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DIProT:A deep learning based interactive toolkit for efficient and effective Protein design

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Synthetic and Systems Biotechnology 2024年第2期9卷 217-222页

作者： Jieling He Wenxu Wu Xiaowo Wang Ministry of Education Key laboratory of bioinformatics Center for Synthetic and Systems BiologyBioinformatics DivisionBeijing National Research Center for Information Science and TechnologyDepartment of AutomationTsinghua UniversityBeijingChina

The protein inverse folding problem,designing amino acid sequences that fold into desired protein structures,is a critical challenge in biological *** numerous data-driven and knowledge-driven methods,there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein *** this paper,we present DIProT,an interactive protein design *** leverages a non-autoregressive deep generative model to solve the inverse folding problem,combined with a protein structure prediction *** integration allows users to incorporate prior knowledge into the design process,evaluate designs in silico,and form a virtual design loop with human *** inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets,with promising sequence recovery and inference *** studies further illustrate how DIProT can facilitate user-guided protein design.

关键词： Computational Protein design Interactive design toolkit Protein inverse folding Non-autoregressive decoding

3D genome structural variations play important roles in regulating seed oil content of Brassica napus

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Plant Communications 2024年第1期5卷 101-115页

作者： Libin Zhang Lin Liu Huaixin Li Jianjie He Hongbo Chao Shuxiang Yan Yontai Yin Weiguo Zhao Maoteng Li College of Life Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Wuhan Frasergen bioinformatics Co. Ltd.Wuhan 430075China Key laboratory of Molecular Biophysics of the Ministry of Education Hubei Bioinformatics and Molecular Imaging Key LaboratoryWuhan 430074China

Dissecting the complex regulatory mechanism of seed oil content(SOC)is one of the main research goals in Brassica *** evidence suggests that genome architecture is linked to multiple biological ***,the effect of genome architecture on SOC regulation remains ***,we used high-throughput chromatin conformation capture to characterize differences in the three-dimen-sional(3D)landscape of genome architecture of seeds from two *** lines,N53-2(with high SOC)and Ken-C8(with low SOC).bioinformatics analysis demonstrated that differentially accessible regions and differentially expressed genes between N53-2 and Ken-C8 were preferentially enriched in regions with quantitative trait loci(QTLs)/associated genomic regions(AGRs)for SOC.A multi-omics analysis demonstrated that expression of SOC-related genes was tightly correlated with genome structural varia-tions in QTLs/AGRs of *** candidate gene BnaA09g48250D,which showed structural variation in a QTL/AGR on chrA09,was identified byfine-mapping of a KN double-haploid population derived from hybridization of N53-2 and *** and knockout of BnaA09g48250D led to significant in-creases and decreases in SOC,respectively,in the transgenic *** together,our results reveal the 3D genome architecture of *** seeds and the roles of genome structural variations in SOC regulation,enriching our understanding of the molecular mechanisms of SOC regulation from the perspective of spatial chromatin structure.

关键词： Brassica napus seed oil content Hi-C QTLfine-mapping genome structural variation

Refining biome labeling for large-scale microbial community samples:Leveraging neural networks and transfer learning

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Environmental Science and Ecotechnology 2024年第1期17卷 128-137页

作者： Nan Wang Teng Wang Kang Ning Key laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingCenter of AI BiologyDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan430074HubeiChina

Microbiome research has generated an extensive amount of data,resulting in a wealth of publicly accessible *** annotation of these samples is crucial for effectively utilizing microbiome data across scientific ***,a notable challenge arises from the lack of essential annotations,particularly regarding collection location and sample biome information,which significantly hinders environmental microbiome *** this study,we introduce Meta-Sorter,a novel approach utilizing neural networks and transfer learning,to enhance biome labeling for thousands of microbiome samples in the MGnify database that have incomplete *** findings demonstrate that Meta-Sorter achieved a remarkable accuracy rate of 96.7%in classifying samples among the 16,507 lacking detailed biome ***,Meta-Sorter provides precise classifications for representative environmental samples that were previously ambiguously labeled as“Marine”in MGnify,thereby elucidating their specific origins in benthic and water column ***,Meta-Sorter effectively distinguishes samples derived from human-environment interactions,enabling clear differentiation between environmental and human-related *** improving the completeness of biome label information for numerous microbial community samples,our research facilitates more accurate knowledge discovery across diverse disciplines,with particular implications for environmental research.

关键词： Microbial community Transfer learning Sample classification Environmental scientific research Novel knowledge discovery

EMT transcription factors activated circuits:A novel tool to study EMT dynamics and its therapeutic implications

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Synthetic and Systems Biotechnology 2024年第1期9卷 1-10页

作者： Tianying Chen Wangyue Jia Bo Zhang Hanqi Xie Qiong Wu MOE Key laboratory of bioinformatics Center for Synthetic and System BiologySchool of Life SciencesTsinghua UniversityBeijing100084China

The epithelial mesenchymal transition(EMT)plays significant roles in the progression of cancer and fibrotic ***,this process is reversible,resulting in mesenchymal epithelial transition(MET),which plays an important role in cancer *** is a lack of methods to trace and target EMT cells using synthetic biology circuits,which makes it difficult to study the cell fate or develop targeted *** this study,we introduced responsive EMT sensing circuits,which sense the EMT using specific promoters that respond to transcription factors typical of EMT activation(EMT-TFs).The transcriptional strength of EMT-sensing promoters decreased more than 13-fold in response to the overexpression of the ***,the NOT gate circuits were built by placing the tetR transcription repressor under the control of EMT sensing promoters and expressed an output signal using the constitutive CMV promoter modified with tetO sites This circuit is named EMT sensing and responding *** the EMT transcription factors was present,we observed a 5.8-fold signal increase in the ***,we successfully distinguished mesenchymal breast cancer cells from epithelial cancer cells and repressed the proliferation of EMT tumor cells using our *** EMT sensing and responding circuits are promising tools for the identification of EMT cells,which is crucial for EMT-related disease therapy and investigating the mechanisms underlying the reversible EMT process.

