Genomic Datasets Research Articles

Functional characteristics and computational model of abundant hyperactive loci in the human genome.

Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.

Deciphering the Role of ASPM in Breast Cancer: A Comprehensive Multicohort Study

Background: Assembly factor for spindle microtubules (ASPM) has gained significant attention in cancer research due to its association with tumor growth and progression. Through the analysis of large-scale genomic datasets, ASPM has been identified as the top upregulated gene in breast cancer (BC), characterized by high proliferation. This multicohort study aimed to investigate the clinicopathological and prognostic significance of ASPM mRNA and protein expression in BC. Methods: ASPM mRNA expression was assessed using the Cancer Genome Atlas (TCGA) BC cohort and has been further validated in the Molecular Taxonomy of BC International Consortium (METABRIC) (n = 1980), The Uppsala cohort (n = 249), in addition to the combined multicentric cohort (n = 7252). ASPM protein expression was evaluated in a large BC cohort (n = 1300) using immunohistochemistry. The correlations between ASPM expression, clinicopathological parameters, molecular subtypes and outcome were assessed. The response to taxane treatment was compared to the clinical prognosis of ASPM using the ROC plotter. Results: High ASPM mRNA and protein expression were significantly associated with aggressive BC features and poor survival across all cohorts. The association with poor outcomes was maintained in the adjuvant chemotherapy and radio-therapy-treated patients. Responders to taxane treatment showed significantly elevated ASPM levels compared to non-responders. Conclusions: High ASPM expression predicts poor prognosis in BC. It may play a role in treatment resistance within a specific subgroup of patients. Further clinical trials are warranted to explore the potential of ASPM as a target for therapeutic interventions in cancer.

Functional characteristics and computational model of abundant hyperactive loci in the human genome

Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.

Enhancing genomic disorder prediction through Feynman Concordance and Interpolated Nearest Centroid techniques.

Clinical biomedical applications of genomic technologies are extensive and provide possibilities to enhance healthcare covering the span of medical talents. Genome disorder prediction is an important issue in biomedical research. Genome disorders cause multivariate diseases such as cancer, dementia, diabetes, Leigh syndrome, etc. Existing machine and deep learning-based methods were introduced to forecast genome disorders. However, the genome prediction outcomes were not sufficient. To address this issue, propose a new method called Quadratic Feynman Polynomial Interpolated and Vector Nearest Centroid-based (QFPI-VNC) for acutely predicting the genome disorder with improved sensitivity and specificity. First, we utilized medical data about children from a public genomes dataset and applied it to Linear Quadratic and Feynman Kac Genome filtering to obtain computationally efficient filtered results. Next, the results are fed to the Concordance Correlated Polynomial Interpolation with the purpose of extracting genome wide data in an accurate manner. Finally, the features extracted are fused and fed to the Support Vector and Nearest Centroid model for genome disorder prediction. Experimental investigations of the proposed method employing the genome dataset confirm that the performance of the proposed method is prospective and in the scope of acceptance with relative to state-of-the-art methods in terms of convergence speed, recognition rate, sensitivity, and specificity. Results suggest that the QFPI-VNC method produces the best performance with a higher genome disease detection rate by 14%, accuracy by 11%, sensitivity by 14% specificity by 12%, and lesser convergence speed by 29% than compared to state-of-the-art methods.

Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools. Here, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets. The analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2 .

Plant pattern recognition receptors: from evolutionary insight to engineering.

The plant immune system relies on germline-encoded pattern recognition receptors (PRRs) that sense foreign and plant-derived molecular patterns, and signal health threats. Genomic and pangenomic data sets provide valuable insights into the evolution of PRRs and their molecular triggers, which is furthering our understanding of plant-pathogen co-evolution and convergent evolution. Moreover, in silico and in vivo methods of PRR identification have accelerated the characterization of receptor-ligand complexes, and advances in protein structure prediction algorithms are revealing novel PRR sensor functions. Harnessing these recent advances to engineer PRRs presents an opportunity to enhance plant disease resistance against a broad spectrum of pathogens, enabling more sustainable agricultural practices. This Review summarizes both established and innovative approaches to leverage genomic data and translate resulting evolutionary insights into engineering PRR recognition specificities.

Thematic Layers of Genomic Susceptibility for Conservation Monitoring.

