Journal of Computational Biology最新文献

{"title":"On Deriving Synteny Blocks by Compacting Elements.","authors":"Leonard Bohnenkämper, Luca Parmigiani, Kamil Hepak, Cedric Chauve, Jens Stoye","doi":"10.1177/15578666261491605","DOIUrl":"https://doi.org/10.1177/15578666261491605","url":null,"abstract":"<p><p>Genomic rearrangements are major drivers of evolution and genetic disease. Studying rearrangements requires segmenting genomes of interest into conserved regions, called synteny blocks, that highlight structural differences between genomes. Synteny blocks are typically defined from annotated genes or derived as a by-product of whole-genome alignment. However, as these procedures are heuristic and do not explicitly model rearrangements, they can obscure real variation, create false similarities, and affect phylogenetic inference. We present a formal framework for deriving synteny blocks by partitioning genomic elements into blocks that do not contain breakpoints. A breakpoint is defined between a pair of genomes as an adjacency of shared elements that occurs in one genome but not in the other. Synteny blocks are therefore not allowed to span such boundaries, ensuring that rearrangements are not obscured. The framework is fully agnostic to the type of genomic element and applies to any genome representation expressed as sequences of elements, such as nonoverlapping alignments, exact matches (MUMs/MEMs), <i>k</i>-mers, unitigs, or minimizers. We formalize two optimization problems: minimizing the total genome length after replacement by synteny blocks (the Minimum-Length Synteny Block Problem) and minimizing the number of distinct blocks (the Minimum-Size Synteny Block Problem). We show that both problems are NP-hard in general. However, when blocks are required to be collinear and to contain a shared element, we provide a linear-time algorithm with respect to the number of input elements that simultaneously minimizes both objectives. The resulting method is simple, efficient, and produces large synteny blocks without obscuring rearrangements.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261491605"},"PeriodicalIF":1.3,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Run-Length-Compressed Skiplist Data Structure for Dynamic GBWTs Supports Time- and Space-Efficient Pangenome Operations over Syncmers.","authors":"Richard Durbin","doi":"10.1177/15578666261495122","DOIUrl":"10.1177/15578666261495122","url":null,"abstract":"<p><p>Skiplists are probabilistic data structures over ordered lists supporting <math><mrow><mi>O</mi><mo>(</mo><mtext>log</mtext><mi>N</mi><mo>)</mo></mrow></math> insertion and search, which share many properties with balanced binary trees. Previously, we introduced the graph Burrows-Wheeler transform (GBWT) to support efficient search over pangenome path sets, but current implementations are static and cumbersome to build and use. Here, we introduce a doubly linked skiplist variant over run-length-compressed Burrows-Wheeler transforms that supports <math><mrow><mi>O</mi><mo>(</mo><mtext>log</mtext><mi>R</mi><mo>)</mo></mrow></math> rank and access operations, as well as a dynamic version that supports <math><mrow><mi>O</mi><mo>(</mo><mtext>log</mtext><mi>R</mi><mo>+</mo><mi>S</mi><mo>)</mo></mrow></math> rank and insert operations, where <i>R</i> is the number of runs and <i>S</i> is the number of symbols in the alphabet. We use these to store and search over paths through a syncmer graph built from Edgar's closed syncmers, equivalent to a sparse de Bruijn graph. Code is available in rskip.[ch] within the syng package at github.com/richarddurbin/syng. This builds a 5.8 GB lossless GBWT representation of 92 full human genomes, single-threaded in 52 minutes, on top of a 4GB 63bp syncmer set built in 37 minutes. Arbitrarily long maximal exact matches can then be found as seeds for sequence matches to the graph at a search rate of ∼1Gbp per 10 seconds per thread.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261495122"},"PeriodicalIF":1.3,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7619509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149194160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scProfiterole: Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters.","authors":"Mustafa Coşkun, Filipa Blasco Lopes, Pınar Kubilay Tolunay, Mark R Chance, Mehmet Koyutürk","doi":"10.1177/15578666261491608","DOIUrl":"10.1177/15578666261491608","url":null,"abstract":"<p><p>Novel technologies for the acquisition of protein expression data at the single cell level are emerging rapidly. Although there exists a substantial body of computational algorithms and tools for the analysis of single cell gene expression (scRNAseq) data, tools for even basic tasks such as clustering or cell type identification for single cell proteomic (scProteomics) data are relatively scarce. Adoption of algorithms that have been developed for scRNAseq into scProteomics is challenged by the larger number of drop-outs, missing data, and noise in single cell proteomic data. Graph contrastive learning (GCL) on cell-to-cell similarity graphs derived from single cell protein expression profiles show promise in cell type identification. However, missing edges and noise in the cell-to-cell similarity graph requires careful design of convolution matrices to overcome the imperfections in these graphs. Here, we introduce scProfiterole (Single Cell Proteomics Clustering via Spectral Filters), a computational framework to facilitate effective use of spectral graph filters in GCL-based clustering of single cell proteomic data. Since clustering assumes a homophilic