NAR Genomics and Bioinformatics最新文献

{"title":"The use of Benzonase to produce ribosome footprints simplifies translational levels quantification by Ribo-seq.","authors":"Guillermo Eastman, George S Bloom, José R Sotelo-Silveira","doi":"10.1093/nargab/lqag049","DOIUrl":"10.1093/nargab/lqag049","url":null,"abstract":"<p><p>Gene expression quantification through genomics methods is crucial for understanding diverse biological contexts. Among these methods, ribosome profiling (Ribo-seq) stands out as a valuable tool for uncovering post-transcriptional gene expression regulation by providing a comprehensive view of the translatome. While current protocols are time-intensive with limited variations, we introduced the use of the Benzonase enzyme to generate ribosome footprints from a polysome-enriched fraction, streamlining the workflow and reducing time, cost and potential sources of bias. Comparing translatome from Benzonase- and RNAse I-derived footprints reveals minimal differences, with both separating from RNA-seq-based transcriptome quantification. Benzonase acts more gently on ribosomes, yielding larger footprints and a weaker triplet periodicity compared to RNAse I, while enabling its application to low-input samples. We further demonstrate Ribo-seq application in primary neuronal cultures, using Benzonase to digest the post-mitochondrial supernatant, thereby bypassing the labor-intensive ribosome/polysome purification step. The introduction of such protocol variations for Ribo-seq, especially for challenging low-input samples, offers a significant advancement of this resource for the community.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag049"},"PeriodicalIF":3.0,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13176768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond chromatin accessibility: bulk ATAC-seq as an integrative assay to portray genomes and epigenomes.","authors":"Islem Toumi, Chaimae Kham, Lucille Stuani, Laurent Le Cam, Pierre-François Roux","doi":"10.1093/nargab/lqag046","DOIUrl":"10.1093/nargab/lqag046","url":null,"abstract":"<p><p>Assay for transposase-accessible chromatin using sequencing (ATAC-seq) is a cornerstone for epigenomic profiling, yet its potential for genomic characterization remains poorly explored. Here, we systematically benchmarked bulk ATAC-seq against whole-genome sequencing (WGS) to assess its capacity for detecting small variants, copy number variations (CNVs), telomere-associated repeat content, and mitochondrial single-nucleotide polymorphisms in cancer cells. Using paired datasets from patient-derived melanoma cell lines and from TCGA primary brain tumors, we demonstrated that ATAC-seq achieves high precision in small variants detection within accessible regions supporting cohort-scale genotyping and genetic stratification, robustly resolves CNVs in the nuclear genome, and supports high-coverage mitogenome profiling, with strong concordance to WGS at standard sequencing depths. Notably, we present the first systematic evaluation of telomere-associated repeat content by ATAC-seq, revealing its untapped potential for studying genome stability. By bridging genomic and epigenomic insights into a single genome-wide approach, bulk ATAC-seq emerges as a cost-effective and versatile tool poised to transform cancer research and to support integrative molecular profiling in clinical settings.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag046"},"PeriodicalIF":3.0,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13158667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147934114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction.","authors":"Shreya Basnet, Jianlin Cheng","doi":"10.1093/nargab/lqag047","DOIUrl":"10.1093/nargab/lqag047","url":null,"abstract":"<p><p>Transcription factors (TFs) regulate gene expression by binding to specific DNA sites on genome, making accurate TF binding site prediction critical for understanding gene regulation and downstream phenotypes. Almost all current deep learning methods use only DNA-related information to predict TF binding sites, ignoring the fact that different TF protein sequences and structures recognize distinct DNA patterns. Not leveraging TF information not only limits prediction accuracy but also makes the methods not generalizable to predicting binding sites of new TFs that do not exist in the training data. Here, we present TransBind, a protein-aware deep learning architecture that integrates DNA sequence information with protein embeddings containing both sequence and structural information derived from a protein language model pretrained on DNA-binding proteins, to improve TF binding site prediction. Through the cross-attention, a TF embedding selectively attends to genomic regions according to its unique binding properties. Evaluated on the data of 690 ChIP-seq experiments spanning 161 TFs across 91 human cell types, TransBind achieves an AUROC of 0.9508 and AUPR of 0.3741-representing a [Formula: see text]11.8% relative AUPR improvement over state-of-the-art methods including TBiNet, EPBDXDNABERT-2, DanQ, and DeepSEA. The model outperformed existing methods in [Formula: see text]98% of TF-cell type combinations. It also recovered 160 known TF binding motifs in the JASPAR database, providing the biological interpretability of the model. Moreover, the approach enables label-zero-shot prediction for unseen TFs, demonstrating its potential of generalizing to new, poorly characterized TFs. The source code of TransBind is available at https://github.com/jianlin-cheng/TransBind. The version used in this work is archived at https://doi.org/10.5281/zenodo.19462292.