UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study

Trozzi, Francesco; Wang, Xinlei; Tao, Peng

doi:10.1021/acs.jpcb.1c02081

Cited by 64 publications

(58 citation statements)

References 64 publications

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“…The top 1500 dispersed genes were used to perform PCA using the RunPCA function. To reduce the dimension, we chose UMAP (Uniform Manifold Approximation and Projection), which has a similar visualization quality to the tSNE (t-Distributed Stochastic Neighbor Embedding) but may preserve more of the global structure [ 26 ]. Through UMAP analyses, the dimension of the first 25 principal components was reduced into 2 components using the RunUMAP function with the parameters n.neighbors = 50 and min.dist = 0.1.…”

Section: Methodsmentioning

confidence: 99%

Single-Cell Transcriptome and Network Analyses Unveil Key Transcription Factors Regulating Mesophyll Cell Development in Maize

Tao

Liu

Shi

et al. 2022

Genes

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Background: Maize mesophyll (M) cells play important roles in various biological processes such as photosynthesis II and secondary metabolism. Functional differentiation occurs during M-cell development, but the underlying mechanisms for regulating M-cell development are largely unknown. Results: We conducted single-cell RNA sequencing (scRNA-seq) to profile transcripts in maize leaves. We then identified coregulated modules by analyzing the resulting pseudo-time-series data through gene regulatory network analyses. WRKY, ERF, NAC, MYB and Heat stress transcription factor (HSF) families were highly expressed in the early stage, whereas CONSTANS (CO)-like (COL) and ERF families were highly expressed in the late stage of M-cell development. Construction of regulatory networks revealed that these transcript factor (TF) families, especially HSF and COL, were the major players in the early and later stages of M-cell development, respectively. Integration of scRNA expression matrix with TF ChIP-seq and Hi-C further revealed regulatory interactions between these TFs and their targets. HSF1 and COL8 were primarily expressed in the leaf bases and tips, respectively, and their targets were validated with protoplast-based ChIP-qPCR, with the binding sites of HSF1 being experimentally confirmed. Conclusions: Our study provides evidence that several TF families, with the involvement of epigenetic regulation, play vital roles in the regulation of M-cell development in maize.

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Section: Methodsmentioning

confidence: 99%

Single-Cell Transcriptome and Network Analyses Unveil Key Transcription Factors Regulating Mesophyll Cell Development in Maize

Tao

Liu

Shi

et al. 2022

Genes

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“…The correlation-based metrics have been widely applied in the comparison between dimensionality reduction methods for biomolecules ( Tian and Tao, 2020 ; Trozzi et al, 2021 ). They are used here for the encoder module to measure how well the information is preserved in the latent space.…”

Section: Methodsmentioning

confidence: 99%

Explore Protein Conformational Space With Variational Autoencoder

Tian

Jiang

Trozzi

et al. 2021

Front. Mol. Biosci.

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Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.

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“…To elucidate the biological phenotype regulated by the NMRGs in the blood of ALS patients, the unsupervised consensus clustering (R package: ConsensuClusterPlus) (Wilkerson and Hayes, 2010) was used to obtain clusters based on "pam" method with 1,000 iterations and resample rate of 80%. Principal component analysis (PCA) and uniform manifold approximation and projection for dimension reduction (UMAP) (Trozzi et al, 2021) methods were used to evaluate gene expression patterns in peripheral blood of different subclusters of ALS. The GSEA software version: 4.1.0 (https://www.gsea-msigdb.org/gsea/index.…”

Section: Additional Bioinformatic Analysesmentioning

confidence: 99%

Circulating NAD+ Metabolism-Derived Genes Unveils Prognostic and Peripheral Immune Infiltration in Amyotrophic Lateral Sclerosis

Zhu

Chen

et al. 2022

Front. Cell Dev. Biol.

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Background: Nicotinamide adenine dinucleotide (NAD+) metabolism has drawn more attention on neurodegeneration research; however, the role in Amyotrophic Lateral Sclerosis (ALS) remains to be fully elucidated. Here, the purpose of this study was to investigate whether the circulating NAD+ metabolic-related gene signature could be identified as a reliable biomarker for ALS survival.Methods: A retrospective analysis of whole blood transcriptional profiles and clinical characteristics of 454 ALS patients was conducted in this study. A series of bioinformatics and machine-learning methods were combined to establish NAD+ metabolic-derived risk score (NPRS) to predict overall survival for ALS patients. The associations of clinical characteristic with NPRS were analyzed and compared. Receiver operating characteristic (ROC) and the calibration curve were utilized to assess the efficacy of prognostic model. Besides, the peripheral immune cell infiltration was assessed in different risk subgroups by applying the CIBERSORT algorithm.Results: Abnormal activation of the NAD+ metabolic pathway occurs in the peripheral blood of ALS patients. Four subtypes with distinct prognosis were constructed based on NAD+ metabolism-related gene expression patterns by using the consensus clustering method. A comparison of the expression profiles of genes related to NAD+ metabolism in different subtypes revealed that the synthase of NAD+ was closely associated with prognosis. Seventeen genes were selected to construct prognostic risk signature by LASSO regression. The NPRS exhibited stronger prognostic capacity compared to traditional clinic-pathological parameters. High NPRS was characterized by NAD+ metabolic exuberant with an unfavorable prognosis. The infiltration levels of several immune cells, such as CD4 naive T cells, CD8 T cells, neutrophils and macrophages, are significantly associated with NPRS. Further clinicopathological analysis revealed that NPRS is more appropriate for predicting the prognostic risk of patients with spinal onset. A prognostic nomogram exhibited more accurate survival prediction compared with other clinicopathological features.Conclusions: In conclusion, it was first proposed that the circulating NAD+ metabolism-derived gene signature is a promising biomarker to predict clinical outcomes, and ultimately facilitating the precise management of patients with ALS.

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UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study

Cited by 64 publications

References 64 publications

Single-Cell Transcriptome and Network Analyses Unveil Key Transcription Factors Regulating Mesophyll Cell Development in Maize

Single-Cell Transcriptome and Network Analyses Unveil Key Transcription Factors Regulating Mesophyll Cell Development in Maize

Explore Protein Conformational Space With Variational Autoencoder

Circulating NAD+ Metabolism-Derived Genes Unveils Prognostic and Peripheral Immune Infiltration in Amyotrophic Lateral Sclerosis

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