Bioinformatics advances最新文献

{"title":"A little longer, a lot better: simulation-guided exploration of extended-length single-end barcoded reads for structural variant detection.","authors":"Can Luo, Yichen Henry Liu, Han Liu, Zhenmiao Zhang, Lu Zhang, Brock A Peters, Xin Maizie Zhou","doi":"10.1093/bioadv/vbag267","DOIUrl":"10.1093/bioadv/vbag267","url":null,"abstract":"<p><strong>Motivation: </strong>Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is essential for comprehensive genomic analysis. While short-read sequencing performs well for SNP and INDEL detection, it remains limited in resolving SVs, particularly in complex genomic regions, due to its short read length. Linked-read sequencing technologies, such as single-tube Long Fragment Read (stLFR), partially address this limitation by incorporating molecular barcodes to provide long-range information.</p><p><strong>Results: </strong>In this study, we evaluate conventional paired-end linked reads (PE100_stLFR) and explore a conceptual extension: long single-end barcoded reads of 500 bp (SE500_stLFR) and 1000 bp (SE1000_stLFR). We developed stLFR-sim, a Python-based simulator that reproduces the stLFR workflow and enables realistic benchmarking. Using a high-quality T2T assembly of HG002, we generated multiple datasets across 12 sequencing configurations. SVs were called using Aquila_stLFR (v2) and benchmarked against the Genome in a Bottle (GIAB) HG002 SV truth set with Truvari. We show that simulated PE100_stLFR has the same trade-off pattern between precision and recall in SV calling compared to real data. Increasing read length consistently improves SV detection accuracy, with SE1000_stLFR achieving the best performance among the evaluated stLFR configurations and showing competitive performance relative to ICLR- and pangenome-based approaches, while approaching the performance of long-read methods. Collectively, our results highlight the potential of extended-length single-end barcoded reads for improving SV detection and demonstrate how simulation can be used to evaluate prospective linked-read sequencing designs.</p><p><strong>Availability and implementation: </strong>stLFR-sim is a Python-based, open-source simulator for paired-end and extended-length single-end barcoded sequencing. The software is freely available under the MIT License at https://github.com/maiziezhoulab/stLFRsim.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag267"},"PeriodicalIF":2.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066696","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":"MosaicLev: modified Levenshtein distance for mobile element-aware genome comparison.","authors":"Harry Stoltz, Thomas E Kuhlman","doi":"10.1093/bioadv/vbag261","DOIUrl":"10.1093/bioadv/vbag261","url":null,"abstract":"<p><strong>Motivation: </strong>Genomes diverge, in part due to the activity of mobile elements, including elements that excise and reinsert (cut-paste) or propagate via an RNA intermediate (copy-paste). Standard sequence comparison methods are not motif-aware, penalizing mobile element insertions based on length rather than recognizing them as single biological events, while alignment-free methods still fail to adequately describe a known mobile-element sequence as a unified change.</p><p><strong>Results: </strong>We introduce a modified Levenshtein distance ( <math><mrow><mi>mlev</mi></mrow> </math> ) that discounts specified block insertions via a tunable parameter ( <math><mrow><mi>m</mi> <mo> </mo> <mo>∈</mo> <mo> </mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo></mrow> </math> ): at <math><mrow><mi>m</mi> <mo>=</mo> <mn>0</mn></mrow> </math> it recovers ordinary Levenshtein distance and at <math><mrow><mi>m</mi> <mo>=</mo> <mn>1</mn></mrow> </math> a whole chunk acts like a single edit. On 67 Cluster G1 mycobacteriophage genomes (a viral group with MPME1 and MPME2), MPME1 targeting yielded <math><mrow><mo>∼</mo> <mn>53</mn> <mi>%</mi></mrow> </math> discount for the 31-genome MPME1-score group and <math><mrow><mo>∼</mo> <mn>9</mn> <mi>%</mi></mrow> </math> for the 11-genome MPME2-score group. MPME2 targeting reversed this pattern, with 18 phages low on both scores. For PV92, a 346-bp Yb8-containing insertion received 99.71% forward reduction at <math><mrow><mi>m</mi> <mo>=</mo> <mn>1</mn></mrow> </math> and none in reverse. Together, these results demonstrate that MosaicLev can quantify the contribution of known mobile elements to sequence differences across distinct genomic settings.</p><p><strong>Availability and implementation: </strong>Python implementation with Numba JIT compilation freely available at https://doi.org/10.5281/zenodo.18452982.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag261"},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042322","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":"Theoretical estimates on the expected number of mutations needed to reconstruct clonal lineage trees.","authors":"Nishat Anjum Bristy, Russell Schwartz","doi":"10.1093/bioadv/vbag264","DOIUrl":"10.1093/bioadv/vbag264","url":null,"abstract":"<p><strong>Motivation: </strong>Phylogenetics faces a growing challenge from increasingly large and complicated data sets enabled by ever-improving sequencing technologies. The issue is particularly acute for somatic evolution studies, such as cancer cell lineages, where single-cell data sets may include tens of thousands of mutations in hundreds of thousands of genetically distinct cells. Simultaneously, the biological complexity of somatic evolution has led to complex phylogeny methods that struggle to scale to even modest data sizes.