Genomic Tools Research Articles

Impact of Polyploidy on Crop Improvement and Plant Breeding Strategies: A Review

Polyploidy, the condition of possessing multiple sets of chromosomes, is a widespread phenomenon in plants that has had a profound impact on crop evolution and improvement. This review explores the role of polyploidy in plant breeding strategies, emphasizing its contributions to enhancing agronomic traits, overcoming genetic barriers, and expanding genetic diversity. Polyploid crops, such as wheat, cotton, and Brassica species, exhibit superior yield, increased biomass, and enhanced stress tolerance compared to their diploid counterparts. Polyploidy can arise naturally or be induced artificially, providing breeders with opportunities to create novel crop varieties through synthetic polyploidization. The complexity of polyploid genomes poses significant challenges, including fertility issues, meiotic instability, and difficulties in genome management. Advances in genomic and biotechnological tools, such as genomic selection and CRISPR-based genome editing, are beginning to address these obstacles, allowing for precise manipulation of polyploid genomes and improved breeding outcomes. Additionally, polyploidy plays a crucial role in overcoming reproductive barriers in interspecific hybrids, facilitating the transfer of desirable traits between species that would otherwise be genetically incompatible. The potential of polyploidy for developing climate-resilient crops is particularly noteworthy, as polyploid plants often exhibit greater tolerance to drought, salinity, and extreme temperatures. This makes polyploid breeding a promising approach for addressing the challenges of climate change and ensuring food security. Future research should focus on understanding the genetic and epigenetic mechanisms underlying polyploid genome stability and trait expression, as well as integrating advanced computational tools to predict and manipulate polyploid gene function. Emerging areas such as synthetic biology and multi-omics integration will further enhance our ability to engineer polyploid crops with complex trait architectures. Polyploidy remains a powerful and versatile tool for plant breeders, offering immense potential for crop improvement, genetic innovation, and the development of resilient agricultural systems. As the understanding and technological capabilities in polyploid breeding continue to advance, polyploid crops will play a central role in sustainable agricultural development and global food production.

SoIR: a comprehensive Solanaceae information resource for comparative and functional genomic study

Abstract The Solanaceae family, which includes economically important crops such as tomatoes, potatoes and peppers, has experienced a rapid expansion in genomic data due to advancements in sequencing technologies. However, existing databases are limited by incomplete species representation, a lack of comprehensive comparative genomic tools and the absence of systematic pan-genomic analyses. To address these gaps, we developed the Solanaceae Information Resource (SoIR, https://soir.bio2db.com), a comprehensive genomics database for the Solanaceae family. SoIR integrates genomic data from 81 species and transcriptomic data from 41 species, encompassing a total of 3 908 408 gene annotations derived from Gene Ontology, nonredundant protein, Pfam, Swiss-Prot and TrEMBL databases. The resource also includes 3 437 115 CRISPR guide sequences, 212 395 transcription factors and 19 086 genes associated with methylation modification. In addition to species-specific analyses, SoIR provides extensive bioinformatics tools for investigating gene family evolution, phylogenetic relationships and karyotype reconstruction across 25 fully sequenced genomes. With advanced tools such as Blast, Synteny and Sequence Alignment, the platform provides users with interactive and intuitive visualizations for conducting cross-species comparative genomics. As the first comprehensive pan-genomic resource for the entire Solanaceae family, SoIR facilitates in-depth cross-species analysis, supporting global research initiatives in plant evolution, functional genomics and crop improvement.

Identification and Molecular Analysis of Proteins Associated with Non-spinning Disease in the Silkworm Bombyx mori L Induced by BmNPV and BmDNV