关键词： Epithelial mesenchymal transition Synthetic biology Tumor killing

Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach

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Big Data Mining and Analytics 2024年第1期7卷 107-130页

作者： Nadezhda Yu.Biziukova Sergey M.Ivanov Oga A.Tarasova Department of bioinformatics Institute of Biomedical ChemistryMoscow 119121Russia Department of bioinformatics Pirogov Russian National Research Medical UniversityMoscow 117997Russia

Analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine,because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment *** of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic *** accumulation of various types of biological data,including interactions between proteins,regulation of genes transcription,proteomics,and metabolomics experiments results,allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic *** aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components,as well as for identifying their roles in the development of various types of *** automatically collect sets of abstract texts from the NCBI PubMed bibliographic *** recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach,while for all other proteins and genes machine learning method is *** association extraction,we develop a set of semantic *** complete the results of the text analysis with the gene set enrichment *** identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.

关键词： text-mining data mining Hedgehog pathway neoplastic processes enrichment analysis pathology molecularmechanisms

An enhanced method for predicting and analysing forest fires using an attention-based CNN model

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Journal of Forestry Research 2024年第4期35卷 115-127页

作者： Shaifali Bhatt Usha Chouhan Department of Mathematics Bioinformatics and Computer ApplicationsMANIT BhopalBhopalIndia

Prediction,prevention,and control of forest fires are crucial on at all *** effective fire detection systems can aid in their *** study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural *** suggested model focuses on predicting the damage affected/burned areas due to possible wildfires and evaluating the multilateral interactions between the pertinent *** results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorological *** selected meteorological data,RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational *** findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.

关键词： CNN Attention module Fire prediction Ecosystem Damage prediction

Oryza CLIMtools:A genome–environment association resource reveals adaptive roles for heterotrimeric G proteins in the regulation of rice agronomic traits

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Plant Communications 2024年第4期5卷 145-165页

作者： Angel Ferrero-Serrano David Chakravorty Kobie J.Kirven Sarah M.Assmann Biology Department Pennsylvania State University208 Mueller LaboratoryUniversity ParkPA 16802USA Intercollege Graduate Degree Program in bioinformatics and Genomics Pennsylvania State University208 Mueller LaboratoryUniversity ParkPA 16802USA

Modern crop varieties display a degree of mismatch between their current distributions and the suitability of the local climate for their *** address this issue,we present Oryza CLIMtools(https://***/CLIMtools/oryza_v1.0/),thefirst resource for pan-genome prediction of climate-associated genetic variants in a crop *** CLIMtools consists of interactive web-based databases that enable the user to(1)explore the local environments of traditional rice varieties(landraces)in South-East Asia and(2)investigate the environment by genome associations for 658 Indica and 283 Japonica rice landrace accessions collected from georeferenced local environments and included in the 3K Rice Ge-nomes *** demonstrate the value of these resources by identifying an interplay betweenflowering time and temperature in the local environment that is facilitated by adaptive natural variation in OsHD2 and disrupted by a natural variant in *** quantitative trait locus analysis has suggested the impor-tance of heterotrimeric G proteins in the control of agronomic ***,we analyzed the climate associations of natural variants in the different heterotrimeric G protein *** identified a coordi-nated role of G proteins in adaptation to the prevailing potential evapotranspiration gradient and revealed their regulation of key agronomic traits,including plant height and seed and panicle *** conclude by highlighting the prospect of targeting heterotrimeric G proteins to produce climate-resilient crops.

关键词： Oryza sativa rice G by E adaptation flowering time GWAS OsHD2 OsSOC1 heterotrimeric G pro-tein OsRGA1 d1 OsGS3 OsDEP1 OsGGC2

KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

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Big Data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key laboratory of bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data

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Journal of Genetics and Genomics 2024年第4期51卷 462-465页

作者： Zijing Gao Xiaoyang Chen Zhen Li Xuejian Cui Qun Jiang Keyi Li Shengquan Chen Rui Jiang Ministry of Education Key laboratory of bioinformatics Bioinformatics Division at the Beijing National Research Center for Information Science and TechnologyCenter for Synthetic and Systems BiologyDepartment of AutomationTsinghua UniversityBeijing 100084China School of Mathematical Sciences and LPMC Nankai UniversityTianjin 300071China

Recent advances in single-cell sequencing technologies provide significant implications for understanding cellular heterogeneity,developmental biology,and disease *** fully exploit the potential of these data,numerous tools have been proposed for upstream and downstream *** the the single-cell RNA sequencing(scRNA-seq)community,scRNA-tools(Zappia et al.,2018)was proposed to help researchers navigate the plethora of tools by *** its inception,scRNA-tools has been widely used and its updated version further reveals trends in the field with over 1000 collected tools(Zappia and Theis,2021),providing a valuable guidance in selecting tools for analyses.

关键词： analytic selecting Tools