SubcellulaRVis: an app to visualize subcellular compartment enrichment

High-throughput 'omics methods result in lists of differentially regulated or expressed genes or proteins, whose function is generally studied through statistical methods such as enrichment analyses. One aspect of protein regulation is subcellular localization, which is crucial for their correct processing and function and can change in response to various cellular stimuli. Enrichment of proteins for subcellular compartments is often based on Gene Ontology Cellular Compartment annotations. Results of enrichment are typically visualized using bar-charts, however enrichment analyses can result in a long list of significant annotations which are highly specific, preventing researchers from gaining a broad understanding of the subcellular compartments their proteins of interest may be located in. Schematic visualization of known subcellular locations has become increasingly available for single proteins via the UniProt and COMPARTMENTS platforms. However, it is not currently available for a list of proteins (e.g. from the same experiment) or for visualizing the results of enrichment analyses. To generate an easy-to-interpret visualization of protein subcellular localization after enrichment we developed the SubcellulaRVis web app, which visualizes the enrichment of subcellular locations of gene lists in an easy and impactful manner. SubcellulaRVis projects the results of enrichment analysis on a graphical representation of a eukaryotic cell. Implemented as a web app and an R package, this tool is user-friendly, provides exportable results in different formats, and can be used for gene lists derived from multiple organisms. Here, we show the power of SubcellulaRVis to assign proteins to the correct subcellular compartment using gene list enriched in previously published spatial proteomics datasets. We envision SubcellulaRVis will be useful for cell biologists with limited bioinformatics expertise wanting to perform precise and quick enrichment analysis and immediate visualization of gene lists.

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gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽

10.12688/f1000research.24956.2 ◽

2020 ◽

Vol 9 ◽

pp. 709 ◽

Cited By ~ 1

Author(s):

Liis Kolberg ◽

Uku Raudvere ◽

Ivan Kuzmin ◽

Jaak Vilo ◽

Hedi Peterson

Keyword(s):

Gene List ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Automated Analysis ◽

R Package ◽

Biological Data ◽

Functional Enrichment ◽

Link Type ◽

Functional Profiling ◽

Rest Api

g:Profiler (https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

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gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽

10.12688/f1000research.24956.1 ◽

2020 ◽

Vol 9 ◽

pp. 709 ◽

Cited By ~ 4

Author(s):

Liis Kolberg ◽

Uku Raudvere ◽

Ivan Kuzmin ◽

Jaak Vilo ◽

Hedi Peterson

Keyword(s):

Gene List ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Automated Analysis ◽

R Package ◽

Biological Data ◽

Functional Enrichment ◽

Link Type ◽

Functional Profiling ◽

Rest Api

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TEnGExA: an R package based tool for tissue enrichment and gene expression analysis

Briefings in Bioinformatics ◽

10.1093/bib/bbaa221 ◽

2020 ◽

Author(s):

Hukam C Rawal ◽

Ulavappa Angadi ◽

Tapan Kumar Mondal

Keyword(s):

Gene Expression ◽

Expression Analysis ◽

Gene Expression Analysis ◽

High Throughput Sequencing ◽

Gene List ◽

Enrichment Analysis ◽

R Package ◽

Web Interface ◽

Tissue Specific ◽

Downstream Analysis

Abstract RNA-seq data analysis with rapidly advancing high-throughput sequencing technology, nowadays provides large number of transcripts or genes to perform downstream analysis including functional annotation and pathway analysis. However for the data from multiple tissues, downstream analysis with tissue-specific or tissue-enriched transcripts is highly preferable. However, there is still a need of tool for quickly performing tissue-enrichment and gene expression analysis irrespective of number of input genes or tissues at various fragments per kilobase of transcript per million fragments mapped (FPKM) thresholds. To fulfill this need, we presented a freely available R package and web-interface tool, TEnGExA, which allows tissue-enrichment analysis (TEA) for any number of genes or transcripts for any species provided only a read-count or FPKM-value matrix as input. Based on the different FPKM value and fold thresholds, TEnGExA classifies the user provided gene lists into tissue-enriched or tissue-specific transcripts along with other standard classes. By analyzing the published sample data from human, plant and microorganism, we signifies that TEnGExA can easily handle complex or large data from any species to provided tissue-enriched gene list for downstream analysis in quick time. In summary, TEnGExA is quick, easy to use and an efficient tool for TEA. The R package is freely available at https://github.com/ubagithub/TEnGExA/ and the GUI web interface is accessible at http://webtom.cabgrid.res.in/tissue_enrich/.

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Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein–protein interaction

Bioinformatics ◽

10.1093/bioinformatics/btz007 ◽

2019 ◽

Vol 35 (16) ◽

pp. 2875-2876 ◽

Cited By ~ 2

Author(s):

Diego A A Morais ◽

Rita M C Almeida ◽

Rodrigo J S Dalmolin

Keyword(s):

Differential Expression ◽

Protein Interaction ◽

Topological Analysis ◽

Gene List ◽

Enrichment Analysis ◽

R Package ◽

Transcriptional Analysis ◽

Supplementary Information ◽

Protein Protein Interaction ◽

Data Platform

Abstract Motivation Several freely available tools perform analysis using algorithms developed to identify significant variation of gene expression individually. The transcriptogramer R package uses protein–protein interaction to perform differential expression of functionally associated genes. The software assesses expression profile of entire genetic systems and reveals which biological systems are significantly altered in case-control designed transcriptome experiments. Results R/Bioconductor transcriptogramer package projects expression values on an ordered gene list to perform topological analysis, differential expression and gene ontology enrichment analysis, independently of data platform or operating system. Availability and implementation http://bioconductor.org/packages/transcriptogramer. Supplementary information Supplementary data are available at Bioinformatics online.