Aims: The present study combines morphological and molecular biology investigations to uncover the underlying causes of the non-spinning syndrome in silkworms. To understand the molecular mechanism and immune mechanism after infection of viral pathogen in silkworm Bombyx mori L. Study Design: Infected silkworm samples were collected from the field and isolated the genomic DNA and total proteins. Analyzed the samples by using genomic tools and proteomic tools. Place and Duration of Study: Central Sericultural Research and Training Institute, Mysuru, Karnataka. 01.03.2023 to 31.07.2023 total 04 months. Methodology: Our findings indicate that infections of BmDNV and BmNPV are associated with abnormal growth of the silk gland, leading to the non-spinning phenotype. To confirm pathogen infection in non-spinning silkworms, we collected genomic DNA and conducted PCR analysis using specific primers for DNV and NPV. Results: Our results showed amplification of both viral infections in the isolated samples. SDS-PAGE analysis revealed differential regulation of several proteins involved in physiological and immunological processes in the hemolymph of non-spinning silkworms. MALDI-TOF/MS was used to identify an up-regulated protein of interest, which was found to be a Lepidopteran low molecular weight lipoprotein. This protein is known to play an important role in insect immune mechanisms. Conclusion: Major disease-causing agents such as BmDNV and BmNPV infections and investigate their impact on protein expression patterns in the hemolymph, shedding light on the factors contributing to non-spinning behavior in silkworms.

Comparative genomic analysis provides new insights into non-typhoidal Salmonella population structure in Peru

Non-typhoidal Salmonella (NTS) is one of the leading causes of foodborne outbreaks worldwide, especially in low- and middle-income countries such as Peru. To understand the dynamics of NTS serotypes circulating in the country, the whole genomes of 1122 NTS strains from 1998 to 2018 were analyzed using phylogenomic and comparative genomics tools. A total of 40 different Sequences Type (STs) were identified, the five most frequent being ST-32 (S. Infantis, 37.25%), ST-11 (S. Enteritidis, 23.8%), ST-19 (S. Typhimurium, 14.17%), ST-31 (S. Newport, 6.77%), and ST-413 (S. Mbandaka, 4.72%). Furthermore, the maximum likelihood phylogeny showed high clonality between strains from the same ST recovered from different isolation sources, as well as a variable recombination rate, when comparing each ST individually. Moreover, several virulence factors involved in adherence and invasion, as well as plasmids and prophages, are strongly associated with the most frequent STs, while multidrug resistance markers are mostly linked to ST-32. This work provides an overview of the main genomic characteristics linked to the high-frequency ST, which have undergone few genetic modifications over time, suggesting a high adaptation of these NTS circulating clones in Peru.

Zebrafish (Danio rerio) as a Model System to Investigate the Role of the Innate Immune Response in Human Infectious Diseases

The zebrafish (Danio rerio) has emerged as a valuable model for studying host-pathogen interactions due to its unique combination of characteristics. These include extensive sequence and functional conservation with the human genome, optical transparency in larvae that allows for high-resolution visualization of host cell-microbe interactions, a fully sequenced and annotated genome, advanced forward and reverse genetic tools, and suitability for chemical screening studies. Despite anatomical differences with humans, the zebrafish model has proven instrumental in investigating immune responses and human infectious diseases. Notably, zebrafish larvae rely exclusively on innate immune responses during the early stages of development, as the adaptive immune system becomes fully functional only after 4–6 weeks post-fertilization. This window provides a unique opportunity to isolate and examine infection and inflammation mechanisms driven by the innate immune response without the confounding effects of adaptive immunity. In this review, we highlight the strengths and limitations of using zebrafish as a powerful vertebrate model to study innate immune responses in infectious diseases. We will particularly focus on host-pathogen interactions in human infections caused by various bacteria (Clostridioides difficile, Staphylococcus aureus, and Pseudomonas aeruginosa), viruses (herpes simplex virus 1, SARS-CoV-2), and fungi (Aspergillus fumigatus and Candida albicans).

Benchmarking nanopore sequencing and rapid genomics feasibility: validation at a quaternary hospital in New Zealand

Approximately 200 critically ill infants and children in New Zealand are in high-dependency care, many suspected of having genetic conditions, requiring scalable genomic testing. We adopted an acute care genomics protocol from an accredited laboratory and established a clinical pipeline using Oxford Nanopore Technologies PromethION 2 solo system and Fabric GEM™ software. Benchmarking of the pipeline was performed using Global Alliance for Genomics and Health benchmarking tools and Genome in a Bottle samples (HG002-HG007). Evaluation of single nucleotide variants resulted in a precision and recall of 0.997 and 0.992, respectively. Small indel identification approached a precision of 0.922 and recall of 0.838. Large genomic variations from Coriell Copy Number Variation Reference Panel 1 were reliably detected with ~2 M long reads. Finally, we present results obtained from fourteen trio samples, ten of which were processed in parallel with a clinically accredited short-read rapid genomic testing pipeline (Newborn Genomics Programme; NCT06081075; 2023-10-12).