network topology, we consider three types of homophilic filters: (i) random walks, (ii) heat kernels (HK), (iii) beta kernels (BK). Direct implementation of these filters is computationally prohibitive, thus, the filters are either truncated or approximated in practice. To overcome this limitation, scProfiterole uses Arnoldi orthonormalization to implement polynomial interpolations of any given spectral graph filter. Our results on comprehensive single cell proteomic data show that (i) GCL with learnable polynomial coefficients that are carefully initialized improves the effectiveness and robustness of cell type identification, (ii) HK and BK improve clustering performance over adjacency matrices or random walks, and (iii) polynomial interpolation of spectral filters outperforms approximation or truncation. The source code for scProfiterole and Supplementary Data are available at https://github.com/mustafaCoskunAgu/scProfiterole.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261491608"},"PeriodicalIF":1.3,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149137026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Topologically Associated Domains via Infomap Entropy Minimization.","authors":"Qiushi Liang, Shengjie Zhao, Ruo Han Wang, Yu Wei Zhang, Lingxi Chen, Shuai Cheng Li","doi":"10.1177/15578666261491762","DOIUrl":"https://doi.org/10.1177/15578666261491762","url":null,"abstract":"<p><p>Accurately identifying topologically associated domains (TADs) from high-throughput chromosome conformation capture (Hi-C) contact maps is fundamental to decoding chromatin architecture and its regulatory functions. Despite numerous existing tools, achieving a balance between computational time and the mathematical optimality of TAD partitions remains a significant challenge. In this study, we formulate TAD identification as a graph partitioning problem guided by the principle of Infomap entropy minimization. We introduce InfoTAD, a novel algorithm that models the hierarchical organization of chromatin interactions through an Infomap entropy encoding tree. A primary theoretical contribution of this work is the rigorous proof that finding an optimal encoding tree with minimal Infomap entropy is Nondeterministic Polynomial-time hard (NP-hard). To circumvent this complexity, we propose an efficient approximation algorithm that integrates a discretization strategy with dynamic programming, specifically tailored to the linear constraints of Hi-C contact maps. Comparative benchmarks on simulated datasets show that InfoTAD consistently achieves higher accuracy and lower Infomap entropy than other methods. Furthermore, application to real Hi-C contact matrices from human cell lines (GM12878 and IMR90) reveals that the boundaries identified by InfoTAD are significantly enriched with structural proteins, underscoring its biological precision and potential for advancing genomic research.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261491762"},"PeriodicalIF":1.3,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149136971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pareto Optimization of Masked Superstrings Improves Compression of Pan-Genome <i>k</i>-Mer Sets.","authors":"Ján Plachý, OndŘej Sladký, Karel BŘinda, Pavel Veselý","doi":"10.1177/15578666261491601","DOIUrl":"https://doi.org/10.1177/15578666261491601","url":null,"abstract":"<p><p>The growing interest in <math><mi>k</mi></math>-mer-based methods across bioinformatics calls for compact <math><mi>k</mi></math>-mer set representations that can be optimized for specific downstream applications. Recently, masked superstrings (MS) have provided such flexibility by moving beyond de Bruijn graph paths to general <math><mi>k</mi></math>-mer superstrings equipped with a binary mask, thereby subsuming Spectrum-Preserving String Sets and achieving compactness on arbitrary <math><mi>k</mi></math>-mer sets. However, existing methods optimize superstring length and mask properties in two separate steps, possibly missing solutions where a small increase in superstring length yields a substantial reduction in mask complexity. Here, we introduce the first method for Pareto optimization of <math><mi>k</mi></math>-mer superstrings and masks, and apply it to the problem of compressing pan-genome <math><mi>k</mi></math>-mer sets. We model the compressibility of MS using an objective that combines superstring length and the number of runs in the mask. We prove that the resulting optimization problem is NP-hard and develop a heuristic based on iterative deepening search in the Aho-Corasick automaton. Using microbial pan-genome datasets, we characterize the Pareto front in the superstring-length/mask-run space and show that the front contains points that Pareto-dominate simplitigs and matchtigs. Finally, we demonstrate that Pareto-optimized MS improve pan-genome <math><mi>k</mi></math>-mer set compressibility by 12%-19% when combined with neural-network compressors, achieving less than 1.2 bits per <math><mi>k</mi></math>-mer in common scenarios.