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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

Single-cell Transcriptomes Reveal Characteristics of MicroRNAs in Gene Expression Noise Reduction

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Genomics, Proteomics & bioinformatics 2021年第3期19卷 394-407页

作者： Tao Hu Lei Wei Shuailin Li Tianrun Cheng Xuegong Zhang 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 UniversityBeijing 100084China

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene *** levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell *** this work,we showed evidence from single-cell RNA sequencing data analysis that microRNAs(miRNAs)can reduce gene expression noise at the mRNA level in mouse *** identified that the miRNA expression level,number of targets,target pool abundance,and miRNA-target interaction strength are the key features contributing to noise *** tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific *** building a physical model of post-transcriptional regulation and observing in synthetic gene circuits,we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic ***,through the integrated analysis of single-cell RNA and miRNA expression profiles,we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

关键词： MicroRNA regulation Gene expression noise Competing RNA microRNA regulation network Singlecell RNA sequencing

Sequence signatures of genes with accompanying antisense transcripts in Saccharomyces cerevisiae

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Science China(Life Sciences) 2014年第1期57卷 52-58页

作者： LI YingXue LIU XueNing WANG XiaoWo ZHANG XueGong MOE Key Laboratory of bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology TNLIST/Department of Automation Tsinghua University School of Life Sciences Tsinghua University

Recent studies have found many antisense non-coding transcripts at the opposite strand of some protein-coding *** yeast,it was reported that such antisense transcripts play regulatory roles for their partner genes by forming a feedback loop with the protein-coding *** not all coding genes have accompanying antisense transcripts,it would be interesting to know whether there are sequence signatures in a coding gene that are decisive or associated with the existence of such antisense *** collected all the annotated antisense transcripts in the yeast Saccharomyces cerevisiae,analyzed sequence motifs around the genes with antisense partners,and classified genes with and without accompanying antisense transcripts by using machine learning *** weak but statistically significant sequence features are detected,which indicates that there are sequence signatures around the protein-coding genes that may be decisive or indicative for the existence of accompanying antisense transcripts.

关键词： antisense transcript pattern recognition motif finding yeast

Dropout training for SVMs with data augmentation

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Frontiers of Computer Science 2018年第4期12卷 694-713页

作者： Ning CHEN Jun ZHU Jianfei CHEN Ting CHEN MOE Key lab of bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology TNLIST Tsinghua University Beijing 100084 China State Key Lab of Intelligent Technology and systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China

Dropout and other feature noising schemes have shown promise in controlling over-fitting by artificially corrupting the training data. Though extensive studies have been performed for generalized linear models, little has been done for support vector machines （SVMs）, one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square （IRLS） algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re- weighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we con- sider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected hinge loss and the nonlinearity of latent representa- tions. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.

关键词： dropout SVMs logistic regression data aug- mentation iteratively reweighted least square

Whole genome sequencing and its applications in medical genetics

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Frontiers of Electrical and Electronic Engineering in China 2016年第2期4卷 115-128页

作者： Jiaxin Wu Mengmeng Wu Ting Chen Rui Jiang MOE Key Laboratory of bioinformatics Bioinformatics Division and Center for Synthetic&Systems BiologyTNLIST Department of Automation Tsinghua UniversityBeijing 100084China

Fundamental improvement was made for genome sequencing since the next-generation sequencing （NGS） came out in the 2000s. The newer technologies make use of the power of massively-parallel short-read DNA sequencing, genome alignment and assembly methods to digitally and rapidly search the genomes on a revolutionary scale, which enable large-scale whole genome sequencing （WGS） accessible and practical for researchers. Nowadays, whole genome sequencing is more and more prevalent in detecting the genetics of diseases, studying causative relations with cancers, making genome-level comparative analysis, reconstruction of human population history, and giving clinical implications and instructions. In this review, we first give a typical pipeline of whole genome sequencing, including the lab template preparation, sequencing, genome assembling and quality control, variants calling and annotations. We compare the difference between whole genome and whole exome sequencing （WES）, and explore a wide range of applications of whole genome sequencing for both mendelian diseases and complex diseases in medical genetics. We highlight the impact of whole genome sequencing in cancer studies, regulatory variant analysis, predictive medicine and precision medicine, as well as discuss the challenges of the whole genome sequencing.

关键词： whole genome sequencing whole exome sequencing next-generation sequencing non-coding regulatoryvariant

Toward a unified information framework for cell atlas assembly

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National Science Review 2022年第3期9卷 7-10页

作者： Sijie Chen Yanting Luo Haoxiang Gao Fanhong Li Jiaqi Li Yixin Chen Renke You Hairong Lv Kui Hua Rui Jiang Xuegong Zhang bioinformatics division of BNRIST and Department of Automation MOE Key Lab of BioinformaticsTsinghua University Fuzhou Institute of Data Technology School of Medicine Tsinghua University School of Life Sciences Center for Synthetic and Systems Biology Tsinghua University

Cells are the basic structural and functional unit of the human body. Virtually all cells of a human body possess the same genome, but they exhibit colorful heterogeneities in phenotypes and *** heterogeneities includ...

