Briefings in bioinformatics最新文献

{"title":"Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.","authors":"Jieyi Pan, Qiyuan Guan, Duanchen Sun","doi":"10.1093/bib/bbag531","DOIUrl":"https://doi.org/10.1093/bib/bbag531","url":null,"abstract":"<p><p>Elucidating the spatial organization of cells is fundamental to understanding tissue architecture and function, yet tissue dissociation during single-cell RNA sequencing (scRNA-seq) strips cells of their native architectural context. It is essential to integrate scRNA-seq data with spatial transcriptomics (ST) references to infer the spatial information of individual cells. Despite the development of various tools, achieving precise and biologically relevant spatial reconstructions remains challenging. Here, we present Cell2space, a deep learning framework that integrates scRNA-seq data with ST references to reconstruct multi-scale cellular spatial organization in a stepwise manner. Instead of treating spatial reconstruction as coordinate regression, Cell2space learns a universal spatial affinity function and employs a hierarchical inference strategy that integrates domain-level priors to refine cellular neighborhoods. This approach enables accurate assignment of single cells to spatial domains and inference of cell-cell neighborhood relationships. Comprehensive evaluations demonstrate that Cell2space robustly achieves superior performance in both domain assignment and neighborhood inference. Applied to the mouse visual cortex and human skin, Cell2space identifies layer-specific markers, reveals continuous gene expression gradients, and faithfully reconstructs stratified tissue architecture without requiring prior annotations. Together, Cell2space provides a powerful framework for integrating single-cell and spatial modalities, enabling deeper insights into tissue organization and cellular microenvironments.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative machine learning approaches for enzyme kinetic parameter prediction.","authors":"Rui Zhou, Wenhui Xi, Peng Yin, Sheng Wang, Yong Zhang, Yanjie Wei","doi":"10.1093/bib/bbag533","DOIUrl":"10.1093/bib/bbag533","url":null,"abstract":"<p><p>The prediction of enzyme kinetic parameters, particularly $mathit{k}_{text{cat}}$ and $mathit{K}_{text{m}}$, is rapidly evolving from physics-based modeling toward data-driven artificial intelligence (AI) approaches, driven by expanding biochemical databases and advances in machine learning (ML) and deep learning (DL). We focus on AI-based enzyme kinetic parameter prediction, with an emphasis on $mathit{k}_{text{cat}}$ and $mathit{K}_{text{m}}$, while also considering related parameters, such as $mathit{K}_{text{i}}$ and $mathit{k}_{text{cat}}/mathit{K}_{text{m}}$. The key findings reveal that: (i) Representation learning increasingly relies on pretrained protein language models, while structural embeddings often show limited additional performance gains over sequence-based approaches; (ii) Model performance is highly data-dependent: classical ML regressors can outperform DL architectures on small datasets when using comparable feature representations, though multimodal feature integration can improve generalization; and (iii) Multitask frameworks for the joint prediction of $mathit{k}_{text{cat}}$ and $mathit{K}_{text{m}}$ may provide synergistic benefits, although the magnitude of the improvement may be limited. Future progress will require standardized benchmarks, mechanistically informed model architectures, uncertainty-aware prediction, and robust out-of-distribution validation to improve biochemical relevance, interpretability, and real-world utility.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"InstructPLM-mu: protein fitness prediction from structure instructions.","authors":"Junde Xu, Yapin Shi, Lijun Lang, Taoyong Cui, Zhiming Zhang, Guangyong Chen, Jiezhong Qiu, Pheng-Ann Heng","doi":"10.1093/bib/bbag400","DOIUrl":"10.1093/bib/bbag400","url":null,"abstract":"<p><p>Multimodal protein language models (PLMs) deliver strong performance on mutation-effect prediction, but training such models from scratch demands substantial computational resources. In this paper, we propose a fine-tuning framework called InstructPLM-mu and investigate the question: Can a pretrained sequence-only PLM, augmented with structural information during fine-tuning, match the performance of end-to-end trained multimodal models? Surprisingly, our experiments show that fine-tuning ESM2 with structural inputs can reach performance comparable to ESM3. To understand how this is achieved, we systematically compare three different feature-fusion designs and fine-tuning recipes. Our results reveal that both the fusion method and the tuning strategy strongly affect final accuracy, indicating that the fine-tuning process is not trivial. We hope this work offers practical guidance for injecting structure into pretrained PLMs and motivates further research on better fusion mechanisms and fine-tuning protocols.