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261491601"},"PeriodicalIF":1.3,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149136959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimizer Density Revisited: Models and Multiminimizers.","authors":"Florian Ingels, Lucas Robidou, Igor Martayan, Camille Marchet, Antoine Limasset","doi":"10.1177/15578666261495123","DOIUrl":"https://doi.org/10.1177/15578666261495123","url":null,"abstract":"<p><p>High-throughput sequence analysis commonly relies on k-mers and sampling schemes to improve scalability and data locality. Among these, local schemes are widely used and are typically evaluated by their density, the expected fraction of selected positions. Minimizers are the most common example, but recent near-tight lower bounds suggest diminishing room for improvement under the classical notion of density. Here, we revisit density and broaden its scope. First, we establish a direct link between density and the distance between consecutive selected positions. Under the sole assumption that these distances are identically distributed, we show that density is exactly the inverse of their expected distance, without assumptions on the selection mechanism. This provides a new way to analyze schemes beyond classical local models. Second, we introduce multiminimizers, a meta-scheme combining N minimizer schemes and selecting the candidate whose extends farthest. Multiminimizers are not local schemes, but under independent random component orders and locally distinct m-mers, their expected density converges exponentially to the optimum. Experiments with random minimizers and open-closed mod-minimizers confirm a controllable computation-density trade-off. Third, we introduce deduplicated density, measuring the fraction of distinct minimizers needed to cover all k-mers in a sequence set. We show that multiminimizers also improve this metric, prove that global optimization is NP-complete, and propose an effective local heuristic. Finally, we provide an efficient SIMD-accelerated Rust implementation and demonstrate reduced memory usage on core sequence-analysis tasks.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261495123"},"PeriodicalIF":1.3,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149137012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Super Bloom: Fast and Precise Filter for Streaming <i>k</i>-Mer Queries.","authors":"Etienne Conchon-Kerjan, Timothé Rouzé, Lucas Robidou, Florian Ingels, Antoine Limasset","doi":"10.1177/15578666261493564","DOIUrl":"https://doi.org/10.1177/15578666261493564","url":null,"abstract":"<p><p>Approximate membership query structures are used throughout sequence bioinformatics, from read screening and metagenomic classification to assembly, indexing, and error correction. Among them, Bloom filters remain the default choice. They are not the most efficient structures in either time or memory, but they provide an effective compromise between compactness, speed, simplicity, and dynamic insertions, which explains their widespread adoption in practice. Their main drawback is poor cache locality, since each query typically requires several random memory accesses. Blocked Bloom filters alleviate this issue by restricting accesses for any given element to a single memory block, but this usually comes with a loss in accuracy at fixed memory. In this work, we introduce the Super Bloom Filter, a Bloom filter variant designed for streaming <i>k</i>-mer queries on biological sequences. Super Bloom uses minimizers to group adjacent <i>k</i>-mers into super-<i>k</i>-mers and assigns all <i>k</i>-mers of a group to the same memory block, thereby amortizing random accesses over consecutive <i>k</i>-mer queries and improving cache efficiency. We further combine this layout with the findere scheme, which reduces false positives by requiring consistent evidence across overlapping subwords. We provide a theoretical analysis of the construction of Super Bloom filters, showing how minimizer density controls the expected reduction in memory transfers, and derive a practical parameterization strategy linking memory budget, block size, collision overhead, and the number of hash functions to robust false-positive control. Across a broad range of memory budgets and numbers of hash functions, Super Bloom consistently outperforms existing Bloom filter implementations, with several-fold time improvements. As a practical validation, we integrated it into a Rust reimplementation of BioBloom Tools, a sequence screening tool that builds filters from reference genomes and classifies reads through <i>k</i>-mer membership queries for applications such as host removal and contamination filtering. This replacement yields substantially faster indexing and querying than both the original C++ implementation and Rust variants based on Bloom filters and blocked Bloom filters. The findere scheme also reduces false positives by several orders of magnitude, with some configurations yielding no observed false positives among <math><mrow><mrow><msup><mrow><mrow><mn>10</mn></mrow></mrow><mn>9</mn></msup></mrow></mrow></math> randomly queried <i>k</i>-mers. Code is available at https://github.com/EtienneC-K/SuperBloom and https://github.com/Malfoy/SBB.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261493564"},"PeriodicalIF":1.3,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149127701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random Matrix Theory-Guided Sparse Principal Component Analysis for Single-Cell RNA-Seq Data.","authors":"Victor Chardès","doi":"10.1177/15578666261493157","DOIUrl":"https://doi.org/10.1177/15578666261493157","url":null,"abstract":"<p><p>Single-cell RNA-seq provides detailed molecular snapshots of individual cells but is notoriously noisy. Variability stems from biological differences and technical factors, such as amplification bias and limited RNA capture efficiency, making it challenging to adapt computational pipelines to heterogeneous datasets or evolving technologies. As a result, most studies still rely on principal component analysis (PCA) for dimensionality reduction, valued for its interpretability and robustness, in spite of its