scholarly journals CRSIPR-A-I: a webtool for the efficacy prediction of CRISPR activation and interference

2021 ◽  
Author(s):  
Xiao Zheng ◽  
Jiajun Cui ◽  
Yixuan Wang ◽  
Jing Zhang ◽  
Chaochen Wang
Keyword(s):  
Gene Activation ◽  
K562 Cells ◽  
Mathematic Model ◽  
Rna Seq ◽  
Reliable Prediction ◽  
Wide Range ◽  
Endogenous Genes ◽  
User Friendly ◽  
Efficacy Prediction ◽  
Crispr Activation

AbstractCRISPR-based gene activation (CRISPRa) or interference (CRISPRi) are powerful and easy-to-use approaches to modify the transcription of endogenous genes in eukaryotes. Successful CRISPRa/i requires sgRNA binding and alteration of local chromatin structure, hence largely depends on the original epigenetic status of the target. Consequently, the efficacy of the CRISPRa/i varies in a wide range when applied to target different gene loci, while a reliable prediction tool is unavailable. To address this problem, we integrated published single cell RNA-Seq data involved CRISPRa/i and epigenomic profiles from K562 cells, identified the significant epigenetic features contributing to CRISPRa/i efficacy by ranking the weight of each feature. We further established a mathematic model and built a user-friendly webtool to predict the CRISPRa/i efficacy of customer-designed sgRNA in different cells. Moreover, we experimentally validated our model by employing CROP-Seq assays. Our work provides both the epigenetic insights into CRISPRa/i and an effective tool for the users.

scholarly journals Reproducible Bioconductor workflows using browser-based interactive notebooks and containers

2017 ◽  
Vol 25 (1) ◽  
pp. 4-12 ◽  
Author(s):  
Reem Almugbel ◽  
Ling-Hong Hung ◽  
Jiaming Hu ◽  
Abeer Almutairy ◽  
Nicole Ortogero ◽  
...  
Keyword(s):  
Interactive Software ◽  
Rna Seq ◽  
Software Environment ◽  
Bioinformatics Analyses ◽  
New Methods ◽  
Wide Range ◽  
Cross Platform ◽  
Differential Gene ◽  
User Friendly ◽  
Bioinformatics Workflows

Abstract Objective Bioinformatics publications typically include complex software workflows that are difficult to describe in a manuscript. We describe and demonstrate the use of interactive software notebooks to document and distribute bioinformatics research. We provide a user-friendly tool, BiocImageBuilder, that allows users to easily distribute their bioinformatics protocols through interactive notebooks uploaded to either a GitHub repository or a private server. Materials and methods We present four different interactive Jupyter notebooks using R and Bioconductor workflows to infer differential gene expression, analyze cross-platform datasets, process RNA-seq data and KinomeScan data. These interactive notebooks are available on GitHub. The analytical results can be viewed in a browser. Most importantly, the software contents can be executed and modified. This is accomplished using Binder, which runs the notebook inside software containers, thus avoiding the need to install any software and ensuring reproducibility. All the notebooks were produced using custom files generated by BiocImageBuilder. Results BiocImageBuilder facilitates the publication of workflows with a point-and-click user interface. We demonstrate that interactive notebooks can be used to disseminate a wide range of bioinformatics analyses. The use of software containers to mirror the original software environment ensures reproducibility of results. Parameters and code can be dynamically modified, allowing for robust verification of published results and encouraging rapid adoption of new methods. Conclusion Given the increasing complexity of bioinformatics workflows, we anticipate that these interactive software notebooks will become as necessary for documenting software methods as traditional laboratory notebooks have been for documenting bench protocols, and as ubiquitous.

scholarly journals Reproducible Bioconductor Workflows Using Browser-based Interactive Notebooks and Containers

2017 ◽  
Author(s):  
Reem Almugbel ◽  
Ling-Hong Hung ◽  
Jiaming Hu ◽  
Abeer Almutairy ◽  
Nicole Ortogero ◽  
...  
Keyword(s):  
Interactive Software ◽  
Rna Seq ◽  
Software Environment ◽  
Bioinformatics Analyses ◽  
New Methods ◽  
Wide Range ◽  
Cross Platform ◽  
Differential Gene ◽  
User Friendly ◽  
Bioinformatics Workflows