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DecBin: Bayesian-optimized deep clustering for long-read metagenomic binning.","authors":"Yi-Heng Zhu, Xinchi Li, Zuo Liu, Jing Liu, Rui Ye, Wei Xue, Gaofei Jiang","doi":"10.1093/bib/bbag571","DOIUrl":"10.1093/bib/bbag571","url":null,"abstract":"<p><p>Accurate metagenomic binning is essential for reconstructing microbial genomes, thereby facilitating the discovery of novel taxa, the identification of pathogens, and the characterization of antimicrobial resistance genes. Most existing methods are designed for assembled contigs and cannot be directly applied to raw long reads. Although a few dedicated long-read binning methods have emerged, challenges remain in bin-number estimation and representation learning. Here, we propose Deep Embedded Clustering for Long-read Binning (DecBin), an adaptive method for directly binning raw long reads through integrating a variational autoencoder (VAE), Bayesian optimization, and deep embedded clustering within a unified framework. Computational experiments on large-scale simulated and mock-community datasets demonstrate that DecBin outperforms state-of-the-art methods in both long-read binning and metagenome-assembled genome recovery, while also achieving strain-level resolution in a low-complexity natural microbiome. Further analysis shows that the advantage of DecBin is partly attributable to its Davies-Bouldin index-guided Bayesian optimization, which explicitly evaluates candidate cluster numbers using a global cluster-validity criterion, thereby improving bin-number estimation across communities of varying complexity. Meanwhile, joint fine-tuning of the VAE encoder and clustering layer enables clustering feedback to refine the latent space, yielding representations better aligned with genome partitions. These findings highlight the potential of adaptive deep clustering for metagenomic binning directly from raw long reads without prior assembly. The source code of DecBin is freely available at https://github.com/LorMeBioAI/DecBin.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual cells: progress and challenges from mechanistic modeling to data-driven integration.","authors":"Nuoran Ma, Zihao Wang, Xu Yang, Guoxing Yang, Rui Yang, Zhenxing Wang, Qinghua Jiang, Yanshuo Chu","doi":"10.1093/bib/bbag562","DOIUrl":"10.1093/bib/bbag562","url":null,"abstract":"<p><p>Virtual cells aim to represent cellular states computationally and predict responses to genetic, chemical, environmental, temporal, and spatial conditions. For comparative purposes, this review uses state encoding (E), state transition (T), and observation decoding (D) as a conceptual abstraction spanning explicit mechanistic models, data-driven foundation and perturbation models, multimodal systems, and spatial and multiscale simulation. We examine model boundaries, data foundations, resource disclosure, out-of-distribution evaluation, causal evidence, and experimental validation. Independent benchmarks show that parameter count and training-set size do not automatically yield robust out-of-distribution generalization, while transcriptomic representations alone do not capture the full functional state of a cell. Trustworthy virtual cells will require perturbation-responsive transitions, cross-modal alignment, continuous-time dynamics, mechanistic constraints, calibrated uncertainty, independent experimental validation, and, where appropriate, closed-loop experimental feedback.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CoMR: an integrative scoring pipeline for comprehensive mitochondrial proteome reconstruction across eukaryotes.","authors":"Julie Boisard, Shelby K Williams, Andrew J Roger, Courtney W Stairs","doi":"10.1093/bib/bbag474","DOIUrl":"10.1093/bib/bbag474","url":null,"abstract":"<p><p>Mitochondrial proteome reconstruction from eukaryotic sequence data typically relies on prediction of mitochondrial targeting signals (MTSs). However, MTS predictors are primarily trained on model organisms and may perform poorly in phylogenetically divergent lineages or in organisms with atypical or reduced targeting sequences. Accurate reconstruction therefore requires integration of complementary sources of evidence beyond targeting prediction alone. We developed Comprehensive Mitochondrial Reconstructor (CoMR), an integrative workflow that combines targeting prediction, curated homology searches, large-scale similarity searches, and automated phylogenetic analysis within a unified scoring framework. Benchmarking on the model yeast Saccharomyces cerevisiae yielded strong discriminatory performance [receiver operating characteristic (ROC)-area under the curve (AUC) = 0.92], exceeding standalone prediction with TargetP2, a predictor of N-terminal targeting peptides (ROC-AUC = 0.72). In the divergent anaerobic protist Paratrimastix pyriformis, CoMR maintained robust performance (ROC-AUC = 0.86) validated with an experimental proteome despite extreme class imbalance, achieving a precision-recall AUC of 0.183 (~78-fold enrichment over random expectation and ~10-fold improvement over TargetP2). Ablation analyses demonstrate that predictive performance is robust to individual evidence-layer removal, while overlap analyses showed that homology-based searches recovered candidates missed by targeting predictors, particularly in P. pyriformis. Overall, CoMR improves mitochondrial proteome reconstruction over targeting prediction alone and provides a reproducible workflow for predicting mitochondrial and mitochondrion-related organelle protein repertoires across eukaryotes to aid investigations of organelle evolution and proteome reduction.