scholarly journals Fold-change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups

2020 ◽  
Author(s):  
Daniil S. Wiebe ◽  
Nadezhda A. Omelyanchuk ◽  
Aleksei M. Mukhin ◽  
Ivo Grosse ◽  
Sergey A. Lashin ◽  
...  
Keyword(s):  
Abiotic Factors ◽  
Transcriptional Response ◽  
Enrichment Analysis ◽  
R Package ◽  
Fold Change ◽  
Functional Gene ◽  
Transcriptome Data ◽  
Gene Enrichment ◽  
Specific Manner ◽  
Go Terms

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies GO terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g. weakly, moderately or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, SEA and GSEA. These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server https://webfsgor.sysbio.cytogen.ru/ .

scholarly journals Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 434 ◽  
Author(s):  
Daniil S. Wiebe ◽  
Nadezhda A. Omelyanchuk ◽  
Aleksei M. Mukhin ◽  
Ivo Grosse ◽  
Sergey A. Lashin ◽  
...  
Keyword(s):  
Abiotic Factors ◽  
Transcriptional Response ◽  
Enrichment Analysis ◽  
R Package ◽  
Fold Change ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Go Terms ◽  
Singular Enrichment Analysis

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments, and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, Singular Enrichment Analysis (SEA), and Gene Set Enrichment Analysis (GSEA). These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers, and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server.

scholarly journals GAPGOM—an R package for gene annotation prediction using GO Metrics

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Casper van Mourik ◽  
Rezvan Ehsani ◽  
Finn Drabløs
Keyword(s):  
Gene Ontology ◽  
Gene Annotation ◽  
Enrichment Analysis ◽  
R Package ◽  
Prediction Performance ◽  
Gene Products ◽  
Limited Information ◽  
Non Coding Rnas ◽  
User Friendly ◽  
Go Terms

Abstract Objective Properties of gene products can be described or annotated with Gene Ontology (GO) terms. But for many genes we have limited information about their products, for example with respect to function. This is particularly true for long non-coding RNAs (lncRNAs), where the function in most cases is unknown. However, it has been shown that annotation as described by GO terms to some extent can be predicted by enrichment analysis on properties of co-expressed genes. Results GAPGOM integrates two relevant algorithms, lncRNA2GOA and TopoICSim, into a user-friendly R package. Here lncRNA2GOA does annotation prediction by co-expression, whereas TopoICSim estimates similarity between GO graphs, which can be used for benchmarking of prediction performance, but also for comparison of GO graphs in general. The package provides an improved implementation of the original tools, with substantial improvements in performance and documentation, unified interfaces, and additional features.

scholarly journals Auxin regulates functional gene groups in a fold-change-specific manner in Arabidopsis thaliana roots

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
N. A. Omelyanchuk ◽  
D. S. Wiebe ◽  
D. D. Novikova ◽  
V. G. Levitsky ◽  
N. Klimova ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Fold Change ◽  
Functional Gene ◽  
Specific Manner

scholarly journals Transglutaminases and Obesity in Humans: Association of F13A1 to Adipocyte Hypertrophy and Adipose Tissue Immune Response

2020 ◽  
Vol 21 (21) ◽  
pp. 8289
Author(s):  
Mari T. Kaartinen ◽  
Mansi Arora ◽  
Sini Heinonen ◽  
Aila Rissanen ◽  
Jaakko Kaprio ◽  
...  
Keyword(s):  
Immune Response ◽  
Adipose Tissue ◽  
Family Members ◽  
Enrichment Analysis ◽  
Human Adipose Tissue ◽  
Adipocyte Size ◽  
Gene Enrichment ◽  
Adipocyte Hypertrophy ◽  
Mz Twins

Transglutaminases TG2 and FXIII-A have recently been linked to adipose tissue biology and obesity, however, human studies for TG family members in adipocytes have not been conducted. In this study, we investigated the association of TGM family members to acquired weight gain in a rare set of monozygotic (MZ) twins discordant for body weight, i.e., heavy–lean twin pairs. We report that F13A1 is the only TGM family member showing significantly altered, higher expression in adipose tissue of the heavier twin. Our previous work linked adipocyte F13A1 to increased weight, body fat mass, adipocyte size, and pro-inflammatory pathways. Here, we explored further the link of F13A1 to adipocyte size in the MZ twins via a previously conducted TWA study that was further mined for genes that specifically associate to hypertrophic adipocytes. We report that differential expression of F13A1 (ΔHeavy–Lean) associated with 47 genes which were linked via gene enrichment analysis to immune response, leucocyte and neutrophil activation, as well as cytokine response and signaling. Our work brings further support to the role of F13A1 in the human adipose tissue pathology, suggesting a role in the cascade that links hypertrophic adipocytes with inflammation.

scholarly journals NoRCE: non-coding RNA sets cis enrichment tool

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.

scholarly journals Transcriptome analysis and molecular mechanism of linseed (Linum usitatissimum L.) drought tolerance under repeated drought using single-molecule long-read sequencing