</p><p><strong>Results: </strong>We explore the theoretical and empirical basis for one strategy for managing these large data sets: subsampling mutations for the computationally challenging phylogeny problem followed by faster placement of mutations on a putatively known guide tree. We specifically focus on determining the number of mutations sufficient to recover the true phylogeny at some level of resolution with high probability. We theoretically analyze variants of several common models that underlie popular tools for building clonal lineage trees. We further test these bounds through simulations of these models, extensions of them, and real biological datasets. The results suggest that modest numbers of mutations suffice to reconstruct clonal tree topologies for typical numbers of clones, supporting subsampling as a general strategy for managing the challenges of ever-growing data.</p><p><strong>Availability and implementation: </strong>All analysis code and scripts used for data simulation are implemented in Python 3 and available at https://github.com/CMUSchwartzLab/mutation-subsampling.git.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag264"},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066025","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":"It's a wrap: deriving distinct discoveries with FDR control after a GWAS analysis.","authors":"Benjamin B Chu, Zihuai He, Chiara Sabatti","doi":"10.1093/bioadv/vbag260","DOIUrl":"10.1093/bioadv/vbag260","url":null,"abstract":"<p><strong>Motivation: </strong>The standard analysis pipeline for genome-wide association studies (GWAS) is based on marginal tests of association. These are computationally convenient and portable, but the discoveries are not immediately interpretable and require post-processing such as \"clumping\" and \"fine mapping.\" An interesting alternative is provided by conditional independence hypotheses: their rejections lead to distinct signals across the genome, accounting for measured confounders and pointing to separate causal pathways. Recent work has shown how summary statistics resulting from the standard marginal GWAS can be used as input to test conditional independence hypotheses while controlling the false discovery rate (FDR). We previously developed a pipeline tailored to European genomes.</p><p><strong>Results: </strong>Here we introduce and release new software (solveblock) extending this capability to a much richer collection of studies. Given a set of genotyped samples, or a reference dataset, the new pipeline efficiently estimates the high-dimensional correlation matrices that describe dependencies across the genome, making rather common sparsity assumptions. Taking this sample-specific estimate as input, the software identifies groups of genetic variants that are highly correlated and uses them to define an appropriate resolution for conditional independence hypotheses. Finally, we compute the distribution for the exchangeable negative controls necessary to test these hypotheses. Simulations based on five UK Biobank sub-populations, illustrate the method's FDR control. The analysis of 26 phenotypes of varying polygenicity in British individuals, results in <math><mrow><mo>≈</mo> <mn>19</mn></mrow> </math> additional discoveries, compared to standard marginal association testing.</p><p><strong>Availability and implementation: </strong>Our code, precompiled software, and processed files for these five sub-populations are openly shared.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag260"},"PeriodicalIF":2.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13676318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217348","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":"Fedflow: cloud orchestration for federated learning with the FeatureCloud platform.","authors":"Lukas Weilguny, Niklas Probul, Yani Ren, Nicolas Pons, Jan Baumbach, Mathieu Almeida","doi":"10.1093/bioadv/vbag255","DOIUrl":"10.1093/bioadv/vbag255","url":null,"abstract":"<p><strong>Motivation: </strong>Federated learning (FL) enables collaborative model training on geographically distributed genomic and clinical datasets while complying with data privacy laws and regulatory constraints. FeatureCloud is an existing platform for FL that provides an accessible web-based interface and a large repository of implemented methods. However, due to its graphical interface, FeatureCloud requires manual interaction of all participants, limiting automation, iteration, and reproducibility.