Detection of single nucleotide polymorphisms (SNP) in equine coat color genes using SNaPshotTM multiplex kit or pluronic F-108 tri-block copolymer and capillary electrophoresis.

Molecular methods for the detection of mammalian coat color phenotypes have expanded greatly within the past decade. Many phenotypes are associated with a single nucleotide polymorphism mutation in the genetic sequence. Traditionally, these mutations are detected through sequencing, hybridization assays or mini-sequencing. However, these techniques can be expensive and tedious. Previously, CE-SSCP using the F-108 polymer was able to distinguish SNPs for the melanocortin-1 receptor (mc1r) coat color gene in horses (Equus caballus) that differed by one nucleotide substitution. The objective of this study was to expand the detection of coat color SNPs in horses. The genes for the solute carrier family member 2 (slc45a2/matp), type III receptor protein-tyrosine kinase (kit) and mc1r genes using CE-SSCP and F-108 polymer were compared to mini-sequencing with the SNaPshotTM kit. The F-108 polymer reproducibly resolved homozygous and heterozygous individuals for the mc1r and kit markers, but was unable to resolve heterozygous individuals for slc45a2 at 38ºC. The need for temperatures <15ºC, the SNP position being close to the 5'-end, and conformational structures/free energy with similar values resulted in the inability to resolve the secondary structures. Despite this limitation, the CE-SSCP method could be used to provide a rapid phenotypic description for equine forensic investigations.

F-108 polymer and capillary electrophoresis easily resolves complex environmental DNA mixtures and SNPs.

Ecological studies of microbial communities often use profiling methods but the true community diversity can be underestimated in methods that separate amplicons based on sequence length using performance optimized polymer 4. Taxonomically, unrelated organisms can produce the same length amplicon even though the amplicons have different sequences. F-108 polymer has previously been shown to resolve same length amplicons by sequence polymorphisms. In this study, we showed F-108 polymer, using the ABI Prism 310 Genetic Analyzer and CE, resolved four bacteria that produced the same length amplicon for the 16S rRNA domain V3 but have variable nucleotide content. Second, a microbial mat community profile was resolved and supported by NextGen sequencing where the number of peaks in the F-108 profile was in concordance with the confirmed species numbers in the mat. Third, equine DNA was analyzed for SNPs. The F-108 polymer was able to distinguish heterozygous and homozygous individuals for the melanocortin 1 receptor coat color gene. The method proved to be rapid, inexpensive, reproducible, and uses common CE instruments. The potential for F-108 to resolve DNA mixtures or SNPs can be applied to various sample types-from SNPs to forensic mixtures to ecological communities.

DNA Barcoding and Metabarcoding Protocols for Species Identification.

The article presents the several steps to be performed on a plant, fungal, insect, or soil sample to obtain DNA sequences for DNA barcode analysis. The chapter begins with a description of sample preparation including procedures for cleaning and proceeds to DNA extraction with methods adapted for the specific type of sample. Next, DNA quantification is described so the proper amount is used for the amplification of the selected barcode regions. Information is provided for reaction mixes and amplification conditions for several referenced barcode primer pairs tuned for the individual sample of interest. This is followed by a description of procedures to access the success of amplification, cleanup, and quantification of the product ready for either Sanger sequencing or library preparation for massive parallel sequencing (MPS). Finally, procedures are provided for Sanger sequencing, library preparation, and MPS sequencing. The chapter provides several references of barcode regions for different sample types.

Guidelines for the Analysis of DNA Barcoding/Metabarcoding Sequencing Data and Interpretation of Publicly Available Databases.

This chapter describes procedures for the use of DNA sequence data to obtain and compare taxonomic identification using the public databases GenBank and Barcode of Life Data System (BOLD). The chapter begins by describing procedures used to prepare quality sequences for uploading into GenBank and BOLD. Next, steps used to query the DNA sequences against the public databases are described using GenBank BLAST and BOLD identification engines. Interpretation guidelines for the taxonomic identification assignments are presented. Finally, a procedure for evaluating the accuracy and reliability of sequences from GenBank and BOLD is provided.

Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness.

Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term "manifestations," as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.

Early adverse physiological event detection using commercial wearables: challenges and opportunities.

Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant's healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.

Assessment of detection limits for dyed and mounted textile fibers using Raman spectroscopy.