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag047"},"PeriodicalIF":3.0,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13145115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nuclear genome profiling of two species of <i>Epidendrum</i> (Orchidaceae): genome size, repeatome, and ploidy.","authors":"Miguel A Alcalá-Gaxiola, Gerardo A Salazar, Eric Hágsater, Mónica A Flores-Iniestres, Lidia I Cabrera, Aldo I Aviña-Rivera, Pedro Mercado-Ruaro, Susana Magallon, Carolina Granados Mendoza, Aleida Núñez-Ruiz, Gloria Soldevila, Araxi O Urrutia, Rubi N Meza-Lázaro","doi":"10.1093/nargab/lqag045","DOIUrl":"10.1093/nargab/lqag045","url":null,"abstract":"<p><p>Characterizing genomic properties such as genome size, ploidy level, heterozygosity, and repetitive DNA proportion and composition without relying on genome assembly is crucial for profiling the genomes of non-model species. This study compares genome profiles of <i>Epidendrum anisatum</i> and <i>Epidendrum marmoratum</i>, using flow cytometry and <i>k</i>-mer analysis approaches, as well as bioinformatic ploidy-level estimation and repeatome characterization. Multiple depths of coverage, <i>k</i> values, and software tools for genome size estimation were explored and contrasted with cytometry genome size estimations. Cytometry and <i>k</i>-mer analyses yielded a consistently higher genome size for <i>E. anisatum</i> (2.59 Gb) than <i>E. marmoratum</i> (1.13 Gb), a 2.3-fold genome size difference. Both species were identified as diploid with no evidence of partial endoreplication. The genomes of both species were found to be highly repetitive (63%-73%) and heavily dominated by Ogre Ty3-gypsy retrotransposons. Additionally, the genome of <i>E. anisatum</i> was characterized by the presence of a 172 bp satellite (<i>AniS1</i>), which represented 11% of the genome size. Together, both Ty3-gypsy transposons and <i>AniS1</i> shape the genome size difference between the two genomes. This study highlights the importance of using flow cytometry, cytogenetic approaches, and bioinformatics techniques in conjunction for genome profiling.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag045"},"PeriodicalIF":3.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RNAStructuromeDB: a transcriptome-wide database of predicted RNA secondary structures with integrated APIs for functional annotation and RNA-targeted drug discovery.","authors":"Abdelraouf O Dapour, Jacopo Manigrasso, Warren B Rouse, Giuseppina La Sala, Miles V Aronnax, Leonardo De Maria, Russell S Hamilton, Sergio Martinez Cuesta, Anders Hogner, Walter N Moss","doi":"10.1093/nargab/lqag044","DOIUrl":"10.1093/nargab/lqag044","url":null,"abstract":"<p><p>RNA structure critically governs biological function in both physiological and pathological contexts, making high-resolution structural maps essential for RNA-targeted therapeutics. Yet, despite recent advances, well-validated structural targets for drug design remain limited. To help bridge this gap, we generated the first genome-scale map of the human RNA structurome by applying ScanFold to >230 000 annotated human pre-mRNA transcripts, identifying sequences likely evolved to form highly stable and functional secondary structures. We also performed a global analysis of regions with <i>z</i>-scores ≤ -2 and statistically characterized their two-dimensional folding patterns. In addition, we developed the RNA-Annotator Pipeline to integrate 20 diverse biological annotations, such as tissue-specific expression and protein interactions, with the structural data. Our results reveal local folding propensities and unusually stable structures with high-confidence architectures, providing insights for prioritizing RNA targets and guiding therapeutic design, including antisense oligonucleotides and small molecules. All ScanFold results are publicly available through RNAStructuromeDB. Using the RNA-Annotator Pipeline, analysis of SMN1 and SMN2 pre-mRNAs showed that a single C-to-T transition in SMN2 induces structural rearrangements that disrupt a critical splicing enhancer. This toolkit establishes an integrated workflow that enables researchers to explore RNA structure-function relationships and accelerate advances in RNA-targeted drug discovery and RNA biology.