Population genomics has great potential to inform applied conservation management and associated policy. However, the bioinformatic analyses and interpretation of population genomic datasets can be daunting and difficult to convey to nonspecialists, including on-the-ground conservationists that work with many state, federal and international agencies. We think that individual population genomic metrics of interest can be interpolated and ultimately distilled into thematic GIS layers that represent spatiotemporal genomic potential (or conversely, susceptibility) in conservation monitoring. As examples relevant to ongoing conservation efforts, we use introgressive hybridisation and individual heterozygosity to illustrate a conceptual approach for mapping population genomic susceptibility. The general framework of thematic layers could be extended to integrate key genomic metrics (e.g., runs of homozygosity and genomic load) that are relevant to many conservation efforts.

Clinical, genomic, and proteomic perspectives in the analysis of comorbid conditions in type 2 diabetes mellitus: a retrospective study.

Type-2 Diabetes Mellitus (T2DM) affects millions globally, with escalating rates. It often leads to undiagnosed complications and commonly coexists with other health conditions. This study investigates two types of prevalent comorbidities related to T2DM-the circulatory system (DCM1) and digestive system diseases (DCM2)-using clinical, genomic and proteomic datasets. The aim is to identify new biomarkers by applying existing machine learning (ML) based techniques for early detection, prognosis and diagnosis of these comorbidities. Here, we report a cross-sectional retrospective analysis from a T2DM dataset of T2DM associated concordant comorbidities (diseases with shared pathophysiology and management) from the Diastrat cohort (a T2DM cohort) recruited at the Northern Ireland Centre for Stratified Medicine (NICSM), in Northern Ireland. In the clinical data analysis, we identified that lipidemia was shown to negatively correlate with depression in the DCM1 group while positively correlate with depression in the DCM2 group. In genomic analysis, we identified statistically significant variants rs9844730 in procollagen-lysine (PLOD2), rs73590361 in beta-1,4-N-acetyl- galactosaminyl-transferase (B4GALNT3) and rs964680 in A kinase (PRKA) anchor protein 14 (AKAP14) which appear to differentiate DCM1 and DCM2 groups. In proteomic analysis, we identified 4 statistically significant proteins: natriuretic peptides B (BNP), pro-adrenomedullin (ADM), natriuretic peptides B (NT-proBNP) and discoidin (DCBLD2) that can differentiate DCM1 and DCM2 groups and have built robust ML model using clinical, genomic, and proteomic markers (0.83 receiver operative characteristics curve area, 84% positive predictive value and 83% negative predictive value and a classification accuracy of 83%) for prediction of DCM1 and DCM2 groups. Our study successfully identifies novel clinical, genomic, and proteomic biomarkers that differentiate between circulatory and digestive system comorbidities in Type-2 Diabetes Mellitus patients. The machine learning model we developed demonstrates strong predictive capabilities, providing a promising tool for the early detection, prognosis, and diagnosis of these T2DM-associated comorbidities. These findings have the potential to enhance personalized management strategies for patients with T2DM, ultimately improving clinical outcomes. Further research is needed to validate these biomarkers and integrate them into clinical practice.

EXPRESSO: a multi-omics database to explore multi-layered 3D genomic organization.

The three-dimensional (3D) organization of the human genome plays a crucial role in gene regulation. EXPloration of Regulatory Epigenome with Spatial and Sequence Observations (EXPRESSO) is a novel multi-omics database for exploration and visualization of multi-layered 3D genomic features across 46 different human tissues. Integrating 1360 3D genomic datasets (Hi-C, HiChIP, ChIA-PET) and 842 1D genomic and transcriptomic datasets (ChIP-seq, ATAC-seq, RNA-seq) from the same biosample, EXPRESSO provides a comprehensive resource for studying the interplay between 3D genome architecture and transcription regulation. This database offers diverse 3D genomic feature types (compartments, contact matrix, contact domains, stripes as diagonal lines extending from a genomic locus in contact matrix, chromatin loops, etc.) and user-friendly interface for both data exploration and download. Other key features include REpresentational State Transfer application programming interfaces for programmatic access, advanced visualization tools for 3D genomic features and web-based applications that correlate 3D genomic features with gene expression and epigenomic modifications. By providing extensive datasets and tools, EXPRESSO aims to deepen our understanding of 3D genomic architecture and its implications for human health and disease, serving as a vital resource for the research community. EXPRESSO is freely available at https://expresso.sustech.edu.cn.

