scholarly journals Generalizable sgRNA design for improved CRISPR/Cas9 editing efficiency

2020 ◽  
Vol 36 (9) ◽  
pp. 2684-2689 ◽  
Author(s):  
Kasidet Hiranniramol ◽  
Yuhao Chen ◽  
Weijun Liu ◽  
Xiaowei Wang
Keyword(s):  
Web Application ◽  
Learning Algorithm ◽  
Structural Features ◽  
Design Tool ◽  
Supplementary Information ◽  
Experimental Conditions ◽  
Guide Rna ◽  
Plasmid Library ◽  
Short Palindromic Repeat ◽  
Sgrna Design

Abstract Motivation The development of clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) technology has provided a simple yet powerful system for targeted genome editing. In recent years, this system has been widely used for various gene editing applications. The CRISPR editing efficacy is mainly dependent on the single guide RNA (sgRNA), which guides Cas9 for genome cleavage. While there have been multiple attempts at improving sgRNA design, there is a pressing need for greater sgRNA potency and generalizability across various experimental conditions. Results We employed a unique plasmid library expressed in human cells to quantify the potency of thousands of CRISPR/Cas9 sgRNAs. Differential sequence and structural features among the most and least potent sgRNAs were then used to train a machine learning algorithm for assay design. Comparative analysis indicates that our new algorithm outperforms existing CRISPR/Cas9 sgRNA design tools. Availability and implementation The new sgRNA design tool is freely accessible as a web application, http://crispr.wustl.edu. Supplementary information Supplementary data are available at Bioinformatics online.

CRISPR-Local: a local single-guide RNA (sgRNA) design tool for non-reference plant genomes

2018 ◽  
Vol 35 (14) ◽  
pp. 2501-2503 ◽  
Author(s):  
Jiamin Sun ◽  
Hao Liu ◽  
Jianxiao Liu ◽  
Shikun Cheng ◽  
Yong Peng ◽  
...  
Keyword(s):  
Design Tool ◽  
Supplementary Information ◽  
Guide Rna ◽  
Plant Genomes ◽  
Guide Rnas ◽  
Genome Wide ◽  
Reference Plant ◽  
Sgrna Design ◽  
Computational Resources ◽  
Local Tool

Abstract Summary CRISPR-Local is a high-throughput local tool for designing single-guide RNAs (sgRNAs) in plants and other organisms that factors in genetic variation and is optimized to generate genome-wide sgRNAs. CRISPR-Local outperforms other sgRNA design tools in the following respects: (i) designing sgRNAs suitable for non-reference varieties; (ii) screening for sgRNAs that are capable of simultaneously targeting multiple genes; (iii) saving computational resources by avoiding repeated calculations from multiple submissions and (iv) running offline, with both command-line and graphical user interface modes and the ability to export multiple formats for further batch analysis or visualization. We have applied CRISPR-Local to 71 public plant genomes, using both CRISPR/Cas9 and CRISPR/cpf1 systems. Availability and implementation CRISPR-Local can be freely downloaded from http://crispr.hzau.edu.cn/CRISPR-Local/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PAVOOC: designing CRISPR sgRNAs using 3D protein structures and functional domain annotations

2018 ◽  
Vol 35 (13) ◽  
pp. 2309-2310 ◽  
Author(s):  
Moritz Schaefer ◽  
Djork-Arné Clevert ◽  
Bertram Weiss ◽  
Andreas Steffen
Keyword(s):  
Protein Structures ◽  
Design Tool ◽  
Protein Crystal ◽  
Supplementary Information ◽  
Functional Domain ◽  
Web Based ◽  
Homology Directed Repair ◽  
Guide Rnas ◽  
Sgrna Design ◽  
Repair Template

