MetaGLIMPSE: Meta-imputation of low-coverage sequencing data for modern and ancient genomes.

Objective– This article aims to comprehensively examine the main types of food crop pests and their attack patterns through a systematic literature review approach. The research focuses on the dynamics of pest attacks, changes in ecological patterns due to climate change, and advances in modern identification technology that enable more accurate early detection. This study also highlights the significance of new paradigms of pest identification based on artificial intelligence (AI), genomics, and landscape mapping in supporting food security at the regional and national levels. Design/methodology/approach– This study used the Systematic Literature Review (SLR) method for scientific publications from 2015–2025 from reputable sources such as Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, Taylor & Francis, Wiley, AGRIS, and Google Scholar. Of the 326 articles identified in the initial stage, 30 articles in English and Indonesian were selected through a screening process based on strict inclusion–exclusion criteria. All articles were then analyzed using thematic coding techniques to produce an in-depth, evidence-based synthesis. Findings– The study produced four key findings: (1) there are five dominant pests in global food crops, namely Thrips tabaci, Spodoptera exigua/frugiperda, Helicoverpa armigera, Nilaparvata lugens and Sitophilus oryzae; (2) attack patterns are strongly influenced by temperature, humidity, pesticide resistance, and monoculture; (3) modern identification technology AI, drone imagery, multispectral sensors, and DNA Barcoding have increased detection accuracy to 94–98%; and (4) community-based early warning systems accelerate field response and reduce the risk of crop failure. Practical implications– These findings provide a scientific basis for local governments, agricultural extension workers, and farmers to gradually adopt pest identification technology and strengthen integrated monitoring systems at a regional scale. Authenticity/value– This article offers a new conceptual model of “Pest Identification Pyramid – Attack Pattern – Early Warning System” that integrates pest biology, digital technology, and community response to improve national food security.

Age estimation of single nucleotide polymorphisms associated with autoinflammatory diseases in anatolia: insights from ancient and modern DNA.

Analyzing past ecosystems can improve our understanding of the mechanisms linking biodiversity with environmental changes. Sedimentary ancient DNA (sedaDNA) opens a window to past biodiversity, beyond the fossil record, that can be used to reconstruct ancient environments and ecosystems functions. To this end, modern biodiversity and environmental conditions are used to calibrate transfer functions, that are then applied to past biodiversity data to reconstruct environmental parameters. Doing this with sedaDNA can be challenging, because ancient DNA is often obtained in limited quantities and fragmented into smaller molecules. This leads to noisy datasets, with a low alpha diversity relative to modern DNA, patchy taxa detection patterns and/or skewed relative abundance profiles. How this affects beta-diversity measures, and the performance of transfer functions remain untested. Here we simulated ancient DNA reads counts matrices from synthetic and empirical datasets, and tested 464 combinations of counts transformations (n = 13), beta-diversity indices (n = 16), and ordinations methods (n = 4), and assessed their performance in (i) separating the ecological signal from the noise introduced by DNA degradation and in (ii) predicting ground-truth environmental conditions. Our results show that commonly used workflows in DNA-based community ecology studies are sensitive to the noise associated to ancient DNA signal. Instead, combinations of methods that include more recent ordination methods proved robust to ancient DNA noise and produced better transfer functions. Our study provides a framework for designing postprocessing workflows that are better suited for sedaDNA studies.

Correction to: Testing the Late Pleistocene Arctic Origins of East Asian Psychology Using Ancient and Modern DNA

Abstract This review summarizes approximately twenty-five years of research into Armenians’ genetic history based on modern and ancient DNA samples. Studies of uniparentally inherited traits and whole-genome data indicate a rather homogeneous genetic structure among various geographical groups of historical Armenia. A significant level of genetic continuity, more than six thousand years, is observed in the eastern areas of the Armenian highlands. The genetic findings strongly dismiss the Balkan theory regarding the origins of Armenians and indicate a genetic influx from Levantine sources into the region during/after the second millennium B.C. Another significant finding is that there are almost no traces of Mongol, Turkic, or Arab conquerors in Armenians’ paternal and maternal gene pools, despite these groups having invaded the Armenian highlands multiple times after the region’s ethnogenetic processes were completed.

Serological screening, including immunological lateral flow assays, remains common for body fluid identification in sexual assault investigations but lacks the sensitivity and specificity of modern DNA profiling. To address this gap, alternative molecular approaches, including MS-based proteomics, have been explored. However, adoption is hindered by lengthy bottom-up workflows and reliance on research-grade instrumentation. Here, a streamlined, protease-free assay for the identification of saliva and seminal fluid in sexual assault evidence is described. Casework-type body fluid samples were extracted in a single step and analyzed by targeted DDA on a Q Exactive MS with a 25-min separation and data search using Byos software. The 96-well plate format used is amenable to higher-throughput automation. Discovery data sets included 50 saliva and 60 semen samples (including samples from 5 vasectomized males). This resulted in the identification of 7 saliva biomarkers (PRB1, PRB2, PRB4, PRH1, STATH, HTN1, and SMR3B) and 5 seminal fluid biomarkers (SEMG1, SEMG2, PSA, PAP, and PIP). Peptide standards were synthesized to confirm the discovery results and to develop a targeted assay. The method was successfully validated using 168 forensic casework-type samples, including diluted, laundered, and environmentally challenged samples on a variety of substrates.