Towards Complex-query Referring Image Segmentation: A Novel Benchmark

Referring Image Segmentation (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research investigating how existing algorithms should be benchmarked with complex language queries, which include more informative descriptions of surrounding objects and backgrounds ( e.g. “the black car.” vs. “the black car is parking on the road and beside the bus.” ). Given the significant improvement in the semantic understanding capability of large pre-trained models, it is crucial to take a step further in RIS by incorporating complex language that resembles real-world applications. To close this gap, building upon the existing RefCOCO and Visual Genome datasets, we propose a new RIS benchmark with complex queries, namely RIS-CQ . The RIS-CQ dataset is of high quality and large scale, which challenges the existing RIS with enriched, specific and informative queries, and enables a more realistic scenario of RIS research. Besides, we present a nichetargeting method to better task the RIS-CQ, called dual-modality graph alignment model ( DuMoGa ), which outperforms a series of RIS methods. To provide a valuable foundation for future advancements in the field of RIS with complex queries, we release the datasets, preprocessing and synthetic scripts, and the algorithm implementations at https://github.com/lili0415/DuMoGa .

Genomic Vulnerability to Climate Change of an Australian Migratory Freshwater Fish, the Golden Perch (Macquaria ambigua).

Genomic vulnerability is a measure of how much evolutionary change is required for a population to maintain optimal genotype-environment associations under projected climates. Aquatic species, and in particular migratory ectotherms, are largely underrepresented in studies of genomic vulnerability. Such species might be well equipped for tracking suitable habitat and spreading diversity that could promote adaptation to future climates. We characterised range-wide genomic diversity and genomic vulnerability in the migratory and fisheries-important golden perch (Macquaria ambigua) from Australia's expansive Murray-Darling Basin (MDB). The MDB has a steep hydroclimatic gradient and is one of the world's most variable regions in terms of climate and streamflow. Golden perch are threatened by fragmentation and obstruction of waterways, alteration of flow regimes, and a progressively hotter and drying climate. We gathered a genomic dataset of 1049 individuals from 186 MDB localities. Despite high range-wide gene flow, golden perch in the warmer, northern catchments had higher predicted vulnerability than those in the cooler, southern catchments. A new cross-validation approach showed that these predictions were insensitive to the exclusion of individual catchments. The results raise concern for populations at warm range edges, which may already be close to their thermal limits. However, a population with functional variants beneficial for climate adaptation found in the most arid and hydrologically variable catchment was predicted to be less vulnerable. Native fish management plans, such as captive breeding and stocking, should consider spatial variation in genomic vulnerability to improve conservation outcomes under climate change, even for dispersive species with high connectivity.

Genetic parameters and genome-wide association analyses for lifetime productivity in Chinese Holstein cattle

Lifetime productivity is a trait of great importance to dairy cattle populations, as it combines information from production and longevity variables. Therefore, we investigated the genetic background of lifetime productivity in high-producing dairy cattle by integrating genomics and transcriptomics datasets. A total of 3,365,612 test-day milk yield records from 134,029 Chinese Holstein cows were used to define 6 lifetime productivity traits, including lifetime milk yield covering full lifespan and 5 cumulative milk yield traits covering partial lifespan. Genetic parameters were estimated based on univariate and bivariate linear animal models and the restricted maximum likelihood method. Genome-wide association studies and weighted gene co-expression network analyses (WGCNA) were performed to identify candidate genes associated with lifetime productivity based on genomic data from 3,424 cows and peripheral blood RNA-sequencing data from 23 cows, respectively. Lifetime milk yield averaged 24,800.8 ± 14,396.6 kg (mean ± SD) across an average of 2.4 parities in Chinese Holstein population. The heritability estimates for lifetime productivity traits ranged from 0.05 (±0.01 for SE) to 0.10 (±0.02 for SE). The estimate of genetic correlation between lifetime milk yield and productive life is 0.88 (±0.3 for SE), whereas the genetic correlation with 305-d milk yield in the first lactation was 0.49 (±0.08 for SE). Absolute values for most genetic correlation estimates between lifetime productivity and type traits were lower than 0.30. Moderate genetic correlations were found between udder related traits and lifetime productivity, such as with udder depth (0.33), rear udder attachment height (0.33), and udder system (0.34). Some single nucleotide polymorphisms and gene co-expression modules significantly associated with lifetime milk yield were identified based on GWAS and WGCNA analyses, respectively. Functional enrichment analyses of the candidate genes identified revealed important pathways related to immune system, longevity, energy utilization, and metabolism, and FoxO signaling. The genes NTMT1, FNBP1, and S1PR1 were considered to be the most important candidate genes influencing lifetime productivity in Holstein cows. Overall, our findings indicate that lifetime productivity is heritable in Chinese Holstein cattle, and important candidate genes were identified by integrating genomic and transcriptomic datasets.