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Cell type and subcellular compartment specific APEX2 proximity labeling proteomics in the mouse brain

10.1101/2021.04.08.439091 ◽

2021 ◽

Author(s):

Vasin Dumrongprechachan ◽

Giulia Soto ◽

Matthew L MacDonald ◽

Yevgenia Kozorovitskiy

Keyword(s):

Mouse Brain ◽

Functional Organization ◽

Specific Information ◽

Striatal Neurons ◽

Cell Type ◽

Subcellular Compartment ◽

Vertebrate Brain ◽

Subcellular Compartments ◽

Cell Type Specific ◽

And Function

The vertebrate brain consists of diverse neuronal types, classified by distinct anatomy and function, along with divergent transcriptomes and proteomes. Defining the cell type-specific neuroproteome is important for understanding the development and functional organization of neural circuits. This task remains challenging in complex tissue, due to suboptimal protein isolation techniques that often result in loss of cell-type specific information and incomplete capture of subcellular compartments. Here, we develop a genetically targeted proximity labeling approach to identify cell-type specific subcellular proteome in the mouse brain. Using adenoassociated viral transduction, we express subcellular-localized APEX2 to map the proteome of the nucleus, cytosol, and cell membrane of Drd1 receptor-positive striatal neurons. We show that each APEX2 construct can differentially and rapidly biotinylate proteins in situ across various subcellular compartments, confirmed by imaging, electron microscopy, and mass spectrometry. This method enables flexible, cell-type specific quantitative profiling of subcellular proteome in the mouse brain.

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Pre-miRNA: novel transport mechanism and function in subcellular compartments

10.26226/morressier.5ebd45acffea6f735881b093 ◽

2020 ◽

Author(s):

Eloina Corradi

Keyword(s):

Transport Mechanism ◽

Subcellular Compartments ◽

And Function

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Bend, Push, Stretch: Remarkable Structure and Mechanics of Single Intermediate Filaments and Meshworks

Cells ◽

10.3390/cells10081960 ◽

2021 ◽

Vol 10 (8) ◽

pp. 1960

Author(s):

K. Tanuj Sapra ◽

Ohad Medalia

Keyword(s):

Intermediate Filaments ◽

Eukaryotic Cell ◽

Coiled Coils ◽

Cellular Functions ◽

Mechanical Pressure ◽

Mechanical Feature ◽

Cellular Processes ◽

Nuclear Lamins ◽

Filament Networks ◽

And Function

The cytoskeleton of the eukaryotic cell provides a structural and functional scaffold enabling biochemical and cellular functions. While actin and microtubules form the main framework of the cell, intermediate filament networks provide unique mechanical properties that increase the resilience of both the cytoplasm and the nucleus, thereby maintaining cellular function while under mechanical pressure. Intermediate filaments (IFs) are imperative to a plethora of regulatory and signaling functions in mechanotransduction. Mutations in all types of IF proteins are known to affect the architectural integrity and function of cellular processes, leading to debilitating diseases. The basic building block of all IFs are elongated α-helical coiled-coils that assemble hierarchically into complex meshworks. A remarkable mechanical feature of IFs is the capability of coiled-coils to metamorphize into β-sheets under stress, making them one of the strongest and most resilient mechanical entities in nature. Here, we discuss structural and mechanical aspects of IFs with a focus on nuclear lamins and vimentin.

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NoRCE: non-coding RNA sets cis enrichment tool

BMC Bioinformatics ◽

10.1186/s12859-021-04112-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Gulden Olgun ◽

Afshan Nabi ◽

Oznur Tastan

Keyword(s):

Expression Patterns ◽

Target Prediction ◽

Enrichment Analysis ◽

Fruit Fly ◽

Relevant Information ◽

R Package ◽

Data Repository ◽

Biologically Relevant ◽

Gene Sets ◽

Data Files

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

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DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

International Journal of Molecular Sciences ◽

10.3390/ijms22031399 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1399

Author(s):

Salim Ghannoum ◽

Waldir Leoncio Netto ◽

Damiano Fantini ◽

Benjamin Ragan-Kelley ◽

Amirabbas Parizadeh ◽

...

Keyword(s):

Single Cell ◽

Biomarker Discovery ◽

Enrichment Analysis ◽

Myxoid Liposarcoma ◽

R Package ◽

Differential Analysis ◽

A Cell ◽

Reproducible Analysis ◽

Transcriptomic Level ◽

User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

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A New Analysis Tool for Continuous Glucose Monitor Data

Journal of Diabetes Science and Technology ◽

10.1177/19322968211028909 ◽

2021 ◽

pp. 193229682110289

Author(s):

Evan Olawsky ◽

Yuan Zhang ◽

Lynn E Eberly ◽

Erika S Helgeson ◽

Lisa S Chow

Keyword(s):

Glucose Monitoring ◽

Glycemic Variability ◽

R Package ◽

Analysis Tool ◽

Continuous Glucose Monitor ◽

Analysis Tools ◽

R Shiny ◽

Primary Driver ◽

Rich Information ◽

Web App

Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

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