BMC Bioinformatics最新文献

{"title":"SNPeBoT: a tool for predicting transcription factor allele specific binding.","authors":"Patrick Gohl, Baldo Oliva","doi":"10.1186/s12859-025-06094-4","DOIUrl":"10.1186/s12859-025-06094-4","url":null,"abstract":"<p><strong>Background: </strong>Mutations in non-coding regulatory regions of DNA may lead to disease through the disruption of transcription factor binding. However, our understanding of binding patterns of transcription factors and the effects that changes to their binding sites have on their action remains limited. To address this issue we trained a Deep learning model to predict the effects of Single Nucleotide Polymorphisms (SNP) on transcription factor binding. Allele specific binding (ASB) data from Chromatin Immunoprecipitation sequencing (ChIP-seq) experiments were paired with high sequence-identity DNA binding Domains assessed in Protein Binding Microarray (PBM) experiments. For each transcription factor a paired DNA binding Domain was selected from which we derived E-score profiles for reference and alternate DNA sequences of ASB events. A Convolutional Neural Network (CNN) was trained to predict whether these profiles were indicative of ASB gain/loss or no change in binding. 18211 E-score profiles from 113 transcription factors were split into train, validation and test data. We compared the performance of the trained model with other available platforms for predicting the effect of SNP on transcription factor binding. Our model demonstrated increased accuracy and ASB recall in comparison to the best scoring benchmark tools.</p><p><strong>Conclusion: </strong>In this paper we present our model SNPeBoT (Single Nucleotide Polymorphism effect on Binding of Transcription Factors) in its standalone and web server form. The increased recovery and prediction accuracy of allele specific binding events could prove useful in discovering non-coding mutations relevant to disease.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"81"},"PeriodicalIF":2.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An alignment-free method for phylogeny estimation using maximum likelihood.","authors":"Tasfia Zahin, Md Hasin Abrar, Mizanur Rahman Jewel, Tahrina Tasnim, Md Shamsuzzoha Bayzid, Atif Rahman","doi":"10.1186/s12859-025-06080-w","DOIUrl":"10.1186/s12859-025-06080-w","url":null,"abstract":"<p><strong>Background: </strong>While alignment has traditionally been the primary approach for establishing homology prior to phylogenetic inference, alignment-free methods offer a simplified alternative, particularly beneficial when handling genome-wide data involving long sequences and complex events such as rearrangements. Moreover, alignment-free methods become crucial for data types like genome skims, where assembly is impractical. However, despite these benefits, alignment-free techniques have not gained widespread acceptance since they lack the accuracy of alignment-based techniques, primarily due to their reliance on simplified models of pairwise distance calculation.</p><p><strong>Results: </strong>Here, we present a likelihood based alignment-free technique for phylogenetic tree construction. We encode the presence or absence of k-mers in genome sequences in a binary matrix, and estimate phylogenetic trees using a maximum likelihood approach. A likelihood based alignment-free method for phylogeny estimation is implemented for the first time in a software named PEAFOWL, which is available at: https://github.com/hasin-abrar/Peafowl-repo . We analyze the performance of our method on seven real datasets and compare the results with the state of the art alignment-free methods.</p><p><strong>Conclusions: </strong>Results suggest that our method is competitive with existing alignment-free tools. This indicates that maximum likelihood based alignment-free methods may in the future be refined to outperform alignment-free methods relying on distance calculation as has been the case in the alignment-based setting.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"77"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UTAP2: an enhanced user-friendly transcriptome and epigenome analysis pipeline.","authors":"Jordana Lindner, Bareket Dassa, Noa Wigoda, Gil Stelzer, Ester Feldmesser, Jaime Prilusky, Dena Leshkowitz","doi":"10.1186/s12859-025-06090-8","DOIUrl":"10.1186/s12859-025-06090-8","url":null,"abstract":"<p><strong>Background: </strong>The emergence of next-generation sequencing (NGS) marked a revolution in biological research, enabling comprehensive characterization of the transcriptome and detailed analysis of the epigenome landscape. This technology has made it possible to detect differences across cell types, genotypes, and conditions. Advances in short-read sequencing platforms, have produced user-friendly machines that offer high throughput at a reduced cost per base. However, leveraging this data still requires bioinformatics expertise to develop and execute tailored solutions for each specific application. Democratizing access to sequence analysis tools is crucial to empower researchers from diverse fields to harness the full potential of NGS data.