In silico framework for genome analysis

Genomes hold the complete genetic information of an organism. Examining and analyzing genomic data plays a critical role in properly understanding an organism, particularly the main characteristics, functionalities, and evolving nature of harmful viruses. However, the rapid increase in genomic data poses new challenges and demands for extracting meaningful and valuable insights from large and complex genomic datasets. In this paper, a novel Framework for Genome Data Analysis (F4GDA), is developed that offers various methods for the analysis of viral genomic data in various forms. The framework’s methods can not only analyze the changes in genomes but also various genome contents. As a case study, the genomes of five SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) VoC (variants of concern), which are divided into three types/groups on the basis of geographical locations, are analyzed using this framework to investigate (1) the nucleotides, amino acids and synonymous codon changes in the whole genomes of VoC as well as in the Spike (S) protein, (2) whether different environments affect the rate of changes in genomes, (3) the variations in nucleotide bases, amino acids, and codon base compositions in VoC genomes, and (4) to compare VoC genomes with the reference genome sequence of SARS-CoV-2.

Prevalence of targetable and potentially targetable alterations in metastatic/recurrent cervical cancer specimens in a large genomic dataset

Prevalence of targetable and potentially targetable alterations in metastatic/recurrent cervical cancer specimens in a large genomic dataset

Accurate Detection of scutellata‐Hybrids (Africanized Bees) Using a SNP‐Based Diagnostic Assay

ABSTRACTHybrid populations of Africanized honey bees (scutellata‐hybrids), notable for their defensive behaviour, have spread rapidly throughout South and North America since their unintentional introduction. Although their migration has slowed, the large‐scale trade and movement of honey bee queens and colonies raise concern over the accidental importation of scutellata‐hybrids to previously unoccupied areas. Therefore, developing an accurate and robust assay to detect scutellata‐hybrids is an important first step toward mitigating risk. Here, we used an extensive population genomic dataset to assess the genomic composition of Apis mellifera native populations and patterns of genetic admixture in North and South American commercial honey bees. We used this dataset to develop a SNP assay, where 80 markers, combined with machine learning classification, can accurately differentiate between scutellata‐hybrids and non‐scutellata‐hybrid commercial colonies. The assay was validated on 1263 individuals from colonies located in Canada, the United States, Australia and Brazil. Notably, we demonstrate that using a reduced SNP set of as few as 10 loci can still provide accurate results.

A host shift as the origin of tomato bacterial canker caused by Clavibacter michiganensis.

The Actinomycetota (formerly Actinobacteria) genus Clavibacter includes phytopathogens with devasting effects in several crops. Clavibacter michiganensis, the causal agent of tomato bacterial canker, is the most notorious species of the genus. Yet, its origin and natural reservoirs remain elusive, and its populations show pathogenicity profiles with unpredictable plant disease outcomes. Here, we generate and analyse a decade-long genomic dataset of Clavibacter from wild and commercial tomato cultivars, providing evolutionary insights that directed phenotypic characterization. Our phylogeny situates the last common ancestor of C. michiganensis next to Clavibacter isolates from grasses rather than to the sole strain we could isolate from wild tomatoes. Pathogenicity profiling of C. michiganensis isolates, together with C. phaseoli and C. californiensis as sister taxa and the wild tomato strain, was found to be congruent with the proposed phylogenetic relationships. We then identified gene enrichment after the evolutionary event, leading to the appearance of the C. michiganesis clade, including known pathogenicity factors but also hitherto unnoticed genes with the ability to encode adaptive traits for a pathogenic lifestyle. The holistic perspective provided by our evolutionary analyses hints towards a host shift event as the origin of C. michiganensis as a tomato pathogen and the existence of pathogenic genes that remain to be characterized.

Progressing towards personalised medicine: the Genomic Data Infrastructure (GDI) project