Abstract Summary Single-guide RNAs (sgRNAs) targeting the same gene can significantly vary in terms of efficacy and specificity. PAVOOC (Prediction And Visualization of On- and Off-targets for CRISPR) is a web-based CRISPR sgRNA design tool that employs state of the art machine learning models to prioritize most effective candidate sgRNAs. In contrast to other tools, it maps sgRNAs to functional domains and protein structures and visualizes cut sites on corresponding protein crystal structures. Furthermore, PAVOOC supports homology-directed repair template generation for genome editing experiments and the visualization of the mutated amino acids in 3D. Availability and implementation PAVOOC is available under https://pavooc.me and accessible using modern browsers (Chrome/Chromium recommended). The source code is hosted at github.com/moritzschaefer/pavooc under the MIT License. The backend, including data processing steps, and the frontend are implemented in Python 3 and ReactJS, respectively. All components run in a simple Docker environment. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GuideMaker: Software to design CRISPR-Cas guide RNA pools in non-model genomes

2021 ◽  
Author(s):  
Ravin Poudel ◽  
Lidimarie Trujillo Rodriguez ◽  
Christopher R Reisch ◽  
Adam R Rivers
Keyword(s):  
Web Application ◽  
Bacterial Genome ◽  
Small World ◽  
Design Tool ◽  
Model Organisms ◽  
Guide Rna ◽  
Creative Commons ◽  
Guide Rnas ◽  
Genome Wide ◽  
Protospacer Adjacent Motif

Background: CRISPR-Cas systems have expanded the possibilities for gene editing in bacteria and eukaryotes. There are many excellent tools for designing the CRISPR-Cas guide RNAs for model organisms with standard Cas enzymes. GuideMaker is intended as a fast and easy-to-use design tool for atypical projects with 1) non-standard Cas enzymes, 2) non-model organisms, or 3) projects that need to design a panel of guide RNAs (gRNA) for genome-wide screens. Findings: GuideMaker can rapidly design gRNAs for gene targets across the genome from a degenerate protospacer adjacent motif (PAM) and a GenBank file. The tool applies Hierarchical Navigable Small World (HNSW) graphs to speed up the comparison of guide RNAs. This allows the user to design gRNAs targeting all genes in a typical bacterial genome in about 1-2 minutes. Conclusions: Guidemaker enables the rapid design of genome-wide gRNA for any CRISPR-Cas enzyme in non-model organisms. While GuideMaker is designed with prokaryotic genomes in mind, it can efficiently process smaller eukaryotic genomes as well. GuideMaker is available as command-line software, a stand-alone web application, and a tool in the CyCverse Discovery Environment. All versions are available under a Creative Commons CC0 1.0 Universal Public Domain Dedication.

scholarly journals CRISPR-DT: designing gRNAs for the CRISPR-Cpf1 system with improved target efficiency and specificity

2019 ◽  
Vol 35 (16) ◽  
pp. 2783-2789 ◽  
Author(s):  
Houxiang Zhu ◽  
Chun Liang
Keyword(s):  
Web Service ◽  
Genome Editing ◽  
Support Vector Machine Model ◽  
Structural Features ◽  
Supplementary Information ◽  
Support Vector ◽  
Machine Model ◽  
Guide Rna ◽  
Dna Targeting ◽  
Service Application

Abstract Motivation The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cpf1 system has been successfully applied in genome editing. However, target efficiency of the CRISPR-Cpf1 system varies among different guide RNA (gRNA) sequences. Results In this study, we reanalyzed the published CRISPR-Cpf1 gRNAs data and found many sequence and structural features related to their target efficiency. With the aid of Random Forest in feature selection, a support vector machine model was created to predict target efficiency for any given gRNAs. We have developed the first CRISPR-Cpf1 web service application, CRISPR-DT (CRISPR DNA Targeting), to help users design optimal gRNAs for the CRISPR-Cpf1 system by considering both target efficiency and specificity. CRISPR-DT will empower researchers in genome editing. Availability and implementation CRISPR-DT, mainly implemented in Perl, PHP and JavaScript, is freely available at http://bioinfolab.miamioh.edu/CRISPR-DT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Unusual Structural Features in the Adduct of Dirhodium Tetraacetate with Lysozyme