</p><p><strong>Results: </strong>UTAP2, our enhanced version of UTAP published version in 2019 (Kohen et al. in BMC Bioinform 20(1):154, 2019), empowers researchers to unlock the mysteries of gene expression and epigenetic modifications with ease. This user-friendly, open-source pipeline, built by unit programmers and deep sequencing analysts, streamlines transcriptome and epigenome data analysis, handling everything from sequences to gene or peak counts and differentially expressed genes or genomic regions annotation. Results are delivered in organized folders and rich reports packed with plots, tables, and links for effortless interpretation. Since the debut of UTAP, it has been embraced by many researchers at the Weizmann Institute and over 100 citations, thus highlighting its scientific contribution.</p><p><strong>Conclusion: </strong>Our User-friendly Transcriptome and Epigenome Analysis Pipeline UTAP2 is available to the broader biomedical research community as an open-source installation. With a single image, it can be installed on both local servers and cloud platforms, allowing users to leverage parallel cluster resources. Once installed UTAP2 enables researchers, even those with limited bioinformatics skills to efficiently, accurately and reliably analyse transcriptome and epigenome sequence data.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"79"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GoldPolish-target: targeted long-read genome assembly polishing.","authors":"Emily Zhang, Lauren Coombe, Johnathan Wong, René L Warren, Inanç Birol","doi":"10.1186/s12859-025-06091-7","DOIUrl":"10.1186/s12859-025-06091-7","url":null,"abstract":"<p><strong>Background: </strong>Advanced long-read sequencing technologies, such as those from Oxford Nanopore Technologies and Pacific Biosciences, are finding a wide use in de novo genome sequencing projects. However, long reads typically have higher error rates relative to short reads. If left unaddressed, subsequent genome assemblies may exhibit high base error rates that compromise the reliability of downstream analysis. Several specialized error correction tools for genome assemblies have since emerged, employing a range of algorithms and strategies to improve base quality. However, despite these efforts, many genome assembly workflows still produce regions with elevated error rates, such as gaps filled with unpolished or ambiguous bases. To address this, we introduce GoldPolish-Target, a modular targeted sequence polishing pipeline. Coupled with GoldPolish, a linear-time genome assembly algorithm, GoldPolish-Target isolates and polishes user-specified assembly loci, offering a resource-efficient means for polishing targeted regions of draft genomes.</p><p><strong>Results: </strong>Experiments using Drosophila melanogaster and Homo sapiens datasets demonstrate that GoldPolish-Target can reduce insertion/deletion (indel) and mismatch errors by up to 49.2% and 55.4% respectively, achieving base accuracy values upwards of 99.9% (Phred score Q > 30). This polishing accuracy is comparable to the current state-of-the-art, Medaka, while exhibiting up to 27-fold shorter run times and consuming 95% less memory, on average.</p><p><strong>Conclusion: </strong>GoldPolish-Target, in contrast to most other polishing tools, offers the ability to target specific regions of a genome assembly for polishing, providing a computationally light-weight and highly scalable solution for base error correction.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"78"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-validation for training and testing co-occurrence network inference algorithms.","authors":"Daniel Agyapong, Jeffrey Ryan Propster, Jane Marks, Toby Dylan Hocking","doi":"10.1186/s12859-025-06083-7","DOIUrl":"10.1186/s12859-025-06083-7","url":null,"abstract":"<p><strong>Background: </strong>Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation.</p><p><strong>Results: </strong>Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability.</p><p><strong>Conclusions: </strong>Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method's applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"74"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11883995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust phylogenetic tree-based microbiome association test using repeatedly measured data for composition bias.","authors":"Kangjin Kim, Sungho Won","doi":"10.1186/s12859-024-06002-2","DOIUrl":"10.1186/s12859-024-06002-2","url":null,"abstract":"<p><strong>Background: </strong>The effects of microbiota on the host phenotypes can differ substantially depending on their age. Longitudinally measured microbiome data allow for the detection of the age modification effect and are useful for the detection of microorganisms related to the progression of disease whose identification change over time. Moreover, longitudinal analysis facilitates the estimation of the within-subject covariate effect, is robust to the between-subject confounders, and provides better evidence for the causal relationship than cross-sectional studies. However, this method of analysis is limited by compositional bias, and few statistical methods can estimate the effect of microbiota on host diseases with repeatedly measured 16S rRNA gene data. Herein, we propose mTMAT, which is applicable to longitudinal microbiome data and is robust to compositional bias.</p><p><strong>Results: </strong>mTMAT normalized the microbial abundance and utilized the ratio of the pooled abundance for association analysis. mTMAT is based on generalized estimating equations with a robust variance estimator and can be applied to repeatedly measured microbiome data. The robustness of mTMAT against compositional bias is underscored by its utilization of abundance ratios.</p><p><strong>Conclusions: </strong>With extensive simulation studies, we showed that mTMAT is statistically relatively powerful and is robust to compositional bias. mTMAT enables detection of microbial taxa associated with host diseases using repeatedly measured 16S rRNA gene data and can provide deeper insights into bacterial pathology.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"75"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TRain: T-cell receptor automated immunoinformatics.","authors":"Austin Seamann, Maia Bennett-Boehm, Ryan Ehrlich, Anna Gil, Liisa Selin, Dario Ghersi","doi":"10.1186/s12859-025-06074-8","DOIUrl":"10.1186/s12859-025-06074-8","url":null,"abstract":"<p><strong>Background: </strong>The scarcity of available structural data makes characterizing the binding of T-cell receptors (TCRs) to peptide-Major Histocompatibility Complexes (pMHCs) very challenging. The recent surge in sequencing data makes TCRs an ideal target for protein structure modeling. Through these 3D models, researchers can potentially identify key motifs on the TCR's binding regions. Furthermore, computational methods can be employed to pair a TCR structure with a pMHC, leading to predictions of docked TCRpMHC structures. However, going from sequence to predicted 3D TCRpMHC complexes requires a non-trivial amount of steps and specialized immunoinformatics expertise.</p><p><strong>Results: </strong>We developed a Python tool named TRain (T-cell Receptor Automated ImmunoiNformatics) to streamline this process by: (1) converting single-cell sequencing data into full TCR amino acid sequences; (2) efficiently submitting TCR amino acid sequences to existing TCR-specific modeling pipelines; (3) pairing modeled TCR structures with existing crystal structures of pMHC complexes in a non-biased manner before docking; (3) automating the preparation and submission process of TCRs and pMHCs for docking using the RosettaDock tool; and (4) providing scripts to analyze the predicted TCRpMHC interface. We illustrate the basic functionality of TRain with a case study, while further information can be found in a dedicated manual.</p><p><strong>Conclusions: </strong>We introduced an open-source tool that streamlines going from full TCR sequence information to predicted 3D TCRpMHC complexes, using well-established tools. Analyzing these predicted complexes can provide deeper insights into the binding properties of TCRs, and can help shed light on one of the key steps in adaptive immune responses.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"76"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 distribution-guided Mapper algorithm.","authors":"Yuyang Tao, Shufei Ge","doi":"10.1186/s12859-025-06085-5","DOIUrl":"10.1186/s12859-025-06085-5","url":null,"abstract":"<p><strong>Background: </strong>The Mapper algorithm is an essential tool for exploring the data shape in topological data analysis. With a dataset as an input, the Mapper algorithm outputs a graph representing the topological features of the whole dataset. This graph is often regarded as an approximation of a Reeb graph of a dataset. The classic Mapper algorithm uses fixed interval lengths and overlapping ratios, which might fail to reveal subtle features of a dataset, especially when the underlying structure is complex.