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive evaluation of structural variation detection for germline and somatic analysis with long-read sequencing data.","authors":"Hua Shi, Yihang Lin, Dachen Liu, Hongfeng Wu, Ashidi Nor Mat Isa, Leyi Wei, Quan Zou, Xiang Chen","doi":"10.1093/bib/bbag530","DOIUrl":"10.1093/bib/bbag530","url":null,"abstract":"<p><p>Detecting structural variations (SVs) via long-read sequencing remains difficult due to algorithmic variations and genomic complexity, alongside a shortage of comprehensive benchmarks for somatic variants. We present a unified benchmarking framework covering both germline and somatic SV detection, which evaluates 14 long-read callers across 20 core datasets from Pacific Biosciences (PacBio) Continuous Long Reads (CLR), Circular Consensus Sequencing (CCS), and Oxford Nanopore Technologies (ONT) platforms. Performance was analyzed across 12 dimensions using metrics including baseline artefact rate, Mendelian discordance rate (MDR), and Mendelian inheritance error rate (MIER). For germline SVs, DeBreak, cuteSV2, and SVDF showed stable and accurate detection. cuteSV2 and SVHunter maintained consistent genotyping accuracy across sequencing depths, whereas Severus and cuteSV2 performed well in identifying complex SVs such as inversions, duplications, and translocations. In tumor datasets, tools designed specifically for somatic SVs outperformed germline callers. Severus and SAVANA showed strong overall somatic performance, and nanomonsv showed distinct advantages at low coverage. These results offer practical references for tool selection under various sequencing scenarios, supporting future algorithmic development. The code and resources are available at https://github.com/model-lab/LR-SV-Benchmark.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149069119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high-dimensional benchmark of objective functions and biological resolutions for personalized metabolic phenotyping.","authors":"Mehmet Ali Erdoğan, Ali Cakmak","doi":"10.1093/bib/bbag528","DOIUrl":"https://doi.org/10.1093/bib/bbag528","url":null,"abstract":"<p><p>Genome-scale personalized metabolic modeling relies on objective functions to simulate intracellular flux phenotypes, yet the optimal formulation for characterizing complex human diseases remains unknown. We ask which combination of objective function, biological resolution, and feature-selection strategy yields the most reliable diagnostic signal, treating machine-learning architecture as a controlled factor. We benchmarked these choices across 57 600 experimental configurations spanning six diverse pathologies, from oncology to neurodegeneration. Within this benchmark, pathway-level minimum reaction flux was the best-performing representation, indicating that diagnostic signals are more strongly associated with rate-limiting metabolic bottlenecks than with aggregate pathway activity. Under a leakage-free evaluation, diagnostic accuracy was largely insensitive to feature density (mean $F_{1}$-score varying by <0.003 between the top-10% subset and the full feature space), so aggressive feature selection was not required. Among classifiers, only Logistic Regression lost accuracy at full density, whereas SVM was essentially unaffected and tree-based ensembles were resilient. Statistical regularization (robust_sigma) ensured consistent generalization, while topological objectives (local_k) were highly sensitive to metabolic noise, excelling only in specific high-signal cohorts. Finally, integrating objective function values as competitive features recovered the regulatory context lost during pathway aggregation. Accurate metabolic phenotyping was best captured by isolating restrictive bottleneck reactions. Predictive accuracy was largely insensitive to feature density; tree-based architectures tolerated dense omics data, while feature selection benefited mainly Logistic Regression. Overall, this benchmark offers systematic, evidence-based guidance for personalized metabolic modeling, complementing heuristic parameter tuning with quantitative comparison.