Abstract Funded by the European Commission under the Digital Europe Programme and through co-funding from participating Member States, the European Genomic Data Infrastructure (GDI) aims to support the 1+Million Genome initiative for establishing an effective data infrastructure for genomic data. GDI has the potential to fill the gap between genomic research and clinical practice, promoting the implementation of data-driven precision medicine in Europe with the harmonisation of genomic and related phenotypic and clinical data across adhering countries. Launched in November 2022, the GDI project is a collaborative effort involving partners from 20 European countries, divided into 8 Work Packages across three Pillars. GDI is structured with the development of a framework (Pillar I Long-term sustainability) and the necessary tools (Pillar II 1+MG infrastructure deployment), with the implementations of use cases (Pillar III Application and innovation solution) to support research and innovation, integrate findings into the clinic and healthcare, and improve public health measures. In the first 2 years, we completed the starter kit which we are currently testing with real data. Over the subsequent years, the GDI project aims to provide a federated and sustainable infrastructure for facilitating research that use genomic data. Through authorized secure access, clinicians and researchers can leverage genomic insights for faster and more precise clinical decision-making, diagnosis, and treatments. The GDI project represents a significant opportunity to improve healthcare outcomes and public health measures by leveraging the potential of genomics. By providing secure access to extensive genomic datasets combined with phenotyped data, the infrastructure sets the foundation for international partnerships in personalized medicine. Ultimately, this initiative holds the promise of advancing medical understanding, enhancing patient care, and fostering economic growth across Europe. Key messages • GDI drives personalised medicine via international collaboration, building a sustainable infrastructure. Harmonizing data accelerates innovation, improving outcomes and fostering growth in Europe. • GDI aims to revolutionize healthcare by collecting and harmonizing genomic and clinical data in Europe. It facilitates faster and more precise decisions and treatments, improving healthcare outcomes.

Enrichment Strategies for Low-Abundant Single Nucleotide Mutations.

Over the past three decades, significant advancements have been made in mutation enrichment methods, driven by the increasing need for precise and efficient identification of rare genetic variants associated with diseases. Mutation-enrichment methods have emerged to boost sensitivity and enable easy detection of low-frequency mutations. These methods are crucial in genomics research and clinical diagnostics, allowing for the detection of low-frequency mutations within large genomic datasets. This review presents a summary of technological developments in rare mutation enrichment and emphasizes their mechanisms and applications in liquid biopsies.

A tip of the iceberg: genome survey indicated a complex evolutionary history of Garuga Roxb. species

Background Garuga Roxb. is a genus endemic to southwest China and other tropical regions in Southeast Asia facing risk of extinction due to the loss of tropical forests and changes in land use. Conducting a genome survey of G. forrestii contribute to a deeper understanding and conservation of the genus.ResultsThis study utilized genome survey of G. forrestii generated approximately 54.56 GB of sequence data, with approximately 112 × coverage. K-mer analysis indicated a genome size of approximately 0.48 GB, smaller than 0.52GB estimated by flow cytometry. The heterozygosity is of about 0.54%, and a repeat rate of around 51.54%. All the shotgun data were assembled into 339,729 scaffolds, with an N50 of 17,344 bp. The average content of guanine and cytosine was approximately 35.16%. A total of 330,999 SSRs were detected, with mononucleotide repeats being the most abundant at 70.16%, followed by dinucleotide repeats at 20.40%. We conducted a preliminary ploidy assessment using Smudgeplot and observed a clear bimodal distribution in G. forrestii at 1/2 relative coverage depth and total coverage depth (2n), suggesting a potential diploid genome structure. A pseudo chromosome of G. forrestii and a gemone of Boswellia sacra were used as reference genome to perform a primer population resequencing analysis within three Garuga species. Principal component analysis (PCA) indicated three distinct groups, but genome wide phylogenetics represented conflicting both between the dataset of different reference genomes and between maternal and nuclear genome.ConclusionIn summary, the genome of G. forrestii is small, and the phylogenetic relationships within the Garuga genus are complex. The genetic data presented in this study holds significant value for comprehensive whole-genome analyses, the evaluation of population genetic diversity, investigations into adaptive evolution, the advancement of artificial breeding efforts, and the support of species conservation and restoration initiatives. Ultimately, this research contributes to reinforcing the conservation and management of natural ecosystems, promoting biodiversity conservation, and advancing sustainable development.

Towards the genomic sequence code of DNA fragility for machine learning.

Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzing the differences and commonalities across myriads of genomic breakage datasets, we extract the sequence-linked rules and patterns behind DNA fragility. We show the overall deconvolution of the sequence influence into short-, mid- and long-range effects, and the stressor-dependent differences in defining the range and compositional effects on DNA fragility. We summarize and release our feature compendium as a library that can be seamlessly incorporated into genomic machine learning procedures, where DNA fragility is of concern, and train a generalized DNA fragility model on cancer-associated breakages. Structural variants (SVs) tend to stabilize regions in which they emerge, with the effect most pronounced for pathogenic SVs. In contrast, the effects of chromothripsis are seen across regions less prone to breakages. We find that viral integration may bring genome fragility, particularly for cancer-associated viruses. Overall, this work offers novel insights into the genomic sequence basis of DNA fragility and presents a powerful machine learning resource to further enhance our understanding of genome (in)stability and evolution.