</p><p><strong>Results: </strong>We introduce fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud. This tool uses distributed computing resources such as virtual machines or cloud instances to automate such workflows. This allows for scalable federated computing either in local simulations or deployed in a trusted environment. Further, we demonstrate how fedflow can be used to integrate FeatureCloud in reproducible Snakemake workflows. For this, we reanalyse a metagenomic dataset with two federated algorithms and compare the results to the centralized approach with pooled data. Overall, fedflow enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics pipelines and thereby helps increase reproducibility.</p><p><strong>Availability: </strong>Fedflow is open-source and available at https://github.com/W-L/fedflow.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag255"},"PeriodicalIF":2.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148946337","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":"SHISMA: SHape-driven inference of significant cell type-specific subnetworks from tiMe series single-cell trAnscriptomics.","authors":"Antonio Collesei, Pierangela Palmerini, Emilia Vigolo, Francesco Spinnato","doi":"10.1093/bioadv/vbag258","DOIUrl":"https://doi.org/10.1093/bioadv/vbag258","url":null,"abstract":"<p><strong>Motivation: </strong>Recent advances in DNA and RNA sequencing technologies and the gradual decrease in costs have allowed to design serial experiments with timestamps, even at single cell resolution. This possibility unlocks a finer level of detail, as well as a huge amount of noisy information to decode. Tools inferring regulatory networks, or patterns, from this type of data often focus on trajectories, disregarding local shapes and fundamental time series primitives. Moreover, they fail to target the analysis on a few meaningful results, reporting large and noisy outputs that need further downstream analysis.</p><p><strong>Results: </strong>We describe SHISMA, a novel tool to infer significant cell type-specific co-dynamic gene subnetworks, from time series transcriptomic data, with strong statistical guarantees in terms of <i>P</i> value. SHISMA leverages isolated cell populations thanks to single-cell resolution, constructing cell type-specific pseudobulk time-series datasets. It then exploits a recently proposed time series primitive, the Bag-of-Receptive-Fields, adapted to discretize shorter temporal data and retain local shapes. SHISMA extracts significant groups of genes by performing a random walk approach on a protein-protein interaction network, with nodes identified by genes and scores derived from the shape-induced representation of the data, while properly validating via permutation and correcting for multiple hypothesis testing. Our extensive experimental evaluation on synthetic data shows that our tool is able to retrieve specific and significant subnetworks from time series transcriptomic data. Moreover, the subnetworks identified by SHISMA on real-world data confirm its ability to retrieve known cell type-specific processes, as well as potentially novel patterns and co-dynamic mechanisms.</p><p><strong>Availability and implementation: </strong>https://github.com/antoniocollesei/SHISMA.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag258"},"PeriodicalIF":2.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13623514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119174","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":"EnzymeSifter: a tool for discovery of industrial enzymes from metagenomes.","authors":"Omar Darawsheh, Matthew Bashton","doi":"10.1093/bioadv/vbag262","DOIUrl":"10.1093/bioadv/vbag262","url":null,"abstract":"<p><strong>Summary: </strong>Metagenomes contain vast amounts of sequences, complicating the process of identifying candidate enzymes for industrial applications. Industrial applications require evaluating multiple biochemical properties simultaneously, including solubility, thermal stability, and pH. While separate predictors for each property exist, a score that combines multiple predicted values will be more descriptive than those generated individually by distinct tools. We present EnzymeSifter, a tool that automates enzyme discovery from vast metagenomes and enables multi-property predictions. It identifies the best performing enzymes using a computed composite score of all predicted values and generates a phylogenetic tree to select the top candidate from each clade-ensuring diversity and even sampling of sequence space. It acts as a sieve that filters according to the user inputs and keeps the most promising non-redundant enzymes for experimental validation.</p><p><strong>Availability and implementation: </strong>EnzymeSifter is freely available and released under an MIT licence. EnzymeSifter source code is available at https://github.com/Bashton-Lab/EnzymeSifter.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag262"},"PeriodicalIF":2.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027010","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":"MultiVirusConsensus: an accurate and efficient open-source pipeline for identification and consensus sequence generation of multiple viruses from mixed samples.","authors":"Niema Moshiri","doi":"10.1093/bioadv/vbag256","DOIUrl":"10.1093/bioadv/vbag256","url":null,"abstract":"<p><strong>Motivation: </strong>Viral surveillance from mixed samples (e.g. wastewater) has become critical in public health efforts to track and contain pathogens. However, existing open-source bioinformatics tools for viral consensus sequence generation are optimized for individual viruses (rather than multiple potential viruses of interest).