Graph mining for next generation sequencing: leveraging the assembly graph for biological insights.

BACKGROUND: The assembly of Next Generation Sequencing (NGS) reads remains a challenging task. This is especially true for the assembly of metagenomics data that originate from environmental samples potentially containing hundreds to thousands of unique species. The principle objective of current assembly tools is to assemble NGS reads into contiguous stretches of sequence called contigs while maximizing for both accuracy and contig length. The end goal of this process is to produce longer contigs with the major focus being on assembly only. Sequence read assembly is an aggregative process, during which read overlap relationship information is lost as reads are merged into longer sequences or contigs. The assembly graph is information rich and capable of capturing the genomic architecture of an input read data set. We have developed a novel hybrid graph in which nodes represent sequence regions at different levels of granularity. This model, utilized in the assembly and analysis pipeline Focus, presents a concise yet feature rich view of a given input data set, allowing for the extraction of biologically relevant graph structures for graph mining purposes. RESULTS: Focus was used to create hybrid graphs to model metagenomics data sets obtained from the gut microbiomes of five individuals with Crohn's disease and eight healthy individuals. Repetitive and mobile genetic elements are found to be associated with hybrid graph structure. Using graph mining techniques, a comparative study of the Crohn's disease and healthy data sets was conducted with focus on antibiotics resistance genes associated with transposase genes. Results demonstrated significant differences in the phylogenetic distribution of categories of antibiotics resistance genes in the healthy and diseased patients. Focus was also evaluated as a pure assembly tool and produced excellent results when compared against the Meta-velvet, Omega, and UD-IDBA assemblers. CONCLUSIONS: Mining the hybrid graph can reveal biological phenomena captured by its structure. We demonstrate the advantages of considering assembly graphs as data-mining support in addition to their role as frameworks for assembly.

Victim identification and body completeness based on last known location at the World Trade Center.

This analysis focuses on the identification efforts conducted by the New York City Office of Chief Medical Examiner (NYC OCME) over a 20-year period from September 11, 2001 to September 11, 2021. Due to this unprecedented level of commitment to victim identification, a wealth of data has been collected over the two-decade period and is still being collected as identification efforts are ongoing. The results of this data analysis are not only informative for the World Trade Center (WTC) victims, but may also be instructional for other large-scale, protracted victim identification efforts. Based on available data, most victims are associated with the impact zones and higher in both towers. No correlation was observed in the overall identification rates based on last known location in the buildings, suggesting that location in the towers does not affect the likelihood of a successful identification. There was, however, a significant difference in the body completeness values observed for victims from the upper floors compared to those below the impact zones. The identification rates and body completeness values for victims onboard the two airplanes are significantly different from each other, possibly related to the varying aircraft speeds at the time of impact.

Victim identification from the September 11, 2001 attack on the World Trade Center: Past trends and future projections.

Victim identification following mass fatality events is critically important. Extensive traumatic injuries and body fragmentation add complexity to this process. World Trade Center (WTC) identification efforts have been ongoing for over 20 years and this study tracks identification trends from the 2753 known WTC victims and the 21,905 recovered remains. For identified victims, data include the number of remains identified, date(s) of the identification(s), and identification modalities. Results show a heavy reliance on DNA due to body fragmentation. Other modalities played an important role initially, but DNA eventually became the singular identification modality. For large-scale disasters involving significant body fragmentation, aggressive DNA testing strategies are critical for victim identification. Over time, the number of linked remains (portions of previously identified individuals) will greatly outnumber the new identifications (first-time identifications). A novel approach using statistical modeling from ecology studies was applied to estimate future WTC identification rates using Identification Accumulation Curve extrapolation with the Good-Toulmin estimator. Projections indicate there will be 76 first-time identifications (95% CI: 49-117) through the successful DNA testing of 3404 unidentified, fragmentary remains. The remainder of the identifications would be additional portions of previously identified victims. These results may be instructional for management of other large-scale, protracted victim identification efforts.

Next generation sequencing of STR artifacts produced from historical bone samples.

STR artifacts are commonly observed in electrophoretic data and can complicate interpretation of the profiles produced. Even when a consensus approach is applied, reproducible artifacts have the potential to convolute the analysis. DNA obtained from historical bone samples is often heavily degraded and damaged, requiring the use of more sensitive procedures to increase allele recovery. Additionally, skeletal remains exposed to environmental conditions may be afflicted with microbial DNA contamination that cross-reacts with the primers during short tandem repeat (STR) multiplex amplification. STR artifacts manifested as a result of these circumstances can be sourced and characterized using new sequencing technologies to potentially ease the analysis burden. For this study, PCR product from 17 low-quality bone samples exhibiting reproducible autosomal and Y-chromosomal STR (Y-STR) artifacts in capillary electrophoresis (CE) data were sequenced with next generation sequencing (NGS). Sequenced reads were bioinformatically sorted using STRait Razor to determine the authenticity of alleles and confirm the profile generated by CE. Sequence data from the PCR products and a subset of the associated extracts were further analyzed with Kaiju to classify the microbial species present and identify potential sources of artifact peaks. A suspected Y-STR artifact was similar in sequence to Pseudomonas sp. BAY1663, a species ubiquitously found in soil. Regions of homology were observed between the Pseudomonas genome and the presumed primer binding locations for Y-STRs included in the AmpFlSTR Y-Filer STR kit. Characterization of such supposed artifact peaks may aid in interpretation of CE data and ultimately lead to increased confidence in the reported results.

Z-Transform Method for Pairwise Osteometric Pair-matching.

A new pairwise osteometric pair-matching approach based on the Z-transform method is presented. In contrast to previous methods that perform a global t-test on the summed skeletal element pair measurement distances, this approach performs t-tests on each individual distance, facilitating the capture of measurement-specific variation. This new approach is compared to published pairwise sorting methods using a standard reference dataset of postcranial remains maintained by the Defense POW/MIA Accounting Agency. Receiver operating characteristic curve analysis indicates significantly improved performance for the clavicle and radius over all previous methods (p < 0.01). The z-transform method weighted by the effect size outperformed the t-test (Byrd and Adams) and the mean t-test (Lynch) for all elements (p < 0.01). The method performed better than the absolute value t-test (Lynch) for five elements (p < 0.01) and performed at least as well for the remainder. To facilitate usability all methods are available at: https://github.com/spawaskar-cora/z-transform-method.