</p><p><strong>Results: </strong>In this work, we introduce a distribution-guided Mapper algorithm named D-Mapper, which utilizes the property of the probability model and data intrinsic characteristics to generate density-guided covers and provide enhanced topological features. Moreover, we introduce a metric accounting for both the quality of overlap clustering and extended persistent homology to measure the performance of Mapper-type algorithms. Our numerical experiments indicate that the D-Mapper outperforms the classic Mapper algorithm in various scenarios. We also apply the D-Mapper to a SARS-COV-2 coronavirus RNA sequence dataset to explore the topological structure of different virus variants. The results indicate that the D-Mapper algorithm can reveal both the vertical and horizontal evolutionary processes of the viruses. Our code is available at https://github.com/ShufeiGe/D-Mapper .</p><p><strong>Conclusion: </strong>The D-Mapper algorithm can generate covers from data based on a probability model. This work demonstrates the power of fusing probabilistic models with Mapper algorithms.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"73"},"PeriodicalIF":2.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-approach co-expression analysis framework (D-CAF) enables identification of novel circadian co-regulation from multi-omic timeseries data.","authors":"Joshua Chuah, Carmalena V Cordi, Juergen Hahn, Jennifer M Hurley","doi":"10.1186/s12859-025-06089-1","DOIUrl":"10.1186/s12859-025-06089-1","url":null,"abstract":"<p><strong>Background: </strong>The circadian clock is a central driver of many biological and behavioral processes, regulating the levels of many genes and proteins, termed clock controlled genes and proteins (CCGs/CCPs), to impart biological timing at the molecular level. While transcriptomic and proteomic data has been analyzed to find potential CCGs and CCPs, multi-omic modeling of circadian data, which has the potential to enhance the understanding of circadian control of biological timing, remains relatively rare due to several methodological hurdles. To address this gap, a dual-approach co-expression analysis framework (D-CAF) was created to perform co-expression analysis that is robust to Gaussian noise perturbations on time-series measurements of both transcripts and proteins.</p><p><strong>Results: </strong>Applying this D-CAF framework to previously gathered transcriptomic and proteomic data from mouse macrophages gathered over circadian time, we identified small, highly significant clusters of oscillating transcripts and proteins in the unweighted similarity matrices and larger, less significant clusters of of oscillating transcripts and proteins using the weighted similarity network. Functional enrichment analysis of these clusters identified novel immunological response pathways that appear to be under circadian control.</p><p><strong>Conclusions: </strong>Overall, our findings suggest that D-CAF is a tool that can be used by the circadian community to integrate multi-omic circadian data to improve our understanding of the mechanisms of circadian regulation of molecular processes.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"72"},"PeriodicalIF":2.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ProToDeviseR: an automated protein topology scheme generator.","authors":"Petar Petrov, Valerio Izzi","doi":"10.1186/s12859-025-06088-2","DOIUrl":"10.1186/s12859-025-06088-2","url":null,"abstract":"<p><strong>Background: </strong>Amino acid sequence characterization is a fundamental part of virtually any protein analysis, and creating concise and clear protein topology schemes is of high importance in proteomics studies. Although numerous databases and prediction servers exist, it is challenging to incorporate data from various, and sometimes contending, resources into a publication-ready scheme.</p><p><strong>Results: </strong>Here, we present the Protein Topology Deviser R package (ProToDeviseR) for the automatic generation of protein topology schemes from database accession numbers, raw results from multiple prediction servers, or a manually prepared table of features. The application offers a graphical user interface, implemented in R Shiny, hosting an enhanced version of Pfam's domains generator for the rendering of visually appealing schemes.</p><p><strong>Conclusions: </strong>ProToDeviseR can easily and quickly generate topology schemes by interrogating UniProt or NCBI GenPept databases and elegantly combine features from various resources.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"71"},"PeriodicalIF":2.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143540051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}