</p><p><strong>Results: </strong>MultiVirusConsensus (MVC) is an accurate and efficient open-source pipeline for identification and consensus sequence generation of multiple viruses from mixed samples. It utilizes the memory-efficient ViralConsensus tool to simultaneously perform consensus sequence calling on all viruses of interest (1) completely in parallel, and (2) by piping datastreams between tools without writing/reading intermediate files (thus eliminating slowdowns related to slow disk accesses).</p><p><strong>Availability: </strong>MultiVirusConsensus (MVC) is freely available as an open-source software project at: https://github.com/niemasd/MultiVirusConsensus.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag256"},"PeriodicalIF":2.6,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009448","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":"annoreport: an interactive tool for metagenome annotation.","authors":"Kepler Ridge, Byron J Adams","doi":"10.1093/bioadv/vbag257","DOIUrl":"10.1093/bioadv/vbag257","url":null,"abstract":"<p><strong>Summary: </strong>Gene annotation of metagenome-assembled genomes is a critical step in determining the functional potential of microbial communities from environmental samples. However, annotation workflows using tools such as Prokka or Bakta produce per-bin output with 10 to 14 files per bin, making manual review infeasible at scale. Existing tools incompletely aggregate and visualize gene annotation content across an entire metagenomic dataset. Here we present annoreport, a single-script Python tool requiring no external dependencies beyond Python 3.9+ that accepts output from either Prokka or Bakta, automatically detecting the annotation tool used. annoreport produces an interactive web-based report summarizing gene product frequencies, hypothetical protein rates, feature type distributions, and functional gene clustering via UniProt annotation across all bins. Applied to 206 metagenome-assembled genomes from Antarctic soil metagenomes, annoreport identified 603,799 coding sequences with a 47.1% annotation rate and revealed functional categorization in Transport & Membrane, Nucleotide Binding, and DNA Metabolism categories.</p><p><strong>Availability and implementation: </strong>Freely available at https://github.com/keplerridge/annoreport under MIT license, via Bioconda (annoreport) and PyPI (annoreport).</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag257"},"PeriodicalIF":2.6,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13589607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009477","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":"MeTime: an R package for reproducible longitudinal metabolomics data analysis.","authors":"Bharadwaj Marella, Patrick Weinisch, Lara Vehovec, Vinh Tran, Josef J Bless, Yacoub A Njipouombe Nsangou, Gabi Kastenmüller, Matthias Arnold","doi":"10.1093/bioadv/vbag252","DOIUrl":"10.1093/bioadv/vbag252","url":null,"abstract":"<p><strong>Summary: </strong>MeTime is an opensource R package for reproducible analysis of longitudinal metabolomics data. It builds upon a central S4 container, metime_analyser, that stores multiple datasets, associated metadata and analysis outputs, enabling unified handling of complex longitudinal studies. Analyses are constructed by piping modular functions, beginning with data transformations (mod_*), followed by calculations (calc_*), and optional meta-analysis (meta_*), so entire workflows remain transparent and easy to modify. MeTime wraps numerous existing methods within a consistent interface, including sample and metabolite distributions, correlation/distance matrices, dimensionality reduction (PCA, UMAP, t-SNE), random forest imputation and feature selection via Boruta, eigenmetabolites and WGCNA-based clustering, conservation index analysis, regression models (linear, mixed-effects, and generalized additive), and partial-correlation networks. By retaining all intermediate results and provenance within the container, MeTime facilitates iterative exploration and ensures reproducible reporting via automatically generated HTML/PDF outputs. Comprehensive user guides, case studies and reference documentation accompany the package, making MeTime a versatile platform for longitudinal omics workflows.</p><p><strong>Availability and implementation: </strong>MeTime is available as an open-source R package and can be installed directly from GitHub (https://github.com/compneurobio/MeTime). Source code, installation instructions, documentation, tutorials, and reproducible case-study workflows are provided within the repository. MeTime has been tested on Microsoft Windows, Unix/Linux, and macOS. For users requiring a containerized environment, a Docker implementation is additionally provided through the GitHub Container Registry.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag252"},"PeriodicalIF":2.6,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13568824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957304","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}