The extent to which human adaptations have persisted throughout history despite strong eroding demographic events such as admixture, genetic drift, and fluctuations in selection pressures remains unknown. Understanding which loci are particularly resilient to such forces may shed light on the traits that were important for humans throughout multiple time periods. Yet, detecting ancient selection events is challenging from modern and ancient DNA due to the data and/or signal being severely degraded. Here we use a domain-adaptive neural network (DANN) trained on simulated data and applied to ancient and modern DNA for sweep detection. We show that the DANN can account for simulation misspecification, or discrepancies between the simulations and real aDNA, thereby improving the ability to detect sweeps in real data. Application of the DANN to more than 800 ancient and modern human genomes spanning the last 7000 years recovered 16 known sweeps at loci including LCT, HLA, KITLG, and OCA2/HERC2, and revealed 32 novel sweeps. All identified sweeps were classified as hard, consistent with historically low population sizes. While some sweeps were lost over time, 14 sweeps at loci involved in a range of functions including neuronal, reproductive, pigmentation, and signaling traits were found to persist from the most ancient time periods into the most recent time periods. Notably, the same top haplotype remained at high frequency across time at 9 of these 14 sweeps. Together, these results indicate that hard sweeps predominated in ancient human history and that several ancient selective events were resilient to strong admixture events and experienced sustained selective pressures.

Rediscovery of a name-bearing type of Echinococcus multilocularis (Leuckart, 1863) by museum forensics: a cold case revisited.

Heterologous protein expression is an indispensable strategy for generating recombinant proteins. Escherichia coli (E. coli) is the most widely used microbial host for recombinant protein production due to its rapid growth, well-characterized genetics, and ability to produce recombinant proteins in high yields using modern recombinant DNA technology. However, while there is a plethora of robust protein expression protocols for E. coli, these methods are often unsuitable for high-throughput screening due to their significant resource and time consumption; these protocols are also susceptible to operator error and inconsistency. To address these challenges, we developed Automated Protein EXpression (APEX), a robust and automated pipeline for recombinant protein production in E. coli. APEX leverages the accessible, open-source Opentrons OT-2 platform to automate microbial handling and protein expression with high precision and reproducibility. APEX can be configured to perform heat shock transformation, selective plating, colony sampling, microculturing, and protein expression induction using a low-cost, minimal OT-2 hardware setup. We further demonstrate the efficacy of our automated transformation workflows using a variety of plasmids (2.7-17.7 kb) and exemplify the automated heterologous expression of a diverse array of proteins (27-222 kDa). Designed with customization, modularity, and user-friendliness in mind, APEX can be easily adapted to the operator's needs without requiring any coding expertise. APEX is available at https://github.com/stracquadaniolab/apex-nf under the AGPL3 license.

Motivation: Oncoviruses, pathogens known to cause or increase the risk of cancer, include both common viruses such as human papillomaviruses and rarer pathogens such as human T-lymphotropic viruses. Computational methods for detecting viral DNA from data acquired by modern DNA sequencing technologies have enabled studies of the association between oncoviruses and cancers. Those studies are rendered particularly challenging when multiple species of oncovirus are present in a tumor sample. In such scenarios, merely detecting the presence of a sequencing read of viral origin is insufficiently informative—instead, a more precise characterization of the viral content in the sample is required.Results: We address this need with NextVir, to our knowledge the first multi-class viral classification framework that adapts genomic foundation models to detecting and classifying sequencing reads of oncoviral origin. Specifically, NextVir explores several foundation models—DNABERT-S, Nucelotide Transformer, and HyenaDNA—and efficiently fine-tunes them to enable accurate identification of the sequencing reads’ origin. The results demonstrate superior performance of the proposed framework over existing deep learning methods and suggest downstream potential for foundational models in genomics.

