging algorithms that look solely at the reads can misalign and mis-merge the reads (especially near repetitive sequences).

Results: The C++ program ProSynAR has been written to take the reads' position in the reference into account when performing (and deciding whether to perform) a merge.

Availability: *Nix users can retrieve the source from GitHub (https://github.

Optimization of proteomics sample preparation for forensic analysis of skin samples.

We present an efficient protein extraction and in-solution enzymatic digestion protocol optimized for mass spectrometry-based proteomics studies of human skin samples. Human skin cells are a proteinaceous matrix that can enable forensic identification of individuals. We performed a systematic optimization of proteomic sample preparation for a protein-based human forensic identification application.

MMDIT: A tool for the deconvolution and interpretation of mitochondrial DNA mixtures.

Short tandem repeats of the nuclear genome have been the preferred markers for analyzing forensic DNA mixtures. However, when nuclear DNA in a sample is degraded or limited, mitochondrial DNA (mtDNA) markers provide a powerful alternative. Though historically considered challenging, the interpretation and analysis of mtDNA mixtures have recently seen renewed interest with the advent of massively parallel sequencing.

Population Informative Markers Selected Using Wright's Fixation Index and Machine Learning Improves Human Identification Using the Skin Microbiome.

Microbial DNA, shed from human skin, can be distinctive to its host and, thus, help individualize donors of forensic biological evidence. Previous studies have utilized single-locus microbial DNA markers (e.g.

Evaluation of Promega PowerSeq™ Auto/Y systems prototype on an admixed sample of Rio de Janeiro, Brazil: Population data, sensitivity, stutter and mixture studies.

Forensic DNA typing typically relies on the length-based (LB) separation of PCR products containing short tandem repeat loci (STRs). Massively parallel sequencing (MPS) elucidates an additional level of STR motif and flanking region variation. Also, MPS enables simultaneous analysis of different marker-types - autosomal STRs, SNPs for lineage and identification purposes, reducing both the amount of sample used and the turn-around-time of analysis.

Graph Algorithms for Mixture Interpretation.

The scale of genetic methods are presently being expanded: forensic genetic assays previously were limited to tens of loci, but now technologies allow for a transition to forensic genomic approaches that assess thousands to millions of loci. However, there are subtle distinctions between genetic assays and their genomic counterparts (especially in the context of forensics). For instance, forensic genetic approaches tend to describe a locus as a haplotype, be it a microhaplotype or a short tandem repeat with its accompanying flanking information.

A Continuous Statistical Phasing Framework for the Analysis of Forensic Mitochondrial DNA Mixtures.

Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions.

STRait Razor Online: An enhanced user interface to facilitate interpretation of MPS data.

Since 2013, STRait Razor has enabled analysis of massively parallel sequencing (MPS) data from various marker systems such as short tandem repeats, single nucleotide polymorphisms, insertion/deletions, and mitochondrial DNA. In this paper, STRait Razor Online (SRO), available at https://www.unthsc.

ProDerAl: reference position dependent alignment.

Motivation: Current read-mapping software uses a singular specification of alignment parameters with respect to the reference. In the presence of varying reference structures (such as the repetitive regions of the human genome), alignments can be improved if those parameters are allowed vary.

Results: To that end, the C++ program ProDerAl was written to refine previously generated alignments using varying parameters for these problematic regions.

Reducing noise and stutter in short tandem repeat loci with unique molecular identifiers.

Unique molecular identifiers (UMIs) are a promising approach to contend with errors generated during PCR and massively parallel sequencing (MPS). With UMI technology, random molecular barcodes are ligated to template DNA molecules prior to PCR, allowing PCR and sequencing error to be tracked and corrected bioinformatically. UMIs have the potential to be particularly informative for the interpretation of short tandem repeats (STRs).

mixIndependR: a R package for statistical independence testing of loci in database of multi-locus genotypes.

Background: Multi-locus genotype data are widely used in population genetics and disease studies. In evaluating the utility of multi-locus data, the independence of markers is commonly considered in many genomic assessments. Generally, pairwise non-random associations are tested by linkage disequilibrium; however, the dependence of one panel might be triplet, quartet, or other.

Fractionation of DNA and protein from individual latent fingerprints for forensic analysis.

Human touch samples represent a significant portion of forensic DNA casework. Yet, the generally low abundance of genetic material combined with the predominantly extracellular nature of DNA in these samples makes DNA-based forensic analysis exceptionally challenging. Human proteins present in these same touch samples offer an abundant and environmentally-robust alternative.

Evaluation of 16S rRNA Hypervariable Regions for Bioweapon Species Detection by Massively Parallel Sequencing.

Molecular detection and classification of the bacterial groups in a sample are relevant in several areas, including medical research and forensics. Sanger sequencing of the 16S rRNA gene is considered the gold standard for microbial phylogenetic analysis. However, the development of massively parallel sequencing (MPS) offers enhanced sensitivity and specificity for microbiological analyses.

Numt identification and removal with RtN!

Motivation: Assays in mitochondrial genomics rely on accurate read mapping and variant calling. However, there are known and unknown nuclear paralogs that have fundamentally different genetic properties than that of the mitochondrial genome. Such paralogs complicate the interpretation of mitochondrial genome data and confound variant calling.