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

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/.

scholarly journals Tissue-specific cell sorting from Drosophila embryos: Application to gene expression analysis

Fly ◽  
2011 ◽  
Vol 5 (3) ◽  
pp. 261-265 ◽  
Author(s):  
Pierre-Adiren Salmand ◽  
Magali Iché-Torres ◽  
Laurent Perrin
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Cell Sorting ◽  
Gene Expression Analysis ◽  
Specific Cell ◽  
Tissue Specific ◽  
Drosophila Embryos

scholarly journals Glimma: interactive graphics for gene expression analysis

2017 ◽  
Author(s):  
Shian Su ◽  
Charity W. Law ◽  
Casey Ah-Cann ◽  
Marie-Liesse Asselin-Labat ◽  
Marnie E. Blewitt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
R Package ◽  
Data Exploration ◽  
Interactive Graphics ◽  
Microarray Gene Expression ◽  
Additional Information ◽  
On Demand ◽  
Annotation Information

AbstractMotivationSummary graphics for RNA-sequencing and microarray gene expression analyses may contain upwards of tens of thousands of points. Details about certain genes or samples of interest are easily obscured in such dense summary displays. Incorporating interactivity into summary plots would enable additional information to be displayed on demand and facilitate intuitive data exploration.ResultsThe open-source Glimma package creates interactive graphics for exploring gene expression analysis with a few simple R commands. It extends popular plots found in the limma package, such as multi-dimensional scaling plots and mean-difference plots, to allow individual data points to be queried and additional annotation information to be displayed upon hovering or selecting particular points. It also offers links between plots so that more information can be revealed on demand. Glimma is widely applicable, supporting data analyses from a number of well established Bioconductor workflows (limma, edgeR and DESeq2) and uses D3/JavaScript to produce HTML pages with interactive displays that enable more effective data exploration by end-users. Results from Glimma can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.Availability and ImplementationThe Glimma R package is available from http://bioconductor.org/packages/devel/bioc/html/Glimma.html.

scholarly journals NormExpression: an R package to normalize gene expression data using evaluated methods

2018 ◽  
Author(s):  
Zhenfeng Wu ◽  
Weixiang Liu ◽  
Xiufeng Jin ◽  
Deshui Yu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
R Package ◽  
Data Normalization ◽  
Expression Data ◽  
Normalization Methods ◽  
Normalize Gene Expression

AbstractData normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.

Functional Gene Expression Analysis of Tissue-Specific Isoforms of Mef2c

2011 ◽  
Vol 32 (1) ◽  
pp. 129-139 ◽  
Author(s):  
Yoshiharu Sekiyama ◽  
Hitoshi Suzuki ◽  
Toshifumi Tsukahara
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Functional Gene ◽  
Tissue Specific

scholarly journals intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Yingying Cao ◽  
Simo Kitanovski ◽  
Daniel Hoffmann
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
High Throughput Sequencing ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
R Package ◽  
Integrative Approach ◽  
Integrated Analysis ◽  
Data Sets ◽  
Rna Seq

Abstract Background RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy. Results Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization. Conclusion intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset.

scholarly journals Tissue specific muscle extracellular matrix hydrogel improves skeletal muscle regeneration in vivo over non-matched tissue source

2020 ◽  
Author(s):  
Jessica L. Ungerleider ◽  
Monika Dzieciatkowska ◽  
Kirk C. Hansen ◽  
Karen L. Christman
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Extracellular Matrix ◽  
Regenerative Medicine ◽  
Expression Analysis ◽  
Muscle Regeneration ◽  
Gene Expression Analysis ◽  
Skeletal Muscle Regeneration ◽  
Cross Sectional ◽  
Tissue Specific

AbstractDecellularized extracellular matrix (ECM) hydrogels present a novel, clinical intervention for a myriad of regenerative medicine applications. The source of ECM is typically the same tissue to which the treatment is applied; however, the need for tissue specific ECM sources has not been rigorously studied. We hypothesized that tissue specific ECM would improve regeneration through preferentially stimulating physiologically relevant processes (e.g. progenitor cell proliferation and differentiation). One of two decellularized hydrogels (tissue specific skeletal muscle or non mesoderm-derived lung) or saline were injected intramuscularly two days after notexin injection in mice (n=7 per time point) and muscle was harvested at days 5 and 14 for histological and gene expression analysis. Both injectable hydrogels were decellularized using the same detergent and were controlled for donor characteristics (i.e. species, age). At day 5, the skeletal muscle ECM hydrogel significantly increased the density of Pax7+ satellite cells in the muscle. Gene expression analysis at day 5 showed that skeletal muscle ECM hydrogels increased expression of genes implicated in muscle contractility. By day 14, skeletal muscle ECM hydrogels improved muscle regeneration over saline and lung ECM hydrogels as shown through a shift in fiber cross sectional area distribution towards larger fibers. This data indicates a potential role for muscle-specific regenerative capacity of decellularized, injectable muscle hydrogels. Further transcriptomic analysis of whole muscle mRNA indicates the mechanism of tissue specific ECM-mediated tissue repair may be immune and metabolism pathway-driven. Taken together, this suggests there is benefit in using tissue specific ECM for regenerative medicine applications.

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