Genetic Diversity and Evolutionary Biology of Indian Wildlife- A Review

The genetic diversity and evolutionary biology of Indian wildlife, emphasizing the critical role of genetic variability in ensuring species adaptability and long-term survival. India is one of the world’s megadiverse countries, home to a wide range of unique flora and fauna spread across its biodiversity hotspots, including the Western Ghats and the Eastern Himalayas. However, rapid habitat fragmentation, deforestation, and poaching have severely impacted the genetic health of many wildlife species, leading to reduced gene flow, population bottlenecks, and increased inbreeding. This has heightened the risk of extinction for small, isolated populations such as the Asiatic lion and the Great Indian bustard. Genetic studies using advanced genomic tools have provided key insights into the population structure, phylogenetic relationships, and adaptive potential of species like the Bengal tiger, Asian elephant, and snow leopard. Conservation strategies, including genetic rescue, habitat corridors, and the use of captive breeding programs, are recommended to preserve genetic diversity and prevent further genetic erosion. Technological advances, such as non-invasive sampling and bioinformatics, are transforming wildlife research, enabling more precise and less intrusive genetic monitoring. The integration of genetic data into conservation policies and practices is essential for developing targeted management plans that maintain the evolutionary potential of Indian wildlife.

Increasing nitrogen use efficiency in agronomically important plants: An insight into gene characteristics on a genome-wide scale in barley

Nitrogen (N) is a critical element for plant growth and development. Hence, improving nitrogen use efficiency (NUE) is vital for reducing costs and the environmental impact of agricultural practices. Understanding the genetic control of N metabolism is crucial to improve NUE, especially in agronomically important plants, such as barley (Hordeum vulgare).Using bioinformatics and functional genomics tools, we identified and characterized sixteen barley nitrogen metabolism-related gene families (HvNMGs) on a genome-wide scale, analysing gene features and evolution. These genes, located on six of seven barley chromosomes, are highly conserved in plants (including barley, rice, and Arabidopsis), as shown by phylogenetic analysis. We further explored the evolutionary relationships of NMGs through a genome-to-genome synteny analysis, which indicated higher conservation of NMGs between barley and other monocots, suggesting that these orthologous pairs predate species divergence. Protein-protein interaction analyses revealed that all of the HvNMGs show interactions, mainly with each other. The H. vulgare miRNAs target sites (hvu-miR) prediction identified six hvu-miR in 4 HvNMGs (HvGABA-T2, HvALDH10-1, HvALDH10-2 and HvARGAH), indicating their potential involvement in stress responses. The expression patterns analysis of publicly available RNA-seq data revealed that HvNMGs are expressed in all developmental stages of barley, and they respond to different stress conditions, indicating their essential role in plant growth, development and stress response. The organ-specific expression analysis, conducted using qPCR, of HvNMGs revealed higher expression of HvNiR and HvNRs in the leaf and significantly higher expression of HvARGAH and HvALDH10 in the spike than in other tissues, showing that some of the genes may be particularly important in some tissues than others. This data provides a foundation for understanding HvNMG function and could be used to improve barley yield by enhancing NUE — an important goal for both crop productivity and environmental sustainability.

Crossing the Pacific: Genomics Reveals the Presence of Japanese Sardine (Sardinops melanosticta) in the California Current Large Marine Ecosystem.

Recent increases in frequency and intensity of warm water anomalies and marine heatwaves have led to shifts in species ranges and assemblages. Genomic tools can be instrumental in detecting such shifts. In the early stages of a project assessing population genetic structure in Pacific Sardine (Sardinops sagax), we detected the presence of Japanese Sardine (Sardinops melanosticta) along the west coast of North America for the first time. We assembled a high quality, chromosome-scale reference genome of the Pacific Sardine and generated low coverage, whole genome sequence (lcWGS) data for 345 sardine collected in the California Current Large Marine Ecosystem (CCLME) in 2021 and 2022. Fifty individuals sampled in 2022 were identified as Japanese Sardine based on strong differentiation observed in lcWGS SNP and full mitogenome data. Although we detected a single case of mitochondrial introgression, we did not observe evidence for recent hybridization events. These findings change our understanding of Sardinops spp. distribution and dispersal in the Pacific and highlight the importance of long-term monitoring programs.