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag044"},"PeriodicalIF":3.0,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13129543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multimodal human protein embeddings database: DeepDrug Protein Embeddings Bank (DPEB).","authors":"Md Saiful Islam Sajol, Magesh Rajasekaran, Hayden Gemeinhardt, Adam Bess, Chris Alvin, Supratik Mukhopadhyay","doi":"10.1093/nargab/lqag042","DOIUrl":"10.1093/nargab/lqag042","url":null,"abstract":"<p><p>Computationally predicting protein-protein interactions (PPIs) is challenging due to the lack of integrated, multimodal protein representations. DeepDrug Protein Embeddings Bank (DPEB) is a curated collection of 22 043 human proteins that integrates four embedding types: structural (AlphaFold2), transformer-based sequence (BioEmbeddings), contextual amino acid patterns (ESM-2: Evolutionary Scale Modeling), and sequence-based n-gram statistics (ProtVec). AlphaFold2 protein structures are available through public databases (e.g. AlphaFold2 Protein Structure Database), but the internal neural network embeddings are not. DPEB addresses this gap by providing AlphaFold2-derived embeddings for computational modeling. Our benchmark evaluations show GraphSAGE with BioEmbedding achieved the highest PPI prediction performance (87.37% AUROC, 79.16% accuracy). The framework also achieved 77.42% accuracy for enzyme classification and 86.04% accuracy for protein family classification. DPEB supports multiple graph neural network methods for PPI prediction, enabling applications in systems biology, drug target identification, pathway analysis, and disease mechanism studies.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag042"},"PeriodicalIF":3.0,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13137052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-read nanopore shotgun metagenomic DNA sequencing for river biodiversity, wildlife, pollution, and environmental health monitoring.","authors":"Orestis Nousias, Fiona G Duffy, Isabelle J Duffy, Mark McCauley, Jenny Whilde, David J Duffy","doi":"10.1093/nargab/lqag040","DOIUrl":"10.1093/nargab/lqag040","url":null,"abstract":"<p><p>As the human population expands and global temperatures rise, species, populations, and biodiversity decline at unprecedented rates, while the frequency of infectious disease emergence increases. Therefore, it is more vital than ever to accurately understand the current state of natural habitats and their constituent species. We assess the feasibility of a single assay: long-read shotgun metagenomic sequencing of environmental DNA (eDNA), to monitor species from across the tree of life, from viruses to complex multicellular organisms, across a representative Irish river system (Avoca River, Co. Wicklow). We conducted aquatic eDNA sampling and long-read shotgun metagenomic sequencing from a mountain tributary through to the sea. This approach could detect and quantify organismal DNA present in environmental samples, from microbes (including DNA viruses) to mammals. Rather than the traditional siloing of microbial and multicellular studies of DNA recovered from environmental samples, simultaneously considering viruses, microbes, and eukaryotes (animals, plants, and fungi) can provide deeper insights. This single assay can simultaneously quantify differences in DNA abundance for a broad range of species and pathogens across sites and sample types, enabling wide-ranging biodiversity assessments. This included human, wildlife, plant, and microbial pathogens and parasites with health, agricultural, and economic importance. The environmental genomic data enabled animal phylogeny and transmissible cancer analysis (blue mussel, <i>Mytilus edulis</i>) even from natural complex community settings. Oxford Nanopore sequencing provides a quantitative approach for river biodiversity, pollution, and environmental health monitoring. Long-read shotgun metagenomic sequencing of environmental samples offers the means to assess whole ecosystems and the ecological, trophic, and host-pathogen interactions occurring within them.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag040"},"PeriodicalIF":3.0,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis and visualization of expression patterns with fuzzy sets as FlowSets.","authors":"Felix Offensperger, Markus Joppich, Evi Sinn, Ralf Zimmer","doi":"10.1093/nargab/lqag043","DOIUrl":"10.1093/nargab/lqag043","url":null,"abstract":"<p><p>The growing availability of high-dimensional, complex datasets demands analysis methods that are both interpretable and flexible. We introduce FlowSets, a novel framework for identifying and analysing flows-fuzzy, interpretable patterns-across multiple, diverse, and heterogeneous data sets. Views are constructed as an interpretation of biological features of the data sets by grouping absolute or relative values, summary statistics (such as fold changes or <i>P</i>-values), or higher-order comparisons into fuzzy, linguistically defined categories based on their underlying distributions. FlowSets builds on these fuzzy categorizations and then tracks how features transition between categories across different conditions or data types, uncovering structural patterns that conventional methods often overlook. The FlowSets framework enables users to define, analyse, and manipulate complex patterns across heterogeneous datasets in a flexible and interpretable manner. With FlowSets, users can visualize feature flows, quantify pattern memberships, and perform enrichment analysis explicitly designed for sets with gradual memberships. This approach offers a robust and customizable alternative to rigid clustering or hard thresholding, allowing for a more transparent and insightful interpretation of multidimensional biological data.