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wei Wang ◽  
Lei Wang ◽  
Ling Wang ◽  
Meilian Tan ◽  
Collins O. Ogutu ◽  
...  
Keyword(s):  
Dna Repair ◽  
Drought Tolerance ◽  
Single Molecule ◽  
Linum Usitatissimum ◽  
Expression Patterns ◽  
Transcriptional Response ◽  
Enrichment Analysis ◽  
Resistant Variety ◽  
Proline Biosynthesis ◽  
Interacting Protein

Abstract Background Oil flax (linseed, Linum usitatissimum L.) is one of the most important oil crops., However, the increases in drought resulting from climate change have dramatically reduces linseed yield and quality, but very little is known about how linseed coordinates the expression of drought resistance gene in response to different level of drought stress (DS) on the genome-wide level. Results To explore the linseed transcriptional response of DS and repeated drought (RD) stress, we determined the drought tolerance of different linseed varieties. Then we performed full-length transcriptome sequencing of drought-resistant variety (Z141) and drought-sensitive variety (NY-17) under DS and RD stress at the seedling stage using single-molecule real-time sequencing and RNA-sequencing. Gene Ontology (GO) and reduce and visualize GO (REVIGO) enrichment analysis showed that upregulated genes of Z141 were enriched in more functional pathways related to plant drought tolerance than those of NY-17 were under DS. In addition, 4436 linseed transcription factors were identified, and 1190 were responsive to stress treatments. Moreover, protein-protein interaction (PPI) network analysis showed that the proline biosynthesis pathway interacts with stress response genes through RAD50 (DNA repair protein 50) interacting protein 1 (RIN-1). Finally, proline biosynthesis and DNA repair structural gene expression patterns were verified by RT- PCR. Conclusions The drought tolerance of Z141 may be related to its upregulation of drought tolerance genes under DS. Proline may play an important role in linseed drought tolerance by maintaining cell osmotic and protecting DNA from ROS damage. In summary, this study provides a new perspective to understand the drought adaptability of linseed.

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

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.

scholarly journals Differential Morpho-Physiological and Transcriptomic Responses to Heat Stress in Two Blueberry Species

2021 ◽  
Vol 22 (5) ◽  
pp. 2481
Author(s):  
Jodi Callwood ◽  
Kalpalatha Melmaiee ◽  
Krishnanand P. Kulkarni ◽  
Amaranatha R. Vennapusa ◽  
Diarra Aicha ◽  
...  
Keyword(s):  
Heat Stress ◽  
Molecular Mechanisms ◽  
Stomatal Closure ◽  
Enrichment Analysis ◽  
Leaf Size ◽  
Vaccinium Corymbosum ◽  
Climatic Conditions ◽  
Differentially Expressed ◽  
Transcriptomic Changes ◽  
Go Terms

Blueberries (Vaccinium spp.) are highly vulnerable to changing climatic conditions, especially increasing temperatures. To gain insight into mechanisms underpinning the response to heat stress, two blueberry species were subjected to heat stress for 6 and 9 h at 45 °C, and leaf samples were used to study the morpho-physiological and transcriptomic changes. As compared with Vaccinium corymbosum, Vaccinium darrowii exhibited thermal stress adaptation features such as small leaf size, parallel leaf orientation, waxy leaf coating, increased stomatal surface area, and stomatal closure. RNAseq analysis yielded ~135 million reads and identified 8305 differentially expressed genes (DEGs) during heat stress against the control samples. In V. corymbosum, 2861 and 4565 genes were differentially expressed at 6 and 9 h of heat stress, whereas in V. darrowii, 2516 and 3072 DEGs were differentially expressed at 6 and 9 h, respectively. Among the pathways, the protein processing in the endoplasmic reticulum (ER) was the highly enriched pathway in both the species: however, certain metabolic, fatty acid, photosynthesis-related, peroxisomal, and circadian rhythm pathways were enriched differently among the species. KEGG enrichment analysis of the DEGs revealed important biosynthesis and metabolic pathways crucial in response to heat stress. The GO terms enriched in both the species under heat stress were similar, but more DEGs were enriched for GO terms in V. darrowii than the V. corymbosum. Together, these results elucidate the differential response of morpho-physiological and molecular mechanisms used by both the blueberry species under heat stress, and help in understanding the complex mechanisms involved in heat stress tolerance.

scholarly journals Gene set enrichment analysis for genome-wide DNA methylation data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jovana Maksimovic ◽  
Alicia Oshlack ◽  
Belinda Phipson
Keyword(s):  
Dna Methylation ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Methylation Array ◽  
Gene Set ◽  
Genome Wide ◽  
Genome Methylation ◽  
Unbiased Gene ◽  
Gene Set Testing

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

scholarly journals TFEA.ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets

2019 ◽  
Vol 35 (24) ◽  
pp. 5339-5340 ◽  
Author(s):  
Laura Puente-Santamaria ◽  
Wyeth W Wasserman ◽  
Luis del Peso
Keyword(s):  
Genomic Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Web Based ◽  
Factor Binding Site ◽  
Gene Sets ◽  
Transcription Regulators ◽  
Computational Identification ◽  
On Chip

Abstract Summary The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. Availability and implementation TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/ Supplementary information Supplementary data are available at Bioinformatics online.

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