BIMSA: Accelerating Long Sequence Alignment Using Processing-In-Memory.

Recent advances in sequencing technologies have stressed the critical role of sequence analysis algorithms and tools in genomics and healthcare research. In particular, sequence alignment is a fundamental building block in many sequence analysis pipelines and is frequently a performance bottleneck both in terms of execution time and memory usage. Classical sequence alignment algorithms are based on dynamic programming and often require quadratic time and memory with respect to the sequence length. As a result, classical sequence alignment algorithms fail to scale with increasing sequence lengths and quickly become memory-bound due to data-movement penalties. Processing-In-Memory (PIM) is an emerging architectural paradigm that seeks to accelerate memory-bound algorithms by bringing computation closer to the data to mitigate data-movement penalties. This work presents BIMSA (Bidirectional In-Memory Sequence Alignment), a PIM design and implementation for the state-of-the-art sequence alignment algorithm BiWFA (Bidirectional Wavefront Alignment), incorporating new hardware-aware optimizations for a production-ready PIM architecture (UPMEM). BIMSA supports aligning sequences up to 100K bases, exceeding the limitations of state-of-the-art PIM implementations. First, BIMSA achieves speedups up to 22.24 × (11.95× on average) compared to state-of-the-art PIM-enabled implementations of sequence alignment algorithms. Second, achieves speedups up to 5.84 × (2.83× on average) compared to the highest-performance multicore CPU implementation of BiWFA. Third, BIMSA exhibits linear scalability with the number of compute units in memory, enabling further performance improvements with upcoming PIM architectures equipped with more compute units and achieving speedups up to 9.56 × (4.7× on average). Code and documentation are publicly available at https://github.com/AlejandroAMarin/BIMSA.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Phylogenomic analyses of the Listeriaceae family support species reclassification and proposal of a new family and new genera.

The taxonomy of the Listeriaceae family has undergone substantial revisions, expanding the Listeria genus from 6 to 29 species since 2009. However, these classifications have relied on 16S rRNA gene sequences and conventional polyphasic taxonomy, with limited use of genomic approaches. This study aimed to employ genomic tools, including phylogenomics, Overall Genomic Relatedness Indices (OGRIs), and core-genome phylogenomic analyses, to reevaluate the taxonomy of the Listeriaceae family. The analyses involved the construction of phylogenetic and phylogenomic trees based on 16S rRNA gene sequences and core genomes from 34 type strain genomes belonging to Listeriaceae family. OGRIs, which encompass Average Amino acid Identity (AAI), core-proteome AAI (cAAI), and Percentage of Conserved Proteins (POCP), were calculated, and specific threshold values of 70%, 87%, and 72-73% were established, respectively, to delimitate genera in the Listeriaceae family. These newly proposed OGRI thresholds unveiled distinct evolutionary lineages. The outcomes of this taxonomic re-evaluation were: (i): the division of the Listeria genus into an emended Listeria genus regrouping only Listeria senso stricto species; (ii): the remaining Listeria senso lato species were transferred into three newly proposed genera: Murraya gen. nov., Mesolisteria gen. nov., and Paenilisteria gen. nov. within Listeriaceae; (iii): Brochothrix was transferred to the newly proposed family Brochothricaceae fam. nov. within the Caryophanales order; (iiii): Listeria ivanovii subsp. londonensis was elevated to the species level as Listeria londonensis sp. nov.; and (iiiii): Murraya murrayi comb. nov. was reclassified as a later heterotypic synonym of Murraya grayi comb. nov. This taxonomic framework enables more precise identification of pathogenic Listeriaceae species, with significant implications for important areas such as food safety, clinical diagnostics, epidemiology, and public health.

Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention.