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Differential expression analyses for single-cell RNA-Seq:old questions on new data

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Quantitative biology 2016年第4期4卷 243-260+336页

作者： Zhun Miao Xuegong Zhang MOE Key Laboratory of bioinformatics bioinformatics division and center for synthetic&systems biology TNLIST Department of Automation Tsinghua University School of Life Sciences Tsinghua University

Background:Single-cell RNA sequencing(scRNA-seq)is an emerging technology that enables high resolution detection of heterogeneities between *** important application of scRNA-seq data is to detect differential expression(DE)of ***,some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data,and some new methods for scRNA-seq data have also been *** and single-cell RNA-seq data have different characteristics.A systematic evaluation of the two types of methods on scRNA-seq data is ***:In this study,we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods,including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq *** obtained observations and recommendations for the methods under different ***:DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of *** strategies should be taken for data with different sample sizes and/or different strengths of the expected *** methods for scRNA-seq data show advantages in some aspects,and DEGSeq tends to outperform other methods with respect to consistency,reproducibility and accuracy of predictions on scRNA-seq data.

关键词： single-cell RNA-Seq differential expression

Construction, characterization and application of a genomewide promoter library in Saccharomyces cerevisiae

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Frontiers of Chemical Science and Engineering 2017年第1期11卷 107-116页

作者： Ting Yuan Yakun Guo Junkai Dong Tianyi Li Tong Zhou Kaiwen Sun Mei Zhang Qingyu Wu Zhen Xie Yizhi Cai Limin Cao Junbiao Dai MOE Key Laboratory of bioinformatics and Centre for synthetic and systems biology School of Life Sciences Tsinghua University Beijing 100084 China College of Life Sciences Capital Normal University Beijing 100048 China MOE Key Laboratory of bioinformatics and bioinformatics division Center for Synthetic and Systems Biology TNLIST/Department of Automation Tsinghua University Beijing 100084 China School of Biological Sciences The King's Buildings University of Edinburgh Edinburgh EH9 3BF UK

Promoters are critical elements to control gene expression but could behave differently under various growth conditions. Here we report the construction of a genome-wide promoter library, in which each native promoter in Saccharomyces cerevisiae was cloned upstream of a yellow fluorescent protein （YFP） reporter gene. Nine libraries were arbitrarily defined and assembled in bacteria. The resulting pools of promoters could be prepared and transformed into a yeast strain either as centromeric plasmids or integrated into a genomic locus upon enzymatic treatment. Using fluorescence activated cell sorting, we classified the yeast strains based on YFP fluorescence intensity and arbitrarily divided the entire library into 12 bins, representing weak to strong promoters. Several strong promoters were identified from the most active bins and their activities were assayed under different growth conditions. Finally, these promoters were applied to drive the expression of genes in xylose utilization to improve fermentation efficiency. Together, this library could provide a quick solution to identify and utilize desired promoters under user-defined growth conditions.

关键词： synthetic biology yeast promoter activity,metabolic engineering xylose utilization

OP-synthetic： identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

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Frontiers of Electrical and Electronic Engineering in China 2014年第3期9卷 100-109页

作者： Honglei Liu Yanda Li Xiaowo Wang MOE Key Laboratory of bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology TNLIST/Department of Automation Tsinghua University Beijing100084 China

Constraint-based flux analysis has been widely used in metabolic engineering to predict genetic optimization strategies. These methods seek to find genetic manipulations that maximally couple the desired metabolites with the cellular growth objective. However, such framework does not work well for overproducing chemicals that are not closely correlated with biomass, for example non-native biochemical production by introducing synthetic pathways into heterologous host cells. Here, we present a computational method called OP-synthetic, which can identify effective manipulations （upregulation, downregulation and deletion of reactions） and produce a step-by-step optimization strategy for the overproduction of indigenous and non-native chemicals. We compared OP-synthetic with several state-of-the-art computational approaches on the problems of succinate overproduction and N- acetylneuraminic acid synthetic pathway optimization in Escherichia coli. OP-synthetic showed its advantage for efficiently handling multiple steps optimization problems on genome wide metabolic networks. And more importantly, the optimization strategies predicted by OP-synthetic have a better match with existing engineered strains, especially for the engineering of synthetic metabolic pathways for non-native chemical production. OP- synthetic is freely available at：http：//***/member/xwwang/OPsynthetic/.

关键词： metabolic network flux analysis optimization