DNA is a digital memory storage medium with advantageous properties, including longevity and high information density. Embedding information-bearing oligonucleotides into materials for long-term storage has gained traction by leveraging modern coding, DNA synthesis, and sequencing technologies. Here, we present a versatile way to store digital information in synthetic DNA embedded in polymer fibers. These composite fibers are made of hydrophilic (poly(vinyl alcohol) and poly(ethylene oxide)) and hydrophobic (polycaprolactone and cellulose acetate) polymers synthesized by solution electrospinning and followed by cross-linking to enhance water resistance. We demonstrate the on-demand retrieval from all fiber compositions of short and long messages encoded in a single oligonucleotide and a pool of oligonucleotides, respectively. DNA/cellulose acetate fiber composites are true nondestructive readout memory: repeated access to messages stored in fibers is afforded without damaging the integrity of fibers or DNA. We envisage that our simple and robust manufacturing approach will contribute to the development of scalable and accessible DNA data storage solutions.

ABSTRACTEnvironmental DNA (eDNA) is broadly assumed to be highly fragmented (< 600 bp) in seawater. However, several marine eDNA studies that have successfully amplified longer fragments (from 600 up to 16,000 bp) are challenging this notion. We hypothesized that a small, yet amplifiable, proportion of eDNA templates contain fragment lengths exceeding 600 bp. To test this, we designed primers to target a series of mitochondrial fragment lengths between 119 and 15,727 bp for the tiger shark (Galeocerdo cuvier) and performed qPCR on seawater eDNA samples collected from the offshore, tropical Kimberley and Roebuck Marine Parks in Western Australia. We observed a steep decrease in eDNA copy number with increasing fragment size between 119 and 1518 bp, beyond which amplification was not successful. Importantly, we demonstrate that fragment sizes larger than conventionally targeted (e.g., 636, 840, and 1518 bp) can still be successfully amplified from seawater eDNA samples. Estimated mean nucleotide damage in seawater eDNA samples was found to be 3.9 breaks per 1000 bp; this equates to a mean undamaged fragment size of 256 bp and is less than damage observed in modern fecal DNA and ancient DNA. Characterizing the extent of eDNA fragmentation in various environmental samples will improve understanding of the genetic material available and enable practitioners to target standard length barcodes and longer hypervariable gene regions. Through the recovery of more informative data, eDNA applications will extend to finer‐scale taxonomic resolution, including complex species and sub‐species discrimination, as well as population analyses.

The importance of string matching algorithms in the world of modern DNA forensic technology cannot be over-stated. Short Tandem Repeats (STRs) play an important role in forensic DNA analysis due to their high variability among individuals. Fast and accurate detection of STRs in large-scale genomic data is most important for criminal investigations, identity verification, and population studies. This study introduces a novel exact string matching algorithm, the Tri-Scan for Left, Right, and Middle Character (TSLRMC) approach, tailored for efficient pattern detection in forensic DNA sequences. This study focusses on the limitations found in some famous and widely used exact string matching algorithms. Proposed algorithm improves the running time for scanning pattern string in a long text string. The novelty of proposed algorithm is to optimize the scanning by the scanning of pattern string left, right and middle characters in the long DNA sequence string and then scan the remaining character of the pattern string in that partial text window where the pattern string’s left, right and middle character found. The proposed algorithm shows significant improvement compared with the most popular exact string matching algorithms, based on running time as well as number of characters compared. Time complexity of this proposed novel TSLRMC algorithm is O (n-m) in worst case, O (mn) in average case and O (1) in best case.

Abstract Despite the few numbers of molecular investigations exploring the genetic diversity, very scarce information is available for supporting the taxonomic and anatomical investigations of Asteraceae species in Egypt. Thus, the adoption of modern DNA based marker techniques is an urgent need to dive into the taxonomic authentication, evolutionary relationship in addition to the calculation of genetic variations within and between Asteraceae species. Hence, the current study was conducted to assess the intraspecific genetic diversity and authenticate taxa at the species level among and within 20 different species of Asteraceae family. Using 19 primers combinations belonging to three different molecular marker systems namely, Start Codon Targeted (SCoT), CAAT box-derived Polymorphism (CBDP) and Inter Simple Sequence Repeats (ISSR). A total of 285 amplified DNA fragments were produced, including 273 polymorphic DNA bands, with an average of percentage of polymorphism (PPB) ranged from 91.10 to 100.00% and the average of polymorphism information content (PIC) for SCoT, CBDP, and ISSR markers were 0.349, 0.403, and 0.418 respectively. The population structure analysis suggested that the 20 Asteraceae species could be categorized into 2 or 7 or 8 genetic groups based on SCoT, CBDP, and ISSR markers datasets, respectively. Both factorial and phylogenetic analyses based on the presence or absence of the amplified bands/fragments were performed by Jaccard’s similarity coefficient. Interestingly, Unweighted pair group method with arithmetic mean (UPGMA) cluster dendrograms for all markers system) two main clusters that all subsequently divided into different subgroups of species. Surprisingly, the UPGMA dendrogram generated based on CBDP marker data was divided into main clusters only one species of Centaurea cristata L. that collected from AGERI was clustered as a unique and main cluster separately while the remaining 19 species of Asteraceae family spreading over the first main cluster. Together, this study showed that the combination of these three molecular marker systems rather than independently could be useful tool for comprehensive evaluation of genetic diversity among and within Asteraceae species in Egypt.