known bias in high dimensions. Here, we improve upon PCA with a random matrix theory (RMT)-based approach that guides the inference of sparse principal components using existing sparse PCA algorithms. We first introduce a novel biwhitening algorithm that self-consistently estimates the magnitude of transcriptomic noise affecting each gene in individual cells, without assuming a specific noise distribution. This enables the use of an RMT-based criterion to automatically select the sparsity level, rendering sparse PCA nearly parameter-free. Our mathematically grounded approach retains the interpretability of PCA while enabling robust, hands-off inference of sparse principal components. Across seven single-cell RNA-seq technologies and four sparse PCA algorithms, we show that this method systematically improves the reconstruction of the principal subspace and consistently outperforms PCA-, autoencoder-, and diffusion-based methods in cell type classification tasks.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261493157"},"PeriodicalIF":1.3,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149127684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PrimerDesigner: Designing Efficient Primers for Synthesizing Large Protein Libraries Without Cross-Hybridization.","authors":"Jonathan Mandl, Marcus Bluestone, Scott Longwell, Polly Fordyce, Yaron Orenstein","doi":"10.1177/15578666261491603","DOIUrl":"https://doi.org/10.1177/15578666261491603","url":null,"abstract":"<p><p>Efficient synthesis of large protein libraries is key to making high-throughput biochemistry scalable. Advances in DNA microarray technology now allow more than 170,000 designed oligonucleotides to be synthesized in a single array, dramatically reducing cost and enabling pooled amplification for downstream mutagenesis reactions. Effective amplification of short oligonucleotides into long coding sequences requires efficient primer design (PD) without cross-hybridization. However, no PD method currently guarantees complete coverage of the protein-coding sequences with maximum efficiency and no cross-hybridization over multiple proteins or multiple variants of the same protein. Here, we present PrimerDesigner, a suite of methods to design the most efficient primers for synthesizing large protein libraries with complete protein-coding sequence coverage and without cross-hybridization. We first prove that PD with complete coverage and without cross-hybridization is NP-hard, even for a single protein. As a solution, we formulate the PD problem using integer linear programming (ILP). When no cross-hybridization is possible, we solve the PD problem for a single protein in linear time. Moreover, we extend our ILP formulation to handle multiple variants of the same protein. We demonstrate that PrimerDesigner produces optimal solutions in only a few minutes for a single protein and within a couple of hours for multiple proteins while achieving higher efficiency than a greedy baseline approach. We expect PrimerDesigner to enable the synthesis of large protein libraries at an unprecedented scale and efficiency.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261491603"},"PeriodicalIF":1.3,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149127634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"stPularity: Spatial Domain Identification via Purity-Constrained Louvain Clustering on Spatially Smoothed Cell Type Proportions.","authors":"Chandra Sekhar Reddy Edula, Yuxing Sun, Xiuwei Zhang","doi":"10.1177/15578666261491611","DOIUrl":"https://doi.org/10.1177/15578666261491611","url":null,"abstract":"<p><p>Deconvolution of spatial transcriptomics data returns, for every spot, a full vector of cell type proportions, yet standard spatial domain identification keeps only the dominant cell type assignment (argmax) and discards the remaining composition signal. We present stPularity, a post-deconvolution clustering tool that operates directly on the proportion vectors a deconvolution pipeline already produces: it builds a k-nearest neighbor graph on spatially smoothed cell type proportions and applies a modified Louvain procedure whose objective trades off modularity against a normalized-entropy purity term, automatically determining the number of clusters without ground-truth labels. Its few hyperparameters are set automatically by Bayesian optimization over label-free internal criteria, so the pipeline runs without manual resolution selection. Because stPularity reads only the proportion matrix and spatial coordinates, it is agnostic to the deconvolution method and operates in the low dimensionality of the cell type panel (4-38 cell types). Across four benchmarks, stPularity improves over the dominant-cell-type baseline on three of four datasets, raising the adjusted Rand index (ARI) over argmax by 0.14-0.26. On three of the four datasets, this advantage over argmax persists or widens as the deconvolution proportions are corrupted with increasing noise. An ablation shows that proportion vectors and gene-expression principal component analysis (PCA) embeddings are complementary rather than redundant, their relative strength depending on whether spatial structure is compositional or transcriptomic. Expression-based spatial methods attain higher absolute ARI by reprocessing the full transcriptome; stPularity, instead, targets a distinct goal: recovering interpretable spatial structure from the composition vectors that the argmax workflow would otherwise discard.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261491611"},"PeriodicalIF":1.3,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}