ABSTRACTObjectiveBioinformatics publications typically include complex software workflows that are difficult to describe in a manuscript. We describe and demonstrate the use of interactive software notebooks to document and distribute bioinformatics research. We provide a user-friendly tool, BiocImageBuilder, to allow users to easily distribute their bioinformatics protocols through interactive notebooks uploaded to either a GitHub repository or a private server.Materials and methodsWe present three different interactive Jupyter notebooks using R and Bioconductor workflows to infer differential gene expression, analyze cross-platform datasets and process RNA-seq data. These interactive notebooks are available on GitHub. The analytical results can be viewed in a browser. Most importantly, the software contents can be executed and modified. This is accomplished using Binder, which runs the notebook inside software containers, thus avoiding the need for installation of any software and ensuring reproducibility. All the notebooks were produced using custom files generated by BiocImageBuilder.ResultsBiocImageBuilder facilitates the publication of workflows with a point-and-click user interface. We demonstrate that interactive notebooks can be used to disseminate a wide range of bioinformatics analyses. The use of software containers to mirror the original software environment ensures reproducibility of results. Parameters and code can be dynamically modified, allowing for robust verification of published results and encouraging rapid adoption of new methods.ConclusionGiven the increasing complexity of bioinformatics workflows, we anticipate that these interactive software notebooks will become as ubiquitous and necessary for documenting software methods as traditional laboratory notebooks have been for documenting bench protocols.

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals User-friendly, scalable tools and workflows for single-cell RNA-seq analysis

2021 ◽  
Author(s):  
Pablo Moreno ◽  
Ni Huang ◽  
Jonathan R. Manning ◽  
Suhaib Mohammed ◽  
Andrey Solovyev ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
User Friendly

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

scholarly journals Pooled CRISPR-activation screening coupled with single-cell RNA-seq in mouse embryonic stem cells

2021 ◽  
Vol 2 (2) ◽  
pp. 100426
Author(s):  
Celia Alda-Catalinas ◽  
Melanie A. Eckersley-Maslin ◽  
Wolf Reik
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Single Cell ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cells ◽  
Rna Seq ◽  
Crispr Activation

scholarly journals A Transcriptomic Approach to the Metabolism of Tetrapyrrolic Photosensitizers in a Marine Annelid

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3924
Author(s):  
Maria Leonor Santos ◽  
Mariaelena D’Ambrosio ◽  
Ana P. Rodrigo ◽  
A. Jorge Parola ◽  
Pedro M. Costa
Keyword(s):  
Metabolic Pathways ◽  
Molecular Networks ◽  
Bile Pigments ◽  
Rna Seq ◽  
The Past ◽  
Wide Range ◽  
Genes Encoding ◽  
Organ Specific ◽  
Bright Green ◽  
Green Coloration

The past decade has seen growing interest in marine natural pigments for biotechnological applications. One of the most abundant classes of biological pigments is the tetrapyrroles, which are prized targets due their photodynamic properties; porphyrins are the best known examples of this group. Many animal porphyrinoids and other tetrapyrroles are produced through heme metabolic pathways, the best known of which are the bile pigments biliverdin and bilirubin. Eulalia is a marine Polychaeta characterized by its bright green coloration resulting from a remarkably wide range of greenish and yellowish tetrapyrroles, some of which have promising photodynamic properties. The present study combined metabolomics based on HPLC-DAD with RNA-seq transcriptomics to investigate the molecular pathways of porphyrinoid metabolism by comparing the worm’s proboscis and epidermis, which display distinct pigmentation patterns. The results showed that pigments are endogenous and seemingly heme-derived. The worm possesses homologs in both organs for genes encoding enzymes involved in heme metabolism such as ALAD, FECH, UROS, and PPOX. However, the findings also indicate that variants of the canonical enzymes of the heme biosynthesis pathway can be species- and organ-specific. These differences between molecular networks contribute to explain not only the differential pigmentation patterns between organs, but also the worm’s variety of novel endogenous tetrapyrrolic compounds.

scholarly journals Epigenetic control via allosteric regulation of mammalian protein arginine methyltransferases