The One Health concept underscores the interconnectedness of human, animal, and environmental health, necessitating an integrated, transdisciplinary approach to tackle contemporary health challenges. This perspective paper explores the pivotal role of genomics and bioinformatics in advancing One Health initiatives. By leveraging genomic technologies and bioinformatics tools, researchers can decode complex biological data, enabling comprehensive insights into pathogen evolution, transmission dynamics, and host-pathogen interactions across species and environments (or ecosystems). These insights are crucial for predicting and mitigating zoonotic disease outbreaks, understanding antimicrobial resistance patterns, and developing targeted interventions for health promotion and disease prevention. Furthermore, integrating genomic data with environmental and epidemiological information enhances the precision of public health responses. Here we discuss case studies demonstrating successful applications of genomics and bioinformatics in One Health contexts, such as including data integration, standardization, and ethical considerations in genomic research. By fostering collaboration among geneticists, bioinformaticians, epidemiologists, zoologists, and data scientists, the One Health approach can harness the full potential of genomics and bioinformatics to safeguard global health. This perspective underscores the necessity of continued investment in interdisciplinary education, research infrastructure, and policy frameworks to effectively employ these technologies in the service of a healthier planet.

Hazard Characterization of Antibiotic-resistant Aeromonas spp. Isolated from Mussel and Oyster Shellstock Available for Retail Purchase in Canada

Surveillance and monitoring of foods for the presence of antimicrobial-resistant (AMR) bacteria are required to assess the risks these bacteria pose to human health. Frequently consumed raw or lightly cooked, live bivalve shellfish such as mussels and oysters can be a source of exposure to AMR bacteria. This study sought to determine the prevalence of third-generation cephalosporin (3GC) and carbapenem-resistant bacteria in live mussel and oyster shellstock available for retail purchase through the course of one calendar year. Just over half of the 180 samples (52%) tested positive for the presence of 3GC-resistant bacteria belonging to thirty distinct bacterial species. Speciation of the isolates was carried out using the Bruker MALDI Biotyper. Serratia spp., Aeromonas spp., and Rahnella spp. were the most frequently isolated groups of bacteria. Antibiotic resistance testing confirmed reduced susceptibility for 3GCs and/or carbapenems in 15 of the 29 Aeromonas isolates. Based on AMR patterns, and species identity, a subset of ten Aeromonas strains was chosen for further characterization by whole genome sequence analysis. Genomic analysis revealed the presence of multiple antibiotic resistance and virulence genes. A number of mobile genetic elements were also identified indicating the potential for horizontal gene transfer. Differences in gene detection by the bioinformatic tools and databases used (ResFinder. CARD RGI, PlasmidFinder, and MobSuite) are discussed. This study highlights the strengths and limitations of using genomics tools to perform hazard characterization of diverse foodborne bacterial species.

Chromosome-scale genome assembly of acerola (Malpighia emarginata DC.).

Acerola (Malpighia emarginata DC.) is a tropical evergreen shrub that produces vitamin C-rich fruits. Increasing fruit nutrition is one of the main targets of acerola breeding programs. Genomic tools have been shown to accelerate plant breeding even in fruiting tree species, which generally have a long life cycle; however, the availability of genomic resources in acerola, so far, has been limited. In this study, as a first step toward developing an efficient breeding technology for acerola, we established a chromosome-scale genome assembly of acerola using high-fidelity long-read sequencing and genetic mapping. The resultant assembly comprises 10 chromosome-scale sequences that span a physical distance of 1,032.5Mb and contain 35,892 predicted genes. Phylogenetic analysis of genome-wide SNPs in 60 acerola breeding materials revealed three distinct genetic groups. Overall, the genomic resource of acerola developed in this study, including its genome and gene sequences, genetic map, and phylogenetic relationship among breeding materials, will not only be useful for acerola breeding but will also facilitate genomic and genetic studies on acerola and related species.

Understanding admixture fractions: theory and estimation of gene-flow.

Estimation of admixture proportions has become one of the most commonly used computational tools in population genomics. However, there is remarkably little population genetic theory on statistical properties of these variables. We develop theoretical results that can accurately predict means and variances of admixture proportions within a population using models with recombination and genetic drift. Based on established theory on measures of multilocus disequilibrium, we show that there is a set of recurrence relations that can be used to derive expectations for higher moments of the admixture proportions distribution. We obtain closed form solutions for some special cases. Using these results, we develop a method for estimating admixture parameters from estimated admixture proportions obtained from programs such as Structure or Admixture. We apply this method to HapMap 3 data and find that the population history of African Americans, as expected, is not best explained by a single admixture event between people of European and African ancestry. The model of constant gene flow starting at 8 generations and ending at 2 generations before present gives the best fit.