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag043"},"PeriodicalIF":3.0,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147782875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNV-Hub: an integrated web-based platform for CNV classification and interpretation using multi-algorithm consensus.","authors":"Vignesh Guru Victor Pillay, Anne-Laure Mosca, Davide Callegarin, Nathalie Marle, Tristan Moro, Mélodie Opale, Nada Maaziz, Gregory Egea, Muriel Payet, Clémence Ragon, Julia Mosnier, Alison Bouzenard, Ludwig Serge Aho, Laurence Faivre, Patrick Callier","doi":"10.1093/nargab/lqag001","DOIUrl":"10.1093/nargab/lqag001","url":null,"abstract":"<p><p>Copy number variations (CNVs) are genomic alterations that can cause rare genetic diseases. Their interpretation requires consulting multiple databases and following classification guidelines, such as those from the American College of Medical Genetics (ACMG) and the Clinical Genome Resource (ClinGen). In France, the Achro-Puce working group has also established recommendations to support CNV interpretation. Despite these resources, CNV analysis remains time-consuming, as it requires reviewing gene content, regulatory elements, and associated syndromes. To address this challenge, we developed CNV-Hub, a web-based platform that streamlines CNV classification and interpretation. CNV-Hub integrates five algorithms: two based on ACMG recommendations (AnnotSV, ClassifyCNV), two using machine learning (X-CNV, ISV), and one specifically developed according to French guidelines. In addition to automated pathogenicity predictions, CNV-Hub provides annotations for each CNV, including gene dosage sensitivity scores (pHaplo, pTriplo), syndrome associations, and direct links to databases such as OMIM and PubMed. The platform's user-friendly interface enables rapid, evidence-based CNV evaluation. By incorporating machine learning among its classification algorithms, CNV-Hub improves the interpretation of uncertain variants by integrating additional parameters. This tool reduces the time required for CNV analysis while maintaining accuracy and reliability, representing a significant advance in molecular cytogenetics and supporting geneticists in clinical decision-making.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag001"},"PeriodicalIF":3.0,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147782897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diploid dual assemblies reveal the telocentric structure and extensive allelic heterogeneity of canine genomes.","authors":"Jeffrey M Kidd, Yassine Souilmi, Benjamin D Rosen, Ruqayya Khan, David Weisz, Olga Dudchenko, Erez Lieberman Aiden, Robert Zammit, J William O Ballard","doi":"10.1093/nargab/lqag035","DOIUrl":"10.1093/nargab/lqag035","url":null,"abstract":"<p><p>Although an increasing number of long-read genome assemblies have been created from a diverse collection of dogs and wolves, most published assemblies represent the diploid genome as a single primary sequence. Here, we generate and analyze phase-resolved diploid dual assemblies from five canines. The most contiguous assemblies represent over half of the canine chromosomes as single contigs, permitting an assessment of the sequence and structure of canine chromosomes. Consistent with a telocentric classification, we find that the centromeres of canine autosomes begin an average of 59 kb from the start of the chromosome and are flanked by a 35 kb subtelomeric segment that is repeat-rich and shared across autosomes. Analysis of a pangenome graph constructed from the 10 haplotype-resolved assemblies shows that short tandem repeat loci are three times more common than variable number tandem repeat loci and that the landscape of canine structural variation features extensive allelic heterogeneity. The pangenome graph includes examples of complex, nested allelic variation involving SINEC (a carnivore-specific SINE) and LINE-1 mobile elements. Analysis of 3' transductions implicate an uncharacterized source element with high activity and demonstrates the presence of full-length LINE-1s capable of retrotransposition that are segregating among canines.</p>","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag035"},"PeriodicalIF":3.0,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}