Hearing and balance disorders are the most prevalent sensory impairments, affecting hundreds of millions worldwide, yet their underlying cellular and molecular pathologies remain poorly understood. This knowledge gap stems from the inaccessibility of the ear’s sensory organs—embedded within the temporal bone (TB), the hardest bone in the body—which cannot be biopsied in living patients without causing irreversible damage. Conventional histopathology workflows rely on postmortem en bloc extraction of TBs, followed by lengthy decalcification, celloidin embedding, and manual serial sectioning of these large specimens—a process that takes one to two years, is labor- and cost-intensive, and lacks compatibility with most modern protein, DNA, and RNA assays. Here, we present a rapid, reversible polymethyl methacrylate (rPMMA) workflow that enables advanced molecular histopathology studies on formalin-fixed, calcified TBs. Our protocol uses low-temperature (–40 °C to +4 °C) resin embedding, precision near-serial sectioning (10–50 µm) via femtosecond laser microtomy or precision diamond wire sawing, and subsequent deacrylation to fully restore tissue accessibility for high-fidelity histomorphology, multiplexed immunofluorescence, whole-genome sequencing, and in situ mRNA detection (RNAscope™) assays. Compared to the gold-standard celloidin workflow, our method reduces processing time and costs by approximately 90% while integrating equivalent histomorphology with advanced molecular assays, providing a new benchmark for multidimensional studies in human hearing and balance pathologies.

Abstract Background: VACTERL association is a rare disorder characterized by a non-random co-occurrence of multiple congenital anomalies. Reported incidences of VACTERL are usually sporadic. Case Presentation: Here we present a case of familial VACTERL association in a male infant born full term at 38 weeks of gestation. The infant’s maternal aunt also had significant features of VACTERL association but died in the first year of life. Due to the lack of specialized expertise and absence of detailed anomaly scans, the congenital anomalies were not detected prenatally. The baby also had a very rare co-occurrence of sacrococcygeal teratoma. Conclusion: Skillful personnel and equipment to detect the anomalies in-utero and the use of modern DNA sequencing techniques to detect the possible underlying genetic defects can help us better understand the pathophysiology of various congenital disorders. Early diagnosis is crucial for optimizing management strategies and counselling parents regarding potential outcomes.

High-throughput sequencing (HTS) is a modern DNA sequencing technology used to rapidly read thousands of genomic fragments from microorganisms given a sample. The large amount of data produced by this process makes deep learning, whose performance often scales with dataset size, a suitable fit for processing HTS samples. While deep learning models have utilized sets of DNA sequences to make informed predictions, to our knowledge, there are no models in the current literature capable of generating synthetic HTS samples, a tool which could enable experimenters to predict HTS samples given some environmental parameters. Furthermore, the unordered nature of HTS samples poses a challenge to nearly all deep learning architectures because they have an inherent dependence on input order. To address this gap in the literature, we introduce DNA Generative Adversarial Set Transformer (DNAGAST), the first model capable of generating synthetic HTS samples.We qualitatively and quantitatively demonstrate DNAGAST’s ability to produce realistic synthetic samples and explore various methods to mitigate mode-collapse. Additionally, we propose novel quantitative diversity metrics to measure the effects of mode-collapse for unstructured set-based data.

Abstract A group of perhaps 2,000 Māori, whose main representative is Monica Matāmua, claim that they are not Māori or Polynesian but descendants of a White race they call the Hotu. They claim that they were the first to settle in Aotearoa New Zealand, more than a thousand years before Māori, so that they are the true Indigenous inhabitants of the land. Further, they hold to a conspiracy theory that claims evidence for White Hotu has been covered up by Māori and elites. This article examines the epistemic modes by which modern Hotu defend their contentions. A key foundation is their employment of the biblical motif of Paradise, by which they identify as an originally Edenic people, in contrast to Māori. The Hotu combine the authoritative biblical motif of Paradise with parallel epistemic foundations, including Māori traditions of original peoples, the colonial myth of a Pacific Paradise, and modern DNA testing. This article demonstrates how the Hotu generate epistemic authority not only via each epistemic mode, but via a hermetic-like combination of epistemic modes. The piling up of superficially similar epistemic claims provides a seemingly profound yet spurious basis for asserting White pre-Polynesian identity and origins. It also provides Hotu with a sense of agency that encourages their ongoing fight to reverse the damaging effects of colonization. Yet the Hotu contention to be descendants of a White pre-Polynesian people comes at the significant cost of demonizing Māori, absolving European colonizers, and emboldening many New Zealanders to deny Māori their Indigenous status.

PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms. PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the Supplementary Material.