2021 ◽  
Vol 22 (3) ◽  
pp. 1496
Author(s):  
Domenico Loreto ◽  
Giarita Ferraro ◽  
Antonello Merlino
Keyword(s):  
Anticancer Agents ◽  
Structural Features ◽  
Side Chain ◽  
Side Chains ◽  
Valuable Insight ◽  
Experimental Conditions ◽  
Egg White Lysozyme ◽  
Potential Applications ◽  
Model Protein ◽  
Hen Egg White

The structures of the adducts formed upon reaction of the cytotoxic paddlewheel dirhodium complex [Rh2(μ-O2CCH3)4] with the model protein hen egg white lysozyme (HEWL) under different experimental conditions are reported. Results indicate that [Rh2(μ-O2CCH3)4] extensively reacts with HEWL:it in part breaks down, at variance with what happens in reactions with other proteins. A Rh center coordinates the side chains of Arg14 and His15. Dimeric Rh–Rh units with Rh–Rh distances between 2.3 and 2.5 Å are bound to the side chains of Asp18, Asp101, Asn93, and Lys96, while a dirhodium unit with a Rh–Rh distance of 3.2–3.4 Å binds the C-terminal carboxylate and the side chain of Lys13 at the interface between two symmetry-related molecules. An additional monometallic fragment binds the side chain of Lys33. These data, which are supported by replicated structural determinations, shed light on the reactivity of dirhodium tetracarboxylates with proteins, providing useful information for the design of new Rh-containing biomaterials with an array of potential applications in the field of catalysis or of medicinal chemistry and valuable insight into the mechanism of action of these potential anticancer agents.

Dementia key gene identification with multi-layered SNP-gene-disease network

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i831-i839
Author(s):  
Dong-gi Lee ◽  
Myungjun Kim ◽  
Sang Joon Son ◽  
Chang Hyung Hong ◽  
Hyunjung Shin
Keyword(s):  
Candidate Genes ◽  
Learning Algorithm ◽  
Search Space ◽  
Supplementary Information ◽  
Gene Identification ◽  
Nucleotide Polymorphisms ◽  
Disease Network ◽  
Single Nucleotide ◽  
Key Genes ◽  
Significant Attention

Abstract Motivation Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted. In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. Results The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. Availability and implementation The code for the framework will be available at http://www.alphaminers.net/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ribo-ODDR: Oligo design pipeline for experiment-specific rRNA depletion in ribo-seq

2021 ◽  
Author(s):  
Ferhat Alkan ◽  
Joana Silva ◽  
Eric Pintó Barberà ◽  
William J Faller
Keyword(s):  
Ribosome Profiling ◽  
Supplementary Information ◽  
Experimental Conditions ◽  
Computational Framework ◽  
Rna Translation ◽  
Rrna Depletion ◽  
Selection For ◽  
Nucleotide Resolution ◽  
User Friendly ◽  
Oligo Design

Abstract Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SurfaceGenie: a web-based application for prioritizing cell-type-specific marker candidates

2020 ◽  
Vol 36 (11) ◽  
pp. 3447-3456 ◽  
Author(s):  
Matthew Waas ◽  
Shana T Snarrenberg ◽  
Jack Littrell ◽  
Rachel A Jones Lipinski ◽  
Polly A Hansen ◽  
...  
Keyword(s):  
Cell Surface ◽  
Specific Surface ◽  
Web Application ◽  
Rank Order ◽  
Surface Proteins ◽  
Supplementary Information ◽  
Live Cells ◽  
Specific Marker ◽  
Cell Type ◽  
Cell Type Specific

Abstract Motivation Cell-type-specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing ‘omic’ discovery datasets is the selection of candidate markers that are most applicable for downstream applications. Results Here, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type-specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type-specific markers, respectively. Availability and implementation Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web application: www.cellsurfer.net/surfacegenie. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

2021 ◽  
Author(s):  
Yang Xu ◽  
Priyojit Das ◽  
Rachel Patton McCord
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Single Cell ◽  
Learning Algorithm ◽  
Cellular Systems ◽  
Supplementary Information ◽  
Omics Data ◽  
Learning Approaches ◽  
Rna Seq ◽  
Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

Sign in / Sign up

Export Citation Format

Share Document