A-233 Wastewater-based surveillance for SARS-CoV-2 Subvariants using the Nanopore Sequencing in South Korea

Abstract Background Genomic tools to identify and characterize SARS-CoV-2 and its various subvariants were adapted from clinical settings. Wastewater-based surveillance (WBS) has since been recognized as tool of transformative potential, providing real-time objective, comprehensive data on the prevalence and distribution of the target pathogen across the space and time. We conducted wastewater-based epidemiology (WBE) at sewage treatment plants to find a more efficient mutation monitoring method, and respond to pre-existing infectious diseases. Methods In our study, domestic sewage collected every weeks from 6 sewage treatment plants in urban region from June 2023 to November 2023 was used to concentrate and isolate viruses using wastewater ultra nucleic acid isolation kits with virus enrichment kits. Positive samples confirmed were subjected to SARS-CoV-2 whole genome sequencing (WGS) using GridION (Oxford Nanopore Technologies). The experimental data was aligned through Epi2me, analyzed using Nextclade, that was compared with KCDC official reports and our monitoring results (total 780 cases) ongoing at SCL. Results Various mutations were identified at each collection time, occurring new subvariants over time. The mutations (orf1a:A690V, S:F456L) sequentially confirmed from the sample collected on Aug. 23rd are specific mutations of XBB.1.9.2 and EG.5. In addition, the mutations orf1b: D54N, S:L455F, which were sequentially confirmed from the sample collected on Sep. 6th, are HK.3, a descendent lineage of EG.5, and FLip variants, which show growth advantage, were confirmed. Furthermore, the detection rate of FLip variants detected from Sep.6th gradually increased from 17% to 67% (Fig.1). Taken together, we confirmed the similarity between monthly detected mutations and changes in dominant species over time by comparing with ‘monthly COVID-19 mutation trends’ and ‘HK.3 dominance announcement by KCDC’(Fig.2). Conclusions WBS would be a useful tool for proactively responding to infectious diseases including COVID-19 by exploring new mutations and predicting changes in dominant species.

Why choose the rabbit to work in reproductive technology?

Rabbits have played a significant role in both livestock production and the advancement of reproductive scientific research. Their unique biological traits, including induced ovulation and a reproductive process that closely mirrors that of humans, have been pivotal in their use as a model. Moreover, their body size is perfectly aligned with the 3Rs principles: Replacement, Reduction, and Refinement. Consequently, techniques for gamete collection and embryo recovery, followed by their use in artificial insemination or embryo transfer, are characterized by being minimally invasive. However, refining invitro fertilization and embryo culture techniques continues to present challenges. The incorporation of cutting-edge genomic editing tools, such as CRISPR/Cas9, has reestablished rabbits as essential models in genetic and biomedical research, driving scientific progress. This review aims to describe the most effective reproductive biotechnologies for both male and female rabbits and how these methodologies are in line with the 3Rs principles-Replacement, Reduction, and Refinement-highlighting their significance in conducting ethical research.

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.

Development of a MaizeGerm50K array and application to maize genetic studies and breeding

Genotyping arrays based on single nucleotide polymorphisms (SNPs) provide a low-cost, high-throughput platform. The development of a SNP array that fully reflects the genetic diversity of maize (Zea mays L.) germplasm and is applicable to molecular breeding programs is desirable. In this study, we developed a MaizeGerm50K array comprising 50,852 SNPs selected from the resequencing data of 1604 maize inbred lines and other markers. A genome-wide association study using a landrace panel genotyped with the array permitted mapping of several known genes. We also verified a candidate gene, RNA-binding motif protein 24-like 1 (ZmRBM24L1), delaying flowering through overexpression lines. Genomic selection for yield and agronomic traits showed high prediction accuracy. The MaizeGerm50K array is thus a valuable genomic tool for maize genetic studies and breeding.