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13625657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential analysis of microbial interaction networks.","authors":"Marianna Milano, Pietro Hiram Guzzi","doi":"10.1093/bib/bbag522","DOIUrl":"10.1093/bib/bbag522","url":null,"abstract":"<p><p>Microbiome studies increasingly indicate that disease-associated shifts cannot be understood from compositional changes alone. The functional architecture of microbial communities-encoded in patterns of association among microbial gene families-may reveal how these systems reorganize across biological conditions. Here, we present a network-based framework for characterizing microbiome rewiring across conditions. The approach combines condition-specific network inference, differential network analysis, and pathway-level network analysis to identify associations that are gained, lost, or altered between groups, with a specific focus on sex-dependent differences. We apply the framework to inflammatory bowel disease, type 2 diabetes, and atherosclerotic cardiovascular disease (ACVD), comparing male and female-specific microbial gene family networks within each disease context. Across these settings, differential networks flag large numbers of candidate rewired associations; however, permutation testing (sex labels shuffled, group sizes preserved, 500 permutations for gene-family networks, and 1000 for pathway networks) shows that the global amount of apparent rewiring is not greater than expected under the null at the global or edge level in any cohort, and that most edges exclusive to one group are induced by group-specific feature filtering rather than by a genuine change in association ($sim $80%-83% in ACVD). We therefore present the method as a rigorously validated framework and a cautionary case study: the differential-network machinery is sound, but the headline biological signal in a naive analysis is largely a property of correlation thresholding and, for the longitudinal inflammatory bowel disease (IBD) cohort, of pseudoreplication. The only non-null result across all validations is a SOHPIE-DNA per-taxon test in the IBD disease arm (15 taxa at FDR $< 0.05$), which we report as a single nominal finding requiring independent replication. Code, data, and supplementary information are available at https://github.com/mmilano87/NetMicrobiome.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SpacerScope: binary-vectorized, genome-wide off-target profiling for RNA-guided nucleases without prior candidate-site bias.","authors":"Yanji Qu, Yaxuan Wang, Yan Wang, Haoru Tang, Qing Chen","doi":"10.1093/bib/bbag478","DOIUrl":"10.1093/bib/bbag478","url":null,"abstract":"<p><p>The precision of CRISPR/Cas systems is fundamental to their application in plant and animal biotechnology. However, comprehensive sequence-based off-target candidate discovery remains a computational bottleneck, particularly in large and complex genomes. Here we developed SpacerScope, an off-target candidate discovery framework that enables unbiased, genome-wide discovery by leveraging binary vectorization, bitwise filtering, and right-end-anchored alignment. Benchmarking against human CIRCLE-seq data demonstrated that SpacerScope recovered 100% of validated off-target sites (6142/6142), matching the sensitivity of exhaustive algorithms. Crucially, SpacerScope achieved this maximum candidate recovery while substantially reducing computational overhead. In large-genome evaluations, SpacerScope maintained low peak memory usage of 2.20 GiB and achieved substantial runtime improvements over indel-aware comparator tools, including more than 50-fold speedup relative to Cas-OFFinder 3 (544 s versus 29 185 s). Furthermore, comparative analyses in polyploid species, such as the octoploid strawberry, revealed that SpacerScope identified larger sequence-compatible candidate burdens than standard web-based design platforms. Our results establish SpacerScope as a high-speed framework for sequence-based genome-wide off-target candidate discovery across diverse and highly repetitive genomic landscapes. The source code and program was publicly available at https://github.com/charlesqu666/SpacerScope. Short Abstract CRISPR/Cas sequence-based off-target candidate discovery remains computationally challenging in large, repetitive, and polyploid genomes. Existing tools either miss indel-containing candidate sites or incur prohibitive runtime and memory costs. We developed SpacerScope, a binary-vectorized framework that enables unbiased, genome-wide off-target candidate discovery without pre-selected candidate sites. By integrating bitwise filtering with right-end-anchored alignment, SpacerScope recovered 100% of validated off-target sites in human CIRCLE-seq data while using only 2.20 GiB of memory and achieving more than 10-fold speedup over indel-aware alternatives. Evaluation in plant genomes, including rice and octoploid strawberry, further demonstrated SpacerScope's capacity to identify larger sequence-compatible candidate burdens overlooked by standard tools. SpacerScope thus provides a high-speed framework for sequence-based genome-wide off-target candidate discovery across diverse and highly repetitive genomic landscapes, supporting downstream prioritization.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148918756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}