Trace evidence in the form of textile fibers can be used to link objects and places during an investigation. Raman spectroscopy is a well-established technique that has been used for the examination of various pigments, paints, inks, and dyes. The objective of this study was to determine the capability of Raman spectroscopy to detect several different dye classes and colors on a variety of textile fibers. To test this, four categories of dyes, reactive, disperse, acid, and direct were examined with Raman microscopy while applied to one of five fiber types (cotton, polyester, nylon, wool, and rayon). Each dye category was tested using four colors, black, blue, red, and yellow, while at four concentrations of dye (w/w), 4% (black only), 1%, 0.5%, and 0.05% (blue, red, and yellow). Finally, each dye, fiber, color, and dye concentration combination were examined with Raman using one of two laser excitation sources (532 nm and 780 nm) while mounted in one of two mounting media, Permount™ and Entellan® new, as well as unmounted. Raman spectroscopy could detect some dyes at low concentrations (0.5% and 0.05%) even when mounted in mounting media and covered with a glass coverslip. Excitation source, dye category, dye concentration, fiber type, and mounting method all influence the ability to detect any given dye. These results support the continued study of Raman as a tool for the examination of fiber dyes as it has shown the potential to be effective even under constraints experienced by forensic examiners.

Rapid, presumptive identification of seed-based toxins using direct analysis in real time mass spectrometry (DART-MS) and its variants.

Detection of seed-based toxins is a need for forensic chemists when suspected poisonings occur. The evidence that is found is often physically unidentifiable, as the seeds are mashed to extract the toxin. This work investigates potential strategies for rapid detection of seed-based toxins and seed mashes containing these toxins using chemical signatures obtained by direct analysis in real time mass spectrometry (DART-MS). Seven toxins (digoxin, digitoxin, hypaconitine, hyoscyamine, lanatoside, oleandrin, and scopolamine) and six seeds containing these toxins were studied. While detection of four of the toxins was readily attainable, detection of digoxin, digitoxin, and lanatoside was hindered by the inability to thermally desorb these larger compounds under normal operating conditions. The use of DART-MS variants capable of higher desorption temperatures (thermal desorption (TD)-DART-MS and infrared thermal desorption (IRTD)-DART-MS) enabled detection of these compounds. Detection of toxins from direct analysis of seed mashes and methanolic seed mash extracts was found to be compound and technique dependent. Principal component analysis (PCA) of generated mass spectra enabled differentiation of seed species, even in cases where the toxins were undetectable.

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.

Comparison of polymerases used for amplification of mitochondrial DNA from challenging hairs and hairs of various treatments.

Forensic DNA analysis of hair evidence typically involves the amplification and sequencing of the control region (CR) of the mitochondrial genome (mtgenome). In compromised hair samples, such as shed hairs, the number of mtgenome copies could be low; thus, it is imperative that the polymerase used in PCR is efficient to ensure maximum amplification. Considering this, the first phase of this study compared the yields obtained from 12 polymerases (sourced from a range of commercial companies) when amplifying the CR, hypervariable (HV) region II (HV2), and hypervariable subregion II-B (HV2B). This initial assessment was performed using mitochondrial DNA (mtDNA) extracted from 2 cm of hair adjacent to the root from three donors of different self-reported ancestries and hair color/texture. PrimeSTAR HS and KAPA HiFi HotStart consistently generated significantly higher amplicon yields (p < 0.05, ~5-fold increase) for most regions than AmpliTaq Gold DNA polymerase (the polymerase validated for use in most forensic laboratories). The second phase of this project was focused on assessing the broad utility of these top two performing polymerases for amplifying two regions of the mtgenome (CR and HV2B) from hair samples representing diverse self-reported ancestral origins (European, Latin American, African American, Asian, and Native American), characteristics/treatments (bleached, dyed, and chemically straightened), and anatomical origins (e.g., head and pubic region) (n = 41). These regions were chosen as they are the most challenging to amplify and sequence in compromised hair samples due to length (i.e., the CR is ~1.2 kb) and repeat structure (i.e., the polycytosine stretch within HV2B). The results indicated that regardless of sample type, PrimeSTAR HS and KAPA HiFi HotStart polymerases outperformed (p < 0.05) AmpliTaq Gold DNA polymerase (averaging 11- and 8-fold increased yields, respectively). The results from this study highlight that enhanced commercially available polymerases appear to significantly improve the amplification of mtDNA from challenging hair samples.

Performance Comparison of Massively Parallel Sequencing (MPS) Instruments Using Single-Nucleotide Polymorphism (SNP) Panels for Ancestry.

Thermo Fisher Scientific released the Precision ID Ancestry Panel, a 165-single-nucleotide polymorphism (SNP) panel for ancestry prediction that was initially compatible with the manufacturer's massively parallel sequencer, the Ion Torrent Personal Genome Machine (PGM). The semiautomated workflow using the panel with the PGM involved several time-consuming manual steps across three instruments, including making templating solutions and loading sequencing chips. In 2014, the manufacturer released the Ion Chef robot, followed by the Ion S5 massively parallel sequencer in late 2015. The robot performs the templating with reagent cartridges and loads the chips, thus creating a fully automated workflow across two instruments. The objective of the work reported here is to compare the performance of two massively parallel sequencing systems and ascertain if the change in the workflow produces different ancestry predictions. For performance comparison of the two systems, forensic-type samples (n = 16) were used to make libraries. Libraries were templated either with the Ion OneTouch 2 system (for the PGM) or on the Ion Chef robot (for the S5). Sequencing results indicated that the ion sphere particle performance metrics were similar for the two systems. The total coverages per SNP and SNP quality were both higher for the S5 system. Ancestry predictions were concordant for the mock forensic-type samples sequenced on both massively parallel sequencing systems. The results indicated that automating the workflow with the Ion Chef system reduced the labor involved and increased the sequencing quality.

Assessment of BOLD and GenBank - Their accuracy and reliability for the identification of biological materials.

Taxonomic identification of biological materials can be achieved through DNA barcoding, where an unknown "barcode" sequence is compared to a reference database. In many disciplines, obtaining accurate taxonomic identifications can be imperative (e.g., evolutionary biology, food regulatory compliance, forensics). The Barcode of Life DataSystems (BOLD) and GenBank are the main public repositories of DNA barcode sequences. In this study, an assessment of the accuracy and reliability of sequences in these databases was performed. To achieve this, 1) curated reference materials for plants, macro-fungi and insects were obtained from national collections, 2) relevant barcode sequences (rbcL, matK, trnH-psbA, ITS and COI) from these reference samples were generated and used for searching against both databases, and 3) optimal search parameters were determined that ensure the best match to the known species in either database. While GenBank outperformed BOLD for species-level identification of insect taxa (53% and 35%, respectively), both databases performed comparably for plants and macro-fungi (~81% and ~57%, respectively). Results illustrated that using a multi-locus barcode approach increased identification success. This study outlines the utility of the BLAST search tool in GenBank and the BOLD identification engine for taxonomic identifications and identifies some precautions needed when using public sequence repositories in applied scientific disciplines.

Bioinformatics Approach to Assess the Biogeographical Patterns of Soil Communities: The Utility for Soil Provenance.

Soil DNA profiling has potential as a forensic tool to establish a link between soil collected at a crime scene and soil recovered from a suspect. However, a quantitative measure is needed to investigate the spatial/temporal variability across multiple scales prior to their application in forensic science. In this study, soil DNA profiles across Miami-Dade, FL, were generated using length heterogeneity PCR to target four taxa. The objectives of this study were to (i) assess the biogeographical patterns of soils to determine whether soil biota is spatially correlated with geographic location and (ii) evaluate five machine learning algorithms for their predictive ability to recognize biotic patterns which could accurately classify soils at different spatial scales regardless of seasonal collection. Results demonstrate that soil communities have unique patterns and are spatially autocorrelated. Bioinformatic algorithms could accurately classify soils across all scales with Random Forest significantly outperforming all other algorithms regardless of spatial level.

A study of the intrinsic variability and the effect of environmental conditions on the formation of a postmortem root band.

A postmortem root band (PMRB) is defined as "an opaque ellipsoidal band composed of a collection of parallel elongated air/gas spaces and is approximately 0.5mm above the root bulb and about 2mm below the skin surface" [1]. It is generally accepted that it can appear in the root of hairs attached to remains during decomposition [1]. This study aimed to investigate the underlying cause and mechanism of PMRB formation. This was done (i) by observing the overall frequency and the intrinsic variability in anagen hairs containing a PMRB collected across five regions of a human decedent's scalp at three time points, and (ii) by determining if PMRB-like features can be induced via immersion in in-vitro controlled environments of anagen hairs plucked from the scalp of a human decedent (ex-situ postmortem hairs) not containing a PMRB. The results of the first objective illustrated that as time since death increased, the frequency of hairs containing a PMRB across the scalp sampling regions increased and the intrinsic variability decreased. The results of the second objective demonstrated that both an aqueous environment and microbial activity are essential for the formation of PMRB-like features. This study was the first to statistically analyze the intrinsic variability of PMRB formation, as well as the first to induce PMRB-like features in roots of ex-situ postmortem hairs.