2017 ◽  
Vol 114 (38) ◽  
pp. 10101-10106 ◽  
Author(s):  
Kanishk Jain ◽  
Cyrus Y. Jin ◽  
Steven G. Clarke
Keyword(s):  
Cancer Metastasis ◽  
Gene Activation ◽  
Kinetic Studies ◽  
Arginine Methylation ◽  
Histone H4 ◽  
Protein Arginine Methyltransferases ◽  
Wide Range ◽  
Substrate Concentrations

Arginine methylation on histones is a central player in epigenetics and in gene activation and repression. Protein arginine methyltransferase (PRMT) activity has been implicated in stem cell pluripotency, cancer metastasis, and tumorigenesis. The expression of one of the nine mammalian PRMTs, PRMT5, affects the levels of symmetric dimethylarginine (SDMA) at Arg-3 on histone H4, leading to the repression of genes which are related to disease progression in lymphoma and leukemia. Another PRMT, PRMT7, also affects SDMA levels at the same site despite its unique monomethylating activity and the lack of any evidence for PRMT7-catalyzed histone H4 Arg-3 methylation. We present evidence that PRMT7-mediated monomethylation of histone H4 Arg-17 regulates PRMT5 activity at Arg-3 in the same protein. We analyzed the kinetics of PRMT5 over a wide range of substrate concentrations. Significantly, we discovered that PRMT5 displays positive cooperativity in vitro, suggesting that this enzyme may be allosterically regulated in vivo as well. Most interestingly, monomethylation at Arg-17 in histone H4 not only raised the general activity of PRMT5 with this substrate, but also ameliorated the low activity of PRMT5 at low substrate concentrations. These kinetic studies suggest a biochemical explanation for the interplay between PRMT5- and PRMT7-mediated methylation of the same substrate at different residues and also suggest a general model for regulation of PRMTs. Elucidating the exact relationship between these two enzymes when they methylate two distinct sites of the same substrate may aid in developing therapeutics aimed at reducing PRMT5/7 activity in cancer and other diseases.

scholarly journals RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans

2020 ◽  
Vol 21 (8) ◽  
pp. 2748 ◽  
Author(s):  
Ruth Barral-Arca ◽  
Alberto Gómez-Carballa ◽  
Miriam Cebey-López ◽  
María José Currás-Tuala ◽  
Sara Pischedda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Viral Infections ◽  
Umbilical Vein ◽  
Cell Types ◽  
Dermal Fibroblasts ◽  
Learning Approaches ◽  
Rna Seq ◽  
Wide Range ◽  
Healthy Control ◽  
Umbilical Vein Endothelial Cells

There is a growing interest in unraveling gene expression mechanisms leading to viral host invasion and infection progression. Current findings reveal that long non-coding RNAs (lncRNAs) are implicated in the regulation of the immune system by influencing gene expression through a wide range of mechanisms. By mining whole-transcriptome shotgun sequencing (RNA-seq) data using machine learning approaches, we detected two lncRNAs (ENSG00000254680 and ENSG00000273149) that are downregulated in a wide range of viral infections and different cell types, including blood monocluclear cells, umbilical vein endothelial cells, and dermal fibroblasts. The efficiency of these two lncRNAs was positively validated in different viral phenotypic scenarios. These two lncRNAs showed a strong downregulation in virus-infected patients when compared to healthy control transcriptomes, indicating that these biomarkers are promising targets for infection diagnosis. To the best of our knowledge, this is the very first study using host lncRNAs biomarkers for the diagnosis of human viral infections.

The Intelligent Design of the Gear Reducer

2012 ◽  
Vol 522 ◽  
pp. 823-827
Author(s):  
Jian Jiang Fang ◽  
Wen Jun Qi
Keyword(s):  
Intelligent Design ◽  
Object Oriented ◽  
Data Access ◽  
Mechanical Transmission ◽  
Design Efficiency ◽  
Integrated Technology ◽  
Gear Drive ◽  
Object Oriented Technology ◽  
Wide Range ◽  
User Friendly

The gear drive is the wide range of applications and is particularly important as a form of mechanical transmission, but the design process requires large amounts of data access and computation. In the paper, computer integrated technology and object-oriented technology is used to research and develop the intelligent design of Straight gear reducer system with user-friendly interactive platform